Design of battery management system and estimation of state of charge

Qing Yang; Fangxin Liu; Qian Liu

doi:10.1117/12.2658844

23 November 2022 Design of battery management system and estimation of state of charge

Qing Yang, Fangxin Liu, Qian Liu

Proceedings Volume 12454, International Symposium on Robotics, Artificial Intelligence, and Information Engineering (RAIIE 2022); 124540J (2022) https://doi.org/10.1117/12.2658844
Event: International Symposium on Robotics, Artificial Intelligence, and Information Engineering (RAIIE 2022), 2022, Hohhot, China

Abstract

Battery management technology is one of the key technologies of new energy vehicles. This paper designs the hardware of battery management system (BMS) for new energy vehicles, and uses radial basis function (RBF) neural network to estimate the state of charge (SOC) of batteries. The results show that using RBF neural network algorithm to estimate SOC can avoid the process of modeling the complex electrochemical reaction inside the battery, and can achieve high accuracy.

Citation Download Citation

Qing Yang, Fangxin Liu, and Qian Liu "Design of battery management system and estimation of state of charge", Proc. SPIE 12454, International Symposium on Robotics, Artificial Intelligence, and Information Engineering (RAIIE 2022), 124540J (23 November 2022); https://doi.org/10.1117/12.2658844

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