ARTIFICIAL INTELLIGENCE FOR BATTERY AND DRIVE SYSTEM MANAGEMENT IN ELECTRIC VEHICLES

Artificial Intelligence provides a promising approach for improving battery management, electric-drive control, energy optimization, and fault diagnosis in electric vehicles. Learning methods such as ANN, SVM, LSTM, and CNN can extract useful relationships from battery and vehicle data, while reinforcement learning and hybrid approaches can support adaptive control [7], [16], [19], [28]–[37]. Accurate estimation of SOC, SOH, and RUL helps the BMS make appropriate decisions during charging, acceleration, and regenerative braking. Intelligent electric-drive control can simultaneously improve torque regulation and energy utilization. Fig. 11 summarizes an integrated engineering framework in which battery management, drive control, energy management, and fault diagnosis communicate through an AI-based decision layer. This arrangement enables information from voltage, current, temperature, speed, and battery-state measurements to be considered jointly. However, limited datasets, computational requirements, interpretability, and real-world validation remain significant challenges [16], [19], [34]–[37]. Future research should emphasize explainable AI, federated learning, edge computing, standardized datasets, and integrated battery–drive models. Overall, coordinated AI management can support safer, more efficient, reliable, and sustainable electric mobility while improving the practical utilization of battery and drive-system information

[1] F. Alanazi, “Electric Vehicles: Benefits, Challenges, and Potential Solutions for Widespread Adoption,” Applied Sciences, vol. 13, no. 10, Art. no. 6016, 2023. [2] Y. Miao, P. Hynan, A. von Jouanne, and A. Yokochi, “Current Li-Ion battery technologies in electric vehicles and opportunities for advancements,” Energies, vol. 12, no. 6, 2019. [3] H. A. Gabbar, A. M. Othman, and M. R. Abdussami, “Review of battery management systems (BMS) development and industrial standards,” Technologies, vol. 9, no. 2, Art. no. 28, 2021. [4] N. Khan et al., “A critical review of battery cell balancing techniques, optimal design, converter topologies, and performance,” 2022. [5] M. S. Ramkumar et al., “Review on Li-Ion battery with battery management system in electrical vehicle,” Advances in Materials Science and Engineering, 2022. [6] Y. Gao, C. Zhu, X. Zhang, and B. Guo, “Implementation and evaluation of a practical electrochemical-thermal model of lithium-ion batteries for EV battery management system,” Energy, 2021. [7] R. Xiong, J. Cao, Q. Yu, H. He, and F. Sun, “Critical review on the battery state of charge estimation methods for electric vehicles,” IEEE Access, vol. 6, pp. 1832–1843, 2018. [8] N. Li et al., “An indirect state-of-health estimation method based on improved genetic and back propagation for online lithium-ion battery used in electric vehicles,” IEEE Transactions on Vehicular Technology, vol. 71, no. 12, pp. 12682–12690, 2022. [9] S. Bamati and H. Chaoui, “Lithium-ion batteries long horizon health prognostic using machine learning,” IEEE Transactions on Energy Conversion, vol. 37, no. 2, pp. 1176–1186, 2022. [10] K. A. Severson et al., “Data-driven prediction of battery cycle life before capacity degradation,” Nature Energy, vol. 4, no. 5, pp. 383–391, 2019.