Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7736506 | Journal of Power Sources | 2014 | 19 Pages |
Abstract
Lithium-ion battery packs in hybrid and pure electric vehicles are always equipped with a battery management system (BMS). The BMS consists of hardware and software for battery management including, among others, algorithms determining battery states. The continuous determination of battery states during operation is called battery monitoring. In this paper, the methods for monitoring of the battery state of charge, capacity, impedance parameters, available power, state of health, and remaining useful life are reviewed with the focus on elaboration of their strengths and weaknesses for the use in on-line BMS applications. To this end, more than 350 sources including scientific and technical literature are studied and the respective approaches are classified in various groups.
Keywords
NCAEMSSOALMSBMSRLSRVMRULNMCBEVSOFRCPPHEVUKFFCEVSOHlocal linear model treeFPUOCVAdaptive extended Kalman filterSafe operating areaLOLIMOTPDECPELiBEKFHEVANFISANNECMemfLithium-ion batteryRemaining useful lifePlug-in Hybrid Electric VehicleFuel cell electric vehicleState of chargeAdaptive neuro-fuzzy inference systemSOCEnergy management systemBattery management systemArtificial neuronal networksconstant phase elementUnscented Kalman filterParticle FilterKalman filterextended Kalman filterRelevance vector machineSVMSupport vector machineBattery monitoringopen-circuit voltageEquivalent circuit modelPartial differential equationelectromotive forceNIMHHybrid electric vehiclesBattery electric vehicleState of healthPower prediction
Related Topics
Physical Sciences and Engineering
Chemistry
Electrochemistry
Authors
Wladislaw Waag, Christian Fleischer, Dirk Uwe Sauer,