| Recently,with the increasing economic strength of our country,the urban population is increasing and the problem of urban traffic congestion is becoming increasingly prominent.Urban rail transit is becoming more and more popular due to its advantages of large capacity,punctuality,efficiency,and comfort.Contactless powered urban rail vehicles have special advantages.This thesis was based on the national key R & D plan "Quality Inspection,Monitoring and Operation Maintenance Technology of New-type Power Supply Vehicles for Urban Rail Transit and Vehicle-mounted Energy Storage Technology".This thesis takes the contactless power supply of urban rail vehicle composite power storage system as the research object,evaluates the system’s health status,and provides guarantee for safe and reliable punctual operation of urban rail vehicles.Firstly,the equivalent circuit model of the storage element battery and super capacitor of the train composite energy storage system was built.Starting from the analysis of the basic working principle and characteristics of the battery and super capacitor,the thesis compared the commonly used equivalent circuit models,and the appropriate equivalent circuit models combined with the characteristics of urban rail vehicles was established.For the established equivalent circuit model,the forgetting factor least square algorithm was used to identify the model parameters.Based on MATLAB / Simulink,the second-order RC equivalent circuit model and the time-varying equivalent simulation model of super capacitor were built.The model was verified under the set test conditions,and the results showed that the error between the simulated voltage and the actual measured voltage of the two equivalent circuit models established in this thesis was small(within 5%)and could meet the requirements of further research.Secondly,the state-of-health(SOH)of the battery and the state-of-charge(SOC)of the super capacitor were estimated.According to the equivalent circuit model of battery and supercapacitor,the SOC value was estimated by extended Kalman filter.Because of the disadvantage of EKF caused large error of SOC estimation which used Taylor formula to expand the nonlinear system,the adaptive unscented Kalman filter(AUKF)was used to improve it.The test results showed that the estimation accuracy of AUKF algorithm was greatly improved compared to EKF algorithm.In addition,according to the three evaluation indexes of battery SOH,this thesis applied the adaptive unscented Kalman filter algorithm to jointly estimate its SOC and SOH values from the perspective of the battery’s actual capacity and ohmic internal resistance.SOC was taken as the state variable of the system,and the ohmic internal resistance and capacity were regarded as the fixed values to estimate the SOH of the battery.The experiments verified that the AUKF algorithm could effectively predict the internal resistance and capacity degradation of the battery.Finally,the particle filter algorithm was used to estimate the number of charge and discharge cycles of the battery.The number of battery charge and discharge cycles was another important evaluation index of battery SOH.As the number of charge and discharge cycles increased,its capacity gradually decreased.Based on 4 sets of battery monitoring data,this thesis used particle filter algorithm to track the battery degradation and genetic algorithm was introduced to solve the problem of particle degradation in the particle filter algorithm.The simulation experiment was carried out by programming on MATLAB platform.The experimental resulted show that the fusion algorithm of genetic particle filtering could better predict the decay process of battery capacity than the traditional particle filtering algorithm,and had obvious advantages in the accuracy of prediction results.The prediction accuracy of the number of charge and discharge cycles could be within 3%. |
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