Enhancing electric vehicle battery lifespan: integrating active balancing and machine learning for precise RUL estimation.

Autor: Sultan YA; Mechatronics Department, Faculty of Engineering, Horus University-Egypt, New Damietta, Egypt., Eladl AA; Electrical Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, 35516, Egypt. eladle7@mans.edu.eg., Hassan MA; Electrical Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, 35516, Egypt., Gamel SA; Electronics and Communication Engineering Dept. Faculty of Engineering, Horus University, New Damietta, Egypt.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2025 Jan 04; Vol. 15 (1), pp. 777. Date of Electronic Publication: 2025 Jan 04.
DOI: 10.1038/s41598-024-82778-w
Abstrakt: Electric vehicles (EVs) rely heavily on lithium-ion battery packs as essential energy storage components. However, inconsistencies in cell characteristics and operating conditions can lead to imbalanced state of charge (SOC) levels, resulting in reduced capacity and accelerated degradation. This study presents an active cell balancing method optimized for both charging and discharging scenarios, aiming to equalize SOC across cells and improve overall pack performance. The proposed system includes two balancing strategies: a charging balance that redistributes excess charge from high-SOC cells to maximize capacity, and a discharging balance that addresses low-SOC cells to extend discharge duration. Experimental results confirm that this method effectively reduces SOC disparities, enhancing both charging and discharging capacities. Additionally, to accurately predict battery lifespan and remaining useful life (RUL), seven machine learning models are evaluated using R-squared (R 2 ) and Mean Absolute Error (MAE) metrics. Among these, k-nearest Neighbors and Random Forest models deliver the highest accuracy, achieving R 2 values of 0.996 and above with low MAE, demonstrating strong predictive capability. The integration of active balancing and RUL prediction enables a feedback loop where balanced SOC levels promote battery health, and RUL predictions inform optimal balancing strategies. This comprehensive approach advances EV battery management, enhancing lifespan and reliability through proactive balancing and predictive insights.
Competing Interests: Competing interests: The authors declare no competing interests.
(© 2025. The Author(s).)
Databáze: MEDLINE
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