Publication | Open Access
Review of the Remaining Useful Life Prognostics of Vehicle Lithium-Ion Batteries Using Data-Driven Methodologies
193
Citations
45
References
2016
Year
EngineeringLife PredictionDeterioration ModelingVehicle Lithium-ion BatteryData ScienceVehicle Lithium-ion BatteriesManagementSystems EngineeringBiostatisticsService Life PredictionElectrical EngineeringPredictive AnalyticsLithium-ion BatteriesLithium-ion BatteryEnergy StorageElectric BatteryPrognostic EvaluationEnergy ManagementPredictive MaintenanceLife Cycle AssessmentBatteriesPrognosticsData Modeling
Lithium-ion batteries are the primary power source in electric vehicles, and the prognosis of their remaining useful life is vital for ensuring the safety, stability, and long lifetime of electric vehicles. Accurately establishing a mechanism model of a vehicle lithium-ion battery involves a complex electrochemical process. Remaining useful life (RUL) prognostics based on data-driven methods has become a focus of research. Current research on data-driven methodologies is summarized in this paper. By analyzing the problems of vehicle lithium-ion batteries in practical applications, the problems that need to be solved in the future are identified.
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