Energy Management Strategy Of Hybrid Energy Storage Tram Based On The Recognition Of Driving Cycle And Driving Style

Nowadays,hybrid energy storage tram develop rapidly.The development of hybrid energy storage tram has a great significance for emission reduction,energy efficiency augmentation,light rail transit development.The energy management strategy of the hybrid system is the key to decide the performance of the hybrid energy storage tram.The core issue is the coordinated control of the power system and the power distribution between the lithium battery and the super capacitor.Meeting the power demand of the whole vehicle as a prerequisite,the quality of the energy management strategy will directly affect the energy efficiency of the vehicle.However,there are gaps in the existing energy management strategies research that how the driving cycle and the driver’s driving style affect the tram energy efficiency.Therefore,while optimizing energy management strategy of hybrid electric train,it is of great significance and application value to take the driving cycle and the driving style into account.This paper is based on 100% low floor hybrid tram of Tangshan Railway Vehicle Co.,Ltd.,relying on the National Key Research and Development Project “efficiency optimization strategy of the hybrid energy storage module at full working condition” and“development of key technologies and equipment for non-contact network powered urban rail vehicles”,the research on energy management strategy of hybrid energy storage tram based on driving cycle and driving style is carried out.The main research contents are as follows:Firstly,based on the railway condition and tram running data of Paris,Budapest and other cities,principal component analysis was used to obtain the reduced dimension of driving characteristics.K-mean clustering method is applied to cluster four types of driving cycle,the typical tram driving cycle is constructed which has been verified by simulation to ensure that the typical driving cycle can accurately show the actual tram operation characteristics.Secondly,according to the model and topology of key components of hybrid power system,the simulation test platform was built in MATLAB/simulink environment.The topology is combined with the logic threshold control method,the power loss prediction model is built by using the BP neural network.Third,the tram energy management strategy is based on the logic threshold control method,with the lowest power loss and the highest energy efficiency of the system as the optimization objective,the optimal solution under different driving cycle was found by using the particle swarm optimization algorithm based on the power loss prediction model,and the optimal power distribution control parameters of different driving cycle were determined.LVQ recognition algorithm is adopted to analyze the real-time running state of the tram to obtain the type of driving cycle and apply the corresponding optimal power distribution control parameters.Finally,consider the influence of different driving cycle on driver’s driving style recognition,under each driving cycle,fuzzy logic rules are used to identify driving styles.Based with the characteristics of ultracapacitors and lithium batteries,ultracapacitors is determined as main energy compensation components.The compensation mechanism of traction and braking is discussed respectively.A tram energy management strategy based on drivng cycle and driving style is obtained.According to tram parameters and dynamic performance indexes,the selection and modeling of energy storage components for tram were determined and established,the topological structure was decided.And a simulation test platform is developed for hybrid energy storage tram with MATLAB/simulink.The effectiveness of the energy management strategy is verified by the simulation experiment of multi-driving cycle and multi-driving style.The results show that the energy management strategy proposed in this paper can well adapt to different driving cycle and driving style,and improve the tram energy efficiency.