Methodology Procedure for Hybrid Electric Vehicles Design

Paper #:
  • 2011-24-0071

Published:
  • 2011-09-11
Citation:
Tribioli, L., Fumarola, A., and Martini, F., "Methodology Procedure for Hybrid Electric Vehicles Design," SAE Technical Paper 2011-24-0071, 2011, https://doi.org/10.4271/2011-24-0071.
Pages:
12
Abstract:
Nowadays, fuel economy and pollutant emissions are keenly felt topics and hybrid electric vehicles (HEVs) represent the best opportunity to respond to this problem in the short term. Hybrid electric vehicles meet the high-efficiency of electric motors, with the high reliability of the internal combustion engines, granting optimal results both in terms of emissions and fuel economy.The vehicle and path features highly affect the architecture choice. A parallel architecture, having a more flexible layout and providing a higher drive power, is more suitable for long paths and higher speeds, while the series one better adapts to urban cycles, as can be switched to a pure electric mode. At the same time, a parallel-series architecture is in general a good choice. Another crucial point is the definition of a control strategy suitable for the mission the car is expected to accomplish, that must properly control both the load partitioning, between engine and motors, and the regenerative braking.According to all these considerations, with the present paper the Authors intend to lay the basis of a comprehensive methodology, which can allow to simply define an optimized powertrain layout, i.e. architecture and devices size, and an efficient control strategy. To this aim, our research group has developed an analytical code that simulates the power flows in HEV powertrain and allows to calculate the performances of a specific vehicle upon various and different missions. By knowing the energy required, the model allows to define a range of admissible states for each time step, resulting of the combination of the engine power and the corresponding motor power, considering regenerative braking and constraints imposed by the engine, the motors and the storage system. The best solution between all possible layouts is found with Dijkstra shortest path optimization algorithm electing the configuration allowing the minimum fuel consumption.
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