Design of an Optimal Control Strategy in a Parallel Hybrid Vehicle in Order to Simultaneously Reduce Fuel Consumption and Emissions

Paper #:
  • 2011-01-0894

  • 2011-04-12
Dorri, M. and Shamekhi, A., "Design of an Optimal Control Strategy in a Parallel Hybrid Vehicle in Order to Simultaneously Reduce Fuel Consumption and Emissions," SAE Technical Paper 2011-01-0894, 2011,
In this paper, an optimal control strategy is developed. The control strategy aims to simultaneously reduce fuel consumption and emissions of a parallel hybrid electric vehicle (HEV). A continuously variable transmission (CVT) is implemented in the HEV model. The CVT has a significant role to operate the internal combustion engine (ICE) near its optimal operating points; consequently its proper control will contribute to enhance the fuel economy and emissions. Using a trade-off between the fuel consumption and emission rates, improving the fuel consumption can cause the emission rates to be improved too.First, 5 different modes for the vehicle motion is defined. Afterwards, depending on the state of charge of the battery (SOC) and the requested power from the driver, the best mode, in each time step, is chosen. Knowing the best mode, the control strategy refers to ICE or electric motor (EM) pre-calculated optimal curves, and determines ICE/EM output speed (i.e. input speed to CVT). The CVT output speed is derived from speed-time diagram of drive cycles. Subsequently, the optimal gear ratio is known. This gear ratio helps the ICE/EM to work optimally, resulting in better fuel consumption and reduced emissions.The controller tries to maintain the vehicle performance parameters in a defined region. This will assure that driver's power request is fulfilled all the time. The controller tries to use as much regenerative power as possible.Results of implementing the proposed control strategy are calculated over three different drive cycles. In a part of control strategy, a fuzzy logic controller is used to determine the proper vehicle mode. To achieve better outputs, parameters of the fuzzy controller are optimized using genetic algorithms. Final results showed the optimal control strategy success in reducing fuel consumption and improving or maintaining emissions meanwhile the performance parameters are within the defined limits.
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