A rapid screening and regrouping approach based on neural networks for large-scale retired lithium-ion cells in second-use applications

Retired cells from electric vehicles could provide considerable economic benefits through secondary uses such as energy storage. However, the screening and regrouping of large-scale cells is facing the problem of low efficiency and low accuracy. To address this problem, two rapid and accurate screening approaches are proposed in this study. Firstly, the characteristics of the series-charging curve of large-scale retired cells are investigated. Then, two novel screening models, namely the neural network model and the piecewise linear fitting model, are thus constructed using the capacity and voltage profiles of a small number of sample cells, and the capacity of a large number of cells can be estimated in batches. Moreover, a device for fast switching between series and parallel is designed to improve efficiency, and a regrouping approach for different second-use applications is introduced. Finally, the proposed approaches are verified by simulations and experiments. The main results are as follow: (1) The piecewise linear fitting model should be used in the small sample case, and the neural network model should be adopted in the large sample case for higher estimation accuracy; (2) The proposed approaches are feasible and effective, and several cases illustrate that the capacity estimation error of the proposed approaches is less than 4%; (3) The screening efficiency of the proposed approaches increases with the increase in the number of cells, and screening results of 5000 cells indicate that the screening efficiency of our proposed approach is at least 5 times higher than that of a traditional approach. (C) 2018 Elsevier Ltd. All rights reserved.