Design and Performance Analysis of a Cascaded Model Predictive Controller and Command Governor for Fuel-Efficient Control of Heavy-Duty Trucks

2021 ◽  
Vol 143 (6) ◽  
Author(s):  
Ben Groelke ◽  
John Borek ◽  
Christian Earnhardt ◽  
Chris Vermillion
Keyword(s):  
Control Strategy ◽  
Heavy Duty ◽  
Model Predictive Controller ◽  
Fuel Savings ◽  
Heavy Duty Truck ◽  
Simulink Model ◽  
Car Following Model ◽  
Cascaded Model ◽  
Predictive Controller ◽  
And Performance

Abstract This paper presents the design and analysis of a predictive ecological control strategy for a heavy-duty truck that achieves substantial fuel savings while maintaining safe following distances in the presence of traffic. The hallmark of the proposed algorithm is the fusion of a long-horizon economic model predictive controller (MPC) for ecological driving with a command governor (CG) for safe vehicle following. The performance of the proposed control strategy was evaluated in simulation using a proprietary medium-fidelity Simulink model of a heavy-duty truck. Results show that the strategy yields substantial fuel economy improvements over a baseline, the extent of which are heavily dependent on the horizon length of the CG. The best fuel and vehicle-following performance are achieved when the CG horizon has a length of 20–40 s, reducing fuel consumption by 4–6% when compared to a Gipps car-following model.

A Multirate, Multiscale Economic Model Predictive Control Approach for Velocity Trajectory Optimization of a Heavy Duty Truck

2020 ◽  
Vol 143 (3) ◽  
Author(s):  
Christian Earnhardt ◽  
Ben Groelke ◽  
John Borek ◽  
Mohammad Naghnaeian ◽  
Chris Vermillion
Keyword(s):  
Predictive Control ◽  
Economic Model ◽  
Heavy Duty ◽  
Time Step ◽  
Length Scales ◽  
Fuel Savings ◽  
Heavy Duty Truck ◽  
Economic Model Predictive Control ◽  
Road Grade ◽  
Lead Vehicle

Abstract This paper introduces a hierarchical economic model predictive control (MPC) approach for maximizing the fuel economy of a heavy-duty truck, which simultaneously accounts for aggregate terrain changes that occur over very long length scales, fine terrain changes that occur over shorter length scales, and lead vehicle behavior that can vary over much shorter time/length scales. To accommodate such disparate time and length scales, the proposed approach uses a multilayer MPC approach wherein the upper-level MPC uses a long distance step, a long time-step, and coarse discretization to account for the slower changes in road grade, while the lower-level MPC uses a shorter time-step to account for fine variations in road grade and rapidly changing lead vehicle behavior. The benefit of this multirate, multiscale approach is that the lower-level MPC leverages the upper-level's sufficiently long look-ahead while allowing for safe vehicle following and adjustment to fine road grade variations. The proposed strategy has been evaluated over four real-world road profiles in both open-highway and traffic environments, using a medium-fidelity simulink model furnished by Volvo Group North America. Compared with a conventional cruise control system plus vehicle following controller as a baseline, results show 4–5% fuel savings in an open highway setting and 6–8% fuel savings in the presence of traffic, without compromising trip time.

scholarly journals A Novel Torque Coordination Control Strategy of a Single-Shaft Parallel Hybrid Electric Vehicle Based on Model Predictive Control

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Jing Sun ◽  
Guojing Xing ◽  
Xudong Liu ◽  
Xiaoling Fu ◽  
Chenghui Zhang
Keyword(s):  
Electric Vehicle ◽  
Predictive Control ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Coordination Control ◽  
Model Predictive Controller ◽  
Parallel Hybrid Electric Vehicle ◽  
Parallel Hybrid ◽  
Predictive Controller ◽  
Hybrid Electric

The torque coordination control during mode transition is a very important task for hybrid electric vehicle (HEV) with a clutch serving as the key enabling actuator element. Poor coordination will deteriorate the drivability of the driver and lead to excessive wearing to the clutch friction plates. In this paper, a novel torque coordination control strategy for a single-shaft parallel hybrid electric vehicle is presented to coordinate the motor torque, engine torque, and clutch torque so that the seamless mode switching can be achieved. Different to the existing model predictive control (MPC) methods, only one model predictive controller is needed and the clutch torque is taken as an optimized variable rather than a known parameter. Furthermore, the successful idea of model reference control (MRC) is also used for reference to generate the set-point signal required by MPC. The parameter sensitivity is studied for better performance of the proposed model predictive controller. The simulation results validate that the proposed novel torque coordination control strategy has less vehicle jerk, less torque interruption, and smaller clutch frictional losses, compared with the baseline method. In addition, the sensitivity and adaptiveness of the proposed novel torque coordination control strategy are evaluated.

Multi-parametric programming-based energy control strategy for parallel hybrid heavy-duty truck

2016 ◽  
Vol 23 (3) ◽  
pp. 244
Author(s):  
Stefano Agostoni ◽  
Federico Cheli ◽  
Ferdinando Luigi Mapelli ◽  
Chen Tao ◽  
Davide Tarsitano ◽  
...  
Keyword(s):  
Control Strategy ◽  
Parametric Programming ◽  
Heavy Duty ◽  
Energy Control ◽  
Heavy Duty Truck ◽  
Parallel Hybrid

LEARNING: KEY TO LEVERAGING THE VALUE OF A FRANCHISE

1994 ◽  
Vol 01 (03n04) ◽  
pp. 367-382 ◽  
Author(s):  
PHILLIP H PHAN ◽  
JOHN E BUTLER ◽  
SOO HOON LEE
Keyword(s):  
Organizational Learning ◽  
Learning Process ◽  
Heavy Duty ◽  
Learning Dynamics ◽  
Contractual Arrangement ◽  
Heavy Duty Truck ◽  
Different Types ◽  
Primary Focus ◽  
And Performance ◽  
The Relationship

The organizational learning dynamics inherent in the franchise relationship provide the primary focus for this research. By encoding knowledge of the skills needed to suceed within the contractual arrangement, the franchisor can short cut the learning process for the franchisee. Once the franchising arrangement is established, both franchisees and franchisors have vested interest in maintaining high levels of organizational learning. In this research a model is presented that incorporates these learning dynamics, and relates them to entrepreneurial returns. Using a sample of heavy-duty truck franchisees, the relationship between different types of organizational learning and performance are explored. The results indicate that the franchising contract may actually act to limit the returns to the franchise relationship. Instead, it may encourage the franchisee to direct their learning efforts to skew returns in their favor. Successful franchisees were found to systematically value the franchise contractual and non-contractual information content more than less successful franchisees.

Research and Development of Heavy-Duty Truck Fueled With DME~Development and Performance of a DME Engine

2005 ◽  
Author(s):  
Yoshio Sato ◽  
Seang-wock Lee ◽  
Toshimitsu Takayanagi ◽  
Hisakazu Suzuki ◽  
Akira Nakamura ◽  
...  
Keyword(s):  
Research And Development ◽  
Heavy Duty ◽  
Heavy Duty Truck ◽  
And Performance

Tyre force control strategy for semi-active magnetorheological damper suspension system for light-heavy duty truck

2015 ◽  
Vol 13 (1) ◽  
pp. 65 ◽  
Author(s):  
Syabillah Sulaiman ◽  
Pakharuddin Mohd Samin ◽  
Hishamuddin Jamaluddin ◽  
Roslan Abd Rahman ◽  
Saiful Anuar Abu Bakar
Keyword(s):  
Control Strategy ◽  
Force Control ◽  
Suspension System ◽  
Magnetorheological Damper ◽  
Heavy Duty ◽  
Heavy Duty Truck
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