High-precision state of charge estimation for the power lithium ion batteries by introducing an improved extended Kalman filtering algorithm with complex varying temperatures.

Xu, Wenhua; Wang, Shunli; Fernandez, Carlos; Yu, Chunmei; Fan, Yongcun; Stroe, Daniel-Ioan

Abstract

Accurate estimation of the state of charge is important for the rational use of lithium ion batteries and the development of electric vehicles. In order to solve the problem that the internal parameters of lithium battery are greatly affected by the temperature change, which makes the estimation of state of charge inaccurate, a new method based on different temperature is proposed. The improved extended Kalman filter algorithm is applied to estimate and track the state of charge at different temperatures and working conditions. The experimental results show that the established estimation model can better estimate the state of charge of lithium battery with fast convergence rate, and can estimate the battery state of different working conditions at different temperatures. The tracking effect is good and the estimation error is controlled within 0.03%.

Citation

XU, W., WANG, S., FERNANDEZ, C., YU, C., FAN, Y. and STROE, D.-I. 2020. High-precision state of charge estimation for the power lithium ion batteries by introducting an improved extended Kalman filtering algorithm with complex varying temperatures. In Proceedings of 5th Advanced robotics and mechatronics international conference 2020 (ICARM 2020), 18-21 December 2020, Shenzhen, China [virtual conference]. Piscataway: IEEE [online], pages 700-705. Available from: https://doi.org/10.1109/ICARM49381.2020.9195316

• Conference Name: 5th Advanced robotics and mechatronics international conference 2020 (ICARM 2020)
• Conference Location: Shenzhen, China
• Start Date: Dec 18, 2020
• End Date: Dec 21, 2020
• Acceptance Date: Mar 25, 2020
• Online Publication Date: Sep 14, 2020
• Publication Date: Dec 21, 2020
• Deposit Date: Nov 2, 2020
• Publicly Available Date: Nov 2, 2020
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Pages: 700-705
• Series ISSN: 2161-4407
• DOI: https://doi.org/10.1109/icarm49381.2020.9195316
• Keywords: Power lithium ion batteries; Improved extended Kalman filtering algorithm; Lithium battery; Improved extended Kalman filter algorithm; Estimation model; Estimation error; High-precision state of charge estimation; Accurate estimation; Electric vehicles; I
• Public URL: https://rgu-repository.worktribe.com/output/979120