To overcome the limitations of the sparrow search algorithm and the challenges of dynamic obstacle avoidance in mobile robots, an integrated method combining the enhanced sparrow search algorithm with the dynamic window approach is introduced. First, logistic–tent chaotic mapping is utilized for the initialization of the sparrow population, thereby achieving a uniform distribution of the sparrow population and simultaneously enhancing the exploratory capability of the algorithm. The implementation of the elite reverse learning strategy aims to diversify the sparrow population, thus improving the quality of initial solutions and the algorithm’s search accuracy. Additionally, the position update dynamic self-adaptive adjustment strategy is adopted to enhance the optimization capability of the algorithm by refining the position update formulas for both producers and scroungers. By combining the Lévy flight strategy and the optimal position perturbation strategy, the algorithm’s efficacy in escaping local optima can be improved. Second, an adaptive velocity adjustment strategy is presented for the dynamic window approach and optimized for its evaluation function to enhance the safety of the path. Third, the enhanced sparrow search algorithm is integrated with the dynamic window approach to tackle the problems of the non-smooth global path and inadequate dynamic obstacle avoidance capability. Both simulation and experimental results show the superiority of the enhanced sparrow search algorithm in comparison to other algorithms in terms of the path length, total rotation angle, and algorithm execution time. Notably, in comparison to the basic sparrow search algorithm, there is a decrease in average path lengths by 15.31% and 11.92% in the improved sparrow search algorithm. The integrated algorithm not only crafts local paths rooted in global paths but also adeptly facilitates real-time dynamic obstacle evasion, ensuring the robot’s safe arrival at its destination.

Four-wheel electromechanical braking (EMB) systems may receive nominal brake torque commands that exceed transient actuator capability under low-voltage supply or degradation. This study proposes a brake torque allocation method based on wheel-end dynamic reachable sets, defined here as one-step torque intervals permitted by voltage-dependent magnitude and application/release rate limits from the previous actual output. A single-wheel EMB model identifies the capability boundaries. Each interval is updated using the wheel’s bus voltage, capability coefficient, previous-cycle actual torque, and control period. Nominal commands are projected onto these intervals, and residual demand is redistributed according to the remaining margins. MATLAB/Simulink–CarSim simulations compare the method with fixed-ratio, static saturation, and dynamic-bound quadratic programming (QP-D) allocation. Under a synchronized 24 V supply, DRA reduces integrated brake demand deficit by 28.8% relative to the fixed QP-D benchmark. Under rear-left degradation, it reduces the peak yaw rate by 78.0% relative to static allocation. These results support capability-aware command allocation in the tested model. Strongly unequal voltages nevertheless produce the largest yaw response with DRA, identifying yaw-aware coordination as a necessary extension for this operating regime.

In the field of dynamic characteristic testing of automotive grade IGBT modules, high-frequency noise interference and stray inductance are universal technical challenges faced by the industry. High frequency noise and stray inductance can interfere with signal integrity, seriously reducing the testing accuracy of the platform and causing deviations in the dynamic characteristic evaluation of IGBT modules. To address this issue, this paper proposes a software hardware collaborative optimization strategy and designs a high-precision dynamic characteristic testing platform for automotive grade IGBT modules. At the software level, Savitzky–Golay filters are introduced and designed to process the test data, filter out high-frequency noise interference. After filtering, the noise mean square error and peak to peak value of the platform are generally reduced by 48–68%, and the switch ringing or peak value is reduced by about 45–53%. In terms of hardware optimization, an improved PCB laminated busbar was designed to reduce the overall stray inductance of the platform through simulation analysis of the structural parameters and via factors of the laminated busbar using ANSYS 2023 R1. The test results verification shows that the stray inductance of the testing platform is only 23 nH. The absolute error between the parameters measured by the testing platform and the reference values of the tested module is within 5%.