A novel capacity estimation method for lithium-ion batteries using fusion estimation of charging curve sections and discrete Arrhenius aging model. (1st October 2019)

• Record Type: Journal Article
• Authors: Zheng, Yuejiu; Qin, Chao; Lai, Xin; Han, Xuebing; Xie, Yi
• Is Part Of: Applied energy. Volume 251(2019)
• Publication Date: 2019-10-01
• Subjects: Capacity estimation -- Charging curve sections -- Discrete Arrhenius aging model -- Model parameters -- Sequential extended Kalman filters; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2019.113327
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: An online capacity estimation method based on charging curve sections is proposed. A discrete Arrhenius aging model is applied for the continuous capacity estimation. Parameters of the discrete Arrhenius aging model are updated by the first EKF. The second EKF makes a fusion capacity estimation of the aforementioned methods. The fusion capacity estimation is accurate over the life span of the battery cell. Abstract: Practical open-loop capacity estimation models, such as the Arrhenius aging model, require parameter updating for the real-world capacity estimation in order to guarantee the estimation accuracy. In this paper, a novel capacity estimation method for lithium-ion batteries, based on the fusion estimation of charging curve sections and the discrete Arrhenius aging model using sequential extended Kalman filters, is proposed. The estimation method based on fractional charging curves is developed to estimate the battery capacity during vehicle charging, and the estimation results serve as the feedback using the first Kalman filter to update the model parameters of the discrete Arrhenius aging model. Then, the second Kalman filter makes a fusion capacity estimation based on the results of charging curve sections and the discrete Arrhenius aging model with the modified parameters. The results of the cycle life tests show that the proposed algorithm can modify the parameters of the discrete Arrhenius aging model online. And the fusion capacity estimation error is less than 1% when the model parameters reach a steady state.