Open AccessA comparative study of machine learning based modeling methods for Lithium-ion battery
Author(s) -
Peng Wang,
Jie Fan,
Yang Ou,
Zhe Li,
Yi Wang,
Bo Deng,
Yuanwei Zhang,
Zihao Gao
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/546/5/052045
Subject(s) - battery (electricity) , adaptability , artificial neural network , computer science , support vector machine , lithium ion battery , process (computing) , power (physics) , decision tree , artificial intelligence , voltage , state of charge , machine learning , simulation , engineering , electrical engineering , ecology , physics , quantum mechanics , biology , operating system
A suitable battery model plays an important role in assisting accurate state estimation for power battery used in electric vehicles. This paper compares the applications of four commonly used machine learning methods (decision tree, k-nearest neighbour, support vector machine and neural network) in lithium-ion battery modeling. The adaptability on working condition, temperature and degradation of above four modeling methods are analysed in detail. Results show that neural network performs best when working condition changes. All the models basically have the same performance on adaptability to temperature. The battery dynamic characteristics change significantly in the aging process and it is necessary to include battery test data under different degradation levels into training sets as to obtain a model that can predict the voltage response accurately in various aging states.