SOC estimation of lithium battery based on fractional order model

Yuhang Chen; Yi Guo

doi:10.1117/12.2689433

10 August 2023 SOC estimation of lithium battery based on fractional order model

Yuhang Chen, Yi Guo

Proceedings Volume 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023); 127483B (2023) https://doi.org/10.1117/12.2689433
Event: 5th International Conference on Information Science, Electrical and Automation Engineering (ISEAE 2023), 2023, Wuhan, China

Abstract

For the purpose of the present study of lithium battery SOC estimation, fractional-order calculus theory and the fact that the real capacitance is fractional-order in nature mean that integer-order modeling yields incorrect methods. To improve the accuracy of lithium battery state-of-charge (SOC) estimation, a fractional-order traceless Kalman filter technique is proposed with a second-order RC fractional-order model, and a least-squares approach with a variable forgetting factor is utilized to determine battery parameters. The system gives real-time updates to the battery condition and settings through recursive estimation of state and parameter variables. Simulation analysis is performed using experimental data and UDDS operating parameters. The traceless Kalman filter method's simulated values are compared to the simulation outcomes. These results show that the method beats the integer-order traceless Kalman algorithm and that the maximum estimation error of battery SOC can be maintained below 2%. This proves that the proposed approach works as intended.

Citation Download Citation

Yuhang Chen and Yi Guo "SOC estimation of lithium battery based on fractional order model", Proc. SPIE 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023), 127483B (10 August 2023); https://doi.org/10.1117/12.2689433

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