电力系统保护与控制2026，Vol.54Issue(11)：93-104,12.DOI:10.19783/j.cnki.pspc.251156
基于复合衰减模型与CNN-GRU-AE融合的锂电池健康状态估计方法
State-of-health estimation method for lithium-ion battery based on a coupled degradation model and CNN-GRU-AE fusion network
摘要
Abstract
To address the limitation of existing data-driven models for battery state-of-health(SOH)estimation that neglect electrochemical mechanism constraints,a deep learning framework integrated with electrochemical mechanism constraints is proposed.First,a hybrid neural network architecture based on convolutional neural network-gated recurrent unit-autoencoder(CNN-GRU-AE)is designed to collaboratively extract temporal features from battery data.The CNN unit captures local degradation features to obtain feature vectors,while the GRU-AE unit models temporal dependencies and computes data reconstruction loss.To ensure consistency with electrochemical mechanisms during SOH estimation,a coupled degradation model is embedded into the framework.This model integrates a linear capacity degradation component,a nonlinear active lithium decay model,and a solid electrolyte interphase(SEI)growth mechanism.The entire framework is optimized via differentiable programming,enabling simultaneous learning of neural network weights and mechanistic parameters.Coupled with a dual-task learning architecture,it simultaneously realizes data reconstruction and lithium-ion battery SOH estimation.Finally,experimental results demonstrate that,compared to other models,the proposed model enhances both the accuracy and robustness of lithium-ion battery SOH estimation,achieving deep coupling between electrochemical mechanisms and data-driven modelling.关键词
锂离子电池/健康状态估计/复合衰减模型/复合神经网络架构Key words
lithium-ion battery/state-of-health estimation/coupled degradation model/hybrid neural network architecture引用本文复制引用
王紫仪,武家辉,王维庆,丁洪帅,张华,杨健..基于复合衰减模型与CNN-GRU-AE融合的锂电池健康状态估计方法[J].电力系统保护与控制,2026,54(11):93-104,12.基金项目
This work is supported by the Key Research and Development Project of Xinjiang Uygur Autonomous Region(No.2022B01020-3). 新疆维吾尔自治区重点研发专项项目资助(2022B01020-3) （No.2022B01020-3）
新疆碳中和能源科学与技术研究项目资助(2022TSYCLJ0001) （2022TSYCLJ0001）
