There is rapidly growing interest in autonomous electric vehicles due to their potential in improving safety, accessibility, and environmental outcomes. However, their market penetration rate is dependent on costs. Use of autonomous electric vehicles for shared-use mobility may improve their cost competitiveness. So far, most of the research has focused on the cost impact of autonomy on taxis and ridesourcing services. Singapore is planning for island-wide deployment of autonomous vehicles for both scheduled and on-demand services as part of their transit system in the year 2030. TUMCREATE developed an autonomous electric vehicle concept, a microtransit vehicle with 30-passenger capacity, which can complement the existing bus transit system. This study aims to determine the cost of autonomous electric microtransit vehicles and compare them to those of buses. A total cost of ownership (TCO) approach was used to compare the lifecycle costs. It was shown that although the acquisition costs of autonomous electric vehicles are higher than those of their conventional counterparts, they can reduce the TCO per passenger-km up to 75% and 60% compared to their conventional counterparts and buses, respectively.
One typical application of intelligent transportation systems (ITS) is vehicle platooning where a group of vehicles travel with smaller inter-vehicle distance safely, improving energy efficiency as well as road capacity and traffic safety. Truck platooning on highways has been widely studied and showed the aforementioned effects. However, the platooning of buses in urban environments have not been investigated thoroughly in the literature. This paper examines the effects of bus platooning with respect to traffic control and energy consumption.Microscopic traffic simulations have been conducted to demonstrate that bus platooning improves the quality of service of buses and maintains the quality of the traffic flow. Subsequently, driving cycles of buses generated from the simulation study serve as input for an energy consumption analysis, showing that not only bus platooning itself result in a reduction of energy consumption but the traffic signal prioritisation for bus platooning lead to additional energy savings. I. INTRODUCTIONVehicle platooning is one typical application of intelligent transportation systems (ITS), it refers to an operational practice in which multiple vehicles follow one another closely. The intra-platoon distance is maintained shorter compared to today's practice, which leads to reduced aerodynamic drag, particularly for the vehicles in the middle of a platoon. The change in the aerodynamic drag results in reductions of energy usage, traffic congestion, and hence emissions [1-5]. A. Vehicle PlatooningModern driver assistant systems and vehicle-to-vehicle (V2V) communication enable the formation of an electronically coupled platoon [1,5,6]. The direct connection between the members of such a platoon leads to a decreased reaction time of about 0.1 seconds, which is significantly faster than the reaction time of a driver of about 2.5 seconds [1,2,[5][6][7][8]. It is thereby possible to reduce the headways within the platoon. The intra-platoon distance between the vehicles is a key performance indicator of the platooning [4].
The electrification of bus-based public transportation contributes to the goal of reducing the adverse environmental impacts caused by urban transportation. However, the penetration of electric vehicles has been slow due to their lower vehicle range and total costs in comparison to vehicles driven by internal combustion engines. By improving the powertrain efficiency, the total costs can be reduced for the same vehicle range. Therefore, this paper proposes a holistic design exploration approach to investigate and identify the optimal powertrain concept for electric city buses based on the component costs and energy consumption costs. The load profiles of speed, slope, and passenger occupancy profiles are derived for a selected bus route in Singapore, which is used in a powertrain design exploration for a 30-passenger vehicle. Six different powertrain architectures are analyzed, together with single and multi-speed gearbox configurations, to identify the optimal powertrain architecture and the resulting component sizes. The powertrain configurations are further analyzed in terms of their influence on the vehicle characteristics and total costs. Multi-motor configurations were found to have better vehicle characteristics and lower total costs in comparison to single rear motor configurations. Concepts with motors on the front and a rear axle could shift the load points to a higher efficiency region, resulting in lower energy consumption and energy costs. The optimal powertrain concept was a fixed-speed two-motor configuration, with a booster motor on the front axle and a motor on the rear axle.
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