基于融合神经网络模型的锂离子电池健康状态间接预测  

Indirect Prediction of Lithium-ion Battery State-of-Health Based on Integrating Neural Network Model

作　　者：云丰泽 刘勤明[1] 汪宇杰 YUN Fengze;LIU Qinming;WANG Yujie(Business School,University of Shanghai for Science and Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学管理学院,上海200093

出　　处：《信息与控制》2024年第5期603-614,630,共13页Information and Control

基　　金：国家自然科学基金(71632008,71840003);上海市自然科学基金(19ZR1435600);教育部人文社会科学研究规划基金(20YJAZH068)

摘　　要：针对表征锂离子电池健康状态(SOH)的直接特征难以在线测量,预测模型精度较差以及泛化性不足的问题,本文提出了一种适用于间接特征数据的融合网络预测模型,并建立了一种在间接模式下由数据到模型的健康状态预测流程。首先,从相关数据集中提取可能被使用的间接数据,并引入特征重建技术,进而构建出间接健康特征(HI)。其次利用变分模态分解(VMD)算法对所提取的时间、温度等间接健康特征进行分解处理,并利用相关性分析方法进行有效性验证。再次,考虑预测设备算力与间接预测模式下特征数据的规模及特点等实际情况以及模型在特征提取、序列预测等方面的个性化性能建立VMD-CNN-AttBiGRU融合神经网络健康状态预测模型。经验证,所建立的间接预测流程取得了较低的SOH预测误差,并在剩余使用寿命的维度对比中展现出较高的鲁棒性,解决了传统预测方法中提取直接特征数据的局限,显著优化了锂离子电池SOH在线预测的可能性。This paper addresses the challenges of measuring the state-of-health(SOH)of lithium-ion batteries through direct features,which often suffer from poor prediction accuracy and insufficient generalization.To overcome these issues,we propose a integrating network prediction model suitable for indirect feature data.Our approach involves a comprehensive process for predicting the state-of-health from data to model using indirect methods.First,we extract potential indirect data from relevant data sets and employ feature reconstruction techniques to construct indirect health indicator(HI).We then apply the variational mode decomposition(VMD)algorithm to decompose these indirect HI,such as time and temperature,and verify their effectiveness through correlation analysis.To accommodate the computational power of prediction devices,the scale and characteristics of feature data in the indirect prediction mode,and the individual performance of the model,we construct the VMD-CNN-AttBiGRU integrating neural network for state-of-health prediction.Finally,the validated feature data is used to verify the lithium-ion battery health state-of-health prediction in the indirect mode.Through a two-dimensional comparative analysis,our model achieves high prediction accuracy,demonstrating the effectiveness of the proposed indirect prediction model.

关 键 词：锂离子电池 健康状态预测 健康特征 间接预测 融合神经网络模型 

分 类 号：TM912[电气工程—电力电子与电力传动]