Abstract
State of charge (SOC) plays a significant role in the battery management system (BMS), since it can contribute to the establishment of energy management for electric vehicles. Unfortunately, SOC cannot be measured directly. Various single Kalman filters, however, are capable of estimating SOC. Under different working conditions, the SOC estimation error will increase because the battery parameters cannot be estimated in real time. In order to obtain a more accurate and applicable SOC estimation than that of a single Kalman filter under different driving conditions and temperatures, a second-order resistor capacitor (RC) equivalent circuit model (ECM) of a battery was established in this paper. Thereafter, a dual filter, i.e., an unscented Kalman filter–extended Kalman filter (UKF–EKF) was developed. With the EKF updating battery parameters and the UKF estimating the SOC, UKF–EKF has the ability to identify parameters and predict the SOC of the battery simultaneously. The dual filter was verified under two different driving conditions and three different temperatures, and the results showed that the dual filter has an improvement on SOC estimation.
Highlights
With the increasing energy crisis, alternative energy vehicles have been given full attention.Lithium-ion batteries (LIBs) have become the power source of electric vehicles (EVs) because of their high energy density and long service life [1]
The results show that the unscented Kalman filter–extended Kalman filter (UKF–extended Kalman filter (EKF)), which can identify battery parameters online, has a better performance than the unscented Kalman filter (UKF) in state of charge (SOC) estimation at different temperatures; in addition, the voltage errors of online estimation are smaller
SOC estimation is an important factor for Battery management systems (BMS) in EVs, as it can provide a basis for the energy management of EVs
Summary
With the increasing energy crisis, alternative energy vehicles have been given full attention. In order to resolve the aforementioned shortcomings of the single Kalman filter, many scholars have proposed joint estimation methods to simultaneously achieve SOC estimation and battery parameter identification online. All of them had high accuracy for SOC estimation and parameter identification under low noise interference, but as the unknown interference increased, they showed their own weaknesses in calculating cost, number of parameters to be adjusted, and robustness of estimation These SOC estimators are only based on the first-order RC ECM, and RLS is not suitable for estimating nonlinear systems. In addition to the unknown noise at work, the estimation of the SOC and identification of the parameters of the battery are usually influenced by ambient temperature, which is reflected in the changes of the OCV–SOC function relationship and the battery’s internal resistance. The UKF is used to predict the SOC, which solves the shortcomings of insufficient accuracy of the EKF’s SOC estimation, while the EKF updates the battery parameters, which further enhances the accuracy of the SOC estimation
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