Research On Fractional Order Modeling And Joint State Estimation Strategy Of Lithium Battery

As a major component of the "dual carbon" policy,the vigorous development of lithium batteries in its industry is unstoppable.However,high-precision estimation of the SOC and SOH of lithium batteries is a key and difficult point in battery management systems.With the development of science and technology,in-depth exploration of modeling and state estimation techniques for lithium batteries will provide us with a more efficient management method,thereby ensuring the safety and reliability of lithium battery operation.The accuracy of SOC and SOH estimation for lithium batteries is deeply influenced by the way batteries are modeled and the methods of battery state estimation.Therefore,this article focuses on the modeling,parameter identification,and state estimation of lithium batteries.Main research content:(1)In order to reduce the terminal voltage error of the lithium battery model and improve the accuracy of the state estimation of the lithium battery,based on the second-order RC integer order model,the fractional order theory is introduced to replace the ideal capacitor with the fractional order capacitor,so as to build the second-order RC fractional order model of the lithium battery,effectively balance the contradiction between the model accuracy and the model complexity,and use the G-L definition to discretization the fractional order model,This lays the foundation for parameter identification and joint state estimation of lithium battery models.(2)In response to the problem of traditional algorithms easily falling into local optima when identifying lithium battery model parameters,a lithium battery fractional order model parameter identification method based on AGA-PSO algorithm is proposed by combining adaptive genetic algorithm(AGA)and particle swarm optimization(PSO).This method liberates the global optimal neighborhood of model parameters obtained from PSO algorithm to AGA algorithm for genetic operation,Furthermore,the global optimal solution for the parameters of the lithium battery model is obtained.(3)Based on the established fractional order model of lithium batteries,a joint SOC SOH state estimation method using the Schmidt orthogonal transform based traceless particle filter algorithm(FOSOUPF)is proposed.This method is based on the unscented particle filter algorithm and combines the idea of Schmidt orthogonal transformation during the sampling process to reduce the number of sampling points in unscented particle filter.It can effectively solve the problems in unscented particle filter algorithm and provide a new approach for the application of unscented particle filter algorithm.A fractional order model of lithium batteries was constructed in the Matlab/Simulink environment and the proposed algorithm was simulated and validated.The simulation results show that the fractional order model of lithium batteries has higher accuracy than the integer order model;The AGA-PSO algorithm has a short time for parameter identification,high robustness,and the identification results still maintain high accuracy under different battery operating conditions;Using FOSOUPF algorithm to jointly estimate the state of lithium battery has higher precision,better robustness,and reduces the computational complexity of the algorithm.The results obtained through the above research provide new ideas for lithium battery modeling and state estimation,effectively solving the problems of low accuracy of conventional models and large state estimation errors.