基于改进BPNN-MPF算法的锂离子电池SoE估计  

SoE estimation of lithium-ion batteries based on improved BPNN-MPF algorithm

作　　者：马彦[1,2] 郭则宣 MA Yan;GUO Ze-xuan(College of Communication Engineering,Jilin University,Changchun 130012,China;State Key Laboratory of Automotive Simulation and Control,Jilin University,Changchun 130022,China)

机构地区：[1]吉林大学通信工程学院,长春130012 [2]吉林大学汽车仿真与控制国家重点实验室,长春130022

出　　处：《吉林大学学报（工学版）》2023年第1期263-272,共10页Journal of Jilin University:Engineering and Technology Edition

基　　金：国家自然科学基金项目(61520106008,U1864201);吉林省高校共建项目(SXGJSF2017-2-1-1).

摘　　要：为了提高锂离子电池能量状态(SoE)估计的准确性,考虑到电流或电压传感器噪声会累积误差,提出了一种基于改进反向传播神经网络(BPNN)与模型预测滤波(MPF)相结合的SoE估计方法。基于一阶RC等效电路模型,采用MPF算法估计电池的SoE,并使用改进BPNN对MPF算法的估计结果进行误差补偿。在NEDC工况下验证了本文方法的准确性。结果表明,与传统MPF算法和BPNN-MPF算法相比,本文方法的SoE估计值能较好地收敛到真实值,且最大绝对误差和均方根误差均在1%以内。The State of Energy(SoE)of lithium-ion battery is an important evaluation index for the energy optimization and management of electric vehicles. In order to improve the accuracy and reliability of battery SoE estimation,a SoE estimation method based on the combination of improved Back Propagation Neural Network(BPNN) and Model Predictive Filtering(MPF) was proposed in this paper. Considering the accumulated errors of current or voltage sensor noise,a first-order RC equivalent circuit model was established in this paper. Based on this model,the MPF algorithm was used to estimate the battery SoE. In order to make the estimated results more accurate,the improved BPNN was used to compensate the errors of the estimated results of MPF algorithm. Finally,the accuracy of the proposed method was verified under New European Driving Conditions(NEDC). The results show that the SoE estimate based on the improved BPNN-MPF algorithm well converges to the real SoE value,compared with traditional MPF algorithm and BPNN-MPF algorithm. The Maximum Absolute Error(MAE) and Root-Mean-Square Error(RMSE)of the estimated values are all within 1%.

关 键 词：控制理论与控制工程 锂离子电池 能量状态估计 改进BP神经网络 模型预测滤波 

分 类 号：U463.63[机械工程—车辆工程]