Fuzzy Logic Based Liquid Thermal Management of a Lithium-Ion Battery Pack for Electric Vehicles

Author(s): Ramkumar T, Hemanth G, Jaivarshan G, Kamalesh S, Veerasamy A

Affiliation: Department of Electrical and Electronics Engineering, K. Ramakrishnan College of Technology (Autonomous), Samayapuram, Tiruchirappalli, Tamil Nadu, India Corresponding author: Ramkumar T, ramkumart.eee@krct.ac.in

Page No: 19-31

Volume, Issue & Publishing Year: Volume 3 Issue 10,Nov-2026

Journal: International Journal of Advanced Engineering Application (IJAEA)

ISSN NO: 3048-6807

Abstract:
The temperature of a lithium-ion battery decides how much power it can deliver, how safe it is and how long it lasts, and uneven heating inside the pack shortens its life further. This paper presents the design of a liquid-cooled thermal management system for a 24 V, 10 Ah lithium-ion pack built as seven series groups of four parallel cells (7S4P), together with the prototype assembled from it. An aluminum panel carries heat from the pack to a copper tube, and a 12 V pump moves an inhibited propylene-glycol/water coolant from the tube to a fan-cooled radiator. Six DS18B20 sensors read the pack temperature and an ACS758 sensor reads the battery current. An ESP32 runs a PID controller whose gains are scaled by a fuzzy supervisor using the highest temperature, its rate of rise and the load current, and it drives the pump and fan with PWM signals. A separate check sets full cooling and raises an alert on overtemperature or a sensor fault. Design calculations put the pack heat at about 5 W at 10 A and 47 W at 30 A, which needs only 0.04–0.4 L/min of coolant. In a lumped thermal-model run with the load falling from 10 A to 5 A at 30 °C ambient, fuzzy–PID control peaked at 32.77 °C, 0.77 °C above the 32 °C set-point, and ended at 31.98 °C, while the uncooled pack reached 36.51 °C. In the same model ON/OFF control held the temperature closer to the set-point but used about 42 % more auxiliary energy, and at 30 A the cooling saturated with the pack settling at 38.0 °C. These values come from a model and not from measurements. The prototype has been assembled and its display reports a normal-temperature status, but systematic measurements on it are still to be done.

Keywords: Battery thermal management, Fuzzy logic, Lithium-ion battery, Liquid cooling, PID control

Reference:

• Martin, G. E., El Baghdadi, M., & Hegazy, O. (2025). Advancements in thermal management for electric vehicles: Strategies, architectures, and power electronics cooling. IEEE Access. https://doi.org/10.1109/ACCESS.2025.3599627
• 2. Oztop, M., & Şahinaslan, A. (2022). Control of temperature distribution for Li-ion battery modules via longitudinal fins. Journal of Energy Storage, 52. https://doi.org/10.1016/j.est.2022.104760
• 3. Ma, Y., Ding, H., Mu, H., & Chen, H. (2021). Liquid cooling strategy of power battery based on fuzzy PID algorithm. Control Theory & Applications, 38(5), 549–560. https://doi.org/10.7641/CTA.2020.00373
• 4. Liu, Z., Liu, Z., Liu, J., & Wang, N. (2023). Thermal management with fast temperature convergence based on optimized fuzzy PID algorithm for electric vehicle battery. Applied Energy, 352. https://doi.org/10.1016/j.apenergy.2023.121936
• 5. Siddula, S., & Kota, V. (2025). Fuzzy logic driven intelligent energy optimization for hybrid electric vehicles. In Proceedings of the International Conference on Next Generation Communication & Information Processing (INCIP). https://doi.org/10.1109/INCIP64058.2025.11019316
• 6. Sohail, M., Saeed, M., Khalatbarisoltani, A., et al. (2025). An intelligent control-based charging strategy for lithium-ion batteries. In Proceedings of the IEEE 16th International Symposium on Power Electronics for Distributed Generation Systems (PEDG). https://doi.org/10.1109/PEDG62294.2025.11060309
• 7. Doniparthi, S., Gatla, R. K., Shameem, & Kumar, N. M. G. (2024). Optimized thermal control of EV battery systems with fuzzy logic and PSO on Arduino Uno. In Proceedings of the 3rd International Conference on Advancement in Technology (ICONAT). https://doi.org/10.1109/ICONAT61936.2024.10774666
• 8. Gomathi, S., Janani, G., Jothishwaren, R., & Nalien, A. (2024). Thermo electric cooling system for E-vehicle. In Proceedings of the 5th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI). https://doi.org/10.1109/ICMCSI61536.2024.00133
• 9. Bairwa, B., Pareek, K., Sridhar, J. P., & Yaragatti, U. R. (2025). Design and development of cooling system for electric vehicle batteries. In Proceedings of the IEEE 5th International Conference on Sustainable Energy and Future Electric Transportation (SEFET). https://doi.org/10.1109/SEFET65155.2025.11255175
• 10. Liu, Z., Jiang, Y., Li, Y., & Wang, P. (2025). Physics-informed machine learning for battery pack thermal management. In Proceedings of the 71st Annual Reliability and Maintainability Symposium (RAMS). https://doi.org/10.1109/RAMS48127.2025.10935157