Identification of parameters for equivalent circuit model of Li-ion battery cell with population based optimization algorithms

Abstract

As EVs are sold to worldwide area with different latitudes, it also implies that lithium-ion batteries need to be safely operated in different climatic environments. To ensure the robustness and safeness of EVs, the characteristics of lithium-ion batteries must be taken into considerations. To comprehensively grasp the key information of battery cell in an efficient way, building a battery model is one of the most effective ways. Based on the model, one can get rid of time-consuming experiments. Moreover, the model enables to simulate and observe battery behaviors under extreme test condition safely. In this paper, an equivalent electric circuit (EEC) of a Li-ion battery cell is investigated. Based on 2nd order RC circuit, an Extended Hybrid Pulse Power Characterization (EHPPC) is designed and carried out to observe the dynamic response of battery cell in time domain. Once substantial measurement results are obtained, the parameterization turns out to be an important issue. This paper investigates the most appropriate metaheuristic based method to rapidly and systematically identify the EEC parameters. Totally four different methods, namely Particle Swarm Optimization, Grey Wolf Optimizer, Harmony Search, and Golden Eagle Optimization, are realized and equally compared by 100 implementations. Eventually, a mission profile of driving pattern, Dynamic Stress Testing (DST), is used to validate the proposed EEC model. The 2RC EEC model is able to simulate battery voltage response with root-mean-squared error as low as 0.0045.