Thermal Management Optimization in EV Battery Pack Assembly: A Data-Driven Approach Using AI-Based Feedback Loops
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I2P109Keywords:
Electric Vehicles (EVs), Battery Thermal Management, Artificial Intelligence, Feedback Loops, Machine Learning, Deep Learning, Energy EfficiencyAbstract
The fast development of Electric Vehicles (EVs) has posed one of the burning challenges: proper thermal management of battery pack assemblies. Thermal variation in EV batteries may result in poor performance, early battery failure and safety hazards. This paper provides an in-depth data-driven proposal for maximizing thermal management of EV battery pack assembly through Artificial Intelligence (AI)-powered feedback loops. The system can dynamically respond to variations in its operations through real-time data acquisition, predictive models, and intelligent control algorithms, thereby improving performance and safety. The paper begins by summarizing the thermal issues that are faced in modern EV battery solutions, discussing heat sources, including electrochemical reactions, exposure to the environment, and charge/discharge state. In the research, the hybrid AI framework (a mixture of Machine Learning (ML) and Deep Learning (DL) is used to create intelligent feedback loops that suggest thermal anomalies and automatically control cooling processes. Experiments were performed through simulators and reality with high-fidelity thermal sensors, cloud-based telemetry, and machine learning devices such as Long Short-Term Memory (LSTM) neural networks, Random Forest regressors, and Reinforcement Learning (RL) agents. When measured against conventional passive and rule-based solutions, the proposed system can show up to a 37 percent increase in thermal consistency and a 22 percent decrease in energy consumption in cooling subsystems. Some of the main contributions are a modular AI feedback design, an adaptive control method to thermally control battery thermal systems and a very rich dataset gathered on different environmental conditions and with diverse operations of the battery. The results provide a firm recommendation for the use of AI-driven dynamic systems as a game changer in the EV thermal management area, with an eye on further improvements in terms of EV reliability and sustainability
References
1. Jaguemont, J., Boulon, L., & Dubé, Y. (2016). A comprehensive review of lithium-ion batteries used in hybrid and electric vehicles at cold temperatures. Applied Energy, 164, 99-114.
2. Pesaran, A. A. (2001). Battery Thermal Management in EVs and HEVs: Issues and Solutions. Battery Man, 43(5), 34-49.
3. Saw, L. H., Ye, Y., & Tay, A. A. (2016). Integration issues of lithium-ion battery into the electric vehicle's battery pack. Journal of Cleaner Production, 113, 1032-1045.
4. Rao, Z., & Wang, S. (2011). A review of power battery thermal energy management. Renewable and Sustainable Energy Reviews, 15(9), 4554-4571.
5. T. Wang et al., “Challenges and Strategies in Battery Thermal Management: A Review,” Journal of Energy Storage, vol. 32, p. 101837, 2020.
6. Moore, A. L., & Shi, L. (2014). Emerging challenges and materials for thermal management of electronics. Materials today, 17(4), 163-174.
7. Jiang, G., Diao, L., & Kuang, K. (2012). Advanced thermal management materials. Springer Science & Business Media.
8. Yang, Y., Bilgin, B., Kasprzak, M., Nalakath, S., Sadek, H., Preindl, M., ... & Emadi, A. (2017). Thermal management of electric machines. IET Electrical Systems in Transportation, 7(2), 104-116.
9. Ghahramani, M., Qiao, Y., Zhou, M. C., O'Hagan, A., & Sweeney, J. (2020). AI-based modeling and data-driven evaluation for smart manufacturing processes. IEEE/CAA Journal of Automatica Sinica, 7(4), 1026-1037.
10. Dincer, I., Hamut, H. S., & Javani, N. (2016). Thermal management of electric vehicle battery systems. John Wiley & Sons.
11. Greco, A., Cao, D., Jiang, X., & Yang, H. (2014). A theoretical and computational study of lithium-ion battery thermal management for electric vehicles using heat pipes. Journal of Power Sources, 257, 344-355.
12. Ghalkhani, M., & Habibi, S. (2022). Review of the Li-ion battery, thermal management, and AI-based battery management system for EV application. Energies, 16(1), 185.
13. Zhang, S. S., Xu, K., & Jow, T. R. (2003). The low temperature performance of Li-ion batteries. Journal of Power Sources, 115(1), 137-140.
14. Afzal, A., Mohammed Samee, A. D., Abdul Razak, R. K., & Ramis, M. K. (2021). Thermal management of modern electric vehicle battery systems (MEVBS). Journal of Thermal Analysis & Calorimetry, 144(4).
15. Bagaa, M., Taleb, T., Bernabe, J. B., & Skarmeta, A. (2020). A machine learning security framework for IoT systems. IEEE Access, 8, 114066-114077.
16. Lopez-Sanz, J., Ocampo-Martinez, C., Alvarez-Florez, J., Moreno-Eguilaz, M., Ruiz-Mansilla, R., Kalmus, J., ... & Lux, G. (2017). Thermal management in plug-in hybrid electric vehicles: A real-time nonlinear model predictive control implementation. IEEE Transactions on Vehicular Technology, 66(9), 7751-7760.
17. Zhang, K., Guliani, A., Ogrenci-Memik, S., Memik, G., Yoshii, K., Sankaran, R., & Beckman, P. (2017). Machine learning-based temperature prediction for runtime thermal management across system components. IEEE Transactions on parallel and distributed systems, 29(2), 405-419.
18. Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R. K., & Kumar, P. (2021). Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Molecular diversity, 25(3), 1315-1360.
19. Liu, H., Wen, M., Yang, H., Yue, Z., & Yao, M. (2021). A review of thermal management systems and control strategies for automotive engines. Journal of Energy Engineering, 147(2), 03121001.
20. Liao, L., Zuo, P., Ma, Y., Chen, X., An, Y., Gao, Y., & Yin, G. (2012). Effects of temperature on charge/discharge behaviors of LiFePO4 cathode for Li-ion batteries. Electrochimica Acta, 60, 269-273.
