Optimization of powertrain operating policy for feasibility assessment and calibration: stochastic dynamic programming approach

An approach based on stochastic dynamic programming is proposed to develop optimal operating policies for automotive powertrain systems. The goal is to minimize fuel consumption and tailpipe emissions. Unlike in the conventional approach, the minimization is performed not for a predetermined drive cycle, but in a stochastic "average" sense over a class of trajectories from an underlying Markov chain drive cycle generator. The objective of this paper is to introduce the approach and illustrate its applications. with several examples.