In this study, we consider fuel cell-powered electric trucks (FCETs) as an alternative to conventional medium- and heavy-duty vehicles. FCETs use a battery combined with onboard hydrogen storage for energy storage. The additional battery provides regenerative braking and better fuel economy, but it will also increase the initial cost of the vehicle. Heavier reliance on stored hydrogen might be cheaper initially, but operational costs will be higher because hydrogen is more expensive than electricity. Achieving the right tradeoff between these power and energy choices is necessary to reduce the ownership cost of the vehicle. This paper develops an optimum component sizing algorithm for FCETs. The truck vehicle model was developed in Autonomie, a platform for modelling vehicle energy consumption and performance. The algorithm optimizes component sizes to minimize overall ownership cost, while ensuring that the FCET matches or exceeds the performance and cargo capacity of a conventional vehicle. Class 4 delivery truck and class 8 linehaul trucks are shown as examples. We estimate the ownership cost for various hydrogen costs, powertrain components, ownership periods, and annual vehicle miles travelled.

The mode transition from electric propulsion mode to hybrid propulsion mode is important with regard to the power management strategy of plug-in hybrid electric vehicles (PHEVs). This is because mode transitions can occur frequently depending on the power management strategies and driving cycles, and because inadequate mode transitions worsen the fuel efficiency and drivability. A pre-transmission parallel PHEV uses a clutch between the internal combustion engine (ICE) and the electric motor (EM) to connect or disconnect the power source of the ICE for a mode transition. The mode transition requires additional energy consumption for clutch speed synchronization, and is accompanied by a drivetrain shock due to clutch engagement. This paper proposes a control strategy for the mode transition with gear-shifting to resolve the problems of energy consumption and drivetrain shock. Through the development of a PHEV performance simulator, we analyze the mode transition characteristics and propose a control strategy considering the vehicle acceleration and gear state. The control strategy reduces the duration required for the mode transition by moving the start time of the mode transition. This helps to improve energy efficiency while maintaining adequate drivability.

This paper proposes a control algorithm for IGS (Idle Stop and Go) systems and a stop prediction algorithm. A new ISG system has been introduced to reduce fuel consumption by improving the conventional ISG system for hybrid electric vehicles (HEVs), including plug-in hybrid electric vehicles (PHEVs). When the HEVs stop or pass in front of a traffic light, the control algorithm determines when to turn off the internal combustion engine (ICE) while comparing the driving energy consumption. A stop prediction algorithm was proposed to determine whether a vehicle will stop at traffic lights. Traffic information and driving test results were obtained using a driver-in-the-loop simulator, and the stop prediction algorithm was verified through these results. The proposed ISG with the stop prediction function can reduce fuel consumption. With the developed traffic flow model, the effectiveness of the proposed ISG was evaluated in terms of the transportation system.