Intelligent lithium plating detection and prediction method for Li-ion batteries based on random forest modelOA
Intelligent lithium plating detection and prediction method for Li-ion batteries based on random forest model
Guangying Zhu;Jianguo Chen;Xuyang Liu;Tao Sun;Xin Lai;Yuejiu Zheng;Yue Guo;Rohit Bhagat
School of Mechanical Engineering,University of Shanghai for Science and Technology,Shanghai 200093,ChinaSchool of Mechanical Engineering,University of Shanghai for Science and Technology,Shanghai 200093,ChinaSchool of Mechanical Engineering,University of Shanghai for Science and Technology,Shanghai 200093,ChinaSchool of Mechanical Engineering,University of Shanghai for Science and Technology,Shanghai 200093,ChinaSchool of Mechanical Engineering,University of Shanghai for Science and Technology,Shanghai 200093,ChinaSchool of Mechanical Engineering,University of Shanghai for Science and Technology,Shanghai 200093,ChinaState Key Laboratory of Automotive Safety and Energy,Tsinghua University,Beijing 100084,ChinaCentre for E-Mobility and Clean Growth Research,Coventry University,Coventry,CV1 5FB,United Kingdom
Lithium-ion batteryLithium plating detectionFeature extractionRandom forest algorithm
Lithium-ion batteryLithium plating detectionFeature extractionRandom forest algorithm
《新能源与智能载运（英文）》 2025 (6)
29-37,9
This research is supported by National Natural Science Foundation of China(NSFC)under the Grant number of 52477216 and Natural Science Foundation of Shanghai under the Grant number of 23ZR1444600,in part by the National Natural Science Foundation of China(NSFC)under the Grant number of 52277222.
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