Research On Parameter Online Identification And SOC Estimation Of Lithium-ion Battery Based On Extended Kalman Filter

With the worldwide intensification of the energy crisis and environmental pollution,electric vehicles have received extensive attention and widespread promotion by governments due to their advantages in energy saving and environmental protection.Battery Management System(BMS)is an important link connecting electric vehicles and power batteries,and its research has become a hot spot in recent years.Accurate battery state of charge(SOC)is essential for BMS to formulate control strategies such as charge and discharge control and energy distribution.However,the SOC cannot be directly measured by the sensors at the wheel,so the SOC online estimation technology is a research focus and difficulty in BMS.In this thesis,the single ternary lithium-ion battery is selected as the research object and a method of joint online estimation of battery model parameters and SOC is proposed based on the battery gas-liquid dynamics model and Extended Kalman Filter(EKF).The main research content is as follows:The internal structure and working mechanism of the lithium-ion battery are analyzed,and the gas-liquid physical model parameters are compared with the external characteristic parameters of the battery.Besides,the charging/discharging process and the shelving process of the gas-liquid model are respectively corresponding to the charging/discharging process and the resting process of the battery.According to the ideal gas state equation,solubility balance equation and Bernoulli equation in the above process,the estimating formula of Open Circuit Voltage(OCV)under battery charging and discharging conditions with temperature input is derived and the estimation formulas under the two conditions are uniformly expressed.Based on Hybrid Pulse Power Characteristic(HPPC)and Genetic Algorithm,the model parameters are identified offline.In order to solve the problem that model parameters change under different working conditions which would reduce the estimation accuracy,an online parameter identification method based on EKF is proposed.After that,a joint online estimation method of parameters and SOC is constructed based on the parameter online identification method and SOC estimation method.The accuracy of the SOC estimation method under the four standard conditions is verified.According to the characteristics of the actual operating conditions of the power battery,a combined test condition is built and the effectiveness of the estimation method under this test condition is verified.The verification results show that the estimation method has strong adaptability to different working conditions.The influencing factors of the estimation method proposed in this thesis are studied from four aspects,including the influence of sampling frequency,initial input error,current noise,and battery temperature on the accuracy of SOC estimation.The results demonstrate that the estimation method has the characteristics of high online estimation accuracy,rapid elimination of initial errors,and application of sparse sampling conditions and different temperature conditions.It provides theoretical and technical support for the development of advanced BMS.