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Because of the advantages of excellent power, fuel economy and zero-emission characteristics, electric buses have been used widely as cities’ short-range commuter vehicles. However, the high-frequency noise becomes more prominent for the powertrain system of the electric bus due to the lack of noise masking effect for the traditional internal combustion engine. To improve the noise characteristic of electric bus powertrain, the identification of the main noise source of the powertrain is well needed. In this paper, the effects of the motor and transmission on the noise level of the electric bus powertrain have been studied using lead packaging method. The variations of acoustical power level of the powertrain according to different rotation speed and torque under the conditions of only motor covered and only transmission covered have been discussed.

Due to the complexity and timeliness of the dual power source control system for range extended electric vehicles, a real-time predictive fuzzy energy management strategy based on speed prediction, which comprehensively takes into account the demand power of auxiliary power unit, future average speed and driving distance is proposed in this work. Firstly, to improve the topicality and accuracy of the control system, the convolutional neural network with long short-term memory neural network (CNN-LSTM) algorithm is adopted to predict the future driving speed by the speed features and adjacent speeds.