Parameters Identification of Battery Model Using a Novel Differential Evolution Algorithm Variant

Abstract

In order to deal with the fluctuation and intermittency of photovoltaic (PV) cells, the battery energy storage system (BESS) as a supplementary power source has been widely concerned. In BESS, the unknown parameters of the battery can affect its output, and its structure determines these parameters. Therefore, it is essential to establish the battery model and extract the parameters accurately, and the existing methods cannot effectively solve this problem. This study proposes an adaptive differential evolution algorithm with the dynamic opposite learning strategy (DOLADE) to deal with the issue. In DOLADE, the number of elite particles and particles with poor performance is expanded, the population’s search area is increased, and the population’s exploration capability is improved. The particles’ search area is dynamically changed to ensure the population has a good exploitation capability. The dynamic opposite learning (DOL) strategy increases the population’s diversity and improves the probability of obtaining the global optimum with a considerable convergence rate. The various discharging experiments are performed, the battery model parameters are identified, and the results are compared with the existing well-established algorithms. The comprehensive results indicate that DOLADE has excellent performance and could deal with similar problems.