Utilizing Look-Ahead Information to Minimize Fuel Consumption and NO_x Emissions in Heavy Duty Vehicles

Abstract:

Producing more fuel efficient vehicles as well as lowering emissions are of high importance among heavy duty vehicle manufactures. One functionality of lowering fuel consumption is to use a so called \emph{look-ahead control strategy}, which uses the GPS and topography data to determine the optimal velocity profile in the future. When driving downhill in slopes, no fuel is supplied to the engine which lowers the temperature in the aftertreatment system. This results in a reduced emission reduction capability of the aftertreatment system.

This master thesis investigates the possibilities of using preheating look-ahead control actions to heat the aftertreatment system before entering a downhill slope, with the purpose of lowering fuel consumption and $NO_x$ emissions. A temperature model of a heavy duty aftertreatment system is produced, which is used to analyse the fuel consumption and $NO_x$ reduction performance of a Scania truck. A Dynamic Programming algorithm is also developed with the purpose of defining an optimal control trajectory for minimizing the fuel consumption and released $NO_x$ emissions.

It is concluded that the Dynamic Programming optimization initiates preheating control actions with results of fuel consumption reduction as well as $NO_x$ emissions reductions. The best case for reducing the maximum amount of fuel consumption results in 0.14\% lower fuel consumption and 5.2\% lower $NO_x$ emissions.