Abstract
The battery State of Charge (SoC) estimation is one of the basic and significant functions for Battery Management System (BMS) in Electric Vehicles (EVs). The SoC is the key to interoperability of various modules and cannot be measured directly. An improved Extended Kalman Filter (iEKF) algorithm based on a composite battery model is proposed in this paper. The approach of the iEKF combines the open-circuit voltage (OCV) method, coulomb counting (Ah) method and EKF algorithm. The mathematical model of the iEKF is built and four groups of experiments are conducted based on LiFePO4 battery for offline parameter identification of the model. The iEKF is verified by real battery data. The simulation results with the proposed iEKF algorithm under both static and dynamic operation conditions show a considerable accuracy of SoC estimation.
Highlights
In response to the issues of the energy crisis and environmental pollution, Electric Vehicles (EVs) are described as one of the most significant developments in the transport industry
The simulation results with the proposed improved Extended Kalman Filter (iEKF) algorithm under both static and dynamic operation conditions show a considerable accuracy of state of charge (SoC) estimation
The iEKF method combines the open-circuit voltage (OCV), Ah and extended KF (EKF) methods: the OCV-SoC function in our work provide an initial value; Ah counting module online identifies the parameters of the battery model; the errors in the OCV and Ah estimation are corrected by the EKF algorithm
Summary
In response to the issues of the energy crisis and environmental pollution, Electric Vehicles (EVs) are described as one of the most significant developments in the transport industry. This model will include the effects of temperature, charge/discharge rate, direct current resistance, etc Using this battery model, we implement the iEKF algorithm to estimate the SoC in MATLAB. The iEKF method combines the OCV, Ah and EKF methods: the OCV-SoC function in our work provide an initial value; Ah counting module online identifies the parameters of the battery model; the errors in the OCV and Ah estimation are corrected by the EKF algorithm. Under both static and dynamic operating conditions, it is shown that the iEKF algorithm results in better accuracy of of the.
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