State-of-charge estimation with adaptive extended Kalman filter and extended stochastic gradient algorithm for lithium-ion batteries. (March 2022)

• Record Type: Journal Article
• Authors: Ye, Yuanmao; Li, Zhenpeng; Lin, Jingxiong; Wang, Xiaolin
• Is Part Of: Journal of energy storage. Volume 47(2022)
• Publication Date: 2022-03
• Subjects: SOC estimation -- Extended stochastic gradient -- Kalman filter -- Parameter identification; Energy storage -- Periodicals; Energy storage -- Research -- Periodicals; 621.3126
• DOI: 10.1016/j.est.2021.103611
• Languages: English
• ISSNs: 2352-152X

Abstract

Highlights: New closed-loop algorithm combining parameter identification and State estimation. Computing cost is reduced by omitting the step of parameters extraction. Parameters identification process is conducted by an algorithm of Extended Stochastic Gradient. Robust to different testing profile and easily convergent. Abstract: Online state-of-charge (SOC) estimation is a critical element for battery management systems and it requires lower computing cost and acceptable range of accuracy. This paper proposes a new model-based SOC estimation method for lithium-ion batteries. By utilizing the state estimation to identify the model parameters and then re-estimate the state by using the identified parameters, the two steps of parameter identification and state estimation are integrated into one closed-loop algorithm and they are implemented by using extended stochastic gradient (ESG) algorithm and adaptive extended Kalman filter (AEKF), respectively. In this method, it is unnecessary to calculate each circuit parameter of the model separately resulting in simper structure and lower computing cost. Experimental results indicate that the proposed SOC estimation algorithm has good performance in terms of estimation accuracy and robustness under different test conditions. It is therefore more suitable for online SOC estimation of lithium-ion batteries.