Critical Review on Improved Electrochemical Impedance Spectroscopy-cuckoo Search-Elman Neural Network Modeling Methods for Whole-life-cycle Health State Estimation of Lithium-ion Battery Energy Storage Systems  

作　　者：Ran Xiong Shunli Wang Paul Takyi-Aninakwa Siyu Jin Carlos Fernandez Qi Huang Weihao Hu Wei Zhan 

机构地区：[1]School of Information Engineering,Southwest University of Science and Technology,Mianyang,China [2]School of Information Engineering,Southwest University of Science and Technology,Mianyang,621010 China [3]Mechanical Engineering from Kwame Nkrumah Uni-versity of Science and Technology,Ghana [4]Northeastern University,Shenyang,China [5]Robert Gordon University,Scotland [6]Southwest University of Science and Technology(SWUST),Mian-yang,China [7]University of Electronic Science and Technology of China,Chengdu,China [8]Xi’an Jiaotong University,Xi’an,China [9]SPIC Southwest Energy Research Institute,Chengdu,China

出　　处：《Protection and Control of Modern Power Systems》2024年第2期75-100,共26页现代电力系统保护与控制（英文）

基　　金：supported by the National Natural Science Foundation of China(No.62173281 and No.61801407);the Sichuan Science and Technology Pro-gram(No.2019YFG0427 and No.2023YFG0108);the China Scholarship Council(No.201908515099);the Fund of Robot Technology used for the Special Environment Key Laboratory of Sichuan Province(No.18kftk03).

摘　　要：Efficient and accurate health state estimation is crucial for lithium-ion battery(LIB)performance monitoring and economic evaluation.Effectively estimating the health state of LIBs online is the key but is also the most difficult task for energy storage systems.With high adaptability and applicability advantages,battery health state estimation based on data-driven techniques has attracted extensive attention from researchers around the world.Artificial neural network(ANN)-based methods are often used for state estimations of LIBs.As one of the ANN methods,the Elman neural network(ENN)model has been improved to estimate the battery state more efficiently and accurately.In this paper,an improved ENN estimation method based on electrochemical impedance spectroscopy(EIS)and cuckoo search(CS)is established as the EIS-CS-ENN model to estimate the health state of LIBs.Also,the paper conducts a critical review of various ANN models against the EIS-CS-ENN model.This demonstrates that the EIS-CS-ENN model outperforms other models.The review also proves that,under the same conditions,selecting appropriate health indicators(HIs)according to the mathematical modeling ability and state requirements are the keys in estimating the health state efficiently.In the calculation process,several evaluation indicators are adopted to analyze and compare the modeling accuracy with other existing methods.Through the analysis of the evaluation results and the selection of HIs,conclusions and suggestions are put forward.Also,the robustness of the EIS-CS-ENN model for the health state estimation of LIBs is verified.

关 键 词：Lithium-ion battery health state esti-mation elman neural network electrochemical imped-ance spectroscopy cuckoo search health indicators 

分 类 号：TM91[电气工程—电力电子与电力传动]