Abstract
The accurate state of charge (SoC) online estimation is a significant indicator that relates to driving ranges of electric vehicles (EV). The relationship between open circuit voltage (OCV) and SoC plays an important role in SoC estimation for lithium-ion batteries. To compare with the traditional incremental OCV (IO) test and the low current OCV (LO) test, a novel OCV test which combines IO test with LO test (CIL) is proposed in this paper. Based on the reliable parameters online identification of the dual polarization (DP) battery model, two SoC estimation algorithms are compared on the accuracy, robustness and convergence speed for the entire SoC region. Meanwhile, the comparative study of the three OCV-SoC relationships fits by the corresponding OCV tests is discussed in terms of the SoC online estimation under various temperatures. The results show that the adaptive extended Kalman filter (AEKF) algorithm can better improve the accuracy and robustness of SoC estimation than that of the extended Kalman filter (EKF) algorithm. Most importantly, the OCV-SoC relationship obtains from the CIL OCV test method is applied to the AEKF algorithm, which has higher accuracy and better statistical indices of SoC estimation, especially suitable for the low temperature.
Highlights
With the development of the automobile manufacturing industry towards to the tendency of the cleanliness, efficiency and sustainability, the electric vehicles (EV) is the most potential choice to solve the above problems, which has made great progress and development in the industrial and commercial fields [1], [2]
In order to extend to the complex nonlinear system, the extended Kalman filter (EKF) algorithm is proposed by using the Taylor expansion, which may lead to truncation errors and result in the EKF is divergent at some initial state of charge (SoC) value [37,38]
The SoC online estimator of the open circuit voltage (OCV)-SoC relationship curve fitted by the low current OCV test is named as the LO estimator, the same naming principle is applied to other estimators, respectively are the incremental OCV (IO) estimator and the combines IO test with LO test (CIL) estimator
Summary
With the development of the automobile manufacturing industry towards to the tendency of the cleanliness, efficiency and sustainability, the EV is the most potential choice to solve the above problems, which has made great progress and development in the industrial and commercial fields [1], [2]. Lithium-ion batteries cannot satisfy the requirement of the information transmission, control and management. The battery SoC estimation is an essential ingredient of the BMS, which is approximately equivalent to the fuel gauge of the EV to indicate remaining driving ranges. The accurate SoC estimation can provide a security assurance to the user due to it can avoid over-charge and over-discharge of lithiumion batteries [7]. The mechanism of electrochemical reaction in the lithium-ion battery is difficult to determine, and the driving condition of the EV is complex, so it still needs explore to provide an accurate SoC estimation for the EV
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
