基于AO-AVOA-BP神经网络模型的锂电池SOH预测  

SOH prediction of lithium battery based on AO-AVOA-BP neural network modeling

作　　者：李军毅 汪兴兴[1] 陈祥[1] 陈林飞[1] 邓业林 Li Junyi;Wang Xingxing;Chen Xiang;Chen Linfei;Deng Yelin(School of Mechanical Engineering,Nantong University,Nantong 226019,China;School of Rail Transportation,Soochow University,Suzhou 215131,China)

机构地区：[1]南通大学机械工程学院,南通226019 [2]苏州大学轨道交通学院,苏州215131

出　　处：《电子测量技术》2025年第4期71-79,共9页Electronic Measurement Technology

基　　金：国家自然科学基金面上项目(52477218);江苏省重点研发计划项目(BE2021065)资助。

摘　　要：为提供准确可靠的锂电池健康状态预测,提出了一种基于非洲秃鹫优化算法融合天鹰优化算法优化BP神经网络的预测模型。通过对电池充电过程中的电压、电流和温度数据的分析,基于灰色关联分析验证健康因子与电池SOH的相关性,确定4个健康因子作为模型的输入,结合基于AO-AVOA优化的BP神经网络模型,实现更精确的SOH预测。将提出的模型与其他优化模型对锂电池SOH进行预测,对各项指标进行对比分析,结果表明,所提出的预测模型平均绝对误差小于0.0089,均方根误差小于0.0112,平均绝对百分比误差小于1.4512%,具有精度高、泛化性强等特点,可有效用于锂电池的SOH预测。To provide accurate and reliable prediction of the state of health of lithium batteries,a prediction model based on the African Vulture Optimization Algorithm fused with the Aquila Optimizer to optimize the BP neural network is proposed.Through the analysis of voltage,current and temperature data during the battery charging process,the correlation between the health factors and the SOH of the battery is verified based on grey correlation analysis,and four health factors are identified as inputs to the model,which are combined with the BP neural network model based on AO-AVOA optimization to achieve a more accurate SOH prediction.The proposed model is compared with other optimization models for SOH prediction of lithium batteries,and the results show that the average absolute error of less than 0.0089,the root mean square error below 0.0112,and the average absolute percentage error under 1.4512%.This model is characterized by high accuracy and robust generalization capabilities,making it highly effective for SOH prediction in lithium batteries.

关 键 词：锂电池 健康状态 BP神经网络 非洲秃鹫优化算法 天鹰优化算法 

分 类 号：TM912[电气工程—电力电子与电力传动] TN86[电子电信—信息与通信工程]