Article
Version 1
Preserved in Portico This version is not peer-reviewed
Predictive Optimal Control of Hybrid Line Haul Trucks
Version 1
: Received: 11 October 2022 / Approved: 17 October 2022 / Online: 17 October 2022 (03:40:06 CEST)
A peer-reviewed article of this Preprint also exists.
Pramanik, S.; Anwar, S. Predictive Optimal Control of Mild Hybrid Trucks. Vehicles 2022, 4, 1344-1364. Pramanik, S.; Anwar, S. Predictive Optimal Control of Mild Hybrid Trucks. Vehicles 2022, 4, 1344-1364.
Abstract
Fuel consumption, subsequent emissions and safe operation of class 8 vehicles are of prime importance in recent days. It is imperative that the 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 back tracking 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 are a function of road grade. The result and analysis shows 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.
Keywords
dynamic program; fuel economy; global optimization; predictive control
Subject
Engineering, Control and Systems Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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