In order to improve the clamping force control accuracy of electro-mechanical braking system of electric vehicles, a multi-closed loop control strategy of electro-mechanical braking based on clamping force is proposed. A detailed EMB mathematical model is established. The sliding mode speed controller and improved fuzzy PID clamping force controller are designed, and the joint simulation model of the speed the clamping force controller is established, and simulation experiments are used to verify the effectiveness of the control strategy. Comparative analysis of three simulation conditions, the maximum adjustment time of the proposed control strategy is 0.254 s and the maximum overshoot is 0.45%. The results of research show that the control strategy designed in this paper can quickly and stably reach the target value of clamping force, has a strong anti-interference capability, has some reference value in the electric vehicle braking control.

To solve the optimization issues of interior permanent magnet synchronous motors (IPMSMs) and ensure a large output torque while minimizing torque ripple and core loss, the multi-objective optimization strategy should be employed. In this study, we took an 8-pole, 48-slot IPMSM as a specimen. First, the width and thickness of the permanent magnet (PM) and the rotor bridge structures were pre-selected as optimization parameters, while torque ripple and core loss were taken as optimization targets. Then, the Taguchi method to perform orthogonal experiments was employed to select the multi-parameter combinations that make the experimental results stable and with little fluctuation. To ensure the optimal results, the function equations were obtained by multivariate nonlinear fitting, while the parameters were optimized by particle swarm optimization (PSO). Finally, the optimal results were verified by the Finite Element Method (FEM). The results show that our proposed hybrid method can provide an optimal design strategy with better performance such as smaller torque ripple and core loss while maintaining a larger output torque.

A high-end permanent magnet (PM) synchronous motor’s cogging torque is a significant performance measure (PMSM). During the running of the motor, excessive cogging torque will amplify noise and vibration. Therefore, the cogging torque must be taken into account while optimizing the design of motors with precise motion control. In this research, we proposed a local optimization-seeking approach (RSM+NSGA-II-LR) based on Response Surface Methodology (RSM) and Non-Dominated Sorting Genetic Algorithm-II (NSGA-II), which reduced the cogging torque of a permanent magnet synchronous motor (SPMSM). To reduce the complexity of optimization and increase its efficiency, the sensitivity analysis method was utilized to identify the structural parameters that had a significant impact on the torque performance. Second, RSM was utilized to fit the functional relationship between the structural parameters and each optimization objective, and NSGA-II was integrated to provide the Pareto solution for each optimization objective. The solution with a greater average torque than the initial motor and the lowest cogging torque was chosen, and a new finite element model (FEM) was created. On the basis of the sensitivity analysis, the structural factors that had the highest influence on the cogging torque were selected, and the RSM is utilized for local optimization to lower the cogging torque as much as feasible. The numerical results demonstrated that the optimization strategy presented in this study effectively reduced the cogging torque of the motor without diminishing the motor’s average torque or increasing its torque ripple.