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

scholarly journals Optimal Energy Control Strategy Design for a Hybrid Electric Vehicle

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Yuan Zou ◽  
Hou Shi-jie ◽  
Li Dong-ge ◽  
Gao Wei ◽  
Xiao-song Hu
Keyword(s):  
Optimal Control ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Heavy Duty ◽  
Dynamic Features ◽  
Optimal Control Strategy ◽  
Energy Control ◽  
Optimal Energy ◽  
Parallel Hybrid ◽  
Hybrid Electric

A heavy-duty parallel hybrid electric truck is modeled, and its optimal energy control is studied in this paper. The fundamental architecture of the parallel hybrid electric truck is modeled feed-forwardly, together with necessary dynamic features of subsystem or components. Dynamic programming (DP) technique is adopted to find the optimal control strategy including the gear-shifting sequence and the power split between the engine and the motor subject to a battery SOC-sustaining constraint. Improved control rules are extracted from the DP-based control solution, forming near-optimal control strategies. Simulation results demonstrate that a significant improvement on the fuel economy can be achieved in the heavy-duty vehicle cycle from the natural driving statistics.

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.

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

The Strategies of Energy Control of Parallel Hybrid Electric Vehicle and the Simulation Analysis of Energy Saving Efficiency

2012 ◽  
Vol 472-475 ◽  
pp. 1953-1956
Author(s):  
Fang Xiao Cheng ◽  
Bao Dong Zhang ◽  
Xiao Mei Lin
Keyword(s):  
Energy Saving ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Simulation Analysis ◽  
Power Source ◽  
Energy Control ◽  
Software Simulation ◽  
Parallel Hybrid Electric Vehicle ◽  
Optimization Control ◽  
Parallel Hybrid

This paper refers to Nissan Tino series models as the research object, the power source of the vehicle makes reasonable distribution by a kind of optimization control strategy of the energy, in order to improving the efficiency of the energy conservation. The experimental data was compared by the advisor software simulation to control strategy, it is verified the effectiveness of the energy saving efficiency.

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