基于AEKPF算法对锂离子电池SOC与SOH的联合估计

Joint estimation of SOC and SOH for Li-ion battery based on AEKPF algorithm

为了提高锂离子电池SOC(state of charge)和SOH(state of health)的估计精度,采用自适应扩展卡尔曼粒子滤波(adaptive extended Kalman particle filter,AEKPF)算法估算SOC和SOH,该算法通过修正噪声可以解决运用EKF(extended Kalman filter)算法时的噪声误差累积问题,并且AEKF(adaptive extended Kalman filter)算法作为PF(particle filter)算法的建议分布用来实时更新粒子,可以改善单独采用PF算法时的粒子退化问题.为了提高SOC的估计精度,提出考虑电池的劣化特征,联合SOH实现对SOC的修正估计.在Matlab环境下的仿真结果表明:AEKPF算法与AEKF算法相比,可以得到更加准确的SOC和SOH估计值,而且AEKPF算法联合SOH可以有效提高SOC的估计精度,仿真绝对误差不超过±1%.

To improve the estimation accuracy of SOC and SOH for Li-ion battery,the adaptive extended Kalman particle filter(AEKPF)algorithm was used to estimate SOC and SOH.The algorithm could effectively solve the problem of noise error accumulation when using extended Kalman filter(EKF)algorithm by modifying the noise,and as the proposed distribution of particle filter(PF)algorithm,the adaptive extended Kalman filter(AEKF)algorithm was used to update particles in real time to solve the particle degradation of PF algorithm.To improve the accuracy of SOC,considering the deterioration characteristics of batteries,SOH was combined to realize the modified estimation of SOC.The simulation results in Matlab environment show that AEKPF algorithm can obtain more accurate SOC and SOH estimates than AEKF algorithm,and AEKPF algorithm combining with SOH can effectively improve the estimation accuracy of SOC with absolute error less than±1%.