Fuel-efficient Driving for Motor Vehicles Based on Slope Recognition

Paper #:
  • 2017-01-0037

  • 2017-03-28
The heavy-duty vehicles travel with complex driving conditions and long-distance transportation in the mountainous areas. The driver's hysteretic perception to the environment will affect the fuel economy of the vehicle. Unreasonable acceleration and deceleration on the slope will increase the fuel consumption. Improving the performance of the engine and transmission system has limited energy-saving space, and the most fuel-efficient driving assistant systems don't consider the road conditions. In the research, the low space dimensions of the economic driving optimization algorithm with the fast calculation speed is established to plan the accurate and real-time economic driving scheme based on the slope information. The optimization algorithm with less dependence on the experimental data of the fuel consumption characteristics has the good adaptability to most vehicles. For the first drive on the slope, the slope gradient and length are measured and stored. For the next drive on the same slope, the speed profile for a vehicle passing the total slope will have been optimized by making full use of the stored front slope information before the vehicle reaches the slope. And the economic driving scheme will be planned to guide the driver to adjust the vehicle speed reasonably during the uphill process and distribute the working time of the engine brake and the wheel brake effectively during the downhill process so as to reduce the fuel consumption. The simulations are conducted to validate the effectiveness of the economic driving optimization algorithm. From the simulation results, the economic driving scheme makes the fuel economy increase 7% to 12.3% averagely compared with the uniform motions with different driving speeds on the slope. The transportation costs and the abrasion of the wheel brake will be effectively reduced when the economic driving scheme is applied to the advanced driver assistance systems.
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