Vehicle platooning has been proposed as one of the potential technologies for intelligent transport systems to improve transportation and energy efficiency in urban cities. Despite extensive studies conducted on the platooning of heavy-duty trucks, literature on the analysis of urban vehicle platoons has been limited. To analyse the impact of platooning in urban environments, this paper studies the influence of intervehicle distance, platoon size and vehicle speed on the drag coefficient of the vehicles in a platoon using computational fluid dynamics (CFD). Two vehicle models—a minibus and a passenger car—are analysed to characterise the drag coefficients of the respective platoons. An analysis of energy consumption is conducted to evaluate the energy savings with platooning using a longitudinal dynamics simulation. The results showed a reduction in the average drag coefficient of the platoon of up to 24% at an intervehicle distance of 1 m depending on the number of vehicles in the platoon. With a larger intervehicle distance of 4 m, the reduction in the drag coefficient decreased to 4% of the drag coefficient of the isolated vehicle. Subsequently, energy savings with platooning were calculated to be up to 10% depending on the driving cycle, intervehicle distance and platoon size.

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.

Autonomous electric buses (AEB) have widely been envisioned in future public transportation systems due to their large potential to improve service quality while reducing operational costs. The requirements and specifications for AEBs, however, remain uncertain and strongly depend on the use case. To enable the identification of the optimal vehicle specifications, this paper presents a holistic design optimization framework that explores the impacts of implementing different AEB concepts in a given set of routes/network. To develop the design optimization framework, first, a multi-objective, multi-criteria objective function is formulated by identifying the attributes of bus journeys that represent overall value to the stakeholders. Simulation models are then developed and implemented to evaluate the overall performance of the vehicle concepts. A genetic algorithm is used to find the concepts with the optimal trade-off between the overall value to the stakeholders and the total cost of ownership. A case study is presented of a single bus line in Singapore. The results show an improvement in the waiting time with the use of a smaller sized AEB with a capacity of 20 passengers. However, the costs and emissions increase due to the requirement of a larger fleet and the increase in daily distance traveled compared to a 94-passenger capacity AEB.