储能科学与技术2026，Vol.15Issue(5)：1694-1703,10.DOI:10.19799/j.cnki.2095-4239.2026.0269
基于双层机器学习的非对称超级电容器跨表征EIS参数估计
Estimation of cross-characterized EIS parameters of asymmetric supercapacitors based on double-layer machine learning
摘要
Abstract
Electrochemical impedance spectroscopy(EIS)is a crucial technique for characterizing the dynamic properties of asymmetric supercapacitors.However,for electrode materials with high noise levels,the traditional nonlinear least-squares method often fails to yield accurate fits owing to the difficulty in determining reliable initial impedance parameters.To address this challenge,this study proposes an intelligent EIS parameter identification method based on cross-characterization fusion and a two-layer machine learning architecture.This approach extracts physicochemical information from cyclic voltammetry and constant-current charge-discharge curves to construct a feature matrix,establishing an interpretable mapping between these features and key EIS parameters(e.g.,internal resistance and constant phase element parameters).After dimensionality reduction and feature enhancement through latent variable extraction and time-series normalization,a two-layer machine learning architecture integrating XGBoost,random forest,ridge regression,and elastic net regression is developed for training,enabling high-precision cross-characterization-based EIS parameter estimation.Experimental results demonstrate that the predicted parameters closely align with those derived directly from EIS,with high stability across different material systems,including cobalt-manganese-based electrodes.This study provides a reliable strategy for initializing EIS fitting in asymmetric supercapacitors,significantly improving convergence and accuracy,and offers a practical approach for identifying electrochemical impedance parameters under high-noise conditions.关键词
电化学阻抗谱/非对称超级电容器/机器学习/参数辨识Key words
electrochemical impedance spectroscopy/asymmetric supercapacitor/machine learning/parameter identification分类
信息技术与安全科学引用本文复制引用
赵一冰,李艺多,周瑜欢,左之慧,王瀚博,王岩,田玉美,陆海彦,刘长英..基于双层机器学习的非对称超级电容器跨表征EIS参数估计[J].储能科学与技术,2026,15(5):1694-1703,10.基金项目
吉林省自然科学基金面上项目(20250102066JC) （20250102066JC）
天津市飞机维修与民航地面特种设备技术工程中心资助项目(CAUCGCZX-202501). （CAUCGCZX-202501）
