Driveline Optimization for a Hybrid Electric City Vehicle to Minimize Fuel Consumption

Paper #:
  • 2014-01-1090

Published:
  • 2014-04-01
DOI:
  • 10.4271/2014-01-1090
Citation:
Carello, M., Bonansea, P., and D'Auria, M., "Driveline Optimization for a Hybrid Electric City Vehicle to Minimize Fuel Consumption," SAE Technical Paper 2014-01-1090, 2014, doi:10.4271/2014-01-1090.
Pages:
10
Abstract:
Nowadays gas emissions and fuel consumption are two of the major challenges for the automotive industry arisen from the ever-increasing relevance of environmental issues. Over the last few years, Hybrid Electric Vehicles (HEV) and Fuel Cell Vehicles have been developed as the most promising solutions that can address these challenges.XAM (eXtreme Automotive Mobility) is a parallel hybrid electric vehicle for urban transportation developed at the Politecnico of Turin. Since 2011 it participated to the Shell Eco-marathon Europe, a competition for low consumption vehicles. In the race XAM runs within the Urban Concept category and is powered by bio-ethanol. XAM is a plug-in parallel hybrid where traction can be provided by internal combustion engine or by electric motor fed by supercapacitor.A 1D simulation model of the vehicle and its subsystems has been created in AMESim in order to predict the behavior of the vehicle during the race. A great deal of attention was focused on the following areas: the vehicle dynamics, the electric motor management in traction or recharging conditions and finally to the transmission ratio that directly impacts the acceleration time, the cruise velocity, the recharging time and the rise of the state of charge of supercapacitor during the regenerative brake. This model has been parameterized and used for numerical optimization. In this latter context, by using the Optimus software, the optimal configuration of the model, that minimizes the fuel consumption, has been computed. Respecting the existing geometric constraints a parameterized interval of the transmission ratio of the vehicle and a gain of acceleration and regeneration torque have been defined for the optimization problem considered.
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