Abstract
Batteries are everywhere, in all forms of transportation, electronics, and constitute a method to store clean energy. Among the diverse types available, the lithium-iron-phosphate (LiFePO4) battery stands out for its common usage in many applications. For the battery’s safe operation, the state of charge (SOC) and state of health (SOH) estimations are essential. Therefore, a reliable and robust observer is proposed in this paper which could estimate the SOC and SOH of LiFePO4 batteries simultaneously with high accuracy rates. For this purpose, a battery model was developed by establishing an equivalent-circuit model with the ambient temperature and the current as inputs, while the measured output was adopted to be the voltage where current and terminal voltage sensors are utilized. Another vital contribution is formulating a comprehensive model that combines three parts: a thermal model, an electrical model, and an aging model. To ensure high accuracy rates of the proposed observer, we adopt the use of the dual extend Kalman filter (DEKF) for the SOC and SOH estimation of LiFePO4 batteries. To test the effectiveness of the proposed observer, various simulations and test cases were performed where the construction of the battery system and the simulation were done using MATLAB. The findings confirm that the best observer was a voltage-temperature (VT) observer, which could observe SOC accurately with great robustness, while an open-loop observer was used to observe the SOH. Furthermore, the robustness of the designed observer was proved by simulating ill-conditions that involve wrong initial estimates and wrong model parameters. The results demonstrate the reliability and robustness of the proposed observer for simultaneously estimating the SOC and SOH of LiFePO4 batteries.
Highlights
Monitoring battery operation and measuring battery aging in real life have been a challenging goal that includes a number of complex processes under complicated operating conditions
The model introduced in [7] took into consideration the robustness, accuracy, and low-cost hardware requirements. It was based on the online parameter identification of an electrical model using recursive least square (RLS) with the application of an unscented Kalman filter (UKF) to estimate state of charge (SOC)
A third-order RC circuit model was introduced [9] based on sampling point Kalman joint algorithm in order to SOC estimation error correction
Summary
Monitoring battery operation and measuring battery aging in real life have been a challenging goal that includes a number of complex processes under complicated operating conditions. A combination of SOC and SOH estimation was introduced in [33]; a sequential algorithm to improve estimation performance was proposed It used frequency-scale separation and estimated parameters/states sequentially by different frequencies of currents injection. As a combination of an electro-thermal model and a semi-empirical cycle-life model, a coupled electro-thermal-aging model was introduced in [34], which demonstrated the system dynamics for LiFePO4 batteries It provided an assumption for the behaviors of the battery, which would be discussed in the section of the mathematical model, as well as an open-loop observer for both SOC and SOH. To assure high performance of the proposed observer, the use of the dual extended Kalman filter (DEKF) is adopted for the SOC and SOH estimation of LiFePO4 batteries with current and terminal voltage sensors.
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