摘要：AbstractIncreased forms of connectivity in vehicles creates opportunities to use route, traffic, and other forms of data to inform powertrain control decisions to achieve better fuel economy and/or emissions. Modern diesel engines are equipped with aftertreatment systems which are effective at reducing tailpipe emissions when they are at adequate temperatures – however extended idle or low-load operation can cause low aftertreatment system temperatures, resulting in poor conversion efficiencies and therefore higher observed emissions. Increased tailpipe emissions can be averted through thermal conditioning of the aftertreatment system and/or feedgas emission reductions. A novel model predictive control (MPC) framework is presented that uses engine speed and load preview along with onboard NOXmeasurements to control the engine for best fuel economy subject to emission constraints. To reduce computational complexity the controller output is a decision variable selecting between two engine calibrations, one which prioritizes fuel economy at the expense of increased NOXemissions, and one which prioritizes reduced NOXemissions and increased exhaust heating at the expense of fuel economy. The reduced complexity enables longer preview horizons which is helpful when trying to optimize aftertreatment thermal dynamics that have long time constants. The emission constraints formulations are motivated by moving-average window techniques, and the use of NOXsensor feedback helps to correct issues caused by modeling error and unmodeled phenomena.
