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The recent development pace of the automotive technology is so rapid worldwide. Especially in a green car, hybrid electric vehicles (HEVs) have been studied a lot due to their significant effects on the urban driving. In the vehicle energy management strategy study, the driving speed is assumed to be known in advance, however the speed is not given in a real world. Accordingly, the prediction of vehicle speed is very important. In this study, we study the prediction methodology for the speed prediction using deep learning. Based on the vehicle driving speed data, the supervised deep learning has been used and the speed prediction accuracy using deep learning shows accurate results comparing to the actual speed. The supervised deep learning is used which is suitable for driving cycle database. As a result, the speed prediction after few seconds is feasible.

Research on HEV (hybrid electric vehicle) intracity buses has become a topic of interest because the well-known service routes of intracity buses and the frequent stop/go pattern make the energy management of the vehicle straightforward. Thus, the energy flow and the energy management of the intracity bus have been studied extensively in order to improve fuel economy. However, the HEV buses that have been studied previously were equipped with a single traction motor or with dual motors with the same capacity for the convenience of the equipment without considering the motoring or generating efficiency of the traction motor. Therefore, the energy flow from the engine/generator unit to the traction motor that has been optimized by many kinds of energy distribution strategies could not be transferred to the wheels in the most efficient manner. This paper investigates this aspect of the energy flow.

A waste heat recovery system is applied to a heavy-duty series hybrid electric vehicle. The engine in a series hybrid electric vehicle can operate at steady state for most of the time because the engine and drivetrain are decoupled, providing the waste heat recovery system with a steady state heat source. Thus, it is possible to optimize the waste heat recovery system design while maximizing the amount of useful energy converted in the system. To realize such a waste heat recovery system, the Rankine steam cycle is selected for the bottoming cycle. The heat exchanger is implemented as a quasi-1D simulation model to calculate the accurate quantity of recovered energy and to determine the working fluid state. The optimal geometric characteristics of the heat exchanger and the efficiency are considered according to the working fluid. The Rankine steam cycle model is constructed, and the output power is calculated.