融合自注意力机制与门控循环单元网络的宽工况锂离子电池SOC估计  

SAM-GRU-based fusion neural network for SOC estimation in lithium-ion batteries under a wide range of operating conditions

作　　者：管鸿盛 钱诚 徐炳辉 孙博[1] 任羿[1] GUAN Hongsheng;QIAN Cheng;XU Binghui;SUN Bo;REN Yi(School of Reliability and Systems Engineering,Beihang University,Beijing 100191,China)

机构地区：[1]北京航空航天大学可靠性与系统工程学院,北京100191

出　　处：《储能科学与技术》2023年第7期2229-2237,共9页Energy Storage Science and Technology

基　　金：国家自然科学基金项目(52075028)。

摘　　要：准确估计宽工况条件下的锂离子电池荷电状态(SOC)对于电动汽车的运行安全性和可靠性至关重要,是电池管理系统最重要的任务之一。本工作充分利用门控循环单元(GRU)神经网络短时处理能力与注意力机制(SAM)长时序特征提取能力,提出了一种融合SAM与GRU的神经网络模型学习锂离子电池可测参数(电压、电流)与其SOC的非线性映射关系,实现了高精度的SOC估计,从而解决锂离子电池SOC的长序列相关特征难以有效表征问题。通过北京公交动态应力测试(BBDST)数据的验证表明,与传统GRU网络相比,本文提出的SAM-GRU神经网络模型对于不同放电倍率、环境温度以及放电倍率-环境温度混合工况下工作的锂离子电池均取得了更准确的SOC估计,估计精度提升分别不小于26%、25%和11%。Accurate estimation of the state of charge(SOC) of lithium-ion batteries under a wide range of operating conditions is crucial for ensuring the operational safety and reliability of electric vehicles;therefore,estimating SOC is one of the most important tasks of battery management systems.In this study,a fusion neural network model combining Self-Attention Mechanism(SAM) and Gated Recurrent Unit(GRU) is proposed to capture the long-term nonlinear mapping relationship between the measurable parameters(voltage and current) and SOC of lithium-ion batteries.This SAM-GRU neural network model makes full use of the shorttime processing capability of GRU and the long-time sequence feature-extraction capability of SAM.Additionally,this model simplifies the effective characterization of the long-sequencerelated features of SOC.Based on the results of the Beijing Bus Dynamic Stress Test,the proposed SAM-GRU neural network model yields more accurate SOC estimates than the traditional GRU neural network under different discharge rates,environmental temperatures,and combinations of both.Specifically,the improvements in accuracy are no less than 26%,25%,and 11%,respectively.

关 键 词：锂离子电池 荷电状态 自注意力机制 门控循环单元神经网络 

分 类 号：TM912[电气工程—电力电子与电力传动]