The Taguchi method is adopted to optimize the structure of high-speed permanent magnet synchronous motor for an electric vehicle with rated speed of 5000 r/min and rated power of 60 kW, in an attempt to improve the electromagnetic performance and efficiency of high-speed permanent magnet synchronous motor for electric vehicles. By analyzing the effect of the optimization variables on the motor iron loss, the average electromagnetic torque and the torque ripple, this paper obtaines the optimal optimization solution. The iron loss model of motor is established, and the Sine-equivalence method, harmonic-component method and FEA method are used to calculate the iron loss of stator respectively. Finally, the rationality of iron loss analysis method of the motor is verified by comparing calculated results and measured results.

In order to improve the efficiency of flat wire winding motor, the mathematical calculation model of winding loss is established, this article analyzes the influence of the winding layers as well as parallel branches on the motor performance. According to the loss generation principle, the article proposes winding transposition and the method of loss suppression to improve the winding structure, and analyzes the influence of different line types on the winding loss. For the common working conditions of the motor, the article compares the performance and efficiency of different motor designs. The simulation results show that the optimization method proposed in this article can reduce the winding loss and improve the efficiency of flat wire motor. Finally, the correctness of the simulation analysis is verified by experiments.

To improve the lateral stability of vehicles on middle and low adhesion roads, a coordinated controller of Active Front Steering (AFS) and Direct Yaw-moment Control (DYC) based on Model Predictive Control (MPC) is constructed. The controller is composed of an upper decision-making layer and a lower executive layer. The upper layer is based on MPC to obtain the required additional yaw moment, and the lower one aims to correct the steering angles of front wheels or apply the wheel cylinder pressure by AFS and DYC. The effectiveness of the control strategies in Double Lane Change (DLC) scenarios is validated, which reveals that, with the road adhesion coefficient being 0.25, the vehicle slip angle and yaw rate are stabilized within -3.5°~3.5° and -16~16 (°)/s respectively, the longitudinal vehicle speed stabilized at roughly 88 km/h. With a road adhesion coefficient of 0.4, the relevant stability indices, including the longitudinal vehicle speed, vehicle slip angle and yaw rate, are significantly improved by the coordinative strategy. Analysis indicates that this AFS-DYC coordinated control strategy can significantly improve vehicle handling stability under medium and low adhesion coefficient condition.

In order to meet the real-time wear monitoring demand of smart tires, this paper presents a new tire wear detection method, which uses a three-axis acceleration sensor integrated device to collect the acceleration waveform of the tire, and uses the Caesars maximum normal variance method to perform principal component analysis on the acceleration waveform characteristics to make waveform feature value extraction and filtering based on the analysis results. The filtered feature value data is trained through the Error Back Propagation (BP) neural network, to achieve real-time detection of tire wear values. The test and comparison based on the real vehicle detection data show that the algorithm can reduce the average error of wear detection to 0.1 mm under low computational power demand.

The emission characteristics under different operating modes (engine mode and hybrid mode) and different test cycles (C-WTVC and CHTC) of a heavy-duty HEV was studies on the chassis dynamometer and Portable Emissions Measurement System (PEMS), emission performance of the HEV was analyzed in combination of the working condition characteristic parameters. The results show that NO_x emission of the test vehicle under hybrid mode is higher than that under engine mode, while the CO emission under hybrid mode is lower than engine mode. Under engine mode, the NO_x emission of CHTC is higher than that of C-WTVC. However, under hybrid mode, the NO_x emission of CHTC is lower than C-WTVC. Under engine mode, CO emission is concentrated at low speed and low load condition, while under hybrid mode, CO emission is concentrated at high speed and high load condition.

In order to bring the energy saving potential of urban bus hybrid system to full play under variable passenger capacity and complex conditions during passenger transportation, the paper presents a powertrain parameter optimization method of hybrid electric bus based on operation data. Firstly, based on the Internet of Vechicle(IOV) data, K-Means clustering analysis using Kernel Principal Component Analysis (KPCA) and Particle Swarm Optimization (PSO) is used to construct representative driving conditions of an urban bus. In view of the characteristic of random variation of passenger number of urban bus in operation, a hybrid electric bus powertrain parameter bi-level optimization model is constructed based on the Optimal Latin Hypercube Design (Opt-LHD), Opt-LHD is used in the inner layer to generate number of passengers, the optimal control strategy of engine is used to transfer system response to outer layer optimization algorithm. Simulation results show that the optimized powertrain parameters have better adaptability to uncertain factors, and the fuel consumption is reduced by 9.97% compared with that before optimization.