Decision-making is a crucial and difficult step in the design process of complex systems such as the hybrid powertrain. Finding an optimal solution requires the system feedback. This can be, depending on the granularity of the models at the component level, highly time-consuming. This is even more true when the system’s performance is determined by its control. In fact, various possibilities can be selected to deliver the required torque to the wheels during a driving cycle. In this work, two different design strategies are proposed to minimize the fuel consumption and the cost of the hybrid powertrain. Both strategies adopt the iterative framework which allows for the separation of the powertrain design problem and its control while leading to system optimality. The first approach is based on model reduction, while the second approach relies on improved cycle reduction techniques. They are then applied to a parallel hybrid vehicle case study, leading to important cost reduction in reasonable delays and are compared using different metrics.
The transformations of conventional Internal Combustion Engine (ICE)-powered vehicles into electrified vehicles are of growing interest. The retrofit hybridization of a Peugeot 308 SW passenger car is studied. For this purpose, a complete hybridization system is added to the rear axle of the vehicle. As a result, the control of the initial ICE-powered vehicle has to be updated. The aim of this paper is to deduce the potential fuel saving thanks to this hybridization. The simulation results show an energy saving of at least 9% and potential consumption reduction on a Worldwide harmonized Light-duty Vehicles Test Cycle (WLTC).
Retrofit electrification of engine-powered vehicles to plug-in hybrid electric vehicles is growing. Different hybrid solutions exist, but all of them must compromise between integration constraints, cost and efficiency. Full vehicle simulation, including the powertrain subsystems is thus mandatory. During the pre-design step, some sub-systems are not available. Use a simplified model for some subsystems is then common, to initiate the sizing process. In this paper, two electric drive models are compared: one using a torque and speed dependent loss map and another with a constant efficiency. Using Dynamic Programming, the fuel consumption is estimated for both models. The simple average efficiency leads to only 3% of errors on fuel saving. Such simple model can thus be relevant for the comparison of solutions in terms of fuel saving.
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