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.

In this article, a novel electromagnetic-frictional integrated brake is proposed, and its structure and working principle are introduced. The geometric model and mathematical models of integrated brake were established, and the multi-field coupling mechanism of integrated brake were analyzed. With BYD Qin as a reference vehicle, the boundary conditions of thermal load and force load of integrated brake were determined according to its structure and performance parameters. Based on the COMSOL software, numerical coupling calculations of electric, magnetic, thermal, and solid fields of integrated brake were carried out respectively in the emergency and downhill braking at a constant speed. The axial, circumferential, and radial temperature distributions of integrated brake disc were analyzed respectively, and they were compared with those of the traditional friction brake disc. The analysis results show that the proposed integrated brake can effectively improve the heat fading resistance of automotive brake during emergency and continuous braking. Under the two braking conditions, the temperature rise of friction brake was faster than that of an electromagnetic brake, and the effect of the electromagnetic brake on temperature rise of integrated brake was small.

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.

To improve braking energy recovery in pure electric vehicles while maintaining a reasonable braking-force distribution, this study proposes a regenerative braking strategy based on a grey wolf optimizer (GWO)-optimized fuzzy control. A single-motor front-wheel-drive pure electric vehicle is modelled in terms of vehicle longitudinal dynamics, motor characteristics, and battery state of charge (SOC). A front–rear braking-force distribution strategy is developed based on the ideal braking-force distribution I-curve and ECE regulation constraints. A Mamdani fuzzy controller is then designed with braking intensity z and battery SOC as inputs and the front-axle regenerative braking-force distribution coefficient k as the output, enabling coordinated allocation between front-axle regenerative braking and mechanical braking. To reduce the dependence of fuzzy rules on expert experience, the GWO is used to optimize 25 fuzzy rules, and the proposed strategy is verified in MATLAB R2023b under a typical urban driving cycle. The results show that all strategies satisfy the braking demand. Compared with the unoptimized fuzzy control strategy, the optimized strategy reduces SOC consumption by 5.15% and increases recovered braking energy by 59.56%, indicating improved regenerative braking performance.

Real-time and accurate estimation of the road adhesion coefficient is pivotal for enhancing vehicle active safety systems (e.g. ABS, ESP) and ensuring reliable autonomous driving decisions. However, conventional Kalman filter-based methods (EKF, UKF) suffer from slow convergence, insufficient accuracy, and poor robustness under sudden road changes—rooted in fixed process noise assumptions and inadequate adaptation to strong nonlinear tire dynamics. To address these issues, this study proposes an innovation-based adaptive extended Kalman filter (AEKF) for real-time estimation of four-wheel independent adhesion coefficients. A 3-degree-of-freedom (3-DOF) vehicle dynamics model integrated with the Dugoff tire model is established to characterize tire forces under combined slip, forming a nonlinear state-space system with four-wheel adhesion coefficients as state variables and sensor-measurable longitudinal acceleration, lateral acceleration, and yaw rate as observations. The AEKF’s core innovation is a normalized prediction residual statistic-based adaptive mechanism, dynamically adjusting process noise covariance to enhance responsiveness and robustness. Comprehensive CarSim/Simulink co-simulations (high/low adhesion, abrupt transitions) show the AEKF outperforms EKF/UKF, reducing the average root mean square error (RMSE) by 61.94%–70.86% compared with the traditional EKF, and 36.61%–74.24% against the UKF, with convergence time shortened by over 50% under sudden changes. It provides a reliable perception basis for real-time control of active safety systems and decision-making of autonomous driving, effectively improving vehicle stability and collision avoidance capability.