Power Battery Parameter Identification And State Of Charge Estimation

With the continuous development of new energy vehicle technology,pure electric vehicles have become the main direction of global automobile development because of their clean energy and environmental friendliness.As the core component and main power source of new energy vehicles,power battery can effectively improve the mileage of electric vehicles by rationally and comprehensively monitoring the power battery through the battery management system.The state of charge of the power battery is the most important state quantity in the battery management system.The accurate estimation of the battery SOC provides a basis for the management and distribution of battery energy and therefore has very important engineering value.The battery SOC cannot be directly measured,and only the mathematical model of the battery can be established first,and then the control algorithm is used to accurately estimate the SOC of the battery.In order to better understand the characteristics of lithium-ion batteries,a battery test platform was established to test the battery capacity attenuation,maximum available capacity,charge and discharge rate and open circuit voltage at different temperatures.According to the test analysis,a variable-order RC equivalent circuit model is established,which takes into account the applicability and simplicity of the model.The selection of the battery model order is completed based on the BIC criterion.The model parameters are updated in real time using a least squares algorithm with forgetting factors.After the battery model was established,it was verified by the charge and discharge test under constant temperature conditions and variable temperature conditions that the variable order model has higher recognition accuracy than the fixed order model,and the identification accuracy is within 2.8%.After establishing the battery dynamic model,a dual Kalman filter algorithm based on adaptive covariance matching is proposed to estimate the battery SOC in real time.Aiming at the shortcoming of the covariance of system noise in the dual Kalman filter algorithm,the algorithm introduces adaptive covariance matching method to modify the system noise characteristics in real time,thus achieving the purpose of suppressing filter divergence and improving estimation accuracy.Through the experiment of charging and discharging the battery under constant temperature and variable temperature conditions,the extended Kalman filter algorithm,double Kalman filter algorithm and the estimation algorithm proposed in this paper are used to compare and analyze the SOC estimation results,and the double based on adaptive covariance matching is verified.The Kalman filter algorithm has better estimation accuracy,the error is within 3.5%,and the algorithm has strong robustness.Finally,the difference between the SOC estimation method of the power battery pack and the estimation of the single battery is briefly introduced,and a SOC estimation based on the simplified model of the battery pack is proposed.In order to verify the application ability of the algorithm in actual engineering,the battery pack is subjected to a custom charge and discharge test.By collecting the voltage,current and temperature data of the battery pack and introducing it into the battery test equipment,the theoretical value of the SOC is obtained.The data is then input into an adaptive dual Kalman filter algorithm to obtain an estimate.Comparing the estimated value with the theoretical value,the comparison error is within 5%.It can be verified from the comparison results that the algorithm has practical value.