现代制造工程Issue(6)：77-87,11.DOI:10.16731/j.cnki.1671-3133.2026.06.009
基于FOMIAUKPF-EKF算法的新能源汽车锂离子电池SOC估计方法研究
Research on SOC estimation method for new energy vehicle lithium-ion batteries based on FOMIAUKPF-EKF algorithm
摘要
Abstract
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).关键词
荷电状态/粒子滤波/分数阶建模/多新息技术/电池管理装备Key words
State of Charge(SOC)/Particle Filter(PF)/fractional-order modeling/multi-innovation technique/battery manage-ment equipment分类
信息技术与安全科学引用本文复制引用
盛强,寇舒,汪园园,饶宾期,孙健..基于FOMIAUKPF-EKF算法的新能源汽车锂离子电池SOC估计方法研究[J].现代制造工程,2026,(6):77-87,11.基金项目
浙江省高层次人才专项支持计划科技创新领军人才项目(201R52056) （201R52056）
湖州职业技术学院高层次人才专项课题项目(2024TS03) （2024TS03）
