AI-Integrated Battery Management Systems for Performance Optimization in Electric Vehicles

Authors

  • Kushal Lodha,S.W Mohod

Keywords:

Artificial Intelligence, Battery Management Systems, Electric Vehicles, Machine Learning, Predictive Maintenance, Performance Optimization, State of Charge, Decision Tree Regressor, Random Forest Regressor, Linear regression Model

Abstract

This study explores the integration of Artificial Intelligence (AI) in Battery ManagementSystems (BMS) for Electric Vehicles (EVs), emphasizing enhanced efficiency, performance, and longevity.AI-driven BMS utilizes machine learning models for precise state estimation and predictive maintenance.The analysis reveals superior predictive accuracy

References

. Wu, B., Widanage, W.D., Yang, S. and Liu, X., 2020. Battery digital twins: Perspectives on the fusion of models, data and artificial intelligence for smart battery management systems. Energy and AI, 1, p.100016.

. Ghalkhani, M. and Habibi, S., 2022. Review of the Li-ion battery, thermal management, and AI-based battery management system for EV application. Energies, 16(1), p.185.

. Shamami, M.S., Alam, M.S., Ahmad, F., Shariff, S.M., AlSaidan, I., Rafat, Y. and Asghar, M.J., 2020. Artificial intelligence-based performance optimization of electric vehicle-to-home (V2H) energy management system. SAE International Journal of Sustainable Transportation, Energy, Environment, & Policy, 1(13-01-02-0007), pp.115-125.

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Published

2025-08-25

How to Cite

Kushal Lodha,S.W Mohod. (2025). AI-Integrated Battery Management Systems for Performance Optimization in Electric Vehicles . Journal of Computational Analysis and Applications (JoCAAA), 34(8), 98–110. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/3488

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Section

Articles