Capacity estimation of large-scale retired li-ion batteries for second use based on support vector machine

Capacities of li-ion batteries are difficult to estimate quickly and accurately when second use batteries are in large scale with dispersed parameters. This may result in costing too much time and money to reuse batteries. By analyzing the capacity and resistance characteristics, there is no functional relationship between them. In order to solve this nonlinear problem, a SVM model with 3 parameters (i.e. penalty coefficient, kernel function parameter and loss function parameter) is established. The inputs are five calculated values of resistance and the output is the capacity value of batteries. Data of 70 battery modules are adopted to train the model while data of other 30 battery modules are used to test the model. In this process, two parameters optimization methods using genetic algorithm have been proposed. By comparison, the coefficient of determination (COD) value of method 2 is higher than method 1 both in training model and testing model. The average error of method 2 between measured values and estimated values is 0.67% whereas that of method 1 is 1.35%. In method 2, 90% of the estimation errors are under 2.5%. The results provide valuable insights for large-scale retired li-ion batteries into second use.