Research On Lithium-ion Battery Parameter Identification Method And SOC/SOP Estimation

Power battery is not only an important component of electric vehicle,but also the research focus and difficulty of electric vehicle.Its internal state of battery plays an important role in the control of battery and the monitoring of battery management system.The adaptability of automotive power battery to the environment is poor,and the internal state characteristics have strong time-varying nonlinearity.When applied in electric vehicles,it is often greatly affected by the working condition,environment and vehicle configuration of the battery.Therefore,it is an important and difficult challenge to accurately estimate the internal state parameters of the battery.In this paper,the following important research work is carried out for the estimation of the internal state of automobile power battery:At the beginning,establish the battery experimental process and experimental platform to carry out the battery characteristic experiment.Collect and sort out the experimental data,and analyze the voltage characteristics,internal resistance characteristics and capacity characteristics of the battery under different temperatures and different magnification.The results show that the battery capacity and internal resistance are sensitive to the ambient temperature,the battery voltage has a certain relationship with the battery capacity,the battery charge discharge ratio has little influence,and the battery has poor tolerance to low temperature,The battery capacity at low temperature is small and the range is large.Then,aiming at the problem of establishing the mathematical model of the battery,through the analysis of the types of battery models,the equivalent circuit model based on the first-order RC is finally selected as the mathematical model of the battery.Its characteristics are that the model is relatively simple,the parameters are less and the amount of calculation is less.In view of the insufficient operation speed and ability of the current controller,the application of the first-order RC model is more suitable in engineering practice.Secondly,aiming at the parameter identification of power battery model,under different temperatures and fuds complex working conditions,the off-line identification,recursive least square method with forgetting factor and Kalman filter algorithm models are established,and the experimental data of three lithium-ion battery parameter identification algorithm models are verified to judge the accuracy of the algorithm.The simulation results show that the voltage error of Kalman filter algorithm for parameter identification is the smallest,the adaptability between working condition and temperature is strong,and the error is less than 3%.To a certain extent,it solves the problems of high variability and poor reliability of battery model parameters in practical application.Then,for the SOC estimation of power battery: a lithium battery SOC estimation based on extended Kalman filter algorithm is proposed.Under the complex working conditions of multi temperature and fuds,the extended Kalman filter is used to calculate the battery SOC,and the ampere hour integral method is compared to determine the accuracy and effectiveness of extended Kalman filter algorithm.The results show that the SOC estimation error is within 3%,which is more accurate.To a certain extent,the SOC estimation of the battery under uncertain battery working conditions and environment is realized.Finally,aiming at the problem of peak power capability estimation of power battery,by analyzing the relationship between battery power and battery current,voltage and SOC,a joint SOC and SOP estimation algorithm based on extended Kalman filter is proposed.By analyzing the current,voltage and SOC limits of the battery,and combining the extended Kalman filter algorithm to identify the current internal state of the battery,the continuous peak power of the battery can be estimated more accurately under the complex working conditions of fuds.The results show that the estimation error of SOP is maintained at about 2W,the error rate is within 3%,and the error is small.