Parameters identification of Thevenin model for lithium-ion batteries using self-adaptive Particle Swarm Optimization Differential Evolution algorithm to estimate state of charge

State of charge (SOC) estimation is a significant task for lithium-ion batteries. However, the accuracy of SOC estimation is closely related to parameters of battery and system non-linearity. To identify the parameters of lithium-ion battery better, this proposed a self-adaptive particle swarm optimization differential evolution (SaPSODE) algorithm. First, to describe the dynamic behaviors of battery, we presented a first-order RC equivalent circuit model (ECM). Second, to calculate open-circuit voltage (OCV) versus time during the dynamic test procedure, an optimizing objective function was built to minimize errors between the true and optimized terminal voltages. Further, control parameters of F and Cr were also self-adapted according to previous successful records. Third, by using OCV-SOC mapping curves, this work obtained estimated SOC curve which was compared with true SOC curve. Comprehensive experimental results demonstrated effectiveness of the proposed framework and methodology, compared with several highly-cited DE variants.

Automated summary

This paper proposed a self-adaptive particle swarm optimization differential evolution (SaPSODE) algorithm to better identify the parameters of lithium-ion batteries. Experimental results demonstrated the effectiveness of this method in SOC estimation compared to other methods.