IEEE Transactions on Vehicular Technology · 2012 · 433 citations · 18 references
EngineeringAutomotive EngineeringElectrical EngineeringAutomotive ElectronicsElectric Vehicle EngineeringElectric Vehicle TechnologyBattery SystemsBattery TechnologyKalman FilteringPower Electronics For Electric VehiclesState EstimationState Of Charge EstimationBattery State Of Charge ModelingExtended Kalman FilterLithium-ion BatteriesLithium-ion Battery State Of Charge EstimationModel-based State Of Charge EstimationOnline State EstimationOpen-circuit VoltageState Of Charge Kalman Filtering
A more accurate battery state of charge (SOC) estimation method for electric drive vehicles is developed based on a nonlinear battery model and an extended Kalman filter (EKF) supported by experimental data. A nonlinear battery model is constructed by separating the model into a nonlinear open circuit voltage and a two-order resistance-capacitance model. EKF is used to eliminate the measurement and process noise and remove the need of prior knowledge of initial SOC. A hardware-in-the-loop test bench was built to validate the method. The experimental results show that the proposed method can estimate the battery SOC with high accuracy.
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