基于FOMIAUKPF-EKF算法的新能源汽车锂离子电池SOC估计方法研究OA

To address the limitations in lithium-ion battery State of Charge(SOC)estimation accuracy caused by particle degra-dation and time-varying model parameters in traditional Particle Filter(PF)algorithms under complex operating conditions,a joint estimation method based on the Fractional Order Multi-Innovation Adaptive Unscented Kalman Particle Filter-Extended Kal-man Filter(FOMIAUKPF-EKF)algorithm was proposed.This method was based on a fractional-order second-order RC equiva-lent circuit model,in which Extended Kalman Filter(EKF)was employed for online parameter identification to compensate for time-varying effects.Multi-innovation theory and an adaptive noise adjustment mechanism were introduced to improve the Un-scented Kalman Particle Filter(UKPF),effectively addressing particle impoverishment and enhancing nonlinear processing capa-bility.Experiments conducted under Highway Fuel Economy Test(HWFET)and New European Driving Cycle(NEDC)condi-tions demonstrated that the FOMIAUKPF-EKF algorithm reduced modeling errors by 15％～25％,exhibited strong robustness under 20％initial value deviation and 3％noise disturbance,maintained the mean SOC estimation error within 1％,and a-chieved significantly superior accuracy and convergence speed compared with benchmark algorithms such as PF and Fractional Order Unscented Kalman Particle Filter(FOUKPF).