Battery Fire Risk Analysis in Battery Powered Ships Using Hazop Analysis and Bayesian Belief Network

Battery Fire Risk Analysis in Battery Powered Ships Using Hazop Analysis and Bayesian Belief Network | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Battery Fire Risk Analysis in Battery Powered Ships Using Hazop Analysis and Bayesian Belief Network Erdeniz Erol, Elif Bal Beşikçi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8244176/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In recent years, hybrid electric and all-electric ships have become emission reduction options for coastal sea transportation, increasing energy efficiency with alternative nonfossil fuels owing to emerging battery developments. On the other hand, efficient electric propulsion ships and safe battery operations require additional operational practices and competencies for seafarers compared with mechanical propulsion ships. This paper is prepared for analyzing hazard and operability (HAZOP) studies for the identification of potential hazards on the basis of a casualty report of a battery retrofitted with a ferry in Norway and the Bayesian Belief Network for the quantification of potential fires of a battery onboard. Although battery management systems (BMSs) address operational safety concerns standalone, such a high-energy-density battery and flammable electrolyte inside may pose a possible fire risk when battery storage is not offline within the ship electrical system. Owing to the lack of sufficient data and experience with batteries in the marine sector, the Bayesian belief network (BBN) is a method that can be used to quantify the fire probability of a ship. The Genie tool is used for the risk evaluation tool of a possible battery ship fire by using the input of weight evaluation factors that are derived from the study of a risk assessment sample of an electric cargo ship in Huzhou. According to our results, battery insulation, overcurrent and high temperature are the most powerful effects for a possible fire onboard. Ocean Engineering HAZOP Bayesian belief network battery fire ship fire ship electrification risk assessment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. INTRODUCTION In recent decades, air pollution caused by rapid growth in fossil fuel consumption has created an attitude toward energy savings. The shipping industry is under great pressure to decrease the negative effects of air pollution on the environment. Ninety percent of overall global trade is transported via the shipping sector; however, ships with diesel-propulsion systems emit large quantities of greenhouse gases such as CO2 and polluting gases such as sulphur oxides and nitrogen oxides (Chu-Van et al. 2018 ; Geertsma et al. 2017 ; Chen et al. 2020 ). While the Paris Convention has set a 2°C target for increasing the global temperature, if no steps are implemented to reduce CO2 emissions are taken, CO2 emissions from ships are expected to increase by 50–250% by 2050. Between 2012 and 2018, the contribution of marine transportation to global CO2 emissions increased slightly from 2.76 to 2.89%. Additionally, shipping currently accounts for 15% of global NOx emissions, with the expectation that the rate will increase unless the maritime industry takes decarbonization measures (IMO 2015 ; IMO 2020 ). While greenhouse gas emissions are the main cause of climate change, which is recognized as one of the greatest threats to human health and biodiversity, NOx and SOx are the contributors to photochemical smog, acid rain and resulting long-term health problems (Goedkoop et al. 2009 ). Therefore, electrification of the maritime industry has accelerated in the last decade. The use of resources such as diesel-electric systems, fuel cells or batteries for partial or full propulsion is referred to as electric/hybrid propulsion. Electrically powered propulsion systems, which provide the highest possible global efficiency and the lowest emissions by aiming for zero emission operations, especially in special areas, are preferred over mechanically driven systems for different types of ships whose operational profiles are suitable for these concepts. Hybrid ships can reduce emissions and fuel consumption by 10%-35% (Lashway 2016). To achieve zero-emissions marine transport goals by 2050, where current electric vehicles represent a low percentage of the global fleet today, there is increasing interest in integrating different battery applications on board (Joung 2020). A battery is an electrochemical system capable of storing highly responsive electrical power. It gives the operator the ability to store unused or surplus energy to operate the engine more stably and then uses the energy when the ship's propulsion system needs it. The price of battery systems is decreasing with recent technological advances, whereas efficiency is increasing, making them competitive and alternative energy sources on board. In the last decade, the chemistry of batteries shaped around lithium ions has stored between two and eight times more energy per unit weight than traditional batteries with water-based electrolytes such as lead acid and nickel cadmium batteries do (Mjøs et al. 2016 ). On the other hand, high energy density and the use of a flammable electrolyte increase the risk of operation. Therefore, lithium-based battery systems should rely on a well-developed and tested electronic control system and physical structure for safe operation, which is called the battery management system (BMS), to enable hybrid electric and all electric operation of ships by using a power management system (PMS) without any risks. Despite these systems being developed by equipment manufacturers and controlled by classification societies, some accidents that were not foreseen before or multiple situations may still arise during the same event. Thus, experts on the marine industry and equipment manufacturers need to identify potential hazards from an operational perspective. In this work, the HAZOP method is used to quantify the potential causal factors that might create a possible fire via the Bayesian belief network. A hazard and operability (HAZOP) study is defined as a structured and systematic examination of a planned or existing process or operation to identify and evaluate problems that may represent risks to personnel or equipment or prevent efficient operation (Rausand 2013 ). A HAZOP is a qualitative method that is dependent on guide words. These guide words are quantitative comparisons such as “more” or “less”, “inverse” or other sequence comparison types of words such as “before” or “after”, which are used to identify the complex operation processes decided by a diversified expert team (HAZOP team) composed of different backgrounds throughout a series of meetings. The history of HAZOP dates back to the early 1970s, when it was first officially introduced by the Institute of Chemical Industry (ICI) from the UK to assess the safety of chemical process facilities and became a risk analysis technique. The first HAZOP guideline was released by the Chemical Industries Association (CIA) in 1977, and throughout the following years, HAZOP analysis was refined. Kletz ( 2012 ) provided descriptive information about process safety and the technical terms of HAZOP analysis. The author also contributed to the development of HAZOP analysis by sharing his experiences with readers. Finally, with the standard IEC 61882: 2001, the International Electrotechnical Commission (IEC) has defined and formalized new requirements for use in HAZOP studies (IEC 61882) (Akman 2015 ). One of the recently published HAZOP analysis-based methods for marine applications is used for risk assessment of the main engine of the 10000 TEU container ship (Zhan et al. 2012 ). Risk rating matrices and evaluations are described for ship fuel systems. This work is funded by the scientific research fund of Shanghai Maritime University. Another HAZOP study used a preliminary analysis to investigate the risk-based decision-making of mooring systems of floating structures for stations at any water depth (Khamidi et al. 2015 ). This study's findings pinpoint the probable reasons for and repercussions of mooring system malfunctions. The risk of hydrogen fuel cell usage was measured efficiently via a Monte Carlo routine that captures the leak's transient behavior (Huser et al.2017). CFD (computational fluid dynamics) software is used with simplified templates for ventilation dispersion and explosion situations, as well as revised leak and ignition frequencies. Fire-related factors were systematically analyzed across all operational stages of the vessel. To address the uncertainty and subjectivity within evaluation parameters, the study incorporated entropy theory and a cloud-based modeling approach for quantitative risk analysis (Zhang and Yan 2017). However, few years have passed to prove the life cycle assessment of batteries. One method for obtaining Li-ion lifecycle data involves applying a supervised learning algorithm with respect to the relevance vector machine (RVM). As a Bayesian support vector machine (SVM) procedure, the RVM method allows arbitrary kernel functions to be used and produces probabilistic predictions (Tang et al. 2019 ). The Bayesian belief network (BBN) is one of the methodologies that can be used for risk assessment, especially for the modeling of rare accidents such as the fire probabilities of an electric ship (Zhang and Thai 2016 ). Since these technologies were commercialized in the last decade, the number of fire casualties due to battery systems has been limited in the quantification of possible probabilities. The Genie tool is used to analyze the danger of a prospective battery ship fire on the basis of the input of weight evaluation elements from a risk assessment example of an electric cargo ship in Huzhou (Zhang and Yan 2017). This paper identifies the probability of operational causes for a possible fire on-board a battery electric ship. To determine the causes, effects, and preventative barriers put in place to lessen the effects of potential accidents, HAZOP and BBN research methodologies are integrated. The effectiveness of the safety barriers in a gas facility is investigated and quantified via BN analysis, and the results are compared with the risk acceptance criteria (Zerrouki and Smadi 2017 ). The remaining sections of this paper are structured as follows. The HAZOP and BBN research approaches for the maritime sector and battery applications are discussed in Section 2. In Section 3, two different case studies based on a real electric ship fire incident and the results of an electric ship fire risk assessment model are used as inputs. The HAZOP study is applied on the basis of an electric ship fire incident, and the operational causes of a fire incident are analyzed via BBN in this work. The main findings of related literature, along with the methods and possible differentiations for battery-powered ships, are presented in Section 4. Finally, key conclusions and suggestions for electric ships and maritime operations are presented in Section 5. 2. METHODOLOGY 2.1 HAZOP Initiation of a HAZOP analysis starts with identification of the system, as its methodology is depicted in the process block in Fig. 1 . Therefore, a complete design representation of the system, which adequately describes the subsystems, parts, functions and properties, is a prerequisite for examination before HAZOP analysis. The system is divided into functional blocks. Every part of the process is analyzed for potential deviations from the design intention. It is determined whether the deviations may cause any hazard or inconvenience. Every phase of the process is identified with the nodes and their deviation parameters. Questions arise for these nodes, and they are detailed around direct words. Each deviation is considered to choose how it could be caused and what the outcomes would be. For hazards, preventive/curing activities are characterized by identifying existing safeguards or any other corrective measures. HAZOP methodology is a widely used preliminary tool for other types or combinations of tools used for the identification of hazards and their possible consequences. The outputs of HAZOP analysis include details of identified hazards and operability problems together with details of any provisions for their detection and/or mitigation; suggested steps for any additional investigations of particular elements of the design using different techniques, if necessary; actions required for addressing ambiguities encountered throughout this investigation; suggestions for alleviating the problems identified, taking into account the group's comprehension of the system (if within the scope of the study); and details of identified hazards and operability problems, along with details of any provisions for their detection and/or mitigation. As this work implies that HAZOP is a preliminary analysis technique, it can complement other techniques as well. An example of an integrated approach using HAZOP and failure modes, effects, and criticality analysis (FMECA) to identify potential unintentional events in the storage system utilized in the LNG regasification plant is a recent study. Compared with other marine fuels, LNG has become one of the most attractive fuel sources for maritime operations because of its low greenhouse gas (GHG) emissions. Compared with single methodologies, a new tool for FMECA and HAZOP integrated analysis (FHIA) has been designed for the development of specific criteria and to obtain more recommendations for reliability and risk data organisations (Giardina and Morale 2015 ). Another combination of HAZOP and a quantitative investigation of the possible threats across the development of fault sources and consequences is fault tree analysis (FTA). The results from FTA permit the organization of preventive and restorative measures to limit the likelihood of possible failure. An examination of a contextual analysis is conducted, which comprises the terminal for emptying chemical and oil-based commodities and the fuel storage facilities belonging to two organizations in the port of Valencia in Spain (Fuentes-Bargues et al. 2017 ). Overall, HAZOP has become a standard preliminary identification tool for process plant design in offshore and marine areas, where procedural HAZOP is widely used for simultaneous operations and assessments of evacuation systems. 2.2 BAYESIAN BELIEF NETWORK Lithium-ion batteries can lose capacity and show increased internal resistance over time. Such degradation is due to cycling using the batteries as well as calendar consequences that will inevitably take place over time. The battery cell deteriorates more quickly at higher temperatures, and there are additional hazards at lower temperatures. Depending on the chemistry, the optimum cell temperature is often between 20°C and 30°C. Higher current (or power) levels as well as greater SOC adjustments (sometimes referred to as depth of discharge (DOD) or delta SOC (DSOC)) can lead to faster degradation. Battery-only storage typically involves frequent deep discharges or charges to meet sudden travel load variations, which can lead to significant battery life degradation (Fang et al. 2019 ). To identify the risks and possible hazards of battery usage, the European Union research projects STABALID and STALLION ( 2015 ) address the risk assessment of large-scale, stationary, nongrid-connected lithium-ion storage systems. A similar approach can be used for marine-type batteries integrated into ships. Possible risk assessment phases for marine batteries include hazard identification on battery vessels; assessment of the following possible risks on battery ships; risk of gas formation; detection, alarm systems and ventilation strategies; risk of explosion and fire; external risks; loss of propulsion or auxiliary power for basic or important services; and reduced battery life. When a battery is charged to an excess level (SOC) above 100%, there is an overcharge. It induces in-cell chemical degradation that can lead to thermal runaway, cell swelling, gas venting, and other extreme events. In contrast, overdischarge occurs when a cell is discharged above 100% discharge depth (DOD). This leads to a sudden decrease in the cell voltage or even a reversal of polarity, resulting in possible electronic control failure. Since the battery depth of discharge (DOD) and number of cycles are arranged according to the year of planned operation, identifying the remaining useful life (RUL) of the batteries is another aspect to address during the battery operation of a ship. Figure 2 shows the RUL estimation techniques (Lipu et al. 2018 ). The risk of hydrogen fuel cell usage was measured efficiently via a Monte Carlo routine that captures the leak's transient behavior (Huser et al.2017). CFD (computational fluid dynamics) software is used with simplified templates for ventilation dispersion and explosion situations, as well as revised leak and ignition frequencies. The key factors resulting in a battery-powered ship fire incident were investigated from the perspective of the entire ship service process (Zhang and Yan 2017). To predict battery degradation, Keil et al. ( 2016 ) applied tests to different chemical types of cells, namely, lithium nickel cobalt aluminum oxide ( NCA : LiNiCoAlO 2 ) NCA, lithium nickel cobalt manganese oxide ( NMC : LiNiCoMnO 2 )) and lithium iron phosphate ( LFP : LiFePO 4 /C ). These datasets are obtained from the thesis of ten Cate Hoedemaker ( 2017 ), which was completed at TU Delft. Predictor variable selection for Bayesian linear regression models for three types of Li-ion batteries is performed according to the Bayes theorem shown below: $$\:P(A/B)\:=\:\:\frac{P(B/A)\:\:P\left(A\right)}{P\left(B\right)}$$ The state of P(B) is the evidence probability of event B, and P(A) is the prior probability of event A. The likelihood, P(B/A), is the conditional probability of event B given event A, and the posterior, P(A\B), is the conditional probability of event A given event B. $$\:posterior\:=\:\:\frac{prior\:.\:likelihood}{evidence}$$ The fluidity and randomness of the evaluation variables, as well as the subjective impact of the scoring levels, are subsequently resolved via the cloud model and entropy theory. Then, the entropy- and cloud-based framework for fire risk assessment is discussed. However, few years have passed to prove the life cycle assessment of batteries. One method for obtaining Li-ion lifecycle data involves applying a supervised learning algorithm with respect to the relevance vector machine (RVM). As a Bayesian support vector machine (SVM) procedure, the RVM method allows arbitrary kernel functions to be used and produces probabilistic predictions (Tang et al. 2019 ). Since electric failures are new onboard, few casualties related to electric blackouts have occurred (Zhang et al. 2019 ). To date, there are only 2 cases of battery fires onboard passenger ferries; neither of them results in total losses (Mrozik et al. 2021 ). 3. CASE STUDY 3.1 HAZOP Results A minor fire was discovered in the battery chamber of the hybrid passenger ferry MF Ytterøyningen on October 10, 2019, as shown in Fig. 3 , followed by a gas explosion on board 12 hours after the fire. Although there were no personal injuries, preliminary reports from relevant stakeholders revealed that the battery system was not connected to the shipside systems at the time of the incident because of ongoing service. Consequently, alarms from the battery system were off through the ship's alarm and monitoring system, where ship personnel were not aware of thermal runaway, resulting in battery fires and switchboard room explosions, as shown in Fig. 3 (Fire onboard Ytterøyningen car-ferry: preliminary investigation findings 2020). The marine battery manufacturer of this ferry presented lessons learned in this accident at a conference in March 2020. In that context, the battery supplier raised their complaints about the lack of seaman training and low industry knowledge on battery safety, which address a clear need to increase training on batteries and awareness among all stakeholders (Battery safety: Lessons learned from the fire aboard the MF Ytterøyningen 2019 ). The reports and documents related to this casualty are systematically inserted into HAZOP study in Table 1 to clarify the guide words, causes, consequences, existing guards, and the required actions of the actors. Guide Word Deviation Possible Cause Consequences Existing Safeguards Action Required Action by Action taken NO NO FLOW no part of the battery system was connected to the shipside systems at the time of the incident. Consequently, no alarms from the battery system were sent through the ship’s alarm system. BMS integration into Alarm Monitoring System Do not power down the battery equipment and check about the alarms about Battery frequently Ship Personnel “The vessel must not be operated without an active communication link between the EMS (Energy Management System) and individual battery packs’ BMS (Battery Management System). Maintaining power to the packs preserves this link and prevents communication loss MORE MORE TEMPERATURE The leakage created arcing between electrical components, at pack voltages of 1000Vdc, igniting a fire Fire was fueled by ethylene glycol components from the coolant and caused external heating of battery modules. Corvus Passive Single Cell Thermal Runaway Isolation safety system worked as designed and intended, most likely limiting the damage from the fire. With early detection via a purpose-built gas sensor or smoke detector, a problem cell can be disconnected before thermal runaway Battery Supplier/Integrator Both Novec 1230 inert gas fire suppression system of the vessel and the supplementary saltwater sprinkler system were activated during the incident. While the saltwater system served as an additional safety measure, available evidence indicates that its activation may have aggravated the event. REVERSE REVERSE FLOW how the extent and severity of the following events were able to develop toward explosion An explosion 12 hours later in the switchboard room next to the battery chamber. Adequate ventilation is required to lower the risk of compartment overpressure and blast from the gas produced in a thermal runaway. Establish procedures to respond to these fire events & programmes for exercises and drills to get ready for emergencies Shipowner Venting mechanisms help redirect flammable gases away from unstable battery units, thereby decreasing explosion risk. Nevertheless, ventilation alone is inadequate if a significant segment of the battery system ignites OTHER THAN Utility failure, Maintenance, Leak, Safety, Corrosion, Instrumentation etc. leakage in the battery system’s liquid cooling circuit The leakage created arcing between electrical components, at pack voltages of 1000Vdc, igniting a fire Corvus Passive Single Cell Thermal Runaway Isolation safety system Seawater fire fighting systems should only be used as back-up or final resource systems Battery Supplier/Integrator Observe the contingency document: “1007814 Guidance Note: Assessment and Response After a Thermal Event involving Orca ESS (Energy Storage System)” PART OF battery module a twisted gasket, intended to seal the cooling plate outside of a battery module, is the most likely cause of the leakage Leakage in the battery system’s liquid cooling circuit To reduce overcharging a hard-wired lock-out function that disconnect BMS The battery monitoring systems of commissioned/active ESS systems must be connected to the ship system at all times – even when the ESS is not in use Battery Supplier/Integrator All shipowners using a battery system should carry out a risk assessment based on the recommendations of the updated safety message. Table-1 HAZOP study based on battery fire casualty reports. 3.2 Case Ship-2 BBN To increase awareness of fire probabilities, the Bayesian belief network (BBN) is one of the technologies that can be utilized. Zhang’s model utilized an integrated cloud-computing algorithm for risk evaluation and validated it via a 500-ton all-electric cargo vessel operating in Huzhou as a case study (Zhang and Yan 2017). A battery safety assessment model based on a Bayesian network was developed in this work using the conditional probability of nodes and the network topological structure. The ship's fire safety is simulated via GeNIe software. First, the construction of an inference network model in GeNIe is shown in Figure-4. Input probability data for this Bayesian analysis were derived from Zhang and Yan’s validated entropy–cloud risk model, which demonstrated reliable predictive capability in prior assessments. (Zhang and Yan 2017). Figure 5 shows similar causes and consequences of a fire and thermal runaway system, which is presented in the Maritime Battery Safety Joint Development Project Technical Reference for Li-ion Battery Explosion Risk and Fire Suppression. The total frequency of a fire (per year) probability is also inserted from that reference, as the Battery System Fire probability is 5,20E-04 and the Engine Room Fire Probability is 6,80E-04 (Maritime Battery Safety Joint Development Project 2019 ). Under certain conditions of abuse, such as overheating, Li-ion cells may vent the electrolyte and/or enter into thermal runaway. The type of chemistry affects the probability of fire risk, but in this work, this effect has not yet been addressed. The tests revealed a significant difference between nickel manganese cadmium (NMC) and lithium iron phosphate (LFP) cells. Compared with the NMC pouch cells, the LFP cylindrical cells were much harder to force into thermal runaway. In particular, NMC batteries are used when better energy efficiency (and more power) is needed, as their energy density is higher, whereas LFP batteries are used where safety is more important because they are more reliable in possible fire situations because of their higher temperature resistance (Ouyang et al. 2017 ). NMC-type battery pack providers have improved this lack by developing battery management and protection systems to comply with marine safety regulations. Another alternative method for preventing thermal runaway is the use of lithium-titan-oxide (LTO) battery chemistry as an anode material for a high life cycle and high-power operational profile. For the same operational performance, LTO batteries only need approximately 50% of the rated energy capacity of the equivalent NMC batteries. Although resulting in a smaller capacity than the NMC counterpart, together with the cost per kWh, the LTO chemistry will be competitive when the lifetimes of the batteries are greater than 20 years of operation and/or when ultrafast charging (> 3C) is needed. The probability distribution of a node is given as a distribution of conditional probabilities on the basis of the state of its parents, called the node probability table (NPT). State 1 represents fire, and State 0 represents no fire. The NPT can be interpreted as described in Table 1. The same principle can be applied to all nodes in a BBN. The colors are used for classifying the different stages of a ship when a fire can occur. The probabilities in Table 2 are input into the BBN. Figure-6 represents the fire-targeted probabilities, and Figure-7 represents the no-fire-targeted probabilities. When they are compared, although the weights are similar, the stage percentages differ in terms of the fire target. The fire probability derived from batteries while sailing in normal and harsh weather conditions is more likely when no fire on a ship is targeted. This means that when a ship fires, it is mostly the consequence of a ship accident. The other battery fire stages cause no ship fires as a result of ship accidents. Figure 8 shows the strength of influence that is affected by the child nodes. As shown in the graph, the thickness represents how strongly the child node affects its parent node. Battery insulation, overcurrent and high temperature are the most powerful effects for a possible fire. Table 2 Updated Node Probability Table for the S/H/C/M/V Structures in a BBN Stage Prob. Risk item Prob. Risk evaluation indicator Prob. Charging the battery (S) 0,17 electric leak (S1) 0,21 Failure of an insulation(S11) 1 Short circuit of the battery (S2) 0,41 Overcharge of the battery (S21) 0,28 Overheat of the battery(S22) 0,41 Overvoltage(S23) 0,31 Charging interface is poorly contacting (S3) 0,38 shore charging station failure (S31) 1 Voyage under standard operating conditions (H) 0,12 Electrical cables are poorly contacting (H1) 0,3 Failure due to human factor (H11) 1 Short circuit of electric system (H2) 0,53 Failure due to equipment(H21) 1 Fire inside cargo (H3) 0,17 Failure due to lack of operational supervision (H31) 1 Operation under adverse environmental conditions (C) 0,3 electric surge current (C1) 0,13 Lightning strike (C11) 1 Ignition or flame (C2) 0,23 Over temperature (C21) 1 electric cables are poorly contacting (C3) 0,4 Intense vibration induced by heavy winds (C31) 1 short circuit on the electrical grid (C4) 0,11 Heavy rain or flood (C41) 1 Fire in electrical cable (C5) 0,13 Over current because of overload (C51) 1 Occurrence of a marine traffic accident (M) 0,32 Short circuit of the battery (M1) 0,27 Battery enclosure distortion (M11) 0,57 Defective battery (M12) 0,43 Explosion of the battery (M2) 0,43 Overpressure (M21) 0,38 Overtemperature (M22) 0,62 cargo corrosion(M3) 0,3 Leak of electrolyte (M31) 1 Vessel berthing and loading stage (V) 0,08 Short circuit of the battery (V1) 0,42 External object impact damaging the battery enclosure (V11) 1 electric cables are poorly contacting (V2) 0,58 Severe vibration caused by cargo handling (V21) 1 4. RESULTS AND DISCUSSION Wang et al. ( 2020 ) used HAZID, fault tree, event tree, and cost‒benefit analyses to evaluate the risk and safety level of a battery-powered high-speed catamaran ferry. A high-speed battery-powered ferry operating in the Norwegian Sea, which has numerous small islands and frequently requires passenger transportation between islands and the mainland, is the subject of these quantifications, which are based on assumptions for assets, human life, and the environment due to the hazards of collision, grounding, contact, and fire of previous hazards of nonelectric ships. There are three parts of the risk assessment: first, design, construction and installation; second, operation, that is, during a voyage and approaching/leaving port; and, finally, an emergency situation. The maritime industry is facing increased focus on sustainable propulsion systems and energy storage capability improvement with battery technologies. On the other hand, another battery room fire occurred in March 2021 in the hybrid vessel ‘Brim’. By a combination of lessons learned from the two cases of MF Ytterøyningen and Brim, the Norwegian Safety Investigation Authority published a report exclusively for the purpose of improving safety at sea (Report on fire on board ‘MS Brim’ in the outer Oslofjord on 11 March 2021 2022). Because neither the ventilation system's nor the battery system's vulnerabilities were effectively detected in that case, the Norwegian Safety Investigation Authority is unable to conclude that the risk assessment accurately reflects the real risk of fire in the battery packs. All pertinent risks that have been discovered by different disciplines and that collectively pose a risk to the vessel should be covered in such a risk assessment. However, our risk assessment approach is based on covering an operational perspective and preventive actions before an incident may occur. The two approaches are combined under HAZOP and BBN for a marine battery fire incident that has not been handled before. 5. CONCLUSION In summary, HAZOP is a widely used, interdisciplinary qualitative tool for better identification of complex systems and preliminary chases for mitigation of their risk-based solutions. The strengths of HAZOP analysis are summarized as follows: it is widely utilized, its benefits and drawbacks are well understood, it makes use of the team's operating staff expertise, it is methodical and thorough, and it should detect all hazardous process variations. It identifies current protections and creates recommendations for new ones, and it works well for both technical and human errors. When dealing with marine dangers in offshore activities that necessitate the collaboration of multiple professions or organizations, the team method is especially suitable. Despite the aforementioned advantages, HAZOP studies have drawbacks in that team expertise and leader facilitation are crucial to their success. It needs to be modified to cover various kinds of dangers because it is optimized for process hazards. This necessitates the creation of procedural descriptions, which are frequently lacking in sufficient detail. Nonetheless, the operation might benefit from the existence of these materials. It takes much time and effort to fully record HAZOP documentation. Battery-related risk assessment and battery usage prognostics are widely under research; however, Li-ion battery usage in maritime and ship propulsion is new, and there are not enough data and experience for batteries in the marine sector. Therefore, in this work, battery usage from stationary grid-connected usage workouts is also examined. 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Mar Technol Soc J 53(3):63–79. https://doi.org/10.4031/MTSJ.53.3.6 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8244176","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":553011033,"identity":"645045ff-9249-4eb7-b891-cefb6a63f9eb","order_by":0,"name":"Erdeniz Erol","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYHACxgMMDBJy/MxgDjNxeoBaLIwlm5mhWtiI01KRuOEAsVr4pQ8fOPBxj0Ti5uP8xyQYKqwTG+R7H+DVItmXlnBwxjMJ422HmdkkGM6kJzawsRvg1WJwhsfgMM8BCVmwFsa2w0AtBFxmD9Ly54AE4+ZmkJZ/RGgx4AFqYTggobiBGaSlgQgtEmfYEg72HJAwljjMbGyRcCzduI0tDb8W/h7mgw9+HKiT4+8/+PDGhxpr2X7mY/i1oIIEBuJichSMglEwCkYBAQAAcH8+ppBkXNIAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-9175-6887","institution":"Istanbul Technical University","correspondingAuthor":true,"prefix":"","firstName":"Erdeniz","middleName":"","lastName":"Erol","suffix":""},{"id":553011034,"identity":"74ec5dc1-2787-4390-87f4-6788ca5d8930","order_by":1,"name":"Elif Bal Beşikçi","email":"","orcid":"https://orcid.org/0000-0002-7882-8292","institution":"Istanbul Technical 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2","display":"","copyAsset":false,"role":"figure","size":46063,"visible":true,"origin":"","legend":"\u003cp\u003eRUL prognostic methods for lithium-ion batteries (Lipu et al. 2018)\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8244176/v1/71c30661021d01072af93f4e.jpeg"},{"id":97672411,"identity":"39b8374b-7030-4294-9fda-06f21c2cdaeb","added_by":"auto","created_at":"2025-12-08 09:37:27","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":55846,"visible":true,"origin":"","legend":"\u003cp\u003eThermal photograph of the MF Ytteroyningen ship during a battery fire\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8244176/v1/57319b6122030e333e289cd9.jpeg"},{"id":97520959,"identity":"33381bbd-0646-4117-b09d-d48ad405229c","added_by":"auto","created_at":"2025-12-05 11:11:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":62858,"visible":true,"origin":"","legend":"\u003cp\u003eShip Fire BN from GeNIe graph view\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8244176/v1/9ac03b1f152a0b8a2288e3db.png"},{"id":97520961,"identity":"8e19f08f-f9cf-4587-a3d6-8fee67474ec8","added_by":"auto","created_at":"2025-12-05 11:11:27","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":99220,"visible":true,"origin":"","legend":"\u003cp\u003eCauses and consequences of thermal runaway in a battery system\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8244176/v1/9f1459a5ab4ac022c3a88c6e.jpeg"},{"id":97520962,"identity":"a72acab3-90e5-4bcb-966b-6a76a8a9307b","added_by":"auto","created_at":"2025-12-05 11:11:27","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":144288,"visible":true,"origin":"","legend":"\u003cp\u003eFire targeted on a ship as a set BBN\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8244176/v1/a41ca5f3e108671aac40278f.jpeg"},{"id":97671567,"identity":"2cc00f01-b437-4427-b3a7-3cddfa12ea8d","added_by":"auto","created_at":"2025-12-08 09:32:45","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":134027,"visible":true,"origin":"","legend":"\u003cp\u003eNo-fire targeted on a ship as a set BBN\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8244176/v1/eee8222a9de673a45a321544.jpeg"},{"id":97672107,"identity":"a0170a32-685f-4e92-a6b1-0df76a1870e4","added_by":"auto","created_at":"2025-12-08 09:34:13","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":189728,"visible":true,"origin":"","legend":"\u003cp\u003eStrength influence diagram of the BBN\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8244176/v1/b6fdd8509ed18691d0020719.jpeg"},{"id":97678126,"identity":"ad87af5c-9862-486d-8ce9-b38abd18ea55","added_by":"auto","created_at":"2025-12-08 09:55:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1722575,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8244176/v1/870e9d47-6871-40bb-a19e-795e637f6306.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eBattery Fire Risk Analysis in Battery Powered Ships Using Hazop Analysis and Bayesian Belief Network\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eIn recent decades, air pollution caused by rapid growth in fossil fuel consumption has created an attitude toward energy savings. The shipping industry is under great pressure to decrease the negative effects of air pollution on the environment. Ninety percent of overall global trade is transported via the shipping sector; however, ships with diesel-propulsion systems emit large quantities of greenhouse gases such as CO2 and polluting gases such as sulphur oxides and nitrogen oxides (Chu-Van et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Geertsma et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Chen et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). While the Paris Convention has set a 2\u0026deg;C target for increasing the global temperature, if no steps are implemented to reduce CO2 emissions are taken, CO2 emissions from ships are expected to increase by 50\u0026ndash;250% by 2050. Between 2012 and 2018, the contribution of marine transportation to global CO2 emissions increased slightly from 2.76 to 2.89%. Additionally, shipping currently accounts for 15% of global NOx emissions, with the expectation that the rate will increase unless the maritime industry takes decarbonization measures (IMO \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; IMO \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). While greenhouse gas emissions are the main cause of climate change, which is recognized as one of the greatest threats to human health and biodiversity, NOx and SOx are the contributors to photochemical smog, acid rain and resulting long-term health problems (Goedkoop et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Therefore, electrification of the maritime industry has accelerated in the last decade. The use of resources such as diesel-electric systems, fuel cells or batteries for partial or full propulsion is referred to as electric/hybrid propulsion. Electrically powered propulsion systems, which provide the highest possible global efficiency and the lowest emissions by aiming for zero emission operations, especially in special areas, are preferred over mechanically driven systems for different types of ships whose operational profiles are suitable for these concepts. Hybrid ships can reduce emissions and fuel consumption by 10%-35% (Lashway 2016). To achieve zero-emissions marine transport goals by 2050, where current electric vehicles represent a low percentage of the global fleet today, there is increasing interest in integrating different battery applications on board (Joung 2020).\u003c/p\u003e\u003cp\u003eA battery is an electrochemical system capable of storing highly responsive electrical power. It gives the operator the ability to store unused or surplus energy to operate the engine more stably and then uses the energy when the ship's propulsion system needs it. The price of battery systems is decreasing with recent technological advances, whereas efficiency is increasing, making them competitive and alternative energy sources on board. In the last decade, the chemistry of batteries shaped around lithium ions has stored between two and eight times more energy per unit weight than traditional batteries with water-based electrolytes such as lead acid and nickel cadmium batteries do (Mj\u0026oslash;s et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). On the other hand, high energy density and the use of a flammable electrolyte increase the risk of operation. Therefore, lithium-based battery systems should rely on a well-developed and tested electronic control system and physical structure for safe operation, which is called the battery management system (BMS), to enable hybrid electric and all electric operation of ships by using a power management system (PMS) without any risks. Despite these systems being developed by equipment manufacturers and controlled by classification societies, some accidents that were not foreseen before or multiple situations may still arise during the same event. Thus, experts on the marine industry and equipment manufacturers need to identify potential hazards from an operational perspective. In this work, the HAZOP method is used to quantify the potential causal factors that might create a possible fire via the Bayesian belief network.\u003c/p\u003e\u003cp\u003eA hazard and operability (HAZOP) study is defined as a structured and systematic examination of a planned or existing process or operation to identify and evaluate problems that may represent risks to personnel or equipment or prevent efficient operation (Rausand \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). A HAZOP is a qualitative method that is dependent on guide words. These guide words are quantitative comparisons such as \u0026ldquo;more\u0026rdquo; or \u0026ldquo;less\u0026rdquo;, \u0026ldquo;inverse\u0026rdquo; or other sequence comparison types of words such as \u0026ldquo;before\u0026rdquo; or \u0026ldquo;after\u0026rdquo;, which are used to identify the complex operation processes decided by a diversified expert team (HAZOP team) composed of different backgrounds throughout a series of meetings. The history of HAZOP dates back to the early 1970s, when it was first officially introduced by the Institute of Chemical Industry (ICI) from the UK to assess the safety of chemical process facilities and became a risk analysis technique. The first HAZOP guideline was released by the Chemical Industries Association (CIA) in 1977, and throughout the following years, HAZOP analysis was refined. Kletz (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) provided descriptive information about process safety and the technical terms of HAZOP analysis. The author also contributed to the development of HAZOP analysis by sharing his experiences with readers. Finally, with the standard IEC 61882: 2001, the International Electrotechnical Commission (IEC) has defined and formalized new requirements for use in HAZOP studies (IEC 61882) (Akman \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). One of the recently published HAZOP analysis-based methods for marine applications is used for risk assessment of the main engine of the 10000 TEU container ship (Zhan et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Risk rating matrices and evaluations are described for ship fuel systems. This work is funded by the scientific research fund of Shanghai Maritime University. Another HAZOP study used a preliminary analysis to investigate the risk-based decision-making of mooring systems of floating structures for stations at any water depth (Khamidi et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This study's findings pinpoint the probable reasons for and repercussions of mooring system malfunctions.\u003c/p\u003e\u003cp\u003eThe risk of hydrogen fuel cell usage was measured efficiently via a Monte Carlo routine that captures the leak's transient behavior (Huser et al.2017). CFD (computational fluid dynamics) software is used with simplified templates for ventilation dispersion and explosion situations, as well as revised leak and ignition frequencies. Fire-related factors were systematically analyzed across all operational stages of the vessel. To address the uncertainty and subjectivity within evaluation parameters, the study incorporated entropy theory and a cloud-based modeling approach for quantitative risk analysis (Zhang and Yan 2017). However, few years have passed to prove the life cycle assessment of batteries. One method for obtaining Li-ion lifecycle data involves applying a supervised learning algorithm with respect to the relevance vector machine (RVM). As a Bayesian support vector machine (SVM) procedure, the RVM method allows arbitrary kernel functions to be used and produces probabilistic predictions (Tang et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe Bayesian belief network (BBN) is one of the methodologies that can be used for risk assessment, especially for the modeling of rare accidents such as the fire probabilities of an electric ship (Zhang and Thai \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Since these technologies were commercialized in the last decade, the number of fire casualties due to battery systems has been limited in the quantification of possible probabilities. The Genie tool is used to analyze the danger of a prospective battery ship fire on the basis of the input of weight evaluation elements from a risk assessment example of an electric cargo ship in Huzhou (Zhang and Yan 2017). This paper identifies the probability of operational causes for a possible fire on-board a battery electric ship.\u003c/p\u003e\u003cp\u003eTo determine the causes, effects, and preventative barriers put in place to lessen the effects of potential accidents, HAZOP and BBN research methodologies are integrated. The effectiveness of the safety barriers in a gas facility is investigated and quantified via BN analysis, and the results are compared with the risk acceptance criteria (Zerrouki and Smadi \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe remaining sections of this paper are structured as follows. The HAZOP and BBN research approaches for the maritime sector and battery applications are discussed in Section 2. In Section 3, two different case studies based on a real electric ship fire incident and the results of an electric ship fire risk assessment model are used as inputs. The HAZOP study is applied on the basis of an electric ship fire incident, and the operational causes of a fire incident are analyzed via BBN in this work. The main findings of related literature, along with the methods and possible differentiations for battery-powered ships, are presented in Section 4. Finally, key conclusions and suggestions for electric ships and maritime operations are presented in Section 5.\u003c/p\u003e"},{"header":"2. METHODOLOGY","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 HAZOP\u003c/h2\u003e\u003cp\u003eInitiation of a HAZOP analysis starts with identification of the system, as its methodology is depicted in the process block in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Therefore, a complete design representation of the system, which adequately describes the subsystems, parts, functions and properties, is a prerequisite for examination before HAZOP analysis. The system is divided into functional blocks. Every part of the process is analyzed for potential deviations from the design intention. It is determined whether the deviations may cause any hazard or inconvenience. Every phase of the process is identified with the nodes and their deviation parameters. Questions arise for these nodes, and they are detailed around direct words. Each deviation is considered to choose how it could be caused and what the outcomes would be. For hazards, preventive/curing activities are characterized by identifying existing safeguards or any other corrective measures.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eHAZOP methodology is a widely used preliminary tool for other types or combinations of tools used for the identification of hazards and their possible consequences. The outputs of HAZOP analysis include details of identified hazards and operability problems together with details of any provisions for their detection and/or mitigation; suggested steps for any additional investigations of particular elements of the design using different techniques, if necessary; actions required for addressing ambiguities encountered throughout this investigation; suggestions for alleviating the problems identified, taking into account the group's comprehension of the system (if within the scope of the study); and details of identified hazards and operability problems, along with details of any provisions for their detection and/or mitigation.\u003c/p\u003e\u003cp\u003eAs this work implies that HAZOP is a preliminary analysis technique, it can complement other techniques as well. An example of an integrated approach using HAZOP and failure modes, effects, and criticality analysis (FMECA) to identify potential unintentional events in the storage system utilized in the LNG regasification plant is a recent study. Compared with other marine fuels, LNG has become one of the most attractive fuel sources for maritime operations because of its low greenhouse gas (GHG) emissions. Compared with single methodologies, a new tool for FMECA and HAZOP integrated analysis (FHIA) has been designed for the development of specific criteria and to obtain more recommendations for reliability and risk data organisations (Giardina and Morale \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Another combination of HAZOP and a quantitative investigation of the possible threats across the development of fault sources and consequences is fault tree analysis (FTA). The results from FTA permit the organization of preventive and restorative measures to limit the likelihood of possible failure. An examination of a contextual analysis is conducted, which comprises the terminal for emptying chemical and oil-based commodities and the fuel storage facilities belonging to two organizations in the port of Valencia in Spain (Fuentes-Bargues et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Overall, HAZOP has become a standard preliminary identification tool for process plant design in offshore and marine areas, where procedural HAZOP is widely used for simultaneous operations and assessments of evacuation systems.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 BAYESIAN BELIEF NETWORK\u003c/h2\u003e\u003cp\u003eLithium-ion batteries can lose capacity and show increased internal resistance over time. Such degradation is due to cycling using the batteries as well as calendar consequences that will inevitably take place over time. The battery cell deteriorates more quickly at higher temperatures, and there are additional hazards at lower temperatures. Depending on the chemistry, the optimum cell temperature is often between 20\u0026deg;C and 30\u0026deg;C. Higher current (or power) levels as well as greater SOC adjustments (sometimes referred to as depth of discharge (DOD) or delta SOC (DSOC)) can lead to faster degradation. Battery-only storage typically involves frequent deep discharges or charges to meet sudden travel load variations, which can lead to significant battery life degradation (Fang et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). To identify the risks and possible hazards of battery usage, the European Union research projects STABALID and STALLION (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) address the risk assessment of large-scale, stationary, nongrid-connected lithium-ion storage systems. A similar approach can be used for marine-type batteries integrated into ships. Possible risk assessment phases for marine batteries include hazard identification on battery vessels; assessment of the following possible risks on battery ships; risk of gas formation; detection, alarm systems and ventilation strategies; risk of explosion and fire; external risks; loss of propulsion or auxiliary power for basic or important services; and reduced battery life.\u003c/p\u003e\u003cp\u003eWhen a battery is charged to an excess level (SOC) above 100%, there is an overcharge. It induces in-cell chemical degradation that can lead to thermal runaway, cell swelling, gas venting, and other extreme events. In contrast, overdischarge occurs when a cell is discharged above 100% discharge depth (DOD). This leads to a sudden decrease in the cell voltage or even a reversal of polarity, resulting in possible electronic control failure.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSince the battery depth of discharge (DOD) and number of cycles are arranged according to the year of planned operation, identifying the remaining useful life (RUL) of the batteries is another aspect to address during the battery operation of a ship. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the RUL estimation techniques (Lipu et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe risk of hydrogen fuel cell usage was measured efficiently via a Monte Carlo routine that captures the leak's transient behavior (Huser et al.2017). CFD (computational fluid dynamics) software is used with simplified templates for ventilation dispersion and explosion situations, as well as revised leak and ignition frequencies. The key factors resulting in a battery-powered ship fire incident were investigated from the perspective of the entire ship service process (Zhang and Yan 2017).\u003c/p\u003e\u003cp\u003eTo predict battery degradation, Keil et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) applied tests to different chemical types of cells, namely, lithium nickel cobalt aluminum oxide (\u003cb\u003eNCA\u003c/b\u003e: \u003cem\u003eLiNiCoAlO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e) NCA, lithium nickel cobalt manganese oxide (\u003cb\u003eNMC\u003c/b\u003e: \u003cem\u003eLiNiCoMnO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e)) and lithium iron phosphate (\u003cb\u003eLFP\u003c/b\u003e: \u003cem\u003eLiFePO\u003c/em\u003e\u003csub\u003e\u003cem\u003e4\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e/C\u003c/em\u003e). These datasets are obtained from the thesis of ten Cate Hoedemaker (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), which was completed at TU Delft.\u003c/p\u003e\u003cp\u003ePredictor variable selection for Bayesian linear regression models for three types of Li-ion batteries is performed according to the Bayes theorem shown below:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:P(A/B)\\:=\\:\\:\\frac{P(B/A)\\:\\:P\\left(A\\right)}{P\\left(B\\right)}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe state of P(B) is the evidence probability of event B, and P(A) is the prior probability of event A. The likelihood, P(B/A), is the conditional probability of event B given event A, and the posterior, P(A\\B), is the conditional probability of event A given event B.\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:posterior\\:=\\:\\:\\frac{prior\\:.\\:likelihood}{evidence}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe fluidity and randomness of the evaluation variables, as well as the subjective impact of the scoring levels, are subsequently resolved via the cloud model and entropy theory. Then, the entropy- and cloud-based framework for fire risk assessment is discussed. However, few years have passed to prove the life cycle assessment of batteries. One method for obtaining Li-ion lifecycle data involves applying a supervised learning algorithm with respect to the relevance vector machine (RVM). As a Bayesian support vector machine (SVM) procedure, the RVM method allows arbitrary kernel functions to be used and produces probabilistic predictions (Tang et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Since electric failures are new onboard, few casualties related to electric blackouts have occurred (Zhang et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). To date, there are only 2 cases of battery fires onboard passenger ferries; neither of them results in total losses (Mrozik et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. CASE STUDY","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.1 HAZOP Results\u003c/h2\u003e\u003cp\u003eA minor fire was discovered in the battery chamber of the hybrid passenger ferry MF Ytter\u0026oslash;yningen on October 10, 2019, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, followed by a gas explosion on board 12 hours after the fire. Although there were no personal injuries, preliminary reports from relevant stakeholders revealed that the battery system was not connected to the shipside systems at the time of the incident because of ongoing service. Consequently, alarms from the battery system were off through the ship's alarm and monitoring system, where ship personnel were not aware of thermal runaway, resulting in battery fires and switchboard room explosions, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (Fire onboard Ytter\u0026oslash;yningen car-ferry: preliminary investigation findings 2020).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe marine battery manufacturer of this ferry presented lessons learned in this accident at a conference in March 2020. In that context, the battery supplier raised their complaints about the lack of seaman training and low industry knowledge on battery safety, which address a clear need to increase training on batteries and awareness among all stakeholders (Battery safety: Lessons learned from the fire aboard the MF Ytter\u0026oslash;yningen \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The reports and documents related to this casualty are systematically inserted into HAZOP study in Table\u0026nbsp;1 to clarify the guide words, causes, consequences, existing guards, and the required actions of the actors.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGuide Word\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDeviation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePossible Cause\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eConsequences\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eExisting Safeguards\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAction Required\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAction by\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eAction taken\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNO\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eNO FLOW\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eno part of the battery system was connected to the shipside systems at the time of the incident.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eConsequently, no alarms from the battery system were sent through the ship\u0026rsquo;s alarm system.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBMS integration into Alarm Monitoring System\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDo not power down the battery equipment and check about the alarms about Battery frequently\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eShip Personnel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026ldquo;The vessel must not be operated without an active communication link between the EMS (Energy Management System) and individual battery packs\u0026rsquo; BMS (Battery Management System). Maintaining power to the packs preserves this link and prevents communication loss\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMORE\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eMORE TEMPERATURE\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eThe leakage created arcing between electrical components, at pack voltages of 1000Vdc, igniting a fire\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eFire was fueled by ethylene glycol components from the coolant and caused external heating of battery modules.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCorvus Passive Single Cell Thermal Runaway Isolation safety system worked as designed and intended, most likely limiting the damage from the fire.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eWith early detection via a purpose-built gas sensor or smoke detector, a problem cell can be disconnected before thermal runaway\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eBattery Supplier/Integrator\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBoth Novec 1230 inert gas fire suppression system of the vessel and the supplementary saltwater sprinkler system were activated during the incident. While the saltwater system served as an additional safety measure, available evidence indicates that its activation may have aggravated the event.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eREVERSE\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eREVERSE FLOW\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003ehow the extent and severity of the following events were able to develop toward explosion\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eAn explosion 12 hours later in the switchboard room next to the battery chamber.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAdequate ventilation is required to lower the risk of compartment overpressure and blast from the gas produced in a thermal runaway.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eEstablish procedures to respond to these fire events \u0026amp; programmes for exercises and drills to get ready for emergencies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eShipowner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eVenting mechanisms help redirect flammable gases away from unstable battery units, thereby decreasing explosion risk. Nevertheless, ventilation alone is inadequate if a significant segment of the battery system ignites\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOTHER THAN\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eUtility failure, Maintenance, Leak, Safety, Corrosion, Instrumentation etc.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eleakage in the battery system\u0026rsquo;s liquid cooling circuit\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eThe leakage created arcing between electrical components, at pack voltages of 1000Vdc, igniting a fire\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCorvus Passive Single Cell Thermal Runaway Isolation safety system\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSeawater fire fighting systems should only be used as back-up or final resource systems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eBattery Supplier/Integrator\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eObserve the contingency document: \u0026ldquo;1007814 Guidance Note: Assessment and Response After a Thermal Event involving Orca ESS (Energy Storage System)\u0026rdquo;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePART OF\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ebattery module\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003ea twisted gasket, intended to seal the cooling plate outside of a battery module, is the most likely cause of the leakage\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eLeakage in the battery system\u0026rsquo;s liquid cooling circuit\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTo reduce overcharging a hard-wired lock-out function that disconnect BMS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eThe battery monitoring systems of commissioned/active ESS systems must be connected to the ship system at all times \u0026ndash; even when the ESS is not in use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eBattery Supplier/Integrator\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eAll shipowners using a battery system should carry out a risk assessment based on the recommendations of the updated safety message.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable-1 HAZOP study based on battery fire casualty reports.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Case Ship-2 BBN\u003c/h2\u003e\u003cp\u003eTo increase awareness of fire probabilities, the Bayesian belief network (BBN) is one of the technologies that can be utilized. Zhang\u0026rsquo;s model utilized an integrated cloud-computing algorithm for risk evaluation and validated it via a 500-ton all-electric cargo vessel operating in Huzhou as a case study (Zhang and Yan 2017).\u003c/p\u003e\u003cp\u003eA battery safety assessment model based on a Bayesian network was developed in this work using the conditional probability of nodes and the network topological structure. The ship's fire safety is simulated via GeNIe software. First, the construction of an inference network model in GeNIe is shown in Figure-4. Input probability data for this Bayesian analysis were derived from Zhang and Yan\u0026rsquo;s validated entropy\u0026ndash;cloud risk model, which demonstrated reliable predictive capability in prior assessments. (Zhang and Yan 2017).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows similar causes and consequences of a fire and thermal runaway system, which is presented in the Maritime Battery Safety Joint Development Project Technical Reference for Li-ion Battery Explosion Risk and Fire Suppression. The total frequency of a fire (per year) probability is also inserted from that reference, as the Battery System Fire probability is 5,20E-04 and the Engine Room Fire Probability is 6,80E-04 (Maritime Battery Safety Joint Development Project \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eUnder certain conditions of abuse, such as overheating, Li-ion cells may vent the electrolyte and/or enter into thermal runaway. The type of chemistry affects the probability of fire risk, but in this work, this effect has not yet been addressed. The tests revealed a significant difference between nickel manganese cadmium (NMC) and lithium iron phosphate (LFP) cells. Compared with the NMC pouch cells, the LFP cylindrical cells were much harder to force into thermal runaway. In particular, NMC batteries are used when better energy efficiency (and more power) is needed, as their energy density is higher, whereas LFP batteries are used where safety is more important because they are more reliable in possible fire situations because of their higher temperature resistance (Ouyang et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). NMC-type battery pack providers have improved this lack by developing battery management and protection systems to comply with marine safety regulations. Another alternative method for preventing thermal runaway is the use of lithium-titan-oxide (LTO) battery chemistry as an anode material for a high life cycle and high-power operational profile. For the same operational performance, LTO batteries only need approximately 50% of the rated energy capacity of the equivalent NMC batteries. Although resulting in a smaller capacity than the NMC counterpart, together with the cost per kWh, the LTO chemistry will be competitive when the lifetimes of the batteries are greater than 20 years of operation and/or when ultrafast charging (\u0026gt;\u0026thinsp;3C) is needed.\u003c/p\u003e\u003cp\u003eThe probability distribution of a node is given as a distribution of conditional probabilities on the basis of the state of its parents, called the node probability table (NPT). State 1 represents fire, and State 0 represents no fire. The NPT can be interpreted as described in Table\u0026nbsp;1. The same principle can be applied to all nodes in a BBN. The colors are used for classifying the different stages of a ship when a fire can occur.\u003c/p\u003e\u003cp\u003eThe probabilities in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e are input into the BBN. Figure-6 represents the fire-targeted probabilities, and Figure-7 represents the no-fire-targeted probabilities. When they are compared, although the weights are similar, the stage percentages differ in terms of the fire target. The fire probability derived from batteries while sailing in normal and harsh weather conditions is more likely when no fire on a ship is targeted. This means that when a ship fires, it is mostly the consequence of a ship accident. The other battery fire stages cause no ship fires as a result of ship accidents. Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows the strength of influence that is affected by the child nodes. As shown in the graph, the thickness represents how strongly the child node affects its parent node. Battery insulation, overcurrent and high temperature are the most powerful effects for a possible fire.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUpdated Node Probability Table for the S/H/C/M/V Structures in a BBN\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProb.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRisk item\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eProb.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRisk evaluation indicator\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eProb.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eCharging the battery (S)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e0,17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eelectric leak (S1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0,21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFailure of an insulation(S11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eShort circuit of the battery (S2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0,41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOvercharge of the battery (S21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0,28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOverheat of the battery(S22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0,41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOvervoltage(S23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0,31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCharging\u003c/p\u003e\u003cp\u003einterface is poorly contacting (S3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0,38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eshore charging station failure (S31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eVoyage under standard operating conditions (H)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0,12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eElectrical cables are poorly contacting (H1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0,3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFailure due to human factor (H11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eShort circuit of electric system (H2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0,53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFailure due to equipment(H21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFire inside cargo (H3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0,17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFailure due to lack of operational supervision (H31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eOperation under adverse environmental conditions (C)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e0,3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eelectric surge current (C1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0,13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLightning strike (C11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIgnition or flame (C2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0,23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOver temperature (C21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eelectric cables are poorly contacting (C3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0,4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIntense vibration induced by heavy winds (C31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eshort circuit on the electrical grid (C4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0,11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHeavy rain or flood (C41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFire in electrical cable (C5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0,13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOver current because of overload (C51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eOccurrence of a marine traffic accident (M)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e0,32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eShort circuit of the battery (M1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0,27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBattery enclosure distortion (M11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0,57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDefective battery (M12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0,43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eExplosion of the battery (M2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0,43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOverpressure (M21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0,38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOvertemperature (M22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0,62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ecargo corrosion(M3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0,3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLeak of electrolyte (M31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVessel berthing and loading stage (V)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0,08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eShort circuit of the battery (V1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0,42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eExternal object impact damaging the battery enclosure (V11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eelectric cables are poorly contacting (V2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0,58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSevere vibration caused by cargo handling (V21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. RESULTS AND DISCUSSION","content":"\u003cp\u003eWang et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) used HAZID, fault tree, event tree, and cost‒benefit analyses to evaluate the risk and safety level of a battery-powered high-speed catamaran ferry. A high-speed battery-powered ferry operating in the Norwegian Sea, which has numerous small islands and frequently requires passenger transportation between islands and the mainland, is the subject of these quantifications, which are based on assumptions for assets, human life, and the environment due to the hazards of collision, grounding, contact, and fire of previous hazards of nonelectric ships. There are three parts of the risk assessment: first, design, construction and installation; second, operation, that is, during a voyage and approaching/leaving port; and, finally, an emergency situation.\u003c/p\u003e\u003cp\u003eThe maritime industry is facing increased focus on sustainable propulsion systems and energy storage capability improvement with battery technologies. On the other hand, another battery room fire occurred in March 2021 in the hybrid vessel \u0026lsquo;Brim\u0026rsquo;. By a combination of lessons learned from the two cases of MF Ytter\u0026oslash;yningen and Brim, the Norwegian Safety Investigation Authority published a report exclusively for the purpose of improving safety at sea (Report on fire on board \u0026lsquo;MS Brim\u0026rsquo; in the outer Oslofjord on 11 March 2021 2022). Because neither the ventilation system's nor the battery system's vulnerabilities were effectively detected in that case, the Norwegian Safety Investigation Authority is unable to conclude that the risk assessment accurately reflects the real risk of fire in the battery packs. All pertinent risks that have been discovered by different disciplines and that collectively pose a risk to the vessel should be covered in such a risk assessment.\u003c/p\u003e\u003cp\u003eHowever, our risk assessment approach is based on covering an operational perspective and preventive actions before an incident may occur. The two approaches are combined under HAZOP and BBN for a marine battery fire incident that has not been handled before.\u003c/p\u003e"},{"header":"5. CONCLUSION","content":"\u003cp\u003eIn summary, HAZOP is a widely used, interdisciplinary qualitative tool for better identification of complex systems and preliminary chases for mitigation of their risk-based solutions. The strengths of HAZOP analysis are summarized as follows: it is widely utilized, its benefits and drawbacks are well understood, it makes use of the team's operating staff expertise, it is methodical and thorough, and it should detect all hazardous process variations. It identifies current protections and creates recommendations for new ones, and it works well for both technical and human errors. When dealing with marine dangers in offshore activities that necessitate the collaboration of multiple professions or organizations, the team method is especially suitable.\u003c/p\u003e\u003cp\u003eDespite the aforementioned advantages, HAZOP studies have drawbacks in that team expertise and leader facilitation are crucial to their success. It needs to be modified to cover various kinds of dangers because it is optimized for process hazards. This necessitates the creation of procedural descriptions, which are frequently lacking in sufficient detail. Nonetheless, the operation might benefit from the existence of these materials. It takes much time and effort to fully record HAZOP documentation.\u003c/p\u003e\u003cp\u003eBattery-related risk assessment and battery usage prognostics are widely under research; however, Li-ion battery usage in maritime and ship propulsion is new, and there are not enough data and experience for batteries in the marine sector. Therefore, in this work, battery usage from stationary grid-connected usage workouts is also examined. The recent explosions in a retrofitted battery ferry and passenger ferry in Norway make people suspicious about the risks and causes of such incidents that may occur if risk assessments are not conducted, as qualitative HAZOP and HAZID works and makes predictions via quantitative research via Bayesian approaches.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAkman A (2015) Kimya sekt\u0026ouml;r\u0026uuml;nde tehlike ve işletilebilirlik (HAZOP) analizi. \u0026Ccedil;alışma D\u0026uuml;nyası Dergisi 3(2):59\u0026ndash;74. 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Mar Technol Soc J 53(3):63\u0026ndash;79. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4031/MTSJ.53.3.6\u003c/span\u003e\u003cspan address=\"10.4031/MTSJ.53.3.6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Istanbul Technical University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"HAZOP, Bayesian belief network, battery fire, ship fire, ship electrification, risk assessment","lastPublishedDoi":"10.21203/rs.3.rs-8244176/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8244176/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn recent years, hybrid electric and all-electric ships have become emission reduction options for coastal sea transportation, increasing energy efficiency with alternative nonfossil fuels owing to emerging battery developments. On the other hand, efficient electric propulsion ships and safe battery operations require additional operational practices and competencies for seafarers compared with mechanical propulsion ships. This paper is prepared for analyzing hazard and operability (HAZOP) studies for the identification of potential hazards on the basis of a casualty report of a battery retrofitted with a ferry in Norway and the Bayesian Belief Network for the quantification of potential fires of a battery onboard. Although battery management systems (BMSs) address operational safety concerns standalone, such a high-energy-density battery and flammable electrolyte inside may pose a possible fire risk when battery storage is not offline within the ship electrical system. Owing to the lack of sufficient data and experience with batteries in the marine sector, the Bayesian belief network (BBN) is a method that can be used to quantify the fire probability of a ship. The Genie tool is used for the risk evaluation tool of a possible battery ship fire by using the input of weight evaluation factors that are derived from the study of a risk assessment sample of an electric cargo ship in Huzhou. According to our results, battery insulation, overcurrent and high temperature are the most powerful effects for a possible fire onboard.\u003c/p\u003e","manuscriptTitle":"Battery Fire Risk Analysis in Battery Powered Ships Using Hazop Analysis and Bayesian Belief Network","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-05 11:11:23","doi":"10.21203/rs.3.rs-8244176/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"82e385ac-d579-4190-9cc5-47a8770dbf1c","owner":[],"postedDate":"December 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":59147736,"name":"Ocean Engineering"}],"tags":[],"updatedAt":"2025-12-05T11:11:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-05 11:11:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8244176","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8244176","identity":"rs-8244176","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}