A chaos genetic algorithm based extended Kalman filter for the available capacity evaluation of lithium-ion batteries

Abstract Lithium-ion batteries are developed rapidly in electric vehicles, whose safety and functional capabilities are influenced greatly by the evaluation of available capacity. Combined with the evolution trend description of state of charge from extended Kalman filter and an adaptive switch mechanism, this paper advances an adaptive chaos genetic algorithm based extended Kalman filter for the state of charge determination of lithium-ion batteries, where a combined state space model is used for simulating their dynamics. It combines the advantage of local linear approximation capability from extended Kalman filter with the global optimal search mechanism from chaos genetic algorithm. The method is applied for the state of charge determination of lithium-ion batteries, and results of lab tests on physical cells, compared with model prediction, are presented. Furthermore, the innovation magnitude bound test and innovation whiteness test are employed for verifying the performance of the proposed method. Results confirm that the advanced method may quickly evaluate state of charge with high accuracy and has great robustness without being affected by the uncertain initial value.