For existing offline real-time fault diagnosis systems for vehicle control motors, efficient and accurate fault detection remains challenging due to weak vibration fault signals under varying working conditions, significant noise interference, and high model complexity. This paper proposes a fault diagnosis method based on the acoustic-vibration signal fusion. The diagnostic framework comprises four modules: raw signal acquisition, data preprocessing, multi-sensor signal fusion, and network-based diagnosis. First, sound and vibration signals are collected using an NVH test rig. The signals are then decomposed and reconstructed using Pearson correlation coefficient filtering, with parameters of the Variational Mode Decomposition (VMD) optimized by the Gray Wolf Optimization (GWO) algorithm. Next, the reconstructed signals undergo continuous wavelet transform (CWT) to produce time-frequency representations, and a multimodal feature fusion network is employed to integrate the sound and vibration signals. Finally, fault detection and classification are achieved by training an enhanced MobileNetV2 network. Experimental results show that this method outperforms single-sensor approaches and other comparative techniques in diagnostic accuracy under variable operating conditions. Furthermore, the total processing time for offline inspection is less than 30 seconds, meeting the real-time diagnostic requirements for automotive control motors.

To address the need for dynamic and static parameter testing of IGBT modules, an integrated test platform was developed. In production-line applications, consistency differences in key parameters between the upper and lower bridge arms of manufactured modules were found to be difficult to quantify using a single metric. To overcome this limitation, a multi-parameter consistency evaluation method was proposed. Based on more than 400 sets of production-level measurements obtained from newly manufactured IGBT modules from the same batch, eight key parameters, including E_on, E_off, dv/dt, di/dt, V_CES, I_CES, V_GEth and I_GES, were selected to construct paired samples of the upper and lower bridge arms. A dimensionless consistency index (CI) was introduced to quantify relative deviations between bridge arms, and consistency characteristics were analyzed using raw-parameter boxplots, CI boxplots, and paired scatter plots in terms of distribution, discrepancy quantification, and sample-wise pairing. The results show that different parameters exhibit different levels of distributional difference and relative deviation between the two bridge arms under identical test conditions, and that the three analyses reveal mutually consistent trends. The proposed method provides an effective basis for within-batch quality assessment, bridge-arm matching, abnormal sample identification, and production process troubleshooting.

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%.

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.