An integrated online adaptive state of charge estimation approach of high-power lithium-ion battery packs.

Wang, Shunli; Fernandez, Carlos; Shang, Liping; Li, Zhanfeng; Yuan, Huifang

Abstract

A novel online adaptive state of charge (SOC) estimation method is proposed, aiming to characterize the capacity state of all the connected cells in lithium-ion battery (LIB) packs. This method is realized using the extended Kalman filter (EKF) combined with Ampere-hour (Ah) integration and open circuit voltage (OCV) methods, in which the time-scale implementation is designed to reduce the computational cost and accommodate uncertain or time-varying parameters. The working principle of power LIBs and their basic characteristics are analysed by using the combined equivalent circuit model (ECM), which takes the discharging current rates and temperature as the core impacts, to realize the estimation. The original estimation value is initialized by using the Ah integral method, and then corrected by measuring the cell voltage to obtain the optimal estimation effect. Experiments under dynamic current conditions are performed to verify the accuracy and the real-time performance of this proposed method, the analysed result of which indicates that its good performance is in line with the estimation accuracy and real-time requirement of high-power LIB packs. The proposed multimodel SOC estimation method may be used in the real-time monitoring of the high-power LIB pack dynamic applications for working state measurement and control.

Citation

WANG, S., FERNANDEZ, C., SHANG, L., LI, Z. and YUAN, H. 2018. An integrated online adaptive state of charge estimation approach of high-power lithium-ion battery packs. Transactions of the Institute of Measurement and Control [online], 40(6), pages 1892-1910. Available from: https://doi.org/10.1177/0142331217694681

• Journal Article Type: Article
• Acceptance Date: Apr 20, 2017
• Online Publication Date: Apr 20, 2017
• Publication Date: Apr 1, 2018
• Deposit Date: Nov 2, 2017
• Publicly Available Date: Nov 2, 2017
• Journal: Transactions of the Institute of Measurement and Control
• Print ISSN: 0142-3312
• Electronic ISSN: 1477-0369
• Publisher: SAGE Publications
• Peer Reviewed: Peer Reviewed
• Volume: 40
• Issue: 6
• Pages: 1892-1910
• DOI: https://doi.org/10.1177/0142331217694681
• Keywords: Equivalent circuit model; Extended Kalman filter; Lithiumion battery; Online estimation; State of charge
• Public URL: http://hdl.handle.net/10059/2568