基于改进GRU模型算法的锂电池SOC估计  

SOC estimation of lithium battery based on improved gated recurrent Unit model algorithm

作　　者：张心成 李翔晟[1] 李藏龙 曾军 秦祥 Zhang Xincheng;Li Xiangsheng;Li Canglong;Zeng Jun;Qin Xiang(School of Mechanical and Electrical Engineering,Central South University of Forestry and Technology,Changsha 410004,China)

机构地区：[1]中南林业科技大学机电工程学院,长沙410004

出　　处：《电子测量技术》2023年第11期57-65,共9页Electronic Measurement Technology

基　　金：湖南省自然科学基金省市联合项目(14JJ5014);中南林业科技大学研究生科技创新基金(CX202102039)项目资助。

摘　　要：锂电池荷电状态(SOC)的准确估计对提高电池的动态性能和能量利用率至关重要。针对现有神经网络SOC估计方法在复杂工况下存在精度低,稳定性差等问题,本文提出一种改进GRU模型算法对SOC进行估计。首先将1DCNN和Bi-GRU相结合并添加注意力机制,构建1DCNN-Bi-GRU-ATT模型。其次,为解决ReLU激活函数易出现死神经元现象,将其改进为PReLU激活函数。同时,为解决MSE-Loss易受复杂工况中电池异常数据影响和MAE-Loss收敛速度较慢等问题,改用Huber-Loss作为网络损失函数。最后,将Adam算法使用Nesterov加速梯度改进为Nadam算法。锂电池SOC估计实验结果表明,在12种复杂工况下该模型算法的均方根误差和平均绝对误差的平均值分别为1.181 7%和0.924 1%,与改进前及其他模型相比,本文模型在12种情况中综合表现更为稳定和准确,有更高的泛化性。Accurate estimation of the state of charge(SOC)of lithium batteries is crucial to improving the dynamic performance and energy utilization of batteries.Aiming at the problems of low accuracy and poor stability of existing neural network SOC estimation methods under complex working conditions,this paper proposes an improved GRU model algorithm to estimate SOC.Firstly,combine 1DCNN and Bi-GRU and add attention mechanism to build 1DCNN-Bi-GRU-ATT model.Secondly,in order to eliminate the phenomenon that the ReLU activation function is prone to dead neurons,it is improved to PReLU activation function.At the same time,in order to solve the problem that MSE-Loss is easily affected by abnormal battery data in complex working conditions and the convergence speed of MAE-Loss is slow,Huber-Loss is used instead as the network loss function.Finally,the Adam algorithm is improved to Nadam algorithm using Nesterov accelerated gradient.The experimental results of lithium battery SOC estimation show that the average values of root mean square error and mean absolute error of the model algorithm under 12 complex operating conditions are 1.1817%and 0.9241%,respectively.Compared with the model before improvement and other models,the comprehensive performance of this model in 12 cases is more stable and accurate,and it has higher generalizability.

关 键 词：锂电池 荷电状态 注意力机制 GRU PReLU Huber-Loss NADAM 

分 类 号：TM921[电气工程—电力电子与电力传动]