Estimation And Realization Of Li-ion Battery State Of Charge Based On Unscented Kalman Filtering

With the shortage of fossil fuels and serious environment problems, governments give more attention to the development of the zero-emission and the new energy electric vehicle. Battery state control and management of the battery management system is one of the key technologies of electric vehicle development to need the breakthrough. Accurate estimation of the battery state of charge (SOC) is a key prerequisite to the battery management system operation and is of great significance to improve vehicle performance and enhance the life. Thus, the research on the power lithium battery SOC estimation is carried out in this paper. The main contents are the following:Firstly, describing the background, clarifying the significance of estimating lithium battery’s SOC, analyzing the current situation of the estimated SOC, definitions and factors. Considering the difficulty of engineering and mathematical algorithms compensating for the accuracy of the equivalent model, the internal resistance of the equivalent circuit model is choosed as a dynamic model of a lithium-ion battery with the basic work principle to understand the power lithium battery. Thereafter the experiments are conducted on the open circuit voltage and the SOC relationship and internal resistance parameter identification of the battery model. The final simulations and the result of the experiments show that the model can simulate the battery characteristics.Secondly, for the open circuit voltage and the SOC relationship of the battery equivalent model is highly non-linear function, unscented Kalman filter algorithm has a better state estimation accuracy compared to the extended Kalman filter in solving nonlinear non-Gaussian stochastic system state issues. So based on the internal resistance model of the battery, this paper apply unscented Kalman filter algorithm to realize lithium battery SOC estimate on the the nonlinear conditions. The algorithm consider the SOC and the internal resistance as the state parameter and realize the mean and variance nonlinear transformation by transfer UT and then on the basis of the standard Kalman filter frame to complete a lithium battery’s SOC estimation. By custom electrical charge and discharge conditions, the MATLAB simulation estimate is conducted. The results prove that the unscented Kalman filter can accurately estimate the battery SOC and make up for the error of the model.Finally, putting up the hardware platform which mainly include STM32minimum system, charge and discharge protection circuit, data acquisition and CAN communications hardware circuit design. In the IAR compiler environment software system program is designed, complete the software programming battery voltage, current, temperature, and estimating the SOC of each module. By collecting data and experimental the accuracy of the measurement system and SOC estimation is verified. Figures(41), tables(4),references(60).