Prediction and simulation of state of charge of li-ion battery for electric vehicle based on data-driven method

Xiangfei Meng; Xin Zhang; Chao Wang; Xiang Yun; Xingming Fan

doi:10.1117/12.3026436

1 April 2024 Prediction and simulation of state of charge of li-ion battery for electric vehicle based on data-driven method

Xiangfei Meng, Xin Zhang, Chao Wang, Xiang Yun, Xingming Fan

Author Affiliations +

Proceedings Volume 13082, Fourth International Conference on Mechanical Engineering, Intelligent Manufacturing, and Automation Technology (MEMAT 2023); 130820Z (2024) https://doi.org/10.1117/12.3026436
Event: 2023 4th International Conference on Mechanical Engineering, Intelligent Manufacturing and Automation Technology (MEMAT 2023), 2023, Guilin, China

Abstract

The Relevance Vector Machine (RVM) which has uncertain expression and management capabilities becomes an effective method for estimating the state of charge (SOC) of Li-ion batteries. However, the algorithm has the problems of low multi-step prediction accuracy and poor online prediction adaptability, which limits its application for battery SOC estimation. So, an improved incremental RVM model is proposed to predict the SOC of Li-ion battery online. Through the ways of dynamic training model and on-line incremental learning to improve the prediction accuracy of the model, and the fast-sequence sparse Bayesian learning algorithm is selected for training to reduce the computational complexity and improve the computational efficiency of the algorithm. The study found that this model can guarantee higher prediction accuracy by adjusting the kernel width automatically. Experimental results show that this method has the characteristics of high prediction accuracy, fast calculation speed, and strong universality, it can provide a reference for Li-ion batteries SOC prediction and application.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiangfei Meng, Xin Zhang, Chao Wang, Xiang Yun, and Xingming Fan "Prediction and simulation of state of charge of li-ion battery for electric vehicle based on data-driven method", Proc. SPIE 13082, Fourth International Conference on Mechanical Engineering, Intelligent Manufacturing, and Automation Technology (MEMAT 2023), 130820Z (1 April 2024); https://doi.org/10.1117/12.3026436

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