首页|Machine learning and neural network supported state of health simulation and forecasting model for lithium-ion battery
Machine learning and neural network supported state of health simulation and forecasting model for lithium-ion battery
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As the intersection of disciplines deepens,the field of battery modeling is increasingly employing vari-ous artificial intelligence(AI)approaches to improve the efficiency of battery management and enhance the stability and reliability of battery operation.This paper reviews the value of AI methods in lithium-ion battery health management and in particular analyses the application of machine learning(ML),one of the many branches of AI,to lithium-ion battery state of health(SOH),focusing on the advantages and strengths of neural network(NN)met-hods in ML for lithium-ion battery SOH simulation and prediction.NN is one of the important branches of ML,in which the application of NNs such as backpropagation NN,convolutional NN,and long short-term memory NN in SOH estimation of lithium-ion batteries has received wide attention.Reports so far have shown that the utiliza-tion of NN to model the SOH of lithium-ion batteries has the advantages of high efficiency,low energy consump-tion,high robustness,and scalable models.In the future,NN can make a greater contribution to lithium-ion battery management by,first,utilizing more field data to play a more practical role in health feature screening and model building,and second,by enhancing the intelligent scree-ning and combination of battery parameters to characterize the actual lithium-ion battery SOH to a greater extent.The in-depth application of NN in lithium-ion battery SOH will certainly further enhance the science,reliability,stability,and robustness of lithium-ion battery management.
machine learninglithium-ion batterystate of healthneural networkartificial intelligence
Nan QI、Kang YAN、Yajuan YU、Rui LI、Rong HUANG、Lai CHEN、Yuefeng SU
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Beijing Institute of Technology Chongqing Innovation Center,Chongqing 401120,China
Department of Energy and Environmental Materials,School of Materials Science and Engineering,Beijing Institute of Technology,Beijing 100081,China
Beijing Electric Vehicle Co.,Ltd.,Beijing 100176,China
国家重点研发计划Research Fund Program for Young Scholars(Chen Lai)of Beijing Institute of Technology国家自然科学基金
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