A Fast Capacity Estimation Approach for Retired Lithium-ion Batteries

Abstract

Capacity is one of the most critical parameters of lithium-ion batteries (LIBs). Retired batteries generally also have 70%-80% of the nominal capacity, so it can bring considerable benefits through echelon use. However, the existing methods cannot simultaneously meet the capacity estimation accuracy and efficiency required for large-scale retired batteries. So this may lead to costing excessive time and energy. To solve the above problems, a fast and accurate capacity evaluation method based on support vector machine (SVM) is proposed in this paper. And the parameters (penalty coefficient and kernel function width) of the SVM model are optimized by the particle swarm optimization (PSO). In order to expand the scope of application, capacity evaluation models in three different SOC are established. The inputs of the model are sampling voltages selected from the featured charging curve at three cases, and the output is the battery capacity. Experimental results demonstrate that the maximum error in the first case is less than 3.02%, and the errors in the other two cases are 1.88% and 1.97% respectively..