Optimization Method for the Energy and Emissions Management of a Hybrid Electric Vehicle with an Exhaust Aftertreatment System

Abstract

This paper presents a real-time optimization method to compute the fuel-optimal torque split, gear selection and engine on/off command for a Diesel hybrid electric vehicle equipped with an exhaust aftertreatment system. We aim to minimize the amount of fuel consumed, while achieving a charge-sustaining operation and keeping the tailpipe NOx emissions below the legislative limit. We simplify the full vehicle model to facilitate the formulation of a mixed-integer convex problem which is then solved using the proposed iterative convex optimization (ICO) algorithm. We validate the result by comparing it to the globally optimal solution computed using dynamic programming (DP). For the simple model, the ICO algorithm finds the same solution as the DP benchmark. The computation time was reduced from one week for the DP benchmark to 49 s for the ICO solution. By comparing the DP solution obtained on the full model with the ICO solution evaluated on the full model, we observe an offset in the solution due to model mismatch, but find that the ICO algorithm captures the qualitative trends of the optimal solution. The proposed algorithm is capable of solving the energy and emissions management problem in real-time, forming the basis for online optimal control.