Parameter Identification of Battery Based on Improved BSO Algorithm

Establishing ECM (equivalent circuit model) and identifying its parameters are very important to the SOC estimation of battery. A third-order Thevenin model of battery is established to improve the accuracy of ECM. In order to improve the performance of BSO (Beetle Swarm Optimization) algorithm effectively, an improved BSO algorithm, based on chaotic initialization and Gaussian perturbation, is proposed and used to identify the parameters of the battery model. Chaotic initialization has ergodicity properties, so it can increase the probability that the optimal value is found, and the estimation accuracy is improved. Gaussian perturbation is introduced for the velocity updating of the particle, it is beneficial to avoid the local optimal solution and accelerates the convergence speed. The optimization performance of the algorithm is tested by five test functions. The results show that: the improved algorithm has faster convergence speed and higher estimation precision compared with PSO and GA algorithm. The improved algorithm is used to identify the parameters of the battery model, and good convergence speed and high precision are achieved.