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OA

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.