Co-Optimization of Speed Planning and Energy Management for Plug-In Hybrid Electric Trucks Passing Through Traffic Light Intersections

To tackle the energy-saving optimization issue of plug-in hybrid electric trucks traversing multiple traffic light intersections continuously, this paper presents a double-layer energy management strategy that utilizes the dynamic programming–twin delayed deep deterministic policy gradient (DP-TD3)...

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Main Authors: Xin Liu, Guojing Shi, Changbo Yang, Enyong Xu, Yanmei Meng
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:Energies
Subjects:
Online Access:https://www.mdpi.com/1996-1073/17/23/6022
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author Xin Liu
Guojing Shi
Changbo Yang
Enyong Xu
Yanmei Meng
author_facet Xin Liu
Guojing Shi
Changbo Yang
Enyong Xu
Yanmei Meng
author_sort Xin Liu
collection DOAJ
description To tackle the energy-saving optimization issue of plug-in hybrid electric trucks traversing multiple traffic light intersections continuously, this paper presents a double-layer energy management strategy that utilizes the dynamic programming–twin delayed deep deterministic policy gradient (DP-TD3) algorithm to synergistically optimize the speed planning and energy management of plug-in hybrid electric trucks, thereby enhancing the vehicle’s passability through traffic light intersections and fuel economy. In the upper layer, the dynamic programming (DP) algorithm is employed to create a speed-planning model. This model effectively converts the nonlinear constraints related to the position, phase, and timing information of each traffic signal on the road into time-varying constraints, thereby improving computational efficiency. In the lower layer, an energy management model is constructed using the twin delayed deep deterministic policy gradient (TD3) algorithm to achieve optimal allocation of demanded power through the interaction of the TD3 agent with the truck environment. The model’s validity is confirmed through testing on a hardware-in-the-loop test machine, followed by simulation experiments. The results demonstrate that the DP-TD3 method proposed in this paper effectively enhances fuel economy, achieving an average fuel saving of 14.61% compared to the dynamic programming–charge depletion/charge sustenance (DP-CD/CS) method.
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publishDate 2024-11-01
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series Energies
spelling doaj-art-c1485d0f71a241e8b4581b6d6d9be9bb2024-12-13T16:25:47ZengMDPI AGEnergies1996-10732024-11-011723602210.3390/en17236022Co-Optimization of Speed Planning and Energy Management for Plug-In Hybrid Electric Trucks Passing Through Traffic Light IntersectionsXin Liu0Guojing Shi1Changbo Yang2Enyong Xu3Yanmei Meng4School of Mechanical Engineering, Guangxi University, Nanning 530004, ChinaSchool of Mechanical Engineering, Guangxi University, Nanning 530004, ChinaDongfeng Liuzhou Motor Co., Ltd., Liuzhou 545005, ChinaDongfeng Liuzhou Motor Co., Ltd., Liuzhou 545005, ChinaSchool of Mechanical Engineering, Guangxi University, Nanning 530004, ChinaTo tackle the energy-saving optimization issue of plug-in hybrid electric trucks traversing multiple traffic light intersections continuously, this paper presents a double-layer energy management strategy that utilizes the dynamic programming–twin delayed deep deterministic policy gradient (DP-TD3) algorithm to synergistically optimize the speed planning and energy management of plug-in hybrid electric trucks, thereby enhancing the vehicle’s passability through traffic light intersections and fuel economy. In the upper layer, the dynamic programming (DP) algorithm is employed to create a speed-planning model. This model effectively converts the nonlinear constraints related to the position, phase, and timing information of each traffic signal on the road into time-varying constraints, thereby improving computational efficiency. In the lower layer, an energy management model is constructed using the twin delayed deep deterministic policy gradient (TD3) algorithm to achieve optimal allocation of demanded power through the interaction of the TD3 agent with the truck environment. The model’s validity is confirmed through testing on a hardware-in-the-loop test machine, followed by simulation experiments. The results demonstrate that the DP-TD3 method proposed in this paper effectively enhances fuel economy, achieving an average fuel saving of 14.61% compared to the dynamic programming–charge depletion/charge sustenance (DP-CD/CS) method.https://www.mdpi.com/1996-1073/17/23/6022plug-in hybrid electric truckeco-drivingenergy management strategytraffic light
spellingShingle Xin Liu
Guojing Shi
Changbo Yang
Enyong Xu
Yanmei Meng
Co-Optimization of Speed Planning and Energy Management for Plug-In Hybrid Electric Trucks Passing Through Traffic Light Intersections
Energies
plug-in hybrid electric truck
eco-driving
energy management strategy
traffic light
title Co-Optimization of Speed Planning and Energy Management for Plug-In Hybrid Electric Trucks Passing Through Traffic Light Intersections
title_full Co-Optimization of Speed Planning and Energy Management for Plug-In Hybrid Electric Trucks Passing Through Traffic Light Intersections
title_fullStr Co-Optimization of Speed Planning and Energy Management for Plug-In Hybrid Electric Trucks Passing Through Traffic Light Intersections
title_full_unstemmed Co-Optimization of Speed Planning and Energy Management for Plug-In Hybrid Electric Trucks Passing Through Traffic Light Intersections
title_short Co-Optimization of Speed Planning and Energy Management for Plug-In Hybrid Electric Trucks Passing Through Traffic Light Intersections
title_sort co optimization of speed planning and energy management for plug in hybrid electric trucks passing through traffic light intersections
topic plug-in hybrid electric truck
eco-driving
energy management strategy
traffic light
url https://www.mdpi.com/1996-1073/17/23/6022
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