CAJ | 학술논문

锂电池荷电状态用来描述电池剩余电量的多少，进而反映电动汽车的续驶里程，是电池管理系统中的核心参数。电池循环次数、瞬间大电流以及温度等因素都会使电池特性发生变化，使用扩展卡尔曼滤波算法对电池荷电状态进行估计，会有较大的误差甚至导致算法不收敛。为了有效地抑制发散以及噪声的影响，基于锂电池混合噪声模型，应用扩展卡尔曼粒子滤波算法对锂电池荷电状态和电流漂移噪声进行同步估计。最后根据充放电试验数据进行仿真分析，结果证明了该算法的优越性。
리전지하전상태용래묘술전지잉여전량적다소，진이반영전동기차적속사리정，시전지관리계통중적핵심삼수。전지순배차수、순간대전류이급온도등인소도회사전지특성발생변화，사용확전잡이만려파산법대전지하전상태진행고계，회유교대적오차심지도치산법불수렴。위료유효지억제발산이급조성적영향，기우리전지혼합조성모형，응용확전잡이만입자려파산법대리전지하전상태화전류표이조성진행동보고계。최후근거충방전시험수거진행방진분석，결과증명료해산법적우월성。
scribed the residual capacity,and indicated the remainder driving range of electric vehicles.The cycles, As the key parameter for power battery management,the SOC of Li-ion battery de-instantaneous high current,abnormal temperatures and other factors would change cell characteristics, which might introduce larger errors even divergence over time if the extended Kalman filter algorithm were applied to the SOC estimation.To suppress the divergence and noise,this paper proposed a meth-od based on EKPF algorithm to realize accurate SOC and the current drift estimation on the Li-ion battery mixed noise model.Finally,the superiority of this method was validated by simulation results.