Enhancing the Lithium-ion battery life predictability using a hybrid method

Abstract

This study contributes to proposing the improved bird swarm algorithm optimization least squares support vector machine (IBSA-LSSVM) model to predict the remaining life of lithium-ion batteries. By improving the prediction accuracy of the model, the safety and reliability of the new energy storage system are improved. In order to avoid the bird swarm algorithm (BSA) getting into the local optimal solution, the levy flight strategy is introduced into the improved bird swarm algorithm (IBSA), which improves the convergence performance of the algorithm. Hence, this study is to verify the effectiveness of the proposed hybrid IBSA-LSSVM model. The following work has been done: (1) test functions are used to test particle swarm optimization (PSO), differential evolution algorithm (DE), BSA and IBSA; (2) the back propagation neural network (BP) model, support vector machine (SVM) model, quantum particle swarm optimization support vector machine (QPSO-SVM) model, BSA-LSSVM model and IBSA-LSSVM model are tested with the B5, B6 and B18 batteries. The following findings are obtained: (1) the five test functions are used to test the PSO, DE, BSA and IBSA algorithms in 20 dimensions, 50 dimensions and 80 dimensions. The results show that the convergence accuracy and convergence stability of IBSA algorithm is higher than those of the other three algorithms; (2) the residual life of B5, B6 and B18 batteries are predicted by the BSA-LSSVM, SVM, QPSO-SVM, BP and IBSA-LSSVM models. The test results show that the root mean square error of the IBSA-LSSVM model for B5 battery is 0.01, the root mean square error for B6 battery is 0.06, and the root mean square error for B18 battery is 0.02. The results show that the prediction accuracy of proposed model is higher than that of the other models.