An integrated approach for real-time model-based state-of-charge estimation of lithium-ion batteries

• Published in: Journal of power sources Vol. 283; pp. 24 - 36
• Main Authors: Zhang, Cheng, Li, Kang, Pei, Lei, Zhu, Chunbo
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.06.2015
• Subjects: Extended Kalman filter; Hysteresis effect; LiFePo4 battery; Real-time SOC estimation; Teaching learning based optimization (TLBO) method; Weighted recursive least square; Weighted recursive least square; Teaching learning based optimization (TLBO) method; LiFePo4 battery; Real-time SOC estimation; Hysteresis effect; Extended Kalman filter
• DOI: 10.1016/j.jpowsour.2015.02.099
• ISSN: 0378-7753

Lithium-ion batteries have been widely adopted in electric vehicles (EVs), and accurate state of charge (SOC) estimation is of paramount importance for the EV battery management system. Though a number of methods have been proposed, the SOC estimation for Lithium-ion batteries, such as LiFePo4 battery, however, faces two key challenges: the flat open circuit voltage (OCV) vs SOC relationship for some SOC ranges and the hysteresis effect. To address these problems, an integrated approach for real-time model-based SOC estimation of Lithium-ion batteries is proposed in this paper. Firstly, an auto-regression model is adopted to reproduce the battery terminal behaviour, combined with a non-linear complementary model to capture the hysteresis effect. The model parameters, including linear parameters and non-linear parameters, are optimized off-line using a hybrid optimization method that combines a meta-heuristic method (i.e., the teaching learning based optimization method) and the least square method. Secondly, using the trained model, two real-time model-based SOC estimation methods are presented, one based on the real-time battery OCV regression model achieved through weighted recursive least square method, and the other based on the state estimation using the extended Kalman filter method (EKF). To tackle the problem caused by the flat OCV-vs-SOC segments when the OCV-based SOC estimation method is adopted, a method combining the coulombic counting and the OCV-based method is proposed. Finally, modelling results and SOC estimation results are presented and analysed using the data collected from LiFePo4 battery cell. The results confirmed the effectiveness of the proposed approach, in particular the joint-EKF method. •An auto-regression battery model is built considering hysteresis nonlinearity.•A hybrid model training method combining TLBO and least square is proposed.•WRLS and joint-EKF approaches are used for real-time model-based SOC estimation.•Flat OCV problem is tackled by combining WRLS method with coulomb counting.