Charging Control Of Lithium Iron Phosphate Battery For Medium And Low Speed Maglev Train

With the rapid development of medium and low speed maglev trains in China,higher requirements are put forward for the battery pack of the energy storage device in the train auxiliary power supply system and the charger that provides electric energy for it.There are some problems in the application of battery pack in maglev train,such as high replacement cost,high requirements for safety performance and strict requirements for charge and discharge rate.The charging process of battery not only affects its charging rate,but also directly affects its service characteristics.Therefore,it is an important research content to control the charging process of the battery pack on medium and low-speed maglev trains,improve the charging rate of battery and prolong the service life of battery.This dissertation takes the on-board lithium iron phosphate battery pack and charger of Guangdong Qingyuan maglev train as the research object.The internal structure of the battery is analyzed and the battery charging control strategy is studied.On this basis,the control mode of the train charger is studied.Firstly,the model structure and parameters of lithium iron phosphate battery are analyzed,and the second-order RC equivalent circuit model of the battery is established.The extended Kalman filtering(EKF)algorithm is used to identify the parameters of the battery model through the discharge test,and the adaptive extended Kalman filtering(AEKF)algorithm is used to estimate the state of charge(SOC)of the battery by introducing noise covariance.According to the obtained results,the simulation model of lithium iron phosphate battery is established,and the parameter identification results and SOC estimation results are introduced into the model.The correctness of the model is verified by comparing the discharge curve of the battery model with the actual discharge curve of the battery.Secondly,this dissertation adopts a five-stage constant current constant voltage charging mode based on SOC.It solves the problem of large polarization voltage when the threshold voltage is used as the switching mark of charging stage in the process of battery charging.In addition,aiming at the different polarization effects of lithium iron phosphate battery when charging with different current magnification,this dissertation takes the charging time and charging polarization voltage as the optimization objectives,the genetic algorithm is used to optimize the charging current in each stage.In the simulation,the battery charging results before and after optimization are compared to verify that the optimized charging mode reduces the charging polarization voltage on the basis of ensuring the charging rate.Finally,the basic topology and control mode of the charger of the Guangdong Qingyuan maglev train is studied,and the simulation model of the charger is established.The PI controller in the double closed-loop control is optimized through fuzzy control,and the optimization effect is verified.Cascade the charger model with the lithium iron phosphate battery model,compare the simulated output waveform with the actual output waveform of the charger,and verify that the output performance under various working conditions meets the actual operation requirements of the train,and the output of the charger during the charging process meets the output accuracy proposed by the charging strategy.