Parameter Identification of Electrical and Thermal Model of a Lithium Polymer Battery Using Particle Swarm Optimization

Due to the complexity of Lithium Polymer (LiP) battery behaviors, the traditional identification methods suffer from the low accuracy. To address this issue, the particle swarm optimization (PSO) algorithm is proposed to estimate the electrical and thermal models of LiP batteries. By incorporating the PSO algorithm into the parameter estimation process, experimental data can be used for iterative comparison and adjustment of identified parameters to improve accuracy. The results in Section IV indicate that the dynamic curve of the battery identified by the PSO algorithm fits well with the actual test waveform. Thus, the effectiveness of this PSO algorithm in improving the accuracy of battery model parameter recognition is verified.