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Identification of road grade and driver mode under complex driving conditions are the key to the commercial application of the autonomous vehicle. Accurate estimation results are helpful for improved vehicle road handling and ride comfort performances. In this paper, the Monte Carlo (MC) method of operating mode recognition is designed using data collected from the full-vehicle road test to identify the driver’s mode and the road surface level information under complex driving conditions. Based on the movement states of the vehicle system, the models of road excitation and driver style are first constructed. Moreover, the standard classification of driver style and road conditions based on the test data is carried out by using the nonlinear statistical theory and Markov chain MC method.

Dual Clutch Automatic Transmission (DCT) has the characteristics of light weight, fast shift speed and high transmission efficiency. Electric vehicles equipped with dual clutch transmission can effectively improve vehicle power performance and economy. Electro-hydraulic control system, as a key component of transmission, determines the quality of shift. In this paper, an electro - hydraulic control system is designed based on two - speed dry dual clutch transmission of electric vehicle. Firstly, the hydraulic components of the system were selected and calculated based on the vehicle parameters. Secondly, the electro-hydraulic control system of the dual clutch transmission was established according to the transmission control strategy and the matching hydraulic valve body assembly was designed. Then, the key components of the system were simulated to analyze their dynamic shift characteristics and response characteristics.

The rubber mount is a key component of the electric vehicle powertrain mounting system, which can reduce the vibration of the powertrain transmitted to the vehicle body. The rubber mount with second-stage isolation means that a rubber vibration isolator is added to the metal bracket of the original rubber mount. Compared with the original rubber mount, the rubber mount with second-stage isolation has smaller dynamic stiffness and better vibration isolation performance in the high-frequency range. In this paper, the static stiffness of a rubber mount with second-stage isolation is tested, and the high-frequency dynamic characteristics of the rubber mount are calculated using the finite element simulation. According to its characteristics, an equivalent mechanical model including equivalent mass is established.

Electric buses have been used widely as cities' short-range commuter vehicles, because of their excellent power, fuel economy and emission characteristics. However, the lack of a noise masking effect for the traditional internal combustion engine, the high-frequency noise becomes more prominent for the powertrain system. The high-frequency noise gives people an unpleasant feeling on psychological and physiological. To control electric vehicle powertrain noise, the identification of the main noise source of the powertrain is well needed. In this paper, Empirical Mode decomposition (EMD) combined with Independent component Analysis (ICA) and continuous Wavelet transform (CWT) was used to identify the main noise source of the electric bus powertrain. The contribution of each noise source to the overall noise level was calculated and compared.

In order to achieve a good shifting quality of pure electric vehicle dual-clutch transmission, this paper adopts linear active disturbance rejection controller (LADRC) to control the shifting strategy. For the uncertainty of transmission dynamics model parameters and the existence of unknown disturbance effects, the linear expansion state observer (LESO) can be used to estimate and compensate the disturbance. The shift control process is converted into tracking the motor speed and clutch speed trajectory, and the linear feedback control law is used to control the motor torque and the solenoid valve current. The simulation and test results show that the control algorithm is effective and good shifting quality is guaranteed.

This paper proposes a hybrid carrier spread spectrum modulation (HCSM) technique based on dynamically adjusting the spatial zero-vectors action time to suppress the sideband vibro-acoustic responses introduced by the discontinuous pulse-width modulation (DPWM) in permanent magnet synchronous motor (PMSM) for electric vehicles (EVs). Firstly, the space vector principle of DPWM is presented, and the relationship between current harmonics and space zero-vectors is analyzed. Then, the HCSM technique is proposed to suppress the current harmonics and vibro-acoustic responses based on the variation trend of zero-vectors action time in six sectors, and the suppression effect of HCSM on sideband components is predicted by the finite element method. Finally, with a 12-slots/10-poles prototype PMSM, the sideband vibro-acoustic responses and the electric drive system loss caused by DPWM and classical space vector pulse-width modulation (SVPWM) before and after using HCSM are analyzed.

To reduce the heating energy consumption of electric vehicles in winter, a switching control strategy for multiple heating modes formed by three heat sources, including air, motor waste heat, and positive temperature coefficient (PTC) heaters, is designed. Firstly, an integrated thermal management system (ITMS) simulation model for the heat pump air conditioning system, battery thermal management system, and motor thermal management system is established based on the AMESim software. Secondly, the influence of ambient temperature and motor outlet coolant temperature on the heating performance of three cabin heating modes is studied. Specifically, the three cabin heating modes include the pure motor waste heat source heat pump mode, the pure air-source heat pump mode, and the dual heat source heat pump mode with waste heat source and air source. Based on the analysis results, the opening and closing strategies for the three cabin heating modes are 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.