An improved model combining machine learning and Kalman filtering architecture for state of charge estimation of lithium-ion batteriesOA
An improved model combining machine learning and Kalman filtering architecture for state of charge estimation of lithium-ion batteries
Yan Li;Min Ye;Qiao Wang;Gaoqi Lian;Baozhou Xia
National Engineering Research Center for Highway Maintenance Equipment,Chang'an University,Xi'an,710064,ChinaNational Engineering Research Center for Highway Maintenance Equipment,Chang'an University,Xi'an,710064,ChinaInstitute for Power Electronics and Electrical Drives(ISEA),RWTH Aachen University,Aachen,52062,GermanyNational Engineering Research Center for Highway Maintenance Equipment,Chang'an University,Xi'an,710064,ChinaNational Engineering Research Center for Highway Maintenance Equipment,Chang'an University,Xi'an,710064,China
Lithium-ion batteryState of charge estimationSupport vector regressionSimulated annealing optimizationKalman filter
Lithium-ion batteryState of charge estimationSupport vector regressionSimulated annealing optimizationKalman filter
《新能源与智能载运（英文）》 2025 (2)
73-83,11
This research was funded by the Key Research and Development Program of Shaanxi Province(2023-GHYB-05 and 2023-YBSF-104).
评论