Research On SOC Estimation Of Lithium Battery Based On Unscented Kalman Filter

In order to cope with the dual pressures of global climate change and energy supply shortage,countries are actively carrying out research on new energy vehicles.Lithium-ion batteries provide energy for electric vehicles,and its management system is responsible for controlling and managing the battery status to ensure driving safety.State of Charge(SOC)is an important basis for battery management system decisionmaking.Accurate SOC estimation can improve energy utilization efficiency and predict driving mileage.In this thesis,18650 lithium-ion battery is taken as the research object,and the research on online estimation of battery SOC is carried out.The main research work is as follows:(1)Based on the experimental platform for charging and discharging characteristics of lithium-ion batteries,the battery was tested for nominal capacity,usable capacity,pulse discharge and charge and discharge at different rates.According to the experimental data,the relevant characteristics of the battery were analyzed,and finally obtained The relationship curve between the battery SOC and the open circuit voltage is obtained,which provides a data basis for the establishment of the battery model and parameter identification in the following.(2)Considering the complexity of the model structure and the amount of calculation,a second-order RC equivalent circuit model is established,and two different methods of offline and online identification are selected for parameters such as ohmic internal resistance,polarization resistance,and polarization capacitance in the battery model.Aiming at the phenomenon of "data saturation" in the recursive least squares method,the forgetting factor is introduced to adjust the weight of old and new data,and the recursive least squares algorithm with the forgetting factor is proposed to identify the model parameters,and the dynamic stress test condition is used The offline and online identification methods are compared and verified,and the results show that the online parameter identification method has higher accuracy.(3)Based on the analysis of the basic principles of the Extended Kalman Filter(EKF)and the Unscented Kalman Filter(UKF),the UKF algorithm assumes that the noise is fixed and causes the filter to diverge.To solve the problem of convergence,an Adaptive Unscented Kalman Filter(AUKF)is proposed to update the system noise in real time.Finally,the estimation results of the three SOC estimation algorithms are compared by using simulated dynamic conditions experiments.The results show that the AUKF algorithm has a fast convergence speed and a relatively stable estimation result,and the average absolute error of SOC estimation is 0.41%.(4)Using the domestic APM32030 chip as the main control chip,the hardware circuit of the battery management system is designed,the control program and the upper computer display software are written,and the functions of battery data collection and SOC estimation are realized,and the performance of the system is verified through test experiments Reliability and accuracy of collected data.