Parameter identification of lithium ion battery using improved SCE algorithm
Aiming at the problems of local optimization and low precision in traditional parameter identification methods,shuffed complex evolution(SCE)is proposed to improve the competitive evolution algorithm of conventional shuffling complex evolution algorithms.Firstly,a second-order RC equivalent circuit model was proposed to describe the dynamic characteristics of the battery,and the parameters to be identified were determined by using the equivalent model of the lithium-ion battery based on the constant current discharge condition test data set.Secondly,the RMS error between the simulated terminal voltage and the real battery test terminal voltage is taken as the objective function,and the optimal parameters of the model are found through the proposed optimization algorithm.Final-ly,the dynamic working condition data sets of DST and FUDS were used for simulation verification,and compared with particle swarm optimization algorithm,gray wolf algorithm and genetic algorithm.The simulation results show that this method has advantages in the identification accuracy.The average ERMS error of the algorithm is 0.016 6 V,which is reduced by 7.8％,8.3％and 14.9％,respectively,compared with other optimization algorithms.
万方数据
维普
