信息与控制2024，Vol.53Issue(5)：603-614,630,13.DOI:10.13976/j.cnki.xk.2024.0283
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
摘要
Abstract
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 suit-able 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 character-istics 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 predic-tion 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Key words
lithium-ion battery/state-of-health prediction/health indicator/integrating prediction/integrating neural network model分类
信息技术与安全科学引用本文复制引用
云丰泽,刘勤明,汪宇杰..基于融合神经网络模型的锂离子电池健康状态间接预测[J].信息与控制,2024,53(5):603-614,630,13.基金项目
国家自然科学基金(71632008,71840003) （71632008,71840003）
上海市自然科学基金(19ZR1435600) （19ZR1435600）
教育部人文社会科学研究规划基金(20YJAZH068) （20YJAZH068）
