Parameter Identification of Lithium Battery Model Based on Improved Fireworks Algorithm

The estimation of SOC of lithium battery is an important index for the safety and reliability of battery energy storage system. So it’s very important to choose the appropriate circuit model. At present, the most extensive model is the second-order Thevenin equivalent model. But the parameters in this model are nonlinear, which brings difficulties to parameter identification. Aiming at this problem, an improved fireworks algorithm (LAFWA) is proposed in this paper. In the improved fireworks algorithm, an adaptive inertia weight based on the rate of cluster focus distance changing is introduced to improve the search and optimization ability of explosive sparks. To solve the problem of Gaussian mutation which can not jump out of the local optimization problem, this paper uses the Levy flight variation method, which has a larger variation range. The proposed algorithm is used to identify the parameters of lithium battery model. Based on the data collected by the pulse charging and discharging experiment, the identification result is obtained by exploiting the LAFWA. The error between the fitting results and the experimental data is within a certain range, which indicates that the identification scheme is accurate and effective.