The design of intelligent vehicle trajectory tracking controllers still has some problems, such as parameter uncertainty and time consumption. To improve the tracking accuracy of the trajectory tracking controller and reduce its computational complexity, an adaptive MPC trajectory tracking control method with a variable prediction horizon is proposed. Firstly, a three-degree-of-freedom vehicle dynamics model is constructed, and the design is improved based on the ordinary MPC controller. Secondly, several groups of different constant vehicle speeds are selected to compare the tracking effect of the ordinary MPC and the improved controller. Then, low speed (30 km/h) and high speed (100 km/h) are selected as representative speeds to solve the calculation time of the controller. The relationship between vehicle speed and prediction horizon is analyzed, and curve fitting is carried out. An adaptive trajectory tracking controller is designed. Finally, it is verified by CarSim and MATLAB/Simulink co-simulation. The results show that compared with ordinary MPC, the improved adaptive trajectory tracking controller can maintain good tracking accuracy and stability according to the speed change and improve the computational efficiency of the controller.

To address the challenge of heat dissipation in electric vehicle in-wheel motors within limited space, this study conducted temperature field analysis and cooling structure optimization based on magnetic–thermal coupling. The three-dimensional finite element model and the magnetic–thermal-coupled mathematical models of synchronous permanent magnet in-wheel motors were established. Using a coupled electromagnetic–thermal finite element analysis method, numerical simulations were performed to investigate the transient temperature fields of the in-wheel motor under different driving conditions. The effects of three different cooling channel structures on the temperature rise and the pressure drop of the in-wheel motor were compared, and a parallel channel structure suitable for the in-wheel motor was selected. The influences of channel quantity, channel width, and coolant flow rate on the temperature field were analyzed. Furthermore, a multi-objective optimization of the cooling structure was carried out using the NSGA-II genetic algorithm. The simulation results demonstrated a significant improvement in the overall thermal performance of the optimized cooling structure. The maximum temperature of the hub motor decreased by 2.25% and 3.32% under the rated and peak speeds, respectively, while the pressure drop in the water channel was reduced by 58.52%. This study provides a theoretical reference for temperature field calculation and cooling structure design of hub motors.