Artificial intelligence-based battery management systems in electric vehicles: models, optimization, and future directions.

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Bibliographic Details
Title:	Artificial intelligence-based battery management systems in electric vehicles: models, optimization, and future directions.
Authors:	Kassem, Hassan¹ Hassan.Kassem@auct.edu.jo, Bishtawi, Tariq¹ t.bishtawi@aau.edu.jo
Source:	International Journal of Electrical & Computer Engineering (2088-8708). Jun2026, Vol. 16 Issue 3, p1645-1654. 10p.
Subjects:	Battery management systems, Artificial intelligence, Deep learning, Electric vehicles, Mathematical optimization, Machine learning
Abstract:	The electric vehicle (EV) depends on the capabilities and durability of the main element of the car -- the battery. Conventional battery management systems (BMS) can generally be challenged with regards to state estimation and lifespan forecasting in the face of complicated real-world scenarios. To address these limitations, this study examines how artificial intelligence (AI) has the potential to transform BMS operations. We introduce an in-depth discussion of AI-controlled BMS by examining the state-of-the-art models of precise state-of-charge and state-of-health estimation. The paper also goes into details of how machine learning and deep learning methods can optimize charging strategy, improve thermal management, and predictive diagnostics. The comparison between the data-driven solutions and the traditional methods is going to reveal that there is a high safety, efficiency, and battery life improvement. Lastly, we map the way ahead, taking into consideration issues such as edge computing, explainable AI, and the way of making the BMS a truly self-optimizing system, essential to the next generation of electric cars. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	The electric vehicle (EV) depends on the capabilities and durability of the main element of the car -- the battery. Conventional battery management systems (BMS) can generally be challenged with regards to state estimation and lifespan forecasting in the face of complicated real-world scenarios. To address these limitations, this study examines how artificial intelligence (AI) has the potential to transform BMS operations. We introduce an in-depth discussion of AI-controlled BMS by examining the state-of-the-art models of precise state-of-charge and state-of-health estimation. The paper also goes into details of how machine learning and deep learning methods can optimize charging strategy, improve thermal management, and predictive diagnostics. The comparison between the data-driven solutions and the traditional methods is going to reveal that there is a high safety, efficiency, and battery life improvement. Lastly, we map the way ahead, taking into consideration issues such as edge computing, explainable AI, and the way of making the BMS a truly self-optimizing system, essential to the next generation of electric cars. [ABSTRACT FROM AUTHOR]
ISSN:	20888708
DOI:	10.11591/ijece.v16i3.pp1645-1654