Predictive Optimal Control of Mild Hybrid Trucks

Abstract

Fuel consumption, subsequent emissions and safe operation of class 8 vehicles are of prime importance in recent days. It is imperative that a vehicle operates in its true optimal operating region, given a variety of constraints such as road grade, load, gear shifts, battery state of charge (for hybrid vehicles), etc. In this paper, a research study is conducted to evaluate the fuel economy and subsequent emission benefits when applying predictive control to a mild hybrid line-haul truck. The problem is solved using a combination of dynamic programming with backtracking and model predictive control. The specific fuel-saving features that are studied in this work are dynamic cruise control, gear shifts, vehicle coasting and torque management. These features are evaluated predictively as compared to a reactive behavior. The predictive behavior of these features is a function of road grade. The result and analysis show significant improvement in fuel savings along with NOx benefits. Out of the control features, dynamic cruise (predictive) control and dynamic coasting showed the most benefits, while predictive gear shifts and torque management (by power splitting between battery and engine) for this architecture did not show fuel benefits but provided other benefits in terms of powertrain efficiency.