Adaptive state of charge estimation of Lithium-ion battery based on battery capacity degradation model. (October 2018)

• Record Type: Journal Article
• Authors: Yang, Guodong; Li, Junqiu; Fu, Zijian; Guo, Lin
• Is Part Of: Energy procedia. Volume 152(2018)
• Page Start: 514
• Page End: 519
• Publication Date: 2018-10
• Subjects: state of charge -- lithium-ion battery -- least squares -- adaptive extended kalman filtering -- battery capacity degradation model; Power resources -- Congresses; Power resources -- Periodicals; Power resources; Conference proceedings; Periodicals; 333.7905
• DOI: 10.1016/j.egypro.2018.09.203
• Languages: English
• ISSNs: 1876-6102

Abstract

Abstract: For electric vehicles (EVs), accurate State of Charge (SoC) estimation of battery contributes to ensure battery safety and improve driving mileage. Therefore, its research has essential application value. However, accurate SoC estimation of the battery relies on precise battery model parameters and capacity. This paper mainly carries out three aspects of work. (1) A battery equivalent circuit model is established, and the Forgetting Factor Recursive Least Squares (FFRLS) method is used to realize online identification of model parameters. (2) Based on the Arrhenius equation, the inverse power law equation and the battery capacity degradation equation, the battery capacity degradation model under dynamic stress is established to achieve the online prediction of battery capacity. (3) Based on equivalent circuit model, battery capacity degradation model and Adaptive Extended Kalman Filtering (AEKF) algorithm, an adaptive SoC estimation method is proposed. Simulation results show that the maximum estimation error of battery capacity and SoC is less than 2.5% and 1.5% respectively.