A rapid classification method of the retired LiCoxNiyMn1−x−yO2 batteries for electric vehicles. (November 2020)

• Record Type: Journal Article
• Authors: Zhou, Ping; He, Zhonglin; Han, Tingting; Li, Xiangjun; Lai, Xin; Yan, Liqin; Lv, Tiaolin; Xie, Jingying; Zheng, Yuejiu
• Is Part Of: Energy reports. Volume 6(2020)
• Page Start: 672
• Page End: 683
• Publication Date: 2020-11
• Subjects: Retired lithium-ion battery -- Rapid classification -- Capacity estimation -- Battery pack -- RBFNN; Power resources -- Periodicals; Energy industries -- Periodicals; Power resources; Periodicals; Electronic journals; 621.04205
• DOI: 10.1016/j.egyr.2020.03.013
• Languages: English
• ISSNs: 2352-4847

Abstract

Abstract: With the aging of Lithium-ion batteries (LIBs) of electric vehicles in the near future, research on the second use of retired LIBs is becoming more and more critical. The classification method of the retired LIBs is challenging before the second use due to large cell variations. This paper proposes a rapid classification method based on battery capacity and internal resistance, because batteries with different capacities and internal resistances have different voltage curves during charge/discharge. First, the piecewise linear fitting method established by the specified tested batteries with capacities and their corresponding characteristic voltages is used to sort the batteries. Then combined with the nonlinear function approximation ability of the radial basis function neural network (RBFNN) model, battery capacity and internal resistance are predicted after the model training. 108 cells are used for the simulation classification with experimental classification performed on 12 cells. The results prove that the classification method is accurate. Highlights: The retired batteries are discharged in series within a short time after full charge. RBFNN with the multi-dimensional inputs is trained to achieve the capacities. The batteries classified by the proposed method have good consistency. Achieving the transition from the small-scale classification to the large-scale.