Research On Health Status Estimation Algorithm Of Vehicle Power Battery During Discharge Proces

In recent years,with the global warming,the carbon emission capacity in the atmosphere has increased.In order to cope with this environmental protection trend and environmental protection needs,the status of electric vehicles in people’s travel transportation has been increasing.The original power mode of fuel vehicles determines its limited development in environmental protection.Electricity can be generated through coal,and oil can also be used to generate electricity.Compared with the power source of traditional fuel vehicles,the power source of traditional fuel vehicles is only limited to fuel,but the power sources of electric vehicles are extensive,and the introduction of electric energy also brings more possibilities for the development of the automobile industry.Lithium-ion battery as the power source of electric vehicles determines its position in the structure of electric vehicles.Among them,it is very important to monitor the state of health(SOH)of the battery.Real-time and accurate grasp of the state of health of the battery SOH is helpful to ensure the safety of the vehicle occupants and the safe driving of the electric vehicle.At present,there are many research methods for the state of health of vehicle power batteries.In order to solve the problem of battery consistency,and at the same time for the irregular and random charging and discharging characteristics of electric vehicles and the need for real-time online accurate detection,this paper proposes an online battery state of health estimation algorithm model during the random discharge of power batteries.The specific research content and innovations of this paper are as follows:(1).An unscented kalman filter(UKF)estimation method with the external indicator function as a priori information is proposed.The algorithm makes full use of the external indication function to obtain relevant prior information,and combines the prior information to perform the filtering algorithm to improve the convergence speed and accuracy of the filtering algorithm;At the same time,the estimation result of the filtering algorithm is used to dynamically correct the external indication function in a closed loop online to achieve the accuracy of the prior information;This kind of benign closed loop architecture can further improve the overall convergence speed of the algorithm and the robustness of the algorithm.(2).Propose the "gray box" equivalent circuit model structure based on the autoregressive(AR)model,and realize the fusion of the "white box" equivalent circuit model and the "black box" model based on the autoregressive model.For the part of the "gray box" model,the current time series equation is used to express the complex internal voltage structure of the vehicle power battery.While ensuring a certain physical meaning,it also reduces the complexity of the model.From the perspective of model accuracy,the estimation accuracy based on the filtering idea is ensured.(3).Using the non-constant current discharge and random walk discharge data in the NASA open source data set,the algorithm model proposed in this paper is simulated and verified.The random walk data set is more suitable for the actual operating environment of the vehicle power battery.At the same time,fully considering the impact of noise in the environment,adding Gaussian white noise to the experimental data to further verify the time complexity and accuracy of the algorithm.Through time complexity and accuracy,it is proved that the algorithm model proposed in this paper can accurately estimate the SOH of the vehicle power battery under the condition of random discharge.