In order to accurately reflect the driving motion characteristics of vehicles on the real road, this paper proposes a vehicle driving cycle construction method based on Markov fusion online map information. In this paper, the actual collected vehicle driving data were cleaned and segmented that were clustered and analyzed by Principal Component Analysis (PCA) method and K-Means clustering method, and a typical working condition fragment library based on Markov was established. The road information of the planned path on the online map was integrated in the fragment database, and the road driving condition of the vehicle was constructed. An electric vehicle was taken as the research object for simulation and analysis, the results show that the energy consumption of online map planning driving condition based on Markov fragment library is closer to the actual road condition than that of online map basic planning condition. The error of characteristic parameters is only 4.29%, and the error of energy consumption is only 4.09%.

In order to improve the recognition accuracy and pre-judgment ability of autonomous vehicles in high-speed dynamic complex traffic scenarios, The lane-changing intention recognition model based on convolutional residual Bidirectional Long Short-Term Memory (BiLSTM) with fusion attention mechanism is proposed. It uses the one-dimensional Convolutional Neural Network (CNN) to extract the vehicle’s motion state features. The constructed feature vector is used as the input information of BiLSTM network. The residual connection is used to solve the problems of optimization bottlenecks and gradient disappearance in multi-layer BiLSTM network. It’s achieved to a adjust the weight of the output of the residual BiLSTM network at different moments with the attention mechanism. And the driving intent probability can be calculated by the Softmax function. The validity of the model is verified by using the expressway data set in NGSIM, the performance and effect of the other 4 models are compared with the model. The results show that the recognition accuracy of the lane-changing intention is the highest, which reaches 97.44%, and prediction accuracy of the vehicle’s lane-changing intention is 90% and higher within 2.5 s before the changing lanes, it shows that the model has better intent recognition accuracy and prediction ability.

This paper presentes a deep reinforcement learning based energy management strategy for Plug-in Hybrid Electric Vehicle (PHEV) of the THS-III platform. Firstly, a forward simulation model of the vehicle was built using MATLAB/Simulink. Secondly, a Markov process for vehicle energy management and a deep reinforcement learning algorithm were built. Finally, simulation and verification were carried out using WLTC-Class3 and ACC-60. The simulation results indicate that compared with the rule-based energy management strategy, the deep reinforcement learning-based energy management strategy saves 16.51% in cost and 15.56% in fuel consumption under WLTC-Class3, and saves 31.95% in cost and 29.96% in fuel consumption under ACC-60.

In order to realize the torque control accuracy of the electric drive system under the influence of the deviation of permanent magnet supply, the temperature deviation of permanent magnet, the current sensor accuracy deviation, the voltage sensor accuracy deviation, the calibration temperature management deviation and the initial angle detection influence deviation, simulation and test were conducted to obtain the influence factors under the independent action of the above factors, and statistical principle was used to obtain the torque control accuracy range of the comprehensive influence factors, which was tested and verified on the prototype. Test results show that the accuracy of the torque value calculated by this method is within the range of -1.1%~2.8%, which meets the product requirements.

To solve the problem that the traditional shift schedule of two-speed transmissions for battery electric vehicles cannot obtain the optimal shift performance under different conditions, this paper proposes a knowledge-based method for shift decision-making. Firstly, the dynamic model of battery electric vehicles was established, the optimal shift data was obtained by dynamic programming. The two-parameter shift schedule was extracted based on support vector machine. Secondly, the data of manual shift was collected to build an exclusive database. The intelligent shift decision model was built based on the long short-term memory network, and the shift decision model was updated online by over-the-air technology. Finally the proposed shift decision method was verified by simulation. The results show that long short-term memory network model has high shift decision accuracy, the proposed knowledge-based shift decision method has better shifting performance than traditional two-parameter shift schedule.

For the transient current overloading when the permanent magnet fan for heat pump is powered down and resumed, this paper proposes a sensorless coasting startup strategy based on current resonant tracking control. Utilizing the current suppression control method and the quasi-resonant tracking method, a two-stage starting process was designed including the current-loop suppression tracking and the speed-loop synchronous recovery for motor free coasting condition, to quickly resume the current-speed closed-loop vector control and make the start smooth. The simulation and the testing results showed that in the medium and high-speed coasting, the proposed starting strategy can effectively reduce the steady-state error of the current and improve its dynamic response, and improve the estimation accuracy of the sensorless algorithm for the current tracking process, so as to enhance the smoothness and reliability of the coasting starting process.

In order to solve the problem of low accuracy of oil stirring loss calculation caused by turbulence in the complex multi-pair gearing reducer, based on the Moving Particle Semi-implicit (MPS) method of Lagrangian system, and combined with Smagorinsky turbulence model and the calculation method of turbulent shear stress on polyhedral wall, the calculation accuracy of stirring loss was improved, which was verified by simulation and test. Finally, the influence of different oil stirring loss on the efficiency of recirculating conditions was analyzed. Research results show that the method can effectively improve the calculation accuracy from 47% to 91% on average of the oil stirring loss of the reducer, and the improvement effect is especially obvious in high-speed conditions. In the recirculating condition, the oil stirring loss has a significant impact on the reducer efficiency, and reducing the oil stirring loss can effectively improve the reducer efficiency in the recirculating condition.

In order to study the head crash conditions of two-wheeler cyclist in traffic accident, this paper verified the car-to-two-wheeler crash model by accident reconstruction, and analyzed the differences of head impact area, speed and angle on sedan and SUV models, proposed the evaluation suggestions in combination with the existing pedestrian protection evaluation requirements. The results show that the head crash speed of the cyclist is not different from that of pedestrians. For the vehicle with Bonnet Leading Edge (BLE) height bellow 850 mm, it is recommended to expand the Wrap Around Distance (WAD) of head crash area from 2 100 to 2 300 mm, and the collision angle is 45°. For vehicles with BLE height higher than 850 mm, there is no need to expand the area.

In order to obtain reliable analytical calculation methods for the steady-state lateral stiffness considering the end constraints of variable diameter springs, the analytical calculation formulas of spring end restraint moment were obtained by using Moore integral according to the mechanical model of variable diameter spring. Based on the restraint moment of variable diameter spring, the analytical calculation formulas of steady-state lateral flexibility and stiffness of variable diameter spring were derived. With a case study, the steady-state lateral stiffness of variable diameter spring was analyzed and simulated, the calculated values are consistent with the simulated values, the relative deviations of flexibility and stiffness are within 0.21%, which means that the analytical formulas are correct. Finally, based on the steady-state lateral stiffness of variable diameter spring, the analytical calculation formula of transverse deflection of variable diameter spring at any position under transverse load was established, the deviation between the calculated value and the simulation value is within 0.21%, indicating that the analytical formula of the relative lateral deflection of the spring is correct.