Impact of Age on Takeover Behavior in Automated Driving in Complex Traffic Situations: A Case Study of Beijing, China

Research on the influence of age on various automated driving conditions will contribute to an understanding of driving behavior characteristics and the development of specific automated driving systems. This study aims to analyze the relationship between age and takeover behavior in automated driving, where 16 test conditions were taken into consideration, including two driving tasks, two warning times and four driving scenarios. Forty-two drivers in Beijing, China in 2020 were recruited to participate in a static driving simulator with Level 3 (L3) conditional automation to obtain detailed test information of the recorded takeover time, mean speed and mean lateral offset. An ANOVA test was proposed to examine the significance among different age groups and conditions. The results confirmed that reaction time increased significantly with age and the driving stability of the older group was worse than the young and middle groups. It was also indicated that the older group could not adapt to complex tasks well when driving due to their limited cognitive driving ability. Additionally, the higher urgency of a scenario explained the variance in the takeover quality. According to the obtained influencing mechanisms, policy implications for the development of vehicle automation, considering the various driving behaviors of drivers, were put forward, so as to correctly identify the high-risk driving conditions in different age groups. For further research, on-road validation will be necessary in order to check for driving simulation-related effects.

CFD MODELLING OF VOLTACAR ELECTRIC VEHICLE BODY FOR THE MOST EFFICIENT DRIVING CONDITIONS

In recent years, fossil fuels prices, greenhouse gas emissions, and need for sustainable energy sources have been increasing day by day. Thus, electric vehicles are seen as a promising candidate in the market due to their low-costs and cleaner fuel options such as electricity, hydrogen etc. Moreover, aerodynamics is one of the most important criteria to consider while designing an automobile for the most efficient driving conditions. For this reason, vehicle developers are studying to reduce drag resistance of the body to improve driving efficiency. On the other hand, Computational Fluid Dynamics (CFD) is one of the main tools for the automotive industry to obtain low-cost results before prototyping of any product. In this study, the aerodynamic characteristics of VoltaCAR electric vehicle is numerically investigated to obtain the best driving velocity. This car participates the TUBITAK-Electromobile car competition every year to achieve low fuel consumption for one hour driving. Thus, it is aimed that to minimize the resistance of the air hitting from the front, side, and roof of the vehicle. In the numerical model, polyhedral mesh structure is preferred to obtain faster convergence with fewer iterations, and shorter computation time is obtained compared to the tetrahedral mesh method. The aerodynamic drag coefficient (Cd) of the car model was calculated as approximately 0.17 at 22.22 and 27.78 m/s. The optimum velocity values were selected as 22.22 and 27.78 m/s by means of their lower Cd.

Analysis and Improvement Measures of Driving Range Attenuation of Electric Vehicles in Winter

A great many EVs in cold areas suffer from range attenuation in winter, which causes driver anxiety concerning the driving range, representing a hot topic. Many researchers have analyzed the reasons for range attenuation but the coupling mechanism of the battery as well as the vehicle and driving conditions have not been clearly estimated. To quantitatively investigate the driving range attenuation of electric vehicles (EVs) during winter, an EV model mainly integrated with a passenger-cabin thermal model, battery model, and vehicle dynamic model was constructed and simulated based on the mass-produced Wuling HongGuang Mini EV. Real vehicle dynamic driving data was used to validate the model. Based on NEDC driving conditions, the driving range calculation formula and energy flow diagram analysis method were used. The reason for attenuation was evaluated quantitatively. Results show that battery energy loss and breaking recovery energy loss contribute nearly half of the range attenuation, which may be alleviated by battery preheating. Suggestions for extending driving range are proposed based on the research.

Capabilities of Nearly Zero Energy Building (nZEB) Electricity Generation to Charge Electric Vehicle (EV) Operating in Real Driving Conditions (RDC)

The growing number of electric vehicles in recent years is observable in almost all countries. The country’s energy transition should accompany this rise in electromobility if it is currently generated from non-renewable sources. Only electric vehicles powered by renewable energy sources can be considered zero-emission. Therefore, it is essential to conduct interdisciplinary research on the feasibility of combining energy recovery/generation structures and testing the energy consumption of electric vehicles under real driving conditions. This work presents a comprehensive approach for evaluating the energy consumption of a modern public building–electric vehicle system within a specific location. The original methodology developed includes surveys that demonstrate the required mobility range to be provided to occupants of the building under consideration. In the next step, an energy balance was performed for a novel near-zero energy building equipped with a 199.8 kWp photovoltaic installation, the energy from which can be used to charge an electric vehicle. The analysis considered the variation in vehicle energy consumption by season (winter/summer), the actual charging profile of the vehicle, and the parking periods required to achieve the target range for the user.