Research On Lifepo₄ Battery Modeling And SOC Estimation Based On Double Kalman Filtering Algorithm

In today's society,with the increasing dependence on energy and environment,energy conservation and environmental protection have been more and more valued by people.The new energy vehicle has become the key research direction of the world's automotive industry field.Power battery as a power source of electric vehicles is an influential factor in the development of electric vehicles.Lithium battery has become more and more widely used in new energy vehicle because of its its superior performance as an electric vehicle power battery.However,there is a problem that the lithium iron phosphate batteries have poor homogeneity among the single cells.So it is critical to design a battery management system(BMS)that manages the battery pack.An accurate estimate of the state of charge(SOC)of the battery is the core and key to the efficient operation of the battery management system.In the dissertation,a method for the state of charge estimation of lithium battery based on the cubature kalman filter has been researched.The main work and results are as follows:1.First of all,the research background of the battery SOC estimation was introduced in detail,and then the advantages and characteristics of the lithium iron phosphate batteries are introduced,then introduced the current research on batteries and battery SOC estimation model of the status quo,for later on in this paper,the research object of lithium iron phosphate batteries battery modeling and SOC estimation basis has been established.2.For four equivalent model of the battery are analyzed and compared,finally confirmed the second order RC model as in this paper,we study the battery model,considering the identity between monomer battery is poorer,therefore,to improve in the second order RC model,second order RC model is put forward,and the model formula,and simulated in MATLAB,the accuracy of the model was verified.3.Introduces in detail the basic principle of kalman algorithm,and on the basis of the classical kalman filtering algorithm is applicable to nonlinear system of extended kalman filtering algorithm introduces the principle and formula is deduced,and the extended kalman filtering algorithm in applications of battery system.Using classical kalman filter and extended kalman filter with the combination of double kalman filtering algorithm joint estimation batteries and battery SOC model parameters,through the experiment and the matlab simulation in cross flow discharge conditions and pulse discharge both double kalman filtering algorithm is proved under the condition of joint estimation accuracy of batteries and battery SOC model parameter method4.Battery SOC estimation method based on CKF and the method based on UKF estimated battery SOC estimation method and were compared,finally found by simulation experiments based on CKF estimate battery SOC has higher accuracy.5.Using the least squares support vector institutions of LSSVM model,on the basis of the implementation of the battery SOC estimation,and introduces the particle swarm optimization algorithm(PSO)in order to improve training efficiency and the precision of model.Through the constant exile electric experiment and pulse charging and discharging experiments validate the PSO-the effectiveness of LSSVM method to estimate the battery SOC.