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 novel driving-behavior-oriented method is proposed in this paper for improving trajectory planning performance of autonomous vehicle driving on urban structural road. Differ from the irregularity and unpredictability of escaping a maze or travelling on off-road, the driving on road emphasizes more on the compliance of road traffic rules and the satisfaction of passenger comfort rather than purely pursuing the shortest route or the shortest time. Therefore, the driving-behavior-oriented framework is employed in trajectory planning, which divides trajectory into lane change, turn and U-turn, according to the basic traffic rules and the daily behaviors of drivers. The presented approach mainly includes basic path planning, fast-bias RRT path planning and velocity planning. The basic path planning consists of lane change, turn and U-turn behaviors, which generates smooth path with continuous curvature. In order to ensure the completeness of the programming algorithm, a fast-bias RRT (FB-RRT) algorithm is embedded. As guiding by the driving behavior, normal random, goal-bias and Gaussian sampling strategies are fused to form FB-RRT, which could make the best use of the basic path planning and reduce the randomness of node’s extension to save the computation time. After collision-free path generating, cubic polynomial curve is employed to schedule velocity profile for coping with vehicle stability requirements, actuator constraints and comfort conditions. The planner has been tested in simulation and a real vehicle in various typical scenarios. Test results illustrate that the presented method could generate a trajectory with controllable extrema of curvature as well as with continuous and smooth enough curvature. Besides, generated trajectory has short length, high success rate (no less than 80% average success rate in complex environment) and real time (the average period is less than 100 ms). Moreover, the velocity profile meets the vehicle stability requirements, actuator constraints, and comfort conditions.

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.

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.

This paper proposes an improved autonomous emergency braking (AEB) algorithm intended for intelligent vehicle. Featuring a combination with the estimation of road adhesion coefficient, the proposed approach takes into account the performance of electronic hydraulic brake. In order for the accurate yet fast estimate of road ahead adhesion coefficient, the expectation maximization framework is applied depending on the reflectivity of ground extracted by multiple beams lidar in four major steps, which are the rough extraction of ground points based on 3σ criterion, the accurate extraction of ground points through principal component analysis (PCA), the main distribution characteristics of ground as extracted using the expectation maximum method (EM) and the estimation of road adhesion coefficient via joint probability. In order to describe the performance of EHB, the response characteristics, as well as the forward and adverse models of both braking pressure and acceleration are obtained. Then, with two typical roads including single homogeneous road and fragment pavement, the safe distance of improved AEB is modeled. To validate the algorithm developed in this paper, various tests have been conducted. According to the test results, the reflectivity of laser point cloud is effective in estimating the road adhesion coefficient. Moreover, considering the performance of EHB system, the improved AEB algorithm is deemed more consistent with the practicalities.

A hard issue in the field of microrobots is path planning in complicated situations with dense obstacle distribution. Although the Dynamic Window Approach (DWA) is a good obstacle avoidance planning algorithm, it struggles to adapt to complex situations and has a low success rate when planning in densely populated obstacle locations. This paper suggests a multi-module enhanced DWA (MEDWA) obstacle avoidance planning algorithm to address the aforementioned issues. An obstacle-dense area judgment approach is initially presented by combining Mahalanobis distance, Frobenius norm, and covariance matrix on the basis of a multi-obstacle coverage model. Second, MEDWA is a hybrid of enhanced DWA (EDWA) algorithms in non-dense areas with a class of two-dimensional analytic vector field methods developed in dense areas. The vector field methods are used instead of the DWA algorithms with poor planning performance in dense areas, which greatly improves the passing ability of microrobots over dense obstacles. The core of EDWA is to extend the new navigation function by modifying the original evaluation function and dynamically adjusting the weights of the trajectory evaluation function in different modules using the improved immune algorithm (IIA), thus improving the adaptability of the algorithm to different scenarios and achieving trajectory optimization. Finally, two scenarios with different obstacle-dense area locations were constructed to test the proposed method 1000 times, and the performance of the algorithm was verified in terms of step number, trajectory length, heading angle deviation, and path deviation. The findings indicate that the method has a smaller planning deviation and that the length of the trajectory and the number of steps can both be reduced by about 15%. This improves the ability of the microrobot to pass through obstacle-dense areas while successfully preventing the phenomenon of microrobots going around or even colliding with obstacles outside of dense areas.