Search Results (3)

This study presents the development of an open-source Driver-in-the-Loop simulation platform, specifically designed to test and analyze advanced automated driving functions. We emphasize the creation of a versatile system architecture that ensures seamless integration and interchangeability of components, supporting diverse research needs. Central to the simulator’s configuration is a hexapod motion platform with six degrees of freedom, chosen through a detailed benchmarking process to ensure dynamic accuracy and fidelity. The simulator employs a half-vehicle cabin, providing an immersive environment where drivers can interact with authentic human–machine interfaces such as pedals, steering, and gear shifters. By projecting complex driving scenarios onto a curved screen, drivers engage with critical maneuvers in a controlled virtual environment. Key innovations include the integration of a motion cueing algorithm and an adaptable, cost-effective open-source framework, facilitating collaboration among researchers and industry experts. The platform enables standardized testing and offers a robust solution for the iterative development and validation of automated driving technologies. Functionality and effectiveness were validated through testing with the ISO lane change maneuver, affirming the simulator’s capabilities. Full article

In this paper, L-band sounding and surface observation data are used to calculate the boundary layer height (BLH) and evaluated CMA (China Metrological Administration Numerical Forecast System) and ERA5 in Xi’an for 2017–2021 using the Richardson (Ri) and Nozaki methods. For different PM_2.5 pollution levels, the correlation between the vertical profile of meteorological factors and BLH is explored. There is a certain negative correlation between BLH and PM_2.5 concentration. The BLH mean values of Nozaki, Ri, ERA5, and CMA from high to low are ~980 m, ~640 m, ~410 m, and ~240 m, respectively. The highest correlation is between ERA5 and CMA BLH with r² > 0.85 for all pollution processes, while it between other methods is significantly lower (r² < 0.58). The observational BLH is generally higher than the model results. Nozaki has a good adaptability on the light pollution, while Ri is more applicable to the stable boundary layer. In moderate and higher pollution, the ERA5 has a slightly better performance than CMA in BLH, while in light pollution there is a significant underestimation for both. Overall, the correlation between any two BLH methods gradually increases with increasing pollution level. In this study, there is about ~30% probability of polluted weather when BLH < 200 m and only <7% probability when BLH > 2000 m. It is difficult to simulate the neutral boundary layer and inversion processes for CMA and ERA5, but ERA5 has higher forecasting skills than CMA. This study can provide the data and theoretical support for the development of haze numerical forecast. Full article

The article describes an elaboration of the X-in-the-loop (XiL) testing environment for a thermal management system (TMS) intended for the traction electric drive of an electric vehicle, which has each of its wheels driven by an in-wheel motor. The TMS features the individual thermal regulation of each electric drive using a hydraulic layout with parallel pipelines and electrohydraulic pumps embedded into them. The XiL system is intended as a tool for studying and developing the TMS design and controls. It consists of the virtual part and the physical part. The former simulates the vehicle operating in a driving cycle with the heat power dissipated by the electric drive components, which entails the change in their temperature regimes. The physical part includes the TMS itself consisting of a radiator, pipelines, and pumps. The physical part also features devices intended for simulation of the electric drive components in terms of their thermal and hydraulic behaviors, as well as devices that simulate airflow induced by the vehicle motion. Bilateral, real-time interactions are established between the two said parts combining them into a cohesive system, which models the studied electric vehicle and its components. The article gives a description of a laboratory setup, which implements the XiL environment including the mathematical models, hardware devices, as well as the control loops that establish the interaction of those components. An example of using this system in a driving cycle test shows the interaction between its parts and operation of the TMS in conditions simulated in both virtual and physical domains. The results constitute calculated and measured quantities including vehicle speed, operating parameters of the electric drives, coolant and air flow rates, and temperatures of the system components. Full article