Smart Battery Optimization and Balancing with AI and Machine  Learning

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

Smart Battery Optimization and Balancing with AI and Machine Learning

Authors

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

Keywords:

Battery Management Systems, Artificial Intelligence (AI),Machine Learning (ML),State of Charge (SoC),State of Health (SoH),Cell Balancing

Abstract

The rapid adoption of electric vehicles (EVs), renewable energy systems, and portable electronicshas increased the demand for efficient, reliable, and intelligent Battery Management Systems(BMS). Traditional BMS approaches often rely on fixed algorithms and rule-based systems

References

. Oyucu, S.; Dümen, S.; Duru, İ.; Aksöz, A.; Biçer, E. Discharge Capacity Estimation for Li-Ion Batteries: A Comparative Study. Symmetry 2024, 16, 436.

. Diao, W.; Saxena, S.; Pecht, M. Accelerated cycle life testing and capacity degradation modeling of LiCoO2-graphite cells. J. Power Sources 2019, 435, 226830.

Downloads

Published

2024-02-15

How to Cite

Hari Prasad Bhupathi, Srikiran Chinta, Dr Vijayalaxmi Biradar, Dr Sanjay Kumar Suman. (2024). Smart Battery Optimization and Balancing with AI and Machine Learning . Journal of Computational Analysis and Applications (JoCAAA), 32(2), 496–504. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/3367