Abstract
Battery energy storage management for electric vehicles (EV) and hybrid EV is the most critical and enabling technology since the dawn of electric vehicle commercialization. A battery system is a complex electrochemical phenomenon whose performance degrades with age and the existence of varying material design. Moreover, it is very tedious and computationally very complex to monitor and control the internal state of a battery’s electrochemical systems. For Thevenin battery model we established a state-space model which had the advantage of simplicity and could be easily implemented and then applied the least square method to identify the battery model parameters. However, accurate state of charge (SoC) estimation of a battery, which depends not only on the battery model but also on highly accurate and efficient algorithms, is considered one of the most vital and critical issue for the energy management and power distribution control of EV. In this paper three different estimation methods, i.e., extended Kalman filter (EKF), particle filter (PF) and unscented Kalman Filter (UKF), are presented to estimate the SoC of LiFePO4 batteries for an electric vehicle. Battery’s experimental data, current and voltage, are analyzed to identify the Thevenin equivalent model parameters. Using different open circuit voltages the SoC is estimated and compared with respect to the estimation accuracy and initialization error recovery. The experimental results showed that these online SoC estimation methods in combination with different open circuit voltage-state of charge (OCV-SoC) curves can effectively limit the error, thus guaranteeing the accuracy and robustness.
Highlights
The experimental results showed that these online SoC estimation methods in combination with different open circuit voltage-state of charge (OCV-SoC) curves can effectively limit the error, guaranteeing the accuracy and robustness
A battery management system is an important component of an electric vehicle especially in pure electric vehicles (PEV) where the battery is the only source of power
One specific factor cannot describe the functioning and operation of the whole battery; (2) an electro-chemical model that captures the significant electrochemical processes of a battery through a complex mathematical equation and as a consequence makes the state estimation process difficult [4,15]; (3) an equivalent circuit model (ECM) captures the electrochemical physics of a battery using only electrical components, and generally includes an nth order resistor-capacitor (RC) circuit with a series impedance factor, making it easy to incorporate into the system model of an electric vehicle with more precision and lower computational cost
Summary
A battery management system is an important component of an electric vehicle especially in pure electric vehicles (PEV) where the battery is the only source of power. Contrary to the discharge test method, the OCV-based SoC estimation method is a promising technique that obtains. (2) an electro-chemical model that captures the significant electrochemical processes of a battery through a complex mathematical equation and as a consequence makes the state estimation process difficult [4,15]; (3) an equivalent circuit model (ECM) captures the electrochemical physics of a battery using only electrical components, and generally includes an nth order resistor-capacitor (RC) circuit with a series impedance factor, making it easy to incorporate into the system model of an electric vehicle with more precision and lower computational cost. Different variants of Kalman filtering techniques are mostly used to estimate the SoC based on different battery models. The remainder of the paper is equivalent battery model and estimates SoC based on EKF, PF and UKF algorithms.
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