Parameter Identification of Lithium Battery Model Based on Chaotic Quantum Sparrow Search Algorithm

An accurate battery model is of great importance for state estimation. This study considers the parameter identification a fractional-order (FOM) battery, which can more realistically describe reaction process cell and provide precise predictions. Firstly, an improved sparrow search algorithm combined with Tent chaotic mapping, quantum behavior strategy Gaussian variation proposed to regulate early population quality, enhance its global ability avoid trapping into local optima. The effectiveness superiority are verified by comparing (CQSSA) particle swarm optimization (PSO), genetic (GA), grey wolf (GWO), Dingo (DOA) (SSA) on benchmark functions. Secondly, parameters FOM identified using six algorithms under hybrid pulse power characterization (HPPC) test. Compared SSA, CQSSA has 4.3%, 5.9% 11.5% improvement in mean absolute error (MAE), root square (RMSE) maximum (MaAE), respectively. Furthermore, these used pulsed discharge test (PULSE) urban dynamometer driving schedule (UDDS) verify adaptability algorithm. Simulation results show that perform well terms MAE, RMSE MaAE terminal voltages all three different tests, demonstrating high accuracy good