Automated System for Automatic Data Acquisition for Pem Electrolyser

Automated System for Automatic Data Acquisition for Pem Electrolyser | 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 Article Automated System for Automatic Data Acquisition for Pem Electrolyser Vishwapranav P, Gowthaman N, Ganesan Pandian, Saikrishna V, Veeramani Vediappan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6154160/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 Proton Exchange Membrane (PEM) electrolyzer are crucial components in hydrogen production systems, where they play a significant role in splitting water into hydrogen and oxygen. However, the performance and longevity of PEM electrolyzer are highly dependent on the condition and integrity of the individual stacks within the system. Regular monitoring and maintenance of these stacks are essential to ensure efficient operation and prevent system failures. Traditionally, this process is labor dependent, requires human inspection and impedance measurement of each stack, which is time-consuming, prone to human error, and may lead to delayed detection of anomalies. Thus, developing an automatic system that constantly acquires data from the cell stack with the integration of sensors (such as current and voltage) can significantly improve cell efficiency, economize cost as cells are made of materials such as platinum which is highly expensive, also preventing changes in torque set and significant time conservation. The main objective is to apply this automatic system to multi cell stack arrangement, where only the total cell voltage and current can be acquired through software but not individual cell’s values. This research introduces an automated system to address the challenges encountered in multi-cell stack configuration. Physical sciences/Engineering Physical sciences/Engineering/Electrical and electronic engineering Physical sciences/Engineering/Energy infrastructure Physical sciences/Engineering/Mechanical engineering Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 1. INTRODUCTION The electrolysis of water in a cell utilizing a Solid Polymer Electrolyte (SPE) which facilitates proton conduction, and gas product separation with bipolar separation material is Proton Exchange Membrane (PEM) electrolysis. A key advantage is that its potential to function at high current densities, lowering operational costs specifically for systems integrated with high erratic energy sources such as solar and wind. Thus, this would be one promising solution for the future energy demands by converting hydrogen into electricity supplying high specific energy and energy density about 141.89 kJg − 1 and 2.5 kWh per gallon of hydrogen. The electrochemical splitting of water into hydrogen and oxygen is considered as an important component using proton exchange membrane (PEM) cell for renewable energy foundation. However, on the long-term functioning of the electrolyzer stack, assembly and balance of plant issues arise. Comprehending current generation, water distribution in each Membrane Electrode Assembly (MEAs) and electrode-electrified interfaces by performing electrochemical impedance spectroscopy which is powerful tool to analyze the overpotential in multiple PEM electrolyzer stacks. Most of the cases, the analysis of stacks is usually carried out manually by removing the stacks with bare hands, analyzing the flow channels, and replacing them. The early diagnosis and recovery from the degradation is challenging for the PEM water electrolysis. Once the impedance spectroscopy is carried out, the alterations in the same can be mapped to the defects. Since stacks can be evaluated in sets, it will be easy to locate the problematic stack and analyse its individual cells. When a greater number of stacks are connected, the system moves around the stacks and connect to the external pins to collect the I and V values. 2. METHODOLOGY 2.1 For single cell: The setup comprises of platinum electrodes, peristaltic pump, dedicated containers for O 2 out and H 2 out, projections from the cell for data collection by using alligator clips. The input voltage is given from the software through which current density is obtained and the vice versa. The peristaltic pump on the other hands regulates water flow through the set pressure to obtain higher hydrogen gas per hour. Figure 1 shows single cell Electrolyzer setup with peristaltic pump circulating water inside the flow channels of the cell. 2.2 Manual Data Acquisition The I-V (current and voltage) values are usually obtained by providing the input voltage and then running the cell for an hour to obtain its respective current density and the hydrogen produced per hour. Before implementing the system for single cell, a manual voltage check was also performed using a multimeter to confirm that the cell produces the same output voltage as specified by the input. The data acquisition system implemented automates the entire data acquisition process ensuring least human intervention and damages to the cell. Figures 2 and 2 shows manual data acquisition process for a single cell where a multimeter is used. 2.3 For multi cell stack: 2.3.1 Problem Faced & Proposed Solution The primary challenge in multi cell stack is that only the total voltage and current across the cells can be measured in software but not individual cell I-V values. Therefore, the objective of this research is to design and develop a system capable of automatically acquiring impedance data from each stack in a PEM electrolyser. The system will autonomously navigate to each stack, measure the impedance, and analyze the data to detect any anomalies. If an anomaly is detected, the system will automatically initiate a safety protocol to shut down the electrolyser by turning off the peristaltic pump, thereby preventing potential damage and ensuring the safety of the operation. The data is collected and stored for further research purposes. 2.3.1 Hardware Setup The automatic system of data acquisition with relays is assembled, controlled and programmed using Arduino. The sensors that are used for acquiring data are integrated with Arduino and the analog to digital converter (ADC) present in the Arduino helps convert the analog voltage into digital values that can be viewed using the serial monitor. Nickel projections are extracted from the cell and welded to measure the I-V characteristics, thereby preventing increases in cell thickness. Figure 4 shows an automated data acquisition system that acquires and communicates the data such as voltage. 2.3.2 Data Acquisition and Programming: The measurement system is structured as follows: The first nickel strip (serving as the reference terminal) is connected directly to the A0 analog input of the Arduino. The remaining four terminals are connected to the four relay channels on their normally closed (NC) contacts. The first three relay channels have their NC terminals wired to subsequent analog input channels (A1, A2, and A3) of the Arduino for individual cell voltage measurements. All normally open (NO) contacts of the relays are interconnected and tied to ground (GND) of the Arduino. 2.3.3 Sequential Measurement Process This setup enables sequential reading of individual cell voltages by selectively grounding each nickel strip, ensuring that the potential difference is measured correctly for each cell: To measure Cell 1 (between Nickel Strip 1 and 2), the system reads from A0, while Relay 1 is activated, grounding the second nickel strip. For Cell 2 (between Nickel Strip 2 and 3), the system reads from A1, activating Relay 2, grounding the third nickel strip. This pattern continues for subsequent cells, ensuring that each voltage measurement is referenced correctly against its preceding terminal. The fourth relay channel is not used for analog measurements, as it does not correspond to a positive potential reading for any cell. 2.3.4 Advantages of This Approach Isolation of Individual Cell Voltages : The relay module allows selective grounding of nickel strips, ensuring each cell’s potential is measured independently without interference from adjacent cells. Accurate Potential Difference Measurement : By grounding one terminal at a time, the Arduino reads the voltage of each cell relative to the grounded strip, mimicking a differential voltage measurement setup. Efficient Data Acquisition : Using a sequential switching mechanism, the system systematically cycles through the cells, allowing real-time monitoring without requiring complex differential amplifier circuits. Automated Data Logging : The measured voltages are processed and printed via serial communication to a connected Python script running on a laptop. The script utilizes PySerial to interface with the Arduino and Pandas to log the data into an Excel sheet for further analysis. 2.4 Anomaly Detection The cell's performance can be anticipated in advance by analysing the real-time data acquired. Any anomalies or damage can be detected early, preventing significant harm to the cell. Further, this movable automatic system is coupled with the Electrochemical impedance spectroscopy to analyze the possible ohmic losses. The impedance measurements will be performed based on the problem in the MEAs, for example a set of 5 MEAs out of 10, total MEAs, affected MEAs or combination of any of MEAs. Hence, the automatic system will support us to do selective impedance analysis. These analyses are carried out in Origaly software using chrono potentiometry method. 2.5 Working The PEM electrolysing system consists of a compilation of parts to ensure that water is split into hydrogen and oxygen through electrolysis. Every part works together to deliver the function of the system which is changing from water management to thermal regulation, gas separation, to electrical supply. Through these, the integration gives the electrolyzer an efficient and reliable performance. A feed of flowing water is supplied to the anode fluid channels because electrolysis needs water. While maintaining constant flow, it also feeds surplus water circulated by a pump, so as not to lose it without being used, feeding into the reaction. On the other side, the anode fluid channels facilitate the passage of water from the feed toward the MEA and break it up within the MEA into protons, electrons, and oxygen through the OER. The recirculation system segregates and controls excess water and oxygen. It is crucial in that the heat exchanger maintains the temperature within desirable optimal operating limits by dissipating excess heat arising from resistive and activation losses in the electrolysis process. In doing so, this ensures no thermal degradation of the MEA and stabilizes the operation of the electrolyzer. The MEA thermal mass assists in maintaining this thermal regulation by providing a buffer to sudden temperature variations and lengthening the service and stability life of the electrolyzer. The electrical (Solar profile, ramp and step voltage) supply supplies the energy required to sustain the electrolysis reaction. A DC power supply applies voltage between the MEA, surmounting activation, Ohmic, and mass transport losses to support hydrogen and oxygen evolution. The electrolysis reaction produces hydrogen at the cathode and oxygen at the anode. The cathode gas channels allow for efficient conduction of the hydrogen gas with minimal water vapour contamination. Residual water vapor is eliminated with the dehumidifiers, which increase the quality of hydrogen coming out. The entire system of electrolyzer along with the data acquisition system is shown in Fig. 5 . Purified hydrogen is transferred to the hydrogen output for collection for storage or future use, thus ensuring the system's outputs are of high quality for downstream applications such as fuel cells or storage. Oxygen can either be vented or recycled, depending on needs of the system.The PEM electrolyser is simulated using Simscape, which combines electrical, thermal, and fluid domains to approximate the system's real performances. For example, this simulation enables detailed examination of behaviours in the system, including voltage against current density, power consumption, and heat dissipation. 3. Results and Discussion The simulation will enable the identification of the key parameters in the process, optimizing the associated operating conditions with regards to efficiency and safety.In brief, the PEM electrolyzer system combines state-of-the-art components and simulation techniques to give a good account of efficiency production of hydrogen without neglecting thermal, electrical, and fluid management challenges. It ensures stable operation, high purity of hydrogen produced, and successful performance monitoring, making it a cornerstone for green hydrogen production technologies. The same simulation can be carried out in Matlab Environment with different forms of electrical input to estimate the hydrogen generation which is shown in Fig. 6 . It is a setup for 5 stacks. Figures 7 , 8 and 9 show the voltage, power and temperature modifications aligning with the hydrogen production. Table 1 Current density and voltage Voltage(V) Current Density(mA/cm 2 ) Hydrogen Production(mL/h) 1.4 2.36 - 1.5 3.25 - 1.6 8.57 - 1.7 26.89 60 1.8 56.44 120 1.9 91.90 200 2.0 130.3 240 2.1 173.3 400 From Table 1 , we can infer that there is a sudden spike and greater variations in current density values from 1.7V. Further, the suitable operating voltage for the cell is between 1.6-1.8V despite the presence of elevated current densities and significant hydrogen generation beyond 1.8V. This is because high voltages for prolonged periods may damage the cell. We can also observe that, there is no hydrogen production for 1.4- 1.6V. Figures 10 and 11 shows the growing trend of the current density with respect to voltage. 3.1 Initial Data In the study of PEM electrolyzer behaviour, the initial dataset typically consists of a small number of discrete data points that represent the relationship between cell voltage and current density. While this dataset provides a foundational understanding, it poses several challenges. First, the data is sparse meaning there are large gaps between adjacent voltage values. This becomes problematic in regions where current density exhibits rapid changes, such as at the onset of exponential surges. Second, calculating results for every possible voltage value within the operational range requires extensive data collection or simulation runs. This approach can be both computationally intensive and time-consuming. 3.2 Data Smoothing Using Cubic Spline Interpolation: In this research paper, the cubic spline interpolation method is used for the dataset to get a smoothened version of the curve showing the cell voltage versus current density. Cubic spline interpolation fits a smooth continuous and differentiable curve through data by fitting piecewise cubic polynomials between consecutive data points. Using the make_interp_spline function from Python's SciPy library, a smooth curve with 100 interpolated points in the original range of 8 data points is obtained. This captures the general trends of the data but gets rid of the jags between points, essential for understanding correctly any inflection points and surges in the curve for the current density. Figure 9 represents the voltage vs current density plot for populated data. 3.3 Detection of Surge in Current Density Accurate understanding of performance limits and stability conditions in operation requires one to detect the exponential increase of the current density in PEM electrolyzers. The surge in the current density accompanying this increase, with a sharp increase in electrical current beyond a certain voltage threshold, indicates a point of significant transition in the system, commonly associated with increased defect on the membrane and the potential for degradation. This point was detected using data interpolation coupled with derivative analysis. Sparse experimental data first were interpolated into a smooth, high-resolution dataset by using cubic splines to dampen the noise and add more detail. Next, first and second derivatives of the smoothened curve were computed in order to look into the rate and acceleration of changes in current density related to the applied voltage. For identifying the true surge point and excluding minor fluctuations caused by noise, a dynamic threshold based on the second derivative distribution was implemented. This guaranteed exact determination of the surge onset but also enabled the identification of operational thresholds critical for optimization, increasing the safety in the systems as shown in Fig. 13 and Table 2 . Table 2 Cell voltages for which surge in current density can be observed S. No Cell Voltage (V) Current Density (mA/cm2) 1 1.477778 3.058886 2 1.484848 3.103348 3 1.491919 3.161577 4 1.498990 3.237485 5 1.513131 3.457978 6 1.520202 3.610385 7 1.527273 3.796113 CONCLUSION From this work, it is concluded that current density exponentially surges from 1.477V. The PEM setup carried out with three different supply types produced the expected output as obtained in the hardware setup. Using the automatic system, the process of retrieving data can be automated and the received data is used to analyze where the surge starts. With this, we can identify where the stack is good to operate at. Further, the simulation results show that as the voltage supply increases, the hydrogen production start after a certain threshold. And the stack temperature also increases along. This increase in stack temperature after a particular region may affect the membrane and this needs to be prevented. Hence identifying where the surge starts helps to determine where the stack is good to operate at to prevent damage of membrane. Declarations Author Contribution A. B. D performed the work and wrote the main manuscript along with taking tests and resultsC. E. F made suggestions and gave ideas on improving the manuscript Acknowledgement The authors thank Vellore Institute of Technology, Chennai Campus for providing Rs. 4,78,000 /- under the scheme VIT-RGMS-SEED funding for the project “Earlier Determination of Membrane Electrode Assembly (MEA) Degradation using the Armed Movable System Coupled with Electrochemical Impedance Spectroscopy (EIS) for Proton Exchange Membrane (PEM) Water Electrolyzer”, SENSE 25. Data Availability The datasets used and/or analysed during the current study can be obtained from the corresponding author on reasonable request. References Norazahar, Norafneeza, et al. "Degradation modelling and reliability analysis of PEM electrolyzer." 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"Membrane degradation in PEM water electrolyzer: Numerical modeling and experimental evidence of the influence of temperature and current density." International Journal of Hydrogen Energy 40.3 (2015): 1353-1366. Ozdemir, Safiye Nur, and Oguzhan Pektezel. "Performance prediction of experimental PEM electrolyzer using machine learning algorithms." Fuel 378 (2024): 132853. Koo, Taehyung, et al. "Development of model-based PEM water electrolysis HILS (Hardware-in-the-Loop simulation) system for state evaluation and fault detection." Energies 16.8 (2023): 3379. Folgado, Francisco Javier, Isaías González, and Antonio José Calderón. "PEM electrolyser digital twin embedded within MATLAB-based graphical user interface." Engineering Proceedings 19.1 (2022): 21. Folgado, F. J., I. González, and A. J. Calderón. "PEM electrolyzer digital twin embedded within MATLAB-based graphical user interface. Proceedings 2022, 69, x." Presented at the 1st International Electronic Conference on Processes: Processes System Innovation . Vol. 17. s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations., 2022. Hong, Jichao, et al. "Review on proton exchange membrane fuel cells: Safety analysis and fault diagnosis." Journal of Power Sources 617 (2024): 235118. Additional Declarations No competing interests reported. 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. 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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-6154160","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":433923493,"identity":"b3f41db7-0e19-4977-b013-861fa1c3a961","order_by":0,"name":"Vishwapranav P","email":"","orcid":"","institution":"Vellore Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Vishwapranav","middleName":"","lastName":"P","suffix":""},{"id":433923494,"identity":"51e640e4-457f-4424-9f05-32acf9b268fb","order_by":1,"name":"Gowthaman N","email":"","orcid":"","institution":"Vellore Institute of 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output\u003c/p\u003e","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6154160/v1/3e370d5c6eb5f02f80a97574.jpg"},{"id":79257193,"identity":"7be02e56-12a3-40a8-80dc-e3f74e3ca71f","added_by":"auto","created_at":"2025-03-26 09:03:56","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":26310,"visible":true,"origin":"","legend":"\u003cp\u003ePlot for Voltage vs Current density for single cell stack\u003c/p\u003e","description":"","filename":"10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6154160/v1/216223d18e8a7a23fbda2ac0.jpg"},{"id":79258715,"identity":"e5544eee-afbe-451d-8aa3-cb0ec85abad1","added_by":"auto","created_at":"2025-03-26 09:11:56","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":57789,"visible":true,"origin":"","legend":"\u003cp\u003eFig. 12 Plot for Voltage vs Current density for populated data\u003c/p\u003e","description":"","filename":"12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6154160/v1/403178290a21dddae9da4863.jpg"},{"id":79257199,"identity":"492df75b-dbc2-4000-a1fa-e2eb9757c80a","added_by":"auto","created_at":"2025-03-26 09:03:56","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":41823,"visible":true,"origin":"","legend":"\u003cp\u003eFig 13 Data points where surge starts\u003c/p\u003e","description":"","filename":"13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6154160/v1/c1e3e5decc7e3cd51e6f08b9.jpg"},{"id":80618806,"identity":"6f66201b-c081-4c73-beae-9f0b05661850","added_by":"auto","created_at":"2025-04-15 09:17:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1518857,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6154160/v1/f1120220-2b5c-418a-8965-125f2369b28b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eAutomated System for Automatic Data Acquisition for Pem Electrolyser\u003c/p\u003e","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eThe electrolysis of water in a cell utilizing a Solid Polymer Electrolyte (SPE) which facilitates proton conduction, and gas product separation with bipolar separation material is Proton Exchange Membrane (PEM) electrolysis. A key advantage is that its potential to function at high current densities, lowering operational costs specifically for systems integrated with high erratic energy sources such as solar and wind. Thus, this would be one promising solution for the future energy demands by converting hydrogen into electricity supplying high specific energy and energy density about 141.89 kJg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 2.5 kWh per gallon of hydrogen. The electrochemical splitting of water into hydrogen and oxygen is considered as an important component using proton exchange membrane (PEM) cell for renewable energy foundation. However, on the long-term functioning of the electrolyzer stack, assembly and balance of plant issues arise. Comprehending current generation, water distribution in each Membrane Electrode Assembly (MEAs) and electrode-electrified interfaces by performing electrochemical impedance spectroscopy which is powerful tool to analyze the overpotential in multiple PEM electrolyzer stacks. Most of the cases, the analysis of stacks is usually carried out manually by removing the stacks with bare hands, analyzing the flow channels, and replacing them. The early diagnosis and recovery from the degradation is challenging for the PEM water electrolysis. Once the impedance spectroscopy is carried out, the alterations in the same can be mapped to the defects. Since stacks can be evaluated in sets, it will be easy to locate the problematic stack and analyse its individual cells. When a greater number of stacks are connected, the system moves around the stacks and connect to the external pins to collect the I and V values.\u003c/p\u003e"},{"header":"2. METHODOLOGY","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 For single cell:\u003c/h2\u003e \u003cp\u003eThe setup comprises of platinum electrodes, peristaltic pump, dedicated containers for O\u003csub\u003e2\u003c/sub\u003e out and H\u003csub\u003e2\u003c/sub\u003e out, projections from the cell for data collection by using alligator clips. The input voltage is given from the software through which current density is obtained and the vice versa. The peristaltic pump on the other hands regulates water flow through the set pressure to obtain higher hydrogen gas per hour. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows single cell Electrolyzer setup with peristaltic pump circulating water inside the flow channels of the cell.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Manual Data Acquisition\u003c/h2\u003e \u003cp\u003eThe I-V (current and voltage) values are usually obtained by providing the input voltage and then running the cell for an hour to obtain its respective current density and the hydrogen produced per hour. Before implementing the system for single cell, a manual voltage check was also performed using a multimeter to confirm that the cell produces the same output voltage as specified by the input. The data acquisition system implemented automates the entire data acquisition process ensuring least human intervention and damages to the cell. Figures\u0026nbsp;\u0026lt;link rid=\"fig2\"\u0026gt;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u0026lt;/link\u0026gt;\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows manual data acquisition process for a single cell where a multimeter is used.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 For multi cell stack:\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Problem Faced \u0026amp; Proposed Solution\u003c/h2\u003e \u003cp\u003eThe primary challenge in multi cell stack is that only the total voltage and current across the cells can be measured in software but not individual cell I-V values. Therefore, the objective of this research is to design and develop a system capable of automatically acquiring impedance data from each stack in a PEM electrolyser. The system will autonomously navigate to each stack, measure the impedance, and analyze the data to detect any anomalies. If an anomaly is detected, the system will automatically initiate a safety protocol to shut down the electrolyser by turning off the peristaltic pump, thereby preventing potential damage and ensuring the safety of the operation. The data is collected and stored for further research purposes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Hardware Setup\u003c/h2\u003e \u003cp\u003eThe automatic system of data acquisition with relays is assembled, controlled and programmed using Arduino. The sensors that are used for acquiring data are integrated with Arduino and the analog to digital converter (ADC) present in the Arduino helps convert the analog voltage into digital values that can be viewed using the serial monitor. Nickel projections are extracted from the cell and welded to measure the I-V characteristics, thereby preventing increases in cell thickness. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows an automated data acquisition system that acquires and communicates the data such as voltage.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Data Acquisition and Programming:\u003c/h2\u003e \u003cp\u003eThe measurement system is structured as follows:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThe first nickel strip (serving as the reference terminal) is connected directly to the A0 analog input of the Arduino.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe remaining four terminals are connected to the four relay channels on their normally closed (NC) contacts.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe first three relay channels have their NC terminals wired to subsequent analog input channels (A1, A2, and A3) of the Arduino for individual cell voltage measurements.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAll normally open (NO) contacts of the relays are interconnected and tied to ground (GND) of the Arduino.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Sequential Measurement Process\u003c/h2\u003e \u003cp\u003eThis setup enables sequential reading of individual cell voltages by selectively grounding each nickel strip, ensuring that the potential difference is measured correctly for each cell:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo measure Cell 1 (between Nickel Strip 1 and 2), the system reads from A0, while Relay 1 is activated, grounding the second nickel strip.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eFor Cell 2 (between Nickel Strip 2 and 3), the system reads from A1, activating Relay 2, grounding the third nickel strip.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThis pattern continues for subsequent cells, ensuring that each voltage measurement is referenced correctly against its preceding terminal.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe fourth relay channel is not used for analog measurements, as it does not correspond to a positive potential reading for any cell.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4 Advantages of This Approach\u003c/h2\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eIsolation of Individual Cell Voltages\u003c/b\u003e: The relay module allows selective grounding of nickel strips, ensuring each cell\u0026rsquo;s potential is measured independently without interference from adjacent cells.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eAccurate Potential Difference Measurement\u003c/b\u003e: By grounding one terminal at a time, the Arduino reads the voltage of each cell relative to the grounded strip, mimicking a differential voltage measurement setup.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eEfficient Data Acquisition\u003c/b\u003e: Using a sequential switching mechanism, the system systematically cycles through the cells, allowing real-time monitoring without requiring complex differential amplifier circuits.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eAutomated Data Logging\u003c/b\u003e: The measured voltages are processed and printed via serial communication to a connected Python script running on a laptop. The script utilizes PySerial to interface with the Arduino and Pandas to log the data into an Excel sheet for further analysis.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Anomaly Detection\u003c/h2\u003e \u003cp\u003eThe cell's performance can be anticipated in advance by analysing the real-time data acquired. Any anomalies or damage can be detected early, preventing significant harm to the cell. Further, this movable automatic system is coupled with the Electrochemical impedance spectroscopy to analyze the possible ohmic losses. The impedance measurements will be performed based on the problem in the MEAs, for example a set of 5 MEAs out of 10, total MEAs, affected MEAs or combination of any of MEAs. Hence, the automatic system will support us to do selective impedance analysis. These analyses are carried out in Origaly software using chrono potentiometry method.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Working\u003c/h2\u003e \u003cp\u003eThe PEM electrolysing system consists of a compilation of parts to ensure that water is split into hydrogen and oxygen through electrolysis. Every part works together to deliver the function of the system which is changing from water management to thermal regulation, gas separation, to electrical supply. Through these, the integration gives the electrolyzer an efficient and reliable performance. A feed of flowing water is supplied to the anode fluid channels because electrolysis needs water. While maintaining constant flow, it also feeds surplus water circulated by a pump, so as not to lose it without being used, feeding into the reaction. On the other side, the anode fluid channels facilitate the passage of water from the feed toward the MEA and break it up within the MEA into protons, electrons, and oxygen through the OER. The recirculation system segregates and controls excess water and oxygen.\u003c/p\u003e \u003cp\u003eIt is crucial in that the heat exchanger maintains the temperature within desirable optimal operating limits by dissipating excess heat arising from resistive and activation losses in the electrolysis process. In doing so, this ensures no thermal degradation of the MEA and stabilizes the operation of the electrolyzer. The MEA thermal mass assists in maintaining this thermal regulation by providing a buffer to sudden temperature variations and lengthening the service and stability life of the electrolyzer.\u003c/p\u003e \u003cp\u003eThe electrical (Solar profile, ramp and step voltage) supply supplies the energy required to sustain the electrolysis reaction. A DC power supply applies voltage between the MEA, surmounting activation, Ohmic, and mass transport losses to support hydrogen and oxygen evolution. The electrolysis reaction produces hydrogen at the cathode and oxygen at the anode. The cathode gas channels allow for efficient conduction of the hydrogen gas with minimal water vapour contamination. Residual water vapor is eliminated with the dehumidifiers, which increase the quality of hydrogen coming out. The entire system of electrolyzer along with the data acquisition system is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePurified hydrogen is transferred to the hydrogen output for collection for storage or future use, thus ensuring the system's outputs are of high quality for downstream applications such as fuel cells or storage. Oxygen can either be vented or recycled, depending on needs of the system.The PEM electrolyser is simulated using Simscape, which combines electrical, thermal, and fluid domains to approximate the system's real performances. For example, this simulation enables detailed examination of behaviours in the system, including voltage against current density, power consumption, and heat dissipation.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cp\u003eThe simulation will enable the identification of the key parameters in the process, optimizing the associated operating conditions with regards to efficiency and safety.In brief, the PEM electrolyzer system combines state-of-the-art components and simulation techniques to give a good account of efficiency production of hydrogen without neglecting thermal, electrical, and fluid management challenges. It ensures stable operation, high purity of hydrogen produced, and successful performance monitoring, making it a cornerstone for green hydrogen production technologies. The same simulation can be carried out in Matlab Environment with different forms of electrical input to estimate the hydrogen generation which is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e. It is a setup for 5 stacks. Figures\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e, \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003e and 9 show the voltage, power and temperature modifications aligning with the hydrogen production.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCurrent density and voltage\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVoltage(V)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrent Density(mA/cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHydrogen Production(mL/h)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.25\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.57\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.89\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56.44\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e91.90\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e130.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e173.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eFrom Table\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, we can infer that there is a sudden spike and greater variations in current density values from 1.7V. Further, the suitable operating voltage for the cell is between 1.6-1.8V despite the presence of elevated current densities and significant hydrogen generation beyond 1.8V. This is because high voltages for prolonged periods may damage the cell. We can also observe that, there is no hydrogen production for 1.4- 1.6V. Figures\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e10\u003c/span\u003e and 11 shows the growing trend of the current density with respect to voltage.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Initial Data\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn the study of PEM electrolyzer behaviour, the initial dataset typically consists of a small number of discrete data points that represent the relationship between cell voltage and current density. While this dataset provides a foundational understanding, it poses several challenges. First, the data is sparse meaning there are large gaps between adjacent voltage values. This becomes problematic in regions where current density exhibits rapid changes, such as at the onset of exponential surges. Second, calculating results for every possible voltage value within the operational range requires extensive data collection or simulation runs. This approach can be both computationally intensive and time-consuming.\u003c/p\u003e \u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Data Smoothing Using Cubic Spline Interpolation:\u003c/h2\u003e \u003cp\u003eIn this research paper, the cubic spline interpolation method is used for the dataset to get a smoothened version of the curve showing the cell voltage versus current density. Cubic spline interpolation fits a smooth continuous and differentiable curve through data by fitting piecewise cubic polynomials between consecutive data points. Using the make_interp_spline function from Python's SciPy library, a smooth curve with 100 interpolated points in the original range of 8 data points is obtained. This captures the general trends of the data but gets rid of the jags between points, essential for understanding correctly any inflection points and surges in the curve for the current density. Figure\u0026nbsp;9 represents the voltage vs current density plot for populated data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Detection of Surge in Current Density\u003c/h2\u003e \u003cp\u003eAccurate understanding of performance limits and stability conditions in operation requires one to detect the exponential increase of the current density in PEM electrolyzers. The surge in the current density accompanying this increase, with a sharp increase in electrical current beyond a certain voltage threshold, indicates a point of significant transition in the system, commonly associated with increased defect on the membrane and the potential for degradation. This point was detected using data interpolation coupled with derivative analysis. Sparse experimental data first were interpolated into a smooth, high-resolution dataset by using cubic splines to dampen the noise and add more detail.\u003c/p\u003e \u003cp\u003eNext, first and second derivatives of the smoothened curve were computed in order to look into the rate and acceleration of changes in current density related to the applied voltage. For identifying the true surge point and excluding minor fluctuations caused by noise, a dynamic threshold based on the second derivative distribution was implemented. This guaranteed exact determination of the surge onset but also enabled the identification of operational thresholds critical for optimization, increasing the safety in the systems as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e13\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCell voltages for which surge in current density can be observed\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS. No\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCell Voltage (V)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCurrent Density (mA/cm2)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.477778\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.058886\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.484848\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.103348\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.491919\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.161577\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.498990\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.237485\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.513131\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.457978\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.520202\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.610385\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.527273\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.796113\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eFrom this work, it is concluded that current density exponentially surges from 1.477V. The PEM setup carried out with three different supply types produced the expected output as obtained in the hardware setup. Using the automatic system, the process of retrieving data can be automated and the received data is used to analyze where the surge starts. With this, we can identify where the stack is good to operate at. Further, the simulation results show that as the voltage supply increases, the hydrogen production start after a certain threshold. And the stack temperature also increases along. This increase in stack temperature after a particular region may affect the membrane and this needs to be prevented. Hence identifying where the surge starts helps to determine where the stack is good to operate at to prevent damage of membrane.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eA. B. D performed the work and wrote the main manuscript along with taking tests and resultsC. E. F made suggestions and gave ideas on improving the manuscript\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Vellore Institute of Technology, Chennai Campus for providing Rs. 4,78,000 /- under the scheme VIT-RGMS-SEED funding for the project \u0026ldquo;Earlier Determination of Membrane Electrode Assembly (MEA) Degradation using the Armed Movable System Coupled with Electrochemical Impedance Spectroscopy (EIS) for Proton Exchange Membrane (PEM) Water Electrolyzer\u0026rdquo;, SENSE 25.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analysed during the current study can be obtained from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eNorazahar, Norafneeza, et al. \u0026quot;Degradation modelling and reliability analysis of PEM electrolyzer.\u0026quot; \u003cem\u003eInternational Journal of Hydrogen Energy\u003c/em\u003e 50 (2024): 842-856.\u003c/li\u003e\n \u003cli\u003eBrezak, Dinko, Ankica Kovač, and Mihajlo Firak. \u0026quot;MATLAB/Simulink simulation of low-pressure PEM electrolyzer stack.\u0026quot; \u003cem\u003eInternational journal of hydrogen energy\u003c/em\u003e 48.16 (2023): 6158-6173.\u003c/li\u003e\n \u003cli\u003eZhang, Caizhi, et al. \u0026quot;Proton exchange membrane water electrolysis system control method.\u0026quot; \u003cem\u003ePEM Water Electrolysis\u003c/em\u003e. Elsevier, 2025. 347-364.\u003c/li\u003e\n \u003cli\u003eFolgado, Francisco Javier, Isa\u0026iacute;as Gonz\u0026aacute;lez, and Antonio Jos\u0026eacute; Calder\u0026oacute;n. \u0026quot;Data acquisition and monitoring system framed in Industrial Internet of Things for PEM hydrogen generators.\u0026quot; \u003cem\u003eInternet of Things\u003c/em\u003e 22 (2023): 100795.\u003c/li\u003e\n \u003cli\u003eCaparros Mancera, Julio Jose, et al. \u0026quot;An optimized balance of plant for a medium-size PEM electrolyzer: design, control and physical implementation.\u0026quot; \u003cem\u003eElectronics\u003c/em\u003e 9.5 (2020): 871.\u003c/li\u003e\n \u003cli\u003eXu, Boshi, et al. \u0026quot;Degradation prediction of PEM water electrolyzer under constant and start-stop loads based on CNN-LSTM.\u0026quot; \u003cem\u003eEnergy and AI\u003c/em\u003e 18 (2024): 100420.\u003c/li\u003e\n \u003cli\u003eChandesris, M., et al. \u0026quot;Membrane degradation in PEM water electrolyzer: Numerical modeling and experimental evidence of the influence of temperature and current density.\u0026quot; \u003cem\u003eInternational Journal of Hydrogen Energy\u003c/em\u003e 40.3 (2015): 1353-1366.\u003c/li\u003e\n \u003cli\u003eOzdemir, Safiye Nur, and Oguzhan Pektezel. \u0026quot;Performance prediction of experimental PEM electrolyzer using machine learning algorithms.\u0026quot; \u003cem\u003eFuel\u003c/em\u003e 378 (2024): 132853.\u003c/li\u003e\n \u003cli\u003eKoo, Taehyung, et al. \u0026quot;Development of model-based PEM water electrolysis HILS (Hardware-in-the-Loop simulation) system for state evaluation and fault detection.\u0026quot; \u003cem\u003eEnergies\u003c/em\u003e 16.8 (2023): 3379.\u003c/li\u003e\n \u003cli\u003eFolgado, Francisco Javier, Isa\u0026iacute;as Gonz\u0026aacute;lez, and Antonio Jos\u0026eacute; Calder\u0026oacute;n. \u0026quot;PEM electrolyser digital twin embedded within MATLAB-based graphical user interface.\u0026quot; \u003cem\u003eEngineering Proceedings\u003c/em\u003e 19.1 (2022): 21.\u003c/li\u003e\n \u003cli\u003eFolgado, F. J., I. Gonz\u0026aacute;lez, and A. J. Calder\u0026oacute;n. \u0026quot;PEM electrolyzer digital twin embedded within MATLAB-based graphical user interface. Proceedings 2022, 69, x.\u0026quot; \u003cem\u003ePresented at the 1st International Electronic Conference on Processes: Processes System Innovation\u003c/em\u003e. Vol. 17. s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations., 2022.\u003c/li\u003e\n \u003cli\u003eHong, Jichao, et al. \u0026quot;Review on proton exchange membrane fuel cells: Safety analysis and fault diagnosis.\u0026quot; \u003cem\u003eJournal of Power Sources\u003c/em\u003e 617 (2024): 235118.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","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":"","lastPublishedDoi":"10.21203/rs.3.rs-6154160/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6154160/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eProton Exchange Membrane (PEM) electrolyzer are crucial components in hydrogen production systems, where they play a significant role in splitting water into hydrogen and oxygen. However, the performance and longevity of PEM electrolyzer are highly dependent on the condition and integrity of the individual stacks within the system. Regular monitoring and maintenance of these stacks are essential to ensure efficient operation and prevent system failures. Traditionally, this process is labor dependent, requires human inspection and impedance measurement of each stack, which is time-consuming, prone to human error, and may lead to delayed detection of anomalies. Thus, developing an automatic system that constantly acquires data from the cell stack with the integration of sensors (such as current and voltage) can significantly improve cell efficiency, economize cost as cells are made of materials such as platinum which is highly expensive, also preventing changes in torque set and significant time conservation. The main objective is to apply this automatic system to multi cell stack arrangement, where only the total cell voltage and current can be acquired through software but not individual cell\u0026rsquo;s values. This research introduces an automated system to address the challenges encountered in multi-cell stack configuration.\u003c/p\u003e","manuscriptTitle":"Automated System for Automatic Data Acquisition for Pem Electrolyser","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-26 09:03:51","doi":"10.21203/rs.3.rs-6154160/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":"fd314a18-cda9-4112-ad52-72e9888ee11d","owner":[],"postedDate":"March 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":46230534,"name":"Physical sciences/Engineering"},{"id":46230535,"name":"Physical sciences/Engineering/Electrical and electronic engineering"},{"id":46230536,"name":"Physical sciences/Engineering/Energy infrastructure"},{"id":46230537,"name":"Physical sciences/Engineering/Mechanical engineering"}],"tags":[],"updatedAt":"2025-04-15T09:08:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-26 09:03:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6154160","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6154160","identity":"rs-6154160","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}