To effectively mitigate the electromagnetic noise (EN) from automotive electronic water pump (AEWP), this paper proposes and validates an EN suppression method based on hybrid random carrier technology. Drawing on the control model of AEWPs and Space Vector Pulse Width Modulation (SVPWM) harmonic theory, the relationship between harmonics and EN in the control system is analyzed, and the mechanism of random signals is explored. Based on the control model and theory of AEWPs, randomized carrier wave mixing is introduced, and a harmonic method of Hybrid Random Carrier Space Vector Pulse Width Modulation (HRCSVPWM) is proposed. Simulation results are spectrally analyzed using the power spectrum transform method to verify the suppression capability of the carrier randomization technique on higher harmonics. Additionally, experimental verification is conducted via a noise characteristics test platform, confirming the method's correctness and effectiveness. This approach significantly reduces the high-frequency vibration and noise of AEWPs without compromising the original control system or requiring additional hardware inputs. Thus, the research findings provide a novel solution for reducing EN in AEWPs, with substantial practical value for electronic water pumps development.
The response speed, accuracy, and stability of the hydraulic clutch are its key performance evaluation indicators. This study focuses on the stability and response quality of the hydraulic clutch, successively conducting mathematical description of physical mechanisms, global sensitivity analysis of multiple parameters, exploration of stability domain boundaries in multi-dimensional parameter spaces, and optimization of dynamic response characteristics. First, the mathematical model of characteristics such as the discontinuous nonlinearity in the flow area, valve core contact nonlinearity, and the nonlinearity of the oil bulk modulus were established. Upon this basis, a nonlinear dynamic mathematical model of the hydraulic clutch system was developed. Then, the Extended Fourier Amplitude Sensitivity Test (EFAST) method was employed, using the weighted objective function of response time and pressure deviation within the clutch as an observation index, followed by sensitivity analysis of the multi-dimensional parameter set. Several parameters with highest sensitivity were selected to construct an optimization framework constrained by the boundaries of the stable domain. The Grey Wolf Optimization Algorithm (GWO) was utilized for parameter optimization and solution. The optimization results demonstrate that, within the stable domain of the parameter set, the optimized structural parameters improve the pressure rise rate of the clutch by approximately 11.2% and the pressure drop rate by approximately 18%. This method offers significant support for enhancing and designing the response performance of the hydraulic clutch system.
The replacement of internal combustion engine (ICE) with electric motor by electric vehicles (EVs) technology is one of the most comprehensive transition in the automotive industry for recent decades. EVs technology causes fundamental changes in vehicle architecture and dynamics including increased overall weight due to battery packs, altered torque delivery, and regenerative braking systems. These changes have significant implications on tires because of the direct interaction of vehicle and road. Therefore, tire performance characteristics are influenced by the type of powertrain. According to the distinctive operating conditions of EVs comparing with the conventional vehicles, determination of the tire tread wear behavior has become a critical challenge for tires. The higher mass and unique driving characteristics of EVs cause fast and unevenly distributed tire wear. This study aims to evaluate the investigation of passenger car tire tread wears for conventional and electric vehicles under controlled road testing. Four commercially available tires are selected for the tests using conventional vehicles on Route-1 and EV testing on Route-2. Key indicators such as average tread depth reduction, wear uniformity, and wear pattern distribution are measured to assess the impact of vehicle type. The results indicate that EVs consistently impose higher tread wear than conventional vehicles. For the tested tire specifications, tread-wear-based mileage on electric-vehicle platforms was markedly lower than on conventional platforms, with the strongest reduction observed on the front axle. Depending on tire type, the estimated mileage reduction from ICE to EV ranged from approximately 35 % to 62 %. The findings indicate that the combined effect of higher vehicle mass, instantaneous torque delivery and regenerative braking intensifies wear severity and front/rear imbalance, especially under urban and stop-go EV duty cycles. These results support the need for EV-oriented tire compounds, axle-specific design strategies and validation procedures that reflect route severity and real operating conditions.
This study presents a reliability-based proportional topology optimization (RPTO) framework for continuum structures, specifically designed to achieve smooth, manufacturing-ready boundaries under multi-source uncertainties. By integrating the Proportional Topology Optimization (PTO) algorithm with a Level Set Method (LSM), the approach bypasses the need for high-order sensitivity information while ensuring high-fidelity boundary representation. A hybrid decoupling strategy is employed to decompose the reliability-based topology optimization (RBTO) problem into a reliability analysis sub-problem and an equivalent deterministic topology optimization (EDTO) sub-problem. Uncertainties in geometry, material volume, and loading are characterized via a probabilistic framework using the First-Order Reliability Method (FORM). Numerical results for 2D and 3D benchmarks demonstrate that the RPTO method effectively captures the influence of the target reliability index on structural morphology, yielding robust designs that satisfy strict probabilistic constraints without the "gray areas" common in density-based methods.
This paper presents a computationally efficient radar sensor modeling methodology incorporating rain-induced propagation attenuation for simulation-based validation of autonomous driving systems. The proposed framework combines the classical radar equation with the rain attenuation model specified in ITU-R Recommenda-tion P.838-3 to estimate received signal power degradation and target-range estimation errors under rainfall conditions. A stochastic False Alarm Probability Generation (FAPG) algorithm is also introduced to reproduce intermittent tar-get detection according to user-defined probabilities of false alarm and detection. The proposed methodology was implemented through real-time co-simulation between MATLAB/Simulink and IPG CarMaker. Simulation results demonstrated that the proposed framework successfully reproduced rain-induced propagation attenuation, target-range estimation errors, received signal power degradation, and stochastic target detection behavior while maintaining computational efficiency suitable for real-time simulation.
