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A rapid screening framework of retired lithium-ion batteries for echelon utilization based on extreme learning machine

In recent years, plenty of lithium-ion batteries have retired from electric vehicles. How to deal with them is getting more and more attention, and echelon utilization is the most cost-effective solution. However, retired cells are highly inconsistent, so they cannot be directly reused. In this paper, a rapid screening framework of retired cells based on extreme learning machine is proposed. Firstly, the characteristic parameters of the cells obtained by dismantling the retired battery modules are measured and analyzed. Secondly, the capacity features of the cells are extracted through the high-rate incremental capacity curve, which are used as the input training extreme learning machine to achieve rapid and accurate screening of retired cells. Finally, the 177 cells are accurately classified into three types by the established model, and the screening accuracy can reach 97.4%. Compared with the traditional neural network, the proposed method improves the speed by 5.9 times while ensuring the accuracy of screening.