Artificial Intelligence and Machine Learning Solutions for Efficient Battery Management and Balancing

Authors

  • Hari Prasad Bhupathi,Srikiran Chinta, Dr Vijayalaxmi Biradar, Dr Sanjay Kumar Suman

Keywords:

Battery management systems, State of Charge, State of Health, optimization

Abstract

Battery management systems (BMS) play a pivotal role in ensuring the performance, safety, andlongevity of energy storage systems, particularly in electric vehicles, renewable energy grids, andportable electronic devices. As battery technologies advance and energy demands increase,traditional rule-based battery management and balancing techniques are proving to be insufficient

References

. Veeranki Srinivasa Rao , Guna Sekhar Sajja , Vishwaraj B Manur Sairaj Arandhakar V.B. Murali Krishna “An exploratory study on intelligent active cell balancing of electric vehicle battery management and performance using machine learning algorithms”, Results in Engineering Volume 25, doi.org/10.1016/j.rineng.2025.104524

(2]. Thiruvonasundari Duraisamy, Deepa Kaliyaperumal “Machine Learning-Based Optimal Cell

Balancing Mechanism for Electric Vehicle Battery Management System”,Journals &

Magazines IEEE Access Volume: 9, Page: 132846 – 132861, doi;10.1109/ACCESS.2021.3115255

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Published

2023-04-20

How to Cite

Hari Prasad Bhupathi,Srikiran Chinta, Dr Vijayalaxmi Biradar, Dr Sanjay Kumar Suman. (2023). Artificial Intelligence and Machine Learning Solutions for Efficient Battery Management and Balancing . Journal of Computational Analysis and Applications (JoCAAA), 31(4), 1545–1554. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/3365

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Articles

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