Air vs. Liquid BTMS for Electric Vehicles:A Thermo‑Fluidic Performance Comparison

Air vs. Liquid BTMS for Electric Vehicles:A Thermo‑Fluidic Performance Comparison | 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 Short Report Air vs. Liquid BTMS for Electric Vehicles:A Thermo‑Fluidic Performance Comparison Tian, Jiantong, Wei, Yuxiang, Wei, Liheng, Wu, Weicheng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9619917/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 The transition to high-energy-density lithium-ion batteries in electric vehicles (EVs) has intensified the need for effective battery thermal management systems (BTMS) to maintain cell temperatures within the optimal 15–35 °C window and prevent thermal runaway. This study presents a comparative thermo-fluidic analysis of the two dominant BTMS strategies—air cooling and indirect liquid cooling—by synthesizing experimentally validated computational fluid dynamics (CFD) data from recent literature. Performance is evaluated through the lens of Newton’s law of cooling and parasitic power consumption (P = V̇ΔP). Fluid property analysis reveals that liquid coolants (water-glycol) offer a convective heat transfer coefficient (1000–10,000 W/m²K) two orders of magnitude higher than air (10–100 W/m²K). Under equivalent 2C discharge conditions, liquid cooling limits the maximum cell temperature to 25–40 °C with a cell-to-cell temperature difference below 1.5 °C, whereas forced-air cooling reaches 42.4 °C with a gradient of ~4.9 °C. The power ratio required to induce a given temperature rise is approximately 860 in favor of liquid cooling. Furthermore, critical design parameters for liquid-cooled systems were examined: increasing the thermal interface material (TIM) compression ratio from 0 % to 25 % reduced the peak module temperature by 3.2 °C without additional pumping power, and coolant inlet temperature was identified as the most influential factor on maximum battery temperature. While air cooling remains cost-effective for low-power applications, liquid cooling demonstrates decisive superiority in peak temperature suppression, thermal uniformity, and energy efficiency, especially at high C-rates. Advanced liquid-based approaches—including immersion cooling, hybrid heat-pipe/cold-plate systems, and triply periodic minimal surface (TPMS) architectures—are identified as essential pathways to meet the thermal demands of next-generation fast-charging EV batteries. Mechanical Engineering Electric vehicle Battery thermal management Air cooling Liquid cooling Thermo-fluidic analysis Lithium-ion battery Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction & Problem Definition The rapid global expansion of electric vehicles (EVs) has positioned them as a key solution for reducing emissions and improving energy efficiency. Hwang et al. (2024) highlight that central to this transition is the widespread adoption of high-energy-density lithium-ion (Li-ion) batteries, which enable competitive driving range and power performance. However, Lu et al. (2020) and Togun et al. (2024) note that the increasing energy density and power demand of these batteries result in significant heat generation during fast charging and high-rate discharging processes. If not properly controlled, excessive heat can elevate cell temperatures, accelerate degradation, reduce efficiency, and potentially trigger hazardous conditions such as thermal runaway (Akbarzadeh et al., 2021; Togun et al., 2024). To ensure safe and efficient operation, Li-ion batteries must function within a narrow temperature range. Hwang et al. (2024) and Zhao et al. (2021) indicate that optimal performance is typically achieved when the battery temperature is maintained between approximately 15°C and 35°C, with minimal temperature gradients across cells. Deviations beyond this range can lead to uneven aging, reduced capacity, and increased safety risks. As EV battery systems continue to scale in size and power, Togun et al. (2024) and Akbarzadeh et al. (2021) emphasise that maintaining thermal stability and uniformity has become an increasingly critical engineering challenge. In this context, the Battery Thermal Management System (BTMS) plays a fundamental role in regulating battery temperature and ensuring operational safety. A well-designed BTMS dissipates excess heat, prevents localized hotspots, and maintains uniform temperature distribution across the battery pack. This not only improves efficiency and extends battery lifespan but also mitigates the risk of thermal runaway and system failure (Hwang et al. 2024; Zhao et al. 2021). Consequently, the development of efficient and reliable BTMS solutions has become a major research focus in the field of electric vehicle engineering. Among various thermal management strategies, air cooling and liquid cooling have emerged as the two dominant approaches. Lu et al. (2020) and Zhao et al. (2021) note that air cooling systems utilize forced airflow to remove heat and are valued for their structural simplicity, low cost, and lightweight design. However, due to the relatively low thermal conductivity and heat capacity of air, their cooling capability is limited, particularly under high thermal loads (Behi et al., 2020; Zhao et al., 2021). In contrast, liquid cooling systems employ circulating coolants through cold plates or channels, offering significantly higher heat transfer efficiency and improved temperature uniformity. Despite their advantages, Hwang et al. (2024) point out that liquid cooling systems involve greater complexity, higher cost, and potential risks such as leakage. This report aims to conduct a comparative thermo-fluidic analysis of air-cooled and liquid-cooled battery thermal management systems in electric vehicles. By applying fundamental principles of thermodynamics and fluid mechanics, the study evaluates their cooling performance, temperature uniformity, energy consumption, and system-level trade-offs. The objective is to identify the relative advantages, limitations, and suitable application scenarios of each approach, thereby providing insights into optimal thermal management strategies for modern and future EV systems. 2. Methods 2.1 Fundamentals of Thermo-Fluid Dynamics in BTMS Battery thermal management is governed by Newton's law of cooling, Q = hA(Ts - T∞), where the convective heat transfer coefficient h dictates cooling effectiveness. For air, h typically reaches only 10–100 W/m²K due to its low thermal conductivity (~ 0.024 W/m·K), whereas liquid coolants (e.g., water-glycol) achieve 1000–10,000 W/m²K (Hwang et al., 2024; Togun et al., 2024). The flow regime, described by the Reynolds number Re = ρvL/µ, further influences heat transfer: turbulent flow enhances h but increases pressure drop ΔP and parasitic pumping power P = V̇ΔP (Akbarzadeh et al., 2021). This creates a design trade-off between cooling performance and energy consumption. 2.2 Air Cooling Architectures Air cooling employs ambient or cabin air via natural convection or forced fans. Its advantages include simple structure, low cost, and easy maintenance (Zhao et al., 2021). However, air’s low heat capacity (~ 1.0 kJ/kg·K) causes severe temperature non-uniformity at high C-rates, as downstream cells receive pre-heated airflow (Akbarzadeh et al., 2021). Improvements have focused on optimised channel geometries and hybrid systems. For instance, Behi et al. (2020) integrated heat pipes into an air-cooled module, reducing maximum temperature by up to 34.5% compared with natural convection alone. 2.3 Liquid Cooling Architectures Liquid cooling, the dominant BTMS for high-performance EVs, circulates coolant through cold plates interfaced with the module via a thermal interface material (TIM) (Gu et al., 2024)(Fig. 1 ). The high specific heat of water-glycol (~ 3.6 kJ/kg·K) enables superior heat rejection. In a comparative CFD study at 2C discharge, Akbarzadeh et al. (2021) showed that a liquid-cooled module’s hottest cell remained ~ 3°C lower than an air-cooled counterpart at equal parasitic power (~ 0.5 W). Key design parameters include cold plate channel geometry, TIM compression ratio, and coolant inlet conditions (Xu et al., 2021; Gu et al., 2024; Zhu et al., 2020). An emerging variant, immersion cooling, submerges cells directly in dielectric fluid, eliminating contact resistances and achieving significantly higher heat transfer coefficients, though cost and material compatibility challenges remain (Wahab et al., 2024). 3. Results 3.1 Comparative Framework This section translates the thermo-fluid principles introduced in Section 2.1 into quantitative performance comparisons. The evaluation is built on experimentally validated CFD simulations and calorimetric measurements. The heat transfer effectiveness is assessed using Newton’s law of cooling, Q=hAΔT, while the parasitic power consumed by fans or pumps is computed as P = V˙Δp, where V˙ is the volumetric flow rate and Δp the pressure drop. All data are drawn from studies that benchmark air- and liquid-cooled battery modules under equivalent thermal loads. 3.2 Direct Thermal Performance Comparison Table 1 lists the thermophysical properties of the two cooling fluids, which explain the order-of-magnitude gap in cooling capacity. Property Air Water Density, ρ (kg/m³) 1.225 998.2 Specific heat, cp (J/kg·K) 1006.4 4182 Thermal conductivity, k (W/m·K) 0.0242 0.60 Dynamic viscosity, µ (kg/m·s) 1.79×10 − 5 1.00×10 − 3 Table 1 . Fluid properties at 25°C (Akbarzadeh et al., 2021) For a 24-cell cylindrical module (18650, 1.5C discharge), natural air convection resulted in a maximum temperature of 64.8°C, well beyond the safe operating limit. Forced air at 2 m/s reduced the peak to 42.4°C; however, a temperature spread of ≈ 4.9°C persisted (Behi et al., 2020). Under equivalent thermal loads, liquid cooling consistently maintained cell temperatures below 40°C. Akbarzadeh et al. (2021) simulated a 48 V prismatic module (12 NMC cells) at a 2C discharge rate. At an equal parasitic power of approximately 0.5 W, the liquid-cooled module’s hottest cell was 3°C cooler than that of the air-cooled module, and the cell-to-cell temperature difference was below 1.5°C compared with > 4.9°C for air cooling. The power ratio required to raise the hottest cell temperature by 13.5°C was PR = P_air/P_liquid ≈ 860, confirming the superior energy efficiency of liquid systems. Table 2 summarises the main performance contrasts. Table 2 Performance comparison (Akbarzadeh et al., 2021; Behi et al., 2020) Metric Forced-air cooling Liquid cooling Max. cell temperature 42.4°C 25–40°C Max. cell-to-cell ΔT ≈ 4.9°C < 1.5°C Power ratio at ΔT = 13.5∘C 860 1 3.3 Critical Liquid-Cooling Design Parameters 3.3.1 Cold-plate channel geometry Xu et al. (2021) optimised a serpentine cold plate for a 40-cell pack using an ensemble-of-surrogates method. The optimised design reduced the maximum temperature difference to 3.46 K (-7.5%) without increasing pump power, demonstrating that channel shape directly governs temperature uniformity. 3.3.2 TIM compression Gu et al. (2024) measured the thermal conductivity of a silicone TIM, which increased from 4.45 to 5.39 W/m·K when the compression ratio rose from 0% to 25%/75%. In a 5C discharge test at 15 LPM, this improvement lowered the maximum module temperature from 63.35°C to 60.15°C (− 3.2°C) while the pressure drop remained fixed at 1.53 kPa, highlighting the importance of mechanical assembly quality. 3.3.3 Coolant inlet conditions Zhu et al. (2020) conducted a sensitivity study on a full-scale EV battery pack. Inlet temperature was the most influential factor: a 40% variation caused a change in maximum battery temperature more than three times larger than the same relative change in flow rate. During an uphill-driving scenario, to keep T_max ≤ 47∘C, the inlet temperature had to be held within 20.9–22.0°C when the flow rate was 6 L/min. 3.4 Concluding Remarks Liquid cooling consistently outperforms air cooling in peak temperature, temperature uniformity, and energy efficiency. The performance gap widens at high C-rates and extreme ambient conditions, where air-cooled systems either breach safety limits or require prohibitive parasitic energy. Within liquid-cooled BTMS, the most influential design levers are the coolant inlet temperature, the cold-plate channel geometry, and the TIM compression ratio. 4. Future Trends in Battery Thermal Management Hybrid BTMSs that combine two or more cooling methods (e.g., air-cooled with liquid-cooled, or thermoelectric coolers coupled with liquid cold plates) can lower maximum battery temperature and improve cooling performance. However, the added structural complexity and parasitic power consumption may reduce overall pack efficiency if the subsystems are not properly balanced (Hwang et al., 2024). Immersion cooling, a direct liquid cooling technique in which cells are submerged in a dielectric fluid, achieves heat transfer rates orders of magnitude higher than conventional methods and offers excellent temperature uniformity, significantly enhancing capacity retention and safety (Wahab et al., 2024). Advanced structures such as Triply Periodic Minimal Surfaces (TPMS) provide interconnected continuous flow channels with high specific surface area and structural robustness. By optimising geometry and porosity, TPMS-based cold plates improve coolant distribution, reduce pressure loss, suppress hotspots, and enable lightweight multifunctional designs that integrate structural support with thermal regulation (Iqbal et al., 2026). As battery energy density and charging rates continue to rise, such advanced liquid-based technologies—hybrid heat-pipe/cold-plate systems (Maalej et al., 2026), immersion cooling, and TPMS architectures—will become essential for next-generation EV battery systems (Togun et al., 2024). 5. Conclusion Liquid cooling consistently outperforms air cooling in maximum temperature control, temperature uniformity, and energy efficiency. Air cooling remains attractive for cost-sensitive, low-power applications owing to its simplicity and light weight (Zhao et al., 2021; Lu et al., 2020), but its low-thermal-capacity limits become critical under high C-rates (Akbarzadeh et al., 2021). Liquid-cooled systems, by contrast, maintain cells within the ideal 25–40°C range with temperature differences below 3.5°C (Hwang et al., 2024). Design parameters play a decisive role: increasing TIM compression ratio significantly reduces contact resistance and improves cooling efficiency without additional pumping power (Gu et al., 2024), while precise inlet coolant temperature control has a greater influence on maximum battery temperature than flow-rate adjustments (Zhu et al., 2020). Looking forward, advanced liquid-based technologies—hybrid systems, immersion cooling, and TPMS architectures—will be crucial to meet the growing thermal demands of high-energy-density, fast-charging EV batteries. Declarations References list Akbarzadeh, M., Kalogiannis, T., Jaguemont, J., Jin, L., Behi, H., Karimi, D., Beheshti, H., Van Mierlo, J. and Berecibar, M. (2021) ‘A comparative study between air cooling and liquid cooling thermal management systems for a high-energy lithium-ion battery module’, Applied Thermal Engineering , 198, 117503. doi: 10.1016/j.applthermaleng.2021.117503 . Behi, H., Jaguemont, J., Berecibar, M., Karimi, D., Sokkeh, M.A., Van Mierlo, J., Behi, M., Gandoman, F.H. and Ghanbarpour, M. (2020) ‘A new concept of thermal management system in Li-ion battery using air cooling and heat pipe for electric vehicles’, Applied Thermal Engineering , 174, 115280. doi: 10.1016/j.applthermaleng.2020.115280 . Gu, J., Kim, H.K. and Jang, S. (2024) ‘Study of Cooling Performance of Liquid-Cooled EV Battery Module According to the TIM Compression Ratio’, International Journal of Automotive Technology , 25, pp. 885–894. doi: 10.1007/s12239-024-00167-8 . Hwang, F.S., Confrey, T., Reidy, C., Picovici, D., Callaghan, D., Culliton, D. and Nolan, C. (2024) ‘Review of battery thermal management systems in electric vehicles’, Renewable and Sustainable Energy Reviews , 192, 114171. doi: 10.1016/j.rser.2023.114171 . Iqbal, F. et al. (2026) ‘Triply Periodic Minimal Surface (TPMS) Architectures for Advanced Battery Thermal Management Systems in EVs: Manufacturing, Integration, and Future Prospects’, Energy and AI . [In Press]. Lu, M., Zhang, X., Ji, J., Xu, X. and Zhang, Y. (2020) ‘Research progress on power battery cooling technology for electric vehicles’, Journal of Energy Storage , 27, 101155. doi: 10.1016/j.est.2019.101155 . Maalej, S., Saad, I., Hamdani, A., Cherif, R., Msaddek, R. and Zaghdoudi, M.C. (2026) ‘Electric vehicle battery thermal management using hybrid heat pipe-cold plate cooling system’, Science and Technology for Energy Transition , 81, pp. 1–15. doi: 10.2516/stet/2026006 . Togun, H., Aljibori, H.S.S., Biswas, N., Mohammed, H.I., Sadeq, A.M., Rashid, F.L., Abdulrazzaq, T. and Zearah, S.A. (2024) ‘A critical review on the efficient cooling strategy of batteries of electric vehicles: Advances, challenges, future perspectives’, Renewable and Sustainable Energy Reviews , 201, 114732. doi: 10.1016/j.rser.2024.114732 . Wahab, A., Najmi, A.U.H., Senobar, H., Kemper, H., Khayyam, H. and Amjady, N. (2024) ‘Immersion cooling innovations and critical hurdles in Li-ion battery cooling for future electric vehicles’, Renewable and Sustainable Energy Reviews , 202, 115268. doi: 10.1016/j.rser.2024.115268 . Xu, H., Zhang, X., Xiang, G. and Li, H. (2021) ‘Optimization of liquid cooling and heat dissipation system of lithium-ion battery packs of automobile’, Case Studies in Thermal Engineering , 26, 101012. doi: 10.1016/j.csite.2021.101012 . Zhao, G., Wang, X., Negnevitsky, M. and Zhang, H. (2021) ‘A review of air-cooling battery thermal management systems for electric and hybrid electric vehicles’, Journal of Power Sources , 501, 230001. doi: 10.1016/j.jpowsour.2021.230001 . Zhu, L., Xiong, F., Chen, H., Wei, D., Li, G. and Ouyang, C. (2020) ‘Thermal analysis and optimization of an EV battery pack for real applications’, International Journal of Heat and Mass Transfer , 158, 120384. doi: 10.1016/j.ijheatmasstransfer.2020.120384 . 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. 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Adapted from Akbarzadeh et al. (2021).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9619917/v1/8e660f2c9d23f8298620f3ee.png"},{"id":108674538,"identity":"6f8bad09-3ffc-4f7d-8141-bdf6fdf6ad1a","added_by":"auto","created_at":"2026-05-07 08:21:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":155772,"visible":true,"origin":"","legend":"\u003cp\u003eCooling performance comparison of air-cooled (a) and liquid-cooled (b) modules. Adapted from Akbarzadeh et al. (2021), Fig. 17\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9619917/v1/6dea948c8fda960cbd1fba9b.png"},{"id":108806025,"identity":"d2bc3895-0a91-4aae-9dda-28623ce7f9c6","added_by":"auto","created_at":"2026-05-08 15:27:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":11909,"visible":true,"origin":"","legend":"\u003cp\u003eTIM thermal conductivity versus compression ratio. Adapted from Gu et al. (2024), Fig. 9.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9619917/v1/018e8b492c04a60554181817.png"},{"id":108674540,"identity":"76dee8fe-ba86-48dc-9963-eb40767a9aee","added_by":"auto","created_at":"2026-05-07 08:21:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":18467,"visible":true,"origin":"","legend":"\u003cp\u003eOptimal inlet temperature and flow rate for cooling condition. Adapted from Zhu et al. (2020), Fig. 17.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9619917/v1/4ab45b16d5656a2a6e614f5d.png"},{"id":108809895,"identity":"5dea11dd-9e16-404e-a993-1739eef720c8","added_by":"auto","created_at":"2026-05-08 15:56:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":408592,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9619917/v1/8187914a-50b5-4bb8-ba60-4c0041d3366c.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eAir vs. Liquid BTMS for Electric Vehicles:A Thermo‑Fluidic Performance Comparison\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction \u0026 Problem Definition","content":"\u003cp\u003eThe rapid global expansion of electric vehicles (EVs) has positioned them as a key solution for reducing emissions and improving energy efficiency. Hwang et al. (2024) highlight that central to this transition is the widespread adoption of high-energy-density lithium-ion (Li-ion) batteries, which enable competitive driving range and power performance. However, Lu et al. (2020) and Togun et al. (2024) note that the increasing energy density and power demand of these batteries result in significant heat generation during fast charging and high-rate discharging processes. If not properly controlled, excessive heat can elevate cell temperatures, accelerate degradation, reduce efficiency, and potentially trigger hazardous conditions such as thermal runaway (Akbarzadeh et al., 2021; Togun et al., 2024).\u003c/p\u003e \u003cp\u003eTo ensure safe and efficient operation, Li-ion batteries must function within a narrow temperature range. Hwang et al. (2024) and Zhao et al. (2021) indicate that optimal performance is typically achieved when the battery temperature is maintained between approximately 15\u0026deg;C and 35\u0026deg;C, with minimal temperature gradients across cells. Deviations beyond this range can lead to uneven aging, reduced capacity, and increased safety risks. As EV battery systems continue to scale in size and power, Togun et al. (2024) and Akbarzadeh et al. (2021) emphasise that maintaining thermal stability and uniformity has become an increasingly critical engineering challenge.\u003c/p\u003e \u003cp\u003eIn this context, the Battery Thermal Management System (BTMS) plays a fundamental role in regulating battery temperature and ensuring operational safety. A well-designed BTMS dissipates excess heat, prevents localized hotspots, and maintains uniform temperature distribution across the battery pack. This not only improves efficiency and extends battery lifespan but also mitigates the risk of thermal runaway and system failure (Hwang et al. 2024; Zhao et al. 2021). Consequently, the development of efficient and reliable BTMS solutions has become a major research focus in the field of electric vehicle engineering.\u003c/p\u003e \u003cp\u003eAmong various thermal management strategies, air cooling and liquid cooling have emerged as the two dominant approaches. Lu et al. (2020) and Zhao et al. (2021) note that air cooling systems utilize forced airflow to remove heat and are valued for their structural simplicity, low cost, and lightweight design. However, due to the relatively low thermal conductivity and heat capacity of air, their cooling capability is limited, particularly under high thermal loads (Behi et al., 2020; Zhao et al., 2021). In contrast, liquid cooling systems employ circulating coolants through cold plates or channels, offering significantly higher heat transfer efficiency and improved temperature uniformity. Despite their advantages, Hwang et al. (2024) point out that liquid cooling systems involve greater complexity, higher cost, and potential risks such as leakage.\u003c/p\u003e \u003cp\u003eThis report aims to conduct a comparative thermo-fluidic analysis of air-cooled and liquid-cooled battery thermal management systems in electric vehicles. By applying fundamental principles of thermodynamics and fluid mechanics, the study evaluates their cooling performance, temperature uniformity, energy consumption, and system-level trade-offs. The objective is to identify the relative advantages, limitations, and suitable application scenarios of each approach, thereby providing insights into optimal thermal management strategies for modern and future EV systems.\u003c/p\u003e "},{"header":"2. Methods","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Fundamentals of Thermo-Fluid Dynamics in BTMS\u003c/h2\u003e \u003cp\u003eBattery thermal management is governed by Newton's law of cooling, Q\u0026thinsp;=\u0026thinsp;hA(Ts - T\u0026infin;), where the convective heat transfer coefficient h dictates cooling effectiveness. For air, h typically reaches only 10\u0026ndash;100 W/m\u0026sup2;K due to its low thermal conductivity (~\u0026thinsp;0.024 W/m\u0026middot;K), whereas liquid coolants (e.g., water-glycol) achieve 1000\u0026ndash;10,000 W/m\u0026sup2;K (Hwang et al., 2024; Togun et al., 2024). The flow regime, described by the Reynolds number Re\u0026thinsp;=\u0026thinsp;ρvL/\u0026micro;, further influences heat transfer: turbulent flow enhances h but increases pressure drop ΔP and parasitic pumping power P\u0026thinsp;=\u0026thinsp;V̇ΔP (Akbarzadeh et al., 2021). This creates a design trade-off between cooling performance and energy consumption.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Air Cooling Architectures\u003c/h2\u003e \u003cp\u003eAir cooling employs ambient or cabin air via natural convection or forced fans. Its advantages include simple structure, low cost, and easy maintenance (Zhao et al., 2021). However, air\u0026rsquo;s low heat capacity (~\u0026thinsp;1.0 kJ/kg\u0026middot;K) causes severe temperature non-uniformity at high C-rates, as downstream cells receive pre-heated airflow (Akbarzadeh et al., 2021). Improvements have focused on optimised channel geometries and hybrid systems. For instance, Behi et al. (2020) integrated heat pipes into an air-cooled module, reducing maximum temperature by up to 34.5% compared with natural convection alone.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Liquid Cooling Architectures\u003c/h2\u003e \u003cp\u003eLiquid cooling, the dominant BTMS for high-performance EVs, circulates coolant through cold plates interfaced with the module via a thermal interface material (TIM) (Gu et al., 2024)(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The high specific heat of water-glycol (~\u0026thinsp;3.6 kJ/kg\u0026middot;K) enables superior heat rejection. In a comparative CFD study at 2C discharge, Akbarzadeh et al. (2021) showed that a liquid-cooled module\u0026rsquo;s hottest cell remained\u0026thinsp;~\u0026thinsp;3\u0026deg;C lower than an air-cooled counterpart at equal parasitic power (~\u0026thinsp;0.5 W). Key design parameters include cold plate channel geometry, TIM compression ratio, and coolant inlet conditions (Xu et al., 2021; Gu et al., 2024; Zhu et al., 2020). An emerging variant, immersion cooling, submerges cells directly in dielectric fluid, eliminating contact resistances and achieving significantly higher heat transfer coefficients, though cost and material compatibility challenges remain (Wahab et al., 2024).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e "},{"header":"3. Results","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Comparative Framework\u003c/h2\u003e \u003cp\u003eThis section translates the thermo-fluid principles introduced in Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2.1\u003c/span\u003e into quantitative performance comparisons. The evaluation is built on experimentally validated CFD simulations and calorimetric measurements. The heat transfer effectiveness is assessed using Newton\u0026rsquo;s law of cooling,\u003c/p\u003e \u003cp\u003eQ=hAΔT, while the parasitic power consumed by fans or pumps is computed as P\u0026thinsp;=\u0026thinsp;V˙Δp, where V˙ is the volumetric flow rate and Δp the pressure drop. All data are drawn from studies that benchmark air- and liquid-cooled battery modules under equivalent thermal loads.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Direct Thermal Performance Comparison\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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\u003elists the thermophysical properties of the two cooling fluids, which explain the order-of-magnitude gap in cooling capacity.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProperty\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAir\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDensity, ρ (kg/m\u0026sup3;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e998.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecific heat, cp (J/kg\u0026middot;K)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1006.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4182\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThermal conductivity, k (W/m\u0026middot;K)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDynamic viscosity, \u0026micro; (kg/m\u0026middot;s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.79\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;3\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\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Fluid properties at 25\u0026deg;C (Akbarzadeh et al., 2021)\u003c/p\u003e \u003cp\u003eFor a 24-cell cylindrical module (18650, 1.5C discharge), natural air convection resulted in a maximum temperature of 64.8\u0026deg;C, well beyond the safe operating limit. Forced air at 2 m/s reduced the peak to 42.4\u0026deg;C; however, a temperature spread of \u0026asymp;\u0026thinsp;4.9\u0026deg;C persisted (Behi et al., 2020). Under equivalent thermal loads, liquid cooling consistently maintained cell temperatures below 40\u0026deg;C.\u003c/p\u003e \u003cp\u003eAkbarzadeh et al. (2021) simulated a 48 V prismatic module (12 NMC cells) at a 2C discharge rate. At an equal parasitic power of approximately 0.5 W, the liquid-cooled module\u0026rsquo;s hottest cell was 3\u0026deg;C cooler than that of the air-cooled module, and the cell-to-cell temperature difference was below 1.5\u0026deg;C compared with \u0026gt;\u0026thinsp;4.9\u0026deg;C for air cooling. The power ratio required to raise the hottest cell temperature by 13.5\u0026deg;C was PR\u0026thinsp;=\u0026thinsp;P_air/P_liquid\u0026thinsp;\u0026asymp;\u0026thinsp;860, confirming the superior energy efficiency of liquid systems. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarises the main performance contrasts.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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\u003ePerformance comparison (Akbarzadeh et al., 2021; Behi et al., 2020)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetric\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForced-air cooling\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLiquid cooling\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMax. cell temperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.4\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u0026ndash;40\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMax. cell-to-cell ΔT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026asymp;\u0026thinsp;4.9\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1.5\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePower ratio at ΔT\u0026thinsp;=\u0026thinsp;13.5∘C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\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 \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Critical Liquid-Cooling Design Parameters\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Cold-plate channel geometry\u003c/h2\u003e \u003cp\u003eXu et al. (2021) optimised a serpentine cold plate for a 40-cell pack using an ensemble-of-surrogates method. The optimised design reduced the maximum temperature difference to 3.46 K (-7.5%) without increasing pump power, demonstrating that channel shape directly governs temperature uniformity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 TIM compression\u003c/h2\u003e \u003cp\u003eGu et al. (2024) measured the thermal conductivity of a silicone TIM, which increased from 4.45 to 5.39 W/m\u0026middot;K when the compression ratio rose from 0% to 25%/75%. In a 5C discharge test at 15 LPM, this improvement lowered the maximum module temperature from 63.35\u0026deg;C to 60.15\u0026deg;C (\u0026minus;\u0026thinsp;3.2\u0026deg;C) while the pressure drop remained fixed at 1.53 kPa, highlighting the importance of mechanical assembly quality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3 Coolant inlet conditions\u003c/h2\u003e \u003cp\u003eZhu et al. (2020) conducted a sensitivity study on a full-scale EV battery pack. Inlet temperature was the most influential factor: a 40% variation caused a change in maximum battery temperature more than three times larger than the same relative change in flow rate. During an uphill-driving scenario, to keep T_max\u0026thinsp;\u0026le;\u0026thinsp;47∘C, the inlet temperature had to be held within 20.9\u0026ndash;22.0\u0026deg;C when the flow rate was 6 L/min.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Concluding Remarks\u003c/h2\u003e \u003cp\u003eLiquid cooling consistently outperforms air cooling in peak temperature, temperature uniformity, and energy efficiency. The performance gap widens at high C-rates and extreme ambient conditions, where air-cooled systems either breach safety limits or require prohibitive parasitic energy. Within liquid-cooled BTMS, the most influential design levers are the coolant inlet temperature, the cold-plate channel geometry, and the TIM compression ratio.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Future Trends in Battery Thermal Management","content":"\u003cp\u003eHybrid BTMSs that combine two or more cooling methods (e.g., air-cooled with liquid-cooled, or thermoelectric coolers coupled with liquid cold plates) can lower maximum battery temperature and improve cooling performance. However, the added structural complexity and parasitic power consumption may reduce overall pack efficiency if the subsystems are not properly balanced (Hwang et al., 2024). Immersion cooling, a direct liquid cooling technique in which cells are submerged in a dielectric fluid, achieves heat transfer rates orders of magnitude higher than conventional methods and offers excellent temperature uniformity, significantly enhancing capacity retention and safety (Wahab et al., 2024). Advanced structures such as Triply Periodic Minimal Surfaces (TPMS) provide interconnected continuous flow channels with high specific surface area and structural robustness. By optimising geometry and porosity, TPMS-based cold plates improve coolant distribution, reduce pressure loss, suppress hotspots, and enable lightweight multifunctional designs that integrate structural support with thermal regulation (Iqbal et al., 2026). As battery energy density and charging rates continue to rise, such advanced liquid-based technologies\u0026mdash;hybrid heat-pipe/cold-plate systems (Maalej et al., 2026), immersion cooling, and TPMS architectures\u0026mdash;will become essential for next-generation EV battery systems (Togun et al., 2024).\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eLiquid cooling consistently outperforms air cooling in maximum temperature control, temperature uniformity, and energy efficiency. Air cooling remains attractive for cost-sensitive, low-power applications owing to its simplicity and light weight (Zhao et al., 2021; Lu et al., 2020), but its low-thermal-capacity limits become critical under high C-rates (Akbarzadeh et al., 2021). Liquid-cooled systems, by contrast, maintain cells within the ideal 25\u0026ndash;40\u0026deg;C range with temperature differences below 3.5\u0026deg;C (Hwang et al., 2024). Design parameters play a decisive role: increasing TIM compression ratio significantly reduces contact resistance and improves cooling efficiency without additional pumping power (Gu et al., 2024), while precise inlet coolant temperature control has a greater influence on maximum battery temperature than flow-rate adjustments (Zhu et al., 2020). Looking forward, advanced liquid-based technologies\u0026mdash;hybrid systems, immersion cooling, and TPMS architectures\u0026mdash;will be crucial to meet the growing thermal demands of high-energy-density, fast-charging EV batteries.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eReferences list\u003c/h2\u003e \u003cp\u003eAkbarzadeh, M., Kalogiannis, T., Jaguemont, J., Jin, L., Behi, H., Karimi, D., Beheshti, H., Van Mierlo, J. and Berecibar, M. (2021) \u0026lsquo;A comparative study between air cooling and liquid cooling thermal management systems for a high-energy lithium-ion battery module\u0026rsquo;, \u003cem\u003eApplied Thermal Engineering\u003c/em\u003e, 198, 117503. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.applthermaleng.2021.117503\u003c/span\u003e\u003cspan address=\"10.1016/j.applthermaleng.2021.117503\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eBehi, H., Jaguemont, J., Berecibar, M., Karimi, D., Sokkeh, M.A., Van Mierlo, J., Behi, M., Gandoman, F.H. and Ghanbarpour, M. (2020) \u0026lsquo;A new concept of thermal management system in Li-ion battery using air cooling and heat pipe for electric vehicles\u0026rsquo;, \u003cem\u003eApplied Thermal Engineering\u003c/em\u003e, 174, 115280. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.applthermaleng.2020.115280\u003c/span\u003e\u003cspan address=\"10.1016/j.applthermaleng.2020.115280\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eGu, J., Kim, H.K. and Jang, S. (2024) \u0026lsquo;Study of Cooling Performance of Liquid-Cooled EV Battery Module According to the TIM Compression Ratio\u0026rsquo;, \u003cem\u003eInternational Journal of Automotive Technology\u003c/em\u003e, 25, pp. 885\u0026ndash;894. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s12239-024-00167-8\u003c/span\u003e\u003cspan address=\"10.1007/s12239-024-00167-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eHwang, F.S., Confrey, T., Reidy, C., Picovici, D., Callaghan, D., Culliton, D. and Nolan, C. (2024) \u0026lsquo;Review of battery thermal management systems in electric vehicles\u0026rsquo;, \u003cem\u003eRenewable and Sustainable Energy Reviews\u003c/em\u003e, 192, 114171. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.rser.2023.114171\u003c/span\u003e\u003cspan address=\"10.1016/j.rser.2023.114171\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eIqbal, F. et al. (2026) \u0026lsquo;Triply Periodic Minimal Surface (TPMS) Architectures for Advanced Battery Thermal Management Systems in EVs: Manufacturing, Integration, and Future Prospects\u0026rsquo;, \u003cem\u003eEnergy and AI\u003c/em\u003e. [In Press].\u003c/p\u003e \u003cp\u003eLu, M., Zhang, X., Ji, J., Xu, X. and Zhang, Y. (2020) \u0026lsquo;Research progress on power battery cooling technology for electric vehicles\u0026rsquo;, \u003cem\u003eJournal of Energy Storage\u003c/em\u003e, 27, 101155. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.est.2019.101155\u003c/span\u003e\u003cspan address=\"10.1016/j.est.2019.101155\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eMaalej, S., Saad, I., Hamdani, A., Cherif, R., Msaddek, R. and Zaghdoudi, M.C. 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(2020) \u0026lsquo;Thermal analysis and optimization of an EV battery pack for real applications\u0026rsquo;, \u003cem\u003eInternational Journal of Heat and Mass Transfer\u003c/em\u003e, 158, 120384. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ijheatmasstransfer.2020.120384\u003c/span\u003e\u003cspan address=\"10.1016/j.ijheatmasstransfer.2020.120384\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Electric vehicle, Battery thermal management, Air cooling, Liquid cooling, Thermo-fluidic analysis, Lithium-ion battery","lastPublishedDoi":"10.21203/rs.3.rs-9619917/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9619917/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe transition to high-energy-density lithium-ion batteries in electric vehicles (EVs) has intensified the need for effective battery thermal management systems (BTMS) to maintain cell temperatures within the optimal 15–35 °C window and prevent thermal runaway. This study presents a comparative thermo-fluidic analysis of the two dominant BTMS strategies—air cooling and indirect liquid cooling—by synthesizing experimentally validated computational fluid dynamics (CFD) data from recent literature. Performance is evaluated through the lens of Newton’s law of cooling and parasitic power consumption (P = V̇ΔP). Fluid property analysis reveals that liquid coolants (water-glycol) offer a convective heat transfer coefficient (1000–10,000 W/m²K) two orders of magnitude higher than air (10–100 W/m²K). Under equivalent 2C discharge conditions, liquid cooling limits the maximum cell temperature to 25–40 °C with a cell-to-cell temperature difference below 1.5 °C, whereas forced-air cooling reaches 42.4 °C with a gradient of ~4.9 °C. The power ratio required to induce a given temperature rise is approximately 860 in favor of liquid cooling. Furthermore, critical design parameters for liquid-cooled systems were examined: increasing the thermal interface material (TIM) compression ratio from 0 % to 25 % reduced the peak module temperature by 3.2 °C without additional pumping power, and coolant inlet temperature was identified as the most influential factor on maximum battery temperature. While air cooling remains cost-effective for low-power applications, liquid cooling demonstrates decisive superiority in peak temperature suppression, thermal uniformity, and energy efficiency, especially at high C-rates. Advanced liquid-based approaches—including immersion cooling, hybrid heat-pipe/cold-plate systems, and triply periodic minimal surface (TPMS) architectures—are identified as essential pathways to meet the thermal demands of next-generation fast-charging EV batteries.\u003c/p\u003e","manuscriptTitle":"Air vs. Liquid BTMS for Electric Vehicles:A Thermo‑Fluidic Performance Comparison","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-07 08:20:58","doi":"10.21203/rs.3.rs-9619917/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":"6f61a6c1-349e-4926-86c9-930679741ebf","owner":[],"postedDate":"May 7th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":67570871,"name":"Mechanical Engineering"}],"tags":[],"updatedAt":"2026-05-07T08:20:58+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-07 08:20:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9619917","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9619917","identity":"rs-9619917","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}