State of charge estimation of lithium-ion battery based on state of temperature estimation using weight clustered-convolutional neural network-long short-term memoryOA
State of charge estimation of lithium-ion battery based on state of temperature estimation using weight clustered-convolutional neural network-long short-term memory
Chaoran Li;Sichen Zhu;Liuli Zhang;Xinjian Liu;Menghan Li;Haiqin Zhou;Qiang Zhang;Zhonghao Rao
Hebei Engineering Research Center of Advanced Energy Storage Technology and Equipment,School of Energy and Environmental Engineering,Hebei University of Technology,Tianjin 300401,ChinaHebei Key Laboratory of Thermal Science and Energy Clean Utilization,School of Energy and Environmental Engineering,Hebei University of Technology,Tianjin 300401,ChinaHebei Engineering Research Center of Advanced Energy Storage Technology and Equipment,School of Energy and Environmental Engineering,Hebei University of Technology,Tianjin 300401,ChinaHebei Key Laboratory of Thermal Science and Energy Clean Utilization,School of Energy and Environmental Engineering,Hebei University of Technology,Tianjin 300401,ChinaPinggao Group Energy Storage Technology Co.,Ltd.,Room 1-4251,Block E,No.6 Huafeng Road,Huaming High-tech Industrial Zone,Dongli District,Tianjin 300308,ChinaHebei Engineering Research Center of Advanced Energy Storage Technology and Equipment,School of Energy and Environmental Engineering,Hebei University of Technology,Tianjin 300401,ChinaHebei Key Laboratory of Thermal Science and Energy Clean Utilization,School of Energy and Environmental Engineering,Hebei University of Technology,Tianjin 300401,ChinaHebei Engineering Research Center of Advanced Energy Storage Technology and Equipment,School of Energy and Environmental Engineering,Hebei University of Technology,Tianjin 300401,China
State of chargeState of temperatureLithium-ion batteryDeep learning methodLong short-term memoryWeight cluster
State of chargeState of temperatureLithium-ion batteryDeep learning methodLong short-term memoryWeight cluster
《新能源与智能载运（英文）》 2025 (6)
69-81,13
This work was supported by the Ministry of Science and Technology of the People's Republic of China(No.2022YFE0207900),the Post-graduate Education and Teaching reform Research project of Hebei Province(YJG2024012),the Science and Technology Planning project of Tianjin(No.23YFZCSN00320)and the Natural Science Foundation of Hebei Province(No.E2022202026).
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