Research And Design Of Energy Storage Lithium Battery Management System

The characteristics of lithium-ion batteries themselves are relatively complex.If the charging and health status during work cannot be estimated in real-time,it will lead to overcharging,discharging,and other issues of lithium-ion batteries,which can lead to safety accidents such as overheating and thermal runaway.Studying an energy storage battery management system that can accurately estimate the charge and health status of lithium batteries is of great significance.This article focuses on the problems existing in the current energy storage battery management system and conducts the following research work:(1)Analyze the working principle and technical parameters of energy storage lithium-ion batteries,construct their second-order RC equivalent circuit model,and calibrate the relationship between SOC(battery state of charge)and open circuit voltage through pulse discharge experiments,further identifying the model parameters.Secondly,multiple innovations of the Multi Innovation Least Squares(MILS)algorithm are used to correct the difference between the output observation values and the model estimation values,solving the problems of low identification accuracy and slow convergence speed of the recursive least squares method.Compared with the recursive least square method,the MILS algorithm has the advantages of high accuracy and fast Rate of convergence in battery parameter identification.(2)Study the joint estimation problem of battery charge and health status.We studied the iterative unscented Kalman particle filter algorithm(RTS-IUPF)with RTS smoothing structure.By optimizing the suggested distribution function of the particle filter algorithm through iterative unscented Kalman,we solved the problem of particle poverty in the later stage of traditional particle filter algorithms.Compared with traditional algorithms,the accuracy and Rate of convergence of the proposed algorithm are improved in the estimation of battery state of charge(SOC)and battery state of health(SOH).(3)Choose TMS320F28035 as the main controller with powerful functions,fast running speed,and convenient algorithm transplantation.The peripheral circuit composed of a high-precision BQ76940 chip is used as the front-end analog signal acquisition unit,and reliable data transmission between the lower computer and the upper computer is achieved through CAN bus.(4)Analyze the estimated results of the algorithm studied on the actual platform.The results show that the absolute error of the estimated battery state of charge(SOC)does not exceed 1.911%,and the absolute error of the estimated battery state of health(SOH)is less than 4%,indicating strong overall performance.This thesis has 49 figures,14 tables,and 63 references.