Research On Fault Diagnosis And Life Prediction Of Super Capacitor For Non-contact Network Urban Rail Vehicles

With the increase of urban traffic congestion and environmental pollution,non-contact urban rail vehicles with the characteristics of green,environmental protection,and large transportation capacity have become the first choice of urban public transportation.At present,the main power sources for non-contact urban rail trains are mainly lithium batteries and supercapacitors.Among them,supercapacitors have the advantages of instantaneous high power and long cycle life,which makes urban rail trains using supercapacitors as the main power source have a wider application prospects.As an important part of the energy storage system,the super capacitor group needs to solve the problem of unbalanced voltage of a large number of super capacitors connected in series,and its working environment is harsh,prone to failure,and affecting the operation of urban rail trains.Therefore,this article mainly studies three aspects of supercapacitors voltage equalization,fault diagnosis and life prediction of super capacitor groups:(1)In order to reduce the failure rate of super capacitors and improve the service life of super capacitors,a supercapacitor voltage equalization strategy with a first-to-end sequential coupling array magnetic integrated converter is proposed.Compared with the previous methods,it adopts a modular design idea and its control circuit structure is simple,and the effect is better in high-power and high-voltage situations such as urban rail trains,which can better solve the problem of series voltage equalization of vehicle supercapacitor.(2)Aiming at the problems of high difficulty in parameter identification and excessive data requirement in the existing supercapacitor fault diagnosis methods,a method based on PCA and bisecting K-means clustering is proposed.Firstly,a fault diagnosis model with selflearning ability is established.The principal component analysis method is used to extract the fault features.Secondly,The mahalanobis distance method is used to calculate the sensitive threshold to identify the state of the supercapacitor.Finally,the Bisecting K-means clustering is used to diagnose the fault of the supercapacitor,and the simulation is performed on the operating conditions of supercapacitor group.(3)The energy storage system of the urban rail train will be charged and discharged during operation.The supercapacitor banks of the energy storage system are subject to cyclical fluctuations in the junction temperature.Frequent fluctuation of junction temperature in periodic cycle can damage the supercapacitor,which makes the supercapacitor component one of the most vulnerable devices in train energy storage system.In order to calculate the junction temperature of the supercapacitor online,firstly,the equivalent thermal network model is established,then the junction temperature curve obtained by the thermal model is extracted by real-time rainflow counting method,and combines with the proposed life prediction model,realizing life prediction of supercapacitor banks.Finally,through simulation experiments,the life of the entire vehicle supercapacitor group under high power operation is predicted,and the cycle life prediction results of the entire vehicle supercapacitor group at different initial temperatures are compared.