An algorithm for state of charge estimation based on a single-particle model. (July 2021)

• Record Type: Journal Article
• Authors: Ren, Lichao; Zhu, Guorong; Kang, Jianqiang; Wang, Jing V.; Luo, Bingyang; Chen, Chaoyang; Xiang, Kui
• Is Part Of: Journal of energy storage. Volume 39(2021)
• Publication Date: 2021-07
• Subjects: Lithium-ion battery -- Extended single-particle model -- State of charge -- Closed-loop estimation -- Robustness; Energy storage -- Periodicals; Energy storage -- Research -- Periodicals; 621.3126
• DOI: 10.1016/j.est.2021.102644
• Languages: English
• ISSNs: 2352-152X

Abstract

Highlights: The extended single-particle model (ESP) with higher accuracy is proposed. A new SOC closed-loop estimation algorithm is proposed. The ESP has higher accuracy than the SP. The closed-loop SOC estimation shows good robustness. Abstract: Compared with the equivalent circuit models or other empirical models, a physics-based model has advantages of accurate and elaborate, and thus becomes a potential candidate used to estimate states of Li-ion batteries in a battery management systems (BMS). The traditional pseudo-two-dimensional (P2D) model couples a large number of nonlinear partial differential equations, leading to the model too complicated to be employed in actual application. The simplified single-particle (SP) model has a trend for the real usage, however, its accuracy needs to be improved, since it meets the demand only under the condition of a low charge/discharge rate. To overcome the drawbacks of the SP model, the extended single-particle model (ESP) with higher accuracy is proposed in this study. We also propose a new state of charge (SOC) closed-loop estimation algorithm based on the combination of ESP model and the ampere-hour integration. Results show that the ESP model can effectively simulate the performance of the battery, and the closed-loop SOC estimation algorithm can correct the initial SOC error without increasing the computational complexity. The mean error of the closed-loop SOC estimation based on ESP is reduced by about 95% and 92.5% than ampere-hour integration under 1 C discharge and FUDS discharge, respectively.