Abstract
The battery state of charge (SoC), whose estimation is one of the basic functions of battery management system (BMS), is a vital input parameter in the energy management and power distribution control of electric vehicles (EVs). In this paper, two methods based on an extended Kalman filter (EKF) and unscented Kalman filter (UKF), respectively, are proposed to estimate the SoC of a lithium-ion battery used in EVs. The lithium-ion battery is modeled with the Thevenin model and the model parameters are identified based on experimental data and validated with the Beijing Driving Cycle. Then space equations used for SoC estimation are established. The SoC estimation results with EKF and UKF are compared in aspects of accuracy and convergence. It is concluded that the two algorithms both perform well, while the UKF algorithm is much better with a faster convergence ability and a higher accuracy.
Highlights
The battery, as an on-board electric energy storage source [1,2,3], is key to the development of electric vehicles (EVs)
The second type is look-up table method based on a black-box battery model, which describes the nonlinear relationship between state of charge (SoC) and its influencing factors
For a lithium-ion battery, the extended Kalman filter (EKF) and unscented Kalman filter (UKF) methods are both adopted in the SoC estimations based on the battery Thevenin model [18,19], and the estimation results are compared and discussed
Summary
The battery, as an on-board electric energy storage source [1,2,3], is key to the development of electric vehicles (EVs). The second type is look-up table method based on a black-box battery model, which describes the nonlinear relationship between SoC and its influencing factors. This approach can often produce a good estimation, it causes problems like heavy computation burden and bad application in real-time. For a lithium-ion battery, the EKF and UKF methods are both adopted in the SoC estimations based on the battery Thevenin model [18,19], and the estimation results are compared and discussed.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have