捷豹路虎

The aim of this study was to examine drivers’ hazard perception in an urban scene, under two different lighting conditions (daytime and after dark) and for two different levels of driving automation (manual driving and hands-off SAE level 2 automation). Forty-eight participants took part in four experimental drives in a driving simulator, encountering six different hazardous/potentially hazardous events in each drive. Results showed that drivers detected hazards significantly earlier and were more likely to react to the hazards in daytime, compared to after dark environments, particularly when pedestrians were approaching the road from the left. However, there was no significant difference in their response time towards the hazards, between the daytime and after dark environments. In terms of driver response in the two automation levels, the majority of drivers were proactive and reacted before the potentially hazardous events turned into actual hazards during manual driving, but responses were more reactive during automated driving. These findings highlight the need to account for context of the driving environment such as lighting conditions and levels of driving automation when designing systems or protocols that aim to support hazard perception and timely driver response.

The rapid electrification and intelligence of modern transportation systems place stringent demands on the electromagnetic compatibility, reliability, and adaptability of automotive power electronics. In electric and autonomous vehicles, electromagnetic interference (EMI) generated by high-frequency switching power converters can compromise safety-critical functions, in-vehicle communications, and system efficiency under dynamic operating conditions. Conventional passive EMI filters, while robust, are often oversized and lack adaptability, leading to increased weight, volume, and energy losses. This paper proposes an intelligent self-tuning active EMI filtering approach for electrified automotive power systems based on reinforcement learning (RL). The EMI mitigation problem is formulated as a Markov decision process, enabling an RL agent to continuously adapt filter parameters in response to time-varying interference characteristics. To improve robustness and generalisation under complex and non-stationary conditions, a variational autoencoder is employed for compact state representation, while a noise-based exploration mechanism enhances learning efficiency and prevents suboptimal convergence. The proposed method is evaluated using experimentally measured EMI spectra from an automotive electric drive unit within a MATLAB/Simulink co-simulation framework. Results demonstrate consistent EMI attenuation improvements of 25-30 dB across a wide frequency range compared with conventional control strategies and passive filtering solutions. By reducing reliance on oversized passive components and enabling adaptive EMI suppression, the proposed framework supports lightweight, energy-efficient, and reliable power-electronic systems for intelligent and green transportation applications.

Wire-arc additive manufacturing (WAAM) is a state-of-the-art near net shape manufacturing technology for manufacturing structures with tailored mechanical properties. However, the process typically requires minor post-processing (e.g., machining) to achieve the desired surface finish and dimensional accuracy. This study investigates the mechanical properties and the concomitant microstructural evolution in a WAAM bimetallic comprising of a low alloy carbon steel (P22) and Inconel 625 nickel alloy (IN625) using gas metal arc welding (GMAW). Microstructural examination revealed distinct microstructural features, including bainite-ferrite morphology in P22 steel and a dendritic γ-austenite matrix in IN625 with intermetallic Laves phase precipitates. The interface exhibited a defect-free metallurgical bond, characterised by martensitic laths within the P22 region, primarily due to rapid cooling, and columnar grains in the IN625 region due to directional solidification. Tensile and Charpy impact tests revealed that IN625 exhibited superior mechanical properties, whereas the bimetallic component displayed moderate strength with reduced ductility. In these tests, fractures appeared to consistently occur on the P22 side, due to the presence of Mn-rich inclusions. Crystallographic texture analysis showed near random texture for the P22 steel, governed by recrystallisation and phase transformation dynamics. In contrast, the texture of the IN625 deposit cannot be concluded due to the small number of directionally grown grains during WAAM.

This paper presents an improved sensorless vector control strategy for a solar and battery powered permanent magnet-assisted synchronous reluctance motor (PMaSynRM) drive intended for light electric vehicle (LEV) applications. To enhance dynamic performance and energy efficiency, a fractional-order PID (FOPID) based vector control scheme integrated with maximum torque per ampere (MTPA) optimization is employed. Space vector pulse width modulation (SVPWM) is utilized for efficient DC-link voltage utilization and reduced harmonic distortion. For wide-speed sensorless operation, a hybrid estimation framework is proposed. High-frequency signal injection (HFSI) is adopted to ensure reliable speed and rotor position estimation in low- and near-zero-speed regions by exploiting rotor saliency, while an adaptive supertwisting sliding mode observer (ASTSMO) governs medium- and high-speed operation using back-EMF estimation. A speed-dependent gain adaptation mechanism is incorporated in the observer to improve robustness and suppress chattering. An enhanced phase-locked loop (ePLL) is employed for accurate angle tracking and noise attenuation. The proposed control architecture enables seamless transition between estimation regions, improved disturbance rejection, and efficient torque production across the entire operating range, making it suitable for solar-assisted traction applications.

Automotive headlamps in Battery Electric Vehicles (BEVs) are exposed to a wide range of environmental and operational conditions that influence their thermal behaviour. Factors such as solar radiation, ambient temperature, lighting features, and nearby heat sources can significantly impact headlamp temperatures, potentially leading to issues like condensation, material degradation, and reduced optical performance. Accurate thermal modelling using Computational Fluid Dynamics (CFD) is essential during the design phase, but its effectiveness depends heavily on the fidelity of boundary conditions, which are often based on internal combustion engine (ICE) vehicle data. This study investigates the thermal behaviour of BEV headlamps under real-world conditions, focusing on parking and charging scenarios. Temperature measurements were taken at various locations on the lens and housing of a Jaguar I-Pace using thermocouples. The results show that lighting features, particularly the high beam, generate localized hotspots on the lens. Vehicle orientation relative to the sun also affects lens temperature, with sun-facing lamps consistently hotter than shaded ones. Notably, during fast charging, headlamp temperatures increased significantly even when the lamps were off, indicating the influence of nearby active thermal systems. These findings highlight the need to incorporate BEV-specific factors—such as lighting configurations, solar exposure, charging conditions, and adjacent heat sources—into CFD boundary condition modelling. This work provides valuable insights for improving the accuracy of thermal simulations and enhancing the durability and performance of headlamps in electric vehicles.