【摘要】 针对卡尔曼滤波算法估计锂离子电池荷电状态存在精度较低的问题，提出了一种基于改进粒子群算法（IPSO）优化双卡尔曼滤波算法（DKF）的方法．在粒子群算法的基础上，引入一种蜘蛛移动策略的黑寡妇优化算法（BWOA）对粒子速度更新方式优化．采用改进粒子群算法优化双卡尔曼滤波算法的噪声协方差矩阵．依据试验数据，基于二阶电阻-电容电路（RC）模型完成参数辨识和电池荷电状态（SOC）估计．对比标准卡尔曼滤波算法与经粒子群算法优化的卡尔曼滤波算法在参数辨识和荷电状态估计方面的结果．结果表明：改进后的算法在参数辨识和荷电状态估计精度方面显著提升，且具有更强的抗干扰能力，其中参数辨识估计精度提高范围为7.9%～38.5%，荷电状态估计精度提高范围为41.0%～51.4%.更多还原

【Abstract】 To solve the problem of Kalman filter algorithm with insufficient accuracy in estimating the state of charge of lithium-ion batteries, the improved particle swarm optimization(IPSO) based dual Kalman filter method was proposed. On the basis of particle swarm optimization algorithm, the spider movement strategy based black widow optimization algorithm(BWOA) was introduced in the particle speed update method. The improved particle swarm optimization algorithm was used to optimize the noise Covariance matrix of the dual Kalman filter. Based on the experimental data, the parameter identification and state of charge(SOC)estimation were achieved by the second-order resistor-capacitor circuit(RC)equivalent circuit model. The parameter identification and SOC estimation by Kalman filter algorithm were compared with those by particle swarm optimization Kalman filter algorithm. The results show that by the improved method, the accuracies of parameter identification and SOC estimation can be significantly improved with better anti-interference ability, and the accuracy improvement range of parameter identification estimation is 7.9%-38.5%, while the accuracy improvement range of state of charge estimation is 41.0%-51.4%.更多还原