Research On SOC Estimation And Design Of Battery Management System Based On Dual Kalman Filter

Facing increasingly severe energy scarcity and environmental pollution,governments of all countries are vigorously promoting the development of new energy vehicles to reduce carbon emissions and ease the energy crisis.The battery management system collects battery data in real time,manages and controls the working state of the power battery,and ensures high-performance battery operation and safe and stable driving.Among them,the estimation of the state of charge of the battery is a core function of the battery management system.The remaining power during the use of the battery affects the performance and safety of the electric vehicle.The accurate SOC estimation of the lithium-ion power battery is the basis for the safe and reliable operation of the power battery.Its research has become a key issue in the field of battery management.Firstly,the effects of temperature,charge/discharge rate,inconsistency of single cells,and self-discharge on the modeling and SOC estimation of lithium ion batteries are analyzed.The advantages and disadvantages of five commonly used equivalent circuit models are compared and analyzed,and the second-order Thevenin equivalent circuit model is selected as the battery equivalent circuit model in this paper.Secondly,the main principles and methods of offline battery parameter identification are described,and the process of offline battery parameter identification is completed.Through the hybrid pulse power test,the relationship curve between open circuit voltage and state of charge is fitted and calculated in different Parameters such as ohmic internal resistance,electrochemical polarization capacitor resistance,and concentration polarization capacitor resistance corresponding to the state of charge.The recursive least square method and Kalman filter algorithm are selected to identify the parameters of the equivalent circuit model online.The parameters are identified online using the recursive least squares method and the Kalman filter method based on the state space equation.Comparing the results of online identification parameters with the results obtained from offline identification parameters,the online parameter identification method can ensure tracking accuracy,which shows that online identification of battery model parameters can effectively reduce the model error and thereby improve the accuracy of SOC estimation.After that,by explaining the principle of the extended Kalman filter algorithm,state space equations and output equations are obtained on the basis of the second-order Thevenin battery equivalent model.The Taylor equation is used to linearize the state equation of the battery,and the parameters of the Kalman filter algorithm are combined online.Identification method.The SOC estimation and verification procedures for double Kalman filtering are written in Matlab.Based on the voltage,current,and SOC values collected from experiments under normal temperature UDDS conditions,the initial value of the algorithm is accurate and the initial value of the algorithm has errors.In this case,the accuracy and convergence of the SOC estimation of the double Kalman filter algorithm are verified.Simulation results show that under UDDS conditions,the DKF algorithm is used to estimate the battery SOC with good convergence and accuracy.Finally,the design and development of the software and hardware of the battery management system are introduced.Firstly,based on the analog front-end BQ76940,the lithium ion battery is used for data collection,and then the collected data is transmitted to the main control unit STM32F103RCT6 through the I~2C interface,and the MCU calculates and analyzes the data.By comparing the error of the data collected by the MCU and the high-precision multimeter,the acquisition accuracy of the battery management system is verified.