Research On State Estimation Method Of Lithium Ion Battery Based On Electro-thermal Model

As an important part of energy storage system,lithium ion battery has been widely used in many industrial fields due to its superior performance.In order to ensure the safe and stable use of lithium ion battery,it is necessary to equip a battery management system(BMS)to accurately estimate its state.In this thesis,lithium battery was taken as the research object,and an electric-thermal coupling model was established considering both electrical and thermal characteristics.The SOC,SOP and SOH of lithium battery were taken as the research objectives,and the following research work was carried out:(1)A simulation model of lithium ion battery considering thermal characteristics was proposed and established.The model is combined with the temperature dependent RC equivalent circuit model and the lumped parameter two-state thermal model of lithium ion battery.The heat generation rate is calculated using the parameters of the equivalent circuit model,and the thermal equivalent circuit model uses the heat generation rate to calculate the battery temperature.The relationship between electrical parameters and open circuit voltage at different temperatures is obtained by identifying the electrothermal model parameters at different temperatures.Finally,the consistency of simulation results and the accuracy of parameter identification results are verified by experiments,and the terminal voltage and temperature prediction results of the dynamic current coupling model are analyzed.(2)In order to obtain accurate real-time SOC and SOP estimation,a joint SOCSOP estimation method based on electrothermal coupling model and multi-parameter constraint was proposed.In this thesis,the relationship between the unscented Kalman filter(UKF)algorithm and gaussian process regression is established,and an SOC estimation method is proposed by combining the UKF algorithm based on Gaussian process regression with the electrothermal coupling model.Temperature is introduced as one of the important constraints of SOP.Considering the internal relationship between SOC and SOP and the influence of voltage,temperature and SOC on SOP estimation,the power state of battery is predicted under multi-parameter constraints.The effectiveness of the method is verified by experiments.Experimental and simulation results show that this method is simple and has high estimation accuracy.(3)A SOH estimation method for lithium ion battery based on NARX neural network model was proposed.The influence of unavoidable factors such as temperature and current on battery working process was studied by experimental data,and the external input nonlinear autoregressive model(NARX)was established to estimate SOH.On the basis of appropriately selecting training data and test data,SWPSO algorithm was introduced for training.The accuracy of SWPSO-NARX SOH estimation was verified by test data.Finally,combined with the SOC estimation method,the SOH joint estimation algorithm is proposed,and the accuracy and practical value of the joint estimation algorithm are verified by experiments.