State Estimation Of Lithium-ion Battery Pack For Electric Vehicle Based On Electrochemical Impedance Spectroscopy

In order to achieve the goal of carbon peak and carbon neutrality in China,electric vehicles as a new energy have broad development prospects.Lithium ion power batteries are widely used in electric vehicles due to their characteristics of large capacity,high safety performance and long service life.The power battery management of electric vehicle can make the vehicle run safely and efficiently,and the accurate estimation of the battery state is one of the most important parts of the battery management system.On the basis of parameter identification,the estimation of the aging degree(SOH)and the state of charge(SOC)of lithium ion battery was completed.Then,the fractional order equivalent circuit of the battery in the time domain was deduced based on the electrochemical impedance spectrum,and the co-estimation of SOC and SOH of the lithium battery was realized based on the fractional order adaptive untraceable Kalman filter and extended Coleman filter algorithm,so as to improve the accuracy and robustness of the state estimation of the lithium ion battery of electric vehicles taking into account the aging degree.Firstly,based on the structure of the lithium iron phosphate battery,the charging and discharging principle of the battery was described,the experimental platform for the battery impedance test was designed,and the fractional battery impedance model was established based on electrochemical impedance spectrum,which took into account the "diffusion effect" of the battery.In order to improve the identification accuracy,the initial values of model parameters were determined according to the geometric shape of impedance spectrum,and the nonlinear least squares L-M method was used to complete the identification of impedance model parameters.The identification results were compared with those of commercial software to prove the accuracy of the designed identification method.Secondly,through the experiment for different cycles and electrochemical impedance spectroscopy(EIS)under the battery SOC value curve,including battery impedance change mechanism based on electrochemical theory,in-depth analysis of cycle number,the influence law of SOC value on EIS curves,extract the corresponding sensitive parameters and typical frequency,determine the computational expressions of the battery SOC and SOH,The reliability of the formula is verified by comparing with experimental values.Thirdly,based on the equivalent circuit model of electrochemical impedance spectroscopy in the frequency domain,the corresponding fractional equivalent circuit model of lithium battery in the time domain was established.The port voltage equation of fractional differential equivalent circuit is constructed in differential form,and the analytical expression of voltage equation in time domain is determined through the analysis of fractional differential theory,and the accuracy and reliability of the fractional differential equivalent circuit established in this thesis are verified by the battery test experiment.Finally,based on the fractional order calculus theory,the battery state equation and measurement equation were constructed,and the fractional order adaptive unscented Kalman filter(FO-AUKF)algorithm was used to estimate the SOC of lithium battery and the extended Kalman filter(EKF)algorithm was used to estimate the SOH of lithium battery with the principle of Kalman filter.Collaborative online estimation of SOC and SOH for lithium batteries is realized.The feasibility and accuracy of the proposed collaborative estimation method are verified by the experimental comparison of multiple static and dynamic conditions.In this thesis,the state estimation of lithium ion battery is completed from the perspective of frequency domain and time domain,and the calculation expression and estimation method of SOC and SOH are obtained,which provides a basis for accurate prediction of battery state in laboratory or online.