Optimal Eco-Driving Control of Autonomous and Electric Trucks in Adaptation to Highway Topography: Energy Minimization and Battery Life Extension

Zhang, Yongzhi; Qu, Xiaobo; Tong, Lang

doi:10.1109/tte.2022.3147214

Cited by 17 publications

(2 citation statements)

References 47 publications

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“…For instance, several studies, including those by Zhuang et al [29], Ding and Jin [30], and Mello and Bauer [31], delve into the intricacies of cruising control during free-driving conditions. Conversely, Zhang et al [32], He et al [33], and Shao and Sun [34] have devoted their efforts to refining car-following control strategies for vehicles trailing preceding counterparts. The realm of signalized intersection crossing has drawn considerable interest as well, prompting research contributions by Katsaros et al [35], Hao et al [36], Sun et al [37], Han et al [38], Lin et al [39], and Dong et al [40].…”

Section: Urban Traffic Management Strategiesmentioning

confidence: 99%

Enhancing Energy Efficiency in Connected Vehicles for Traffic Flow Optimization

Shahbazi,

Nowaczyk

2023

Smart Cities

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In urban settings, the prevalence of traffic lights often leads to fluctuations in traffic patterns and increased energy utilization among vehicles. Recognizing this challenge, this research addresses the adverse effects of traffic lights on the energy efficiency of electric vehicles (EVs) through the introduction of a Multi-Intersections-Based Eco-Approach and Departure strategy (M-EAD). This innovative strategy is designed to enhance various aspects of urban mobility, including vehicle energy efficiency, traffic flow optimization, and battery longevity, all while ensuring a satisfactory driving experience. The M-EAD strategy unfolds in two distinct stages: First, it optimizes eco-friendly green signal windows at traffic lights, with a primary focus on minimizing travel delays by solving the shortest path problem. Subsequently, it employs a receding horizon framework and leverages an iterative dynamic programming algorithm to refine speed trajectories. The overarching objective is to curtail energy consumption and reduce battery wear by identifying the optimal speed trajectory for EVs in urban environments. Furthermore, the research substantiates the real-world efficacy of this approach through on-road vehicle tests, attesting to its viability and practicality in actual road scenarios. In the proposed case, the simulation results showcase notable achievements, with energy consumption reduced by 0.92% and battery wear minimized to a mere 0.0017%. This research, driven by the pressing issue of urban traffic energy efficiency, not only presents a solution in the form of the M-EAD strategy but also contributes to the fields of sustainable urban mobility and EV performance optimization. By tackling the challenges posed by traffic lights, this work offers valuable insights and practical implications for improving the sustainability and efficiency of urban transportation systems.

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Section: Urban Traffic Management Strategiesmentioning

confidence: 99%

Enhancing Energy Efficiency in Connected Vehicles for Traffic Flow Optimization

Shahbazi,

Nowaczyk

2023

Smart Cities

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“…While existing studies focus on minimizing the energy consumption of conventional internal combustion engine (ICE) trucks [5], [6], [7], electric trucks [8], [9] and electric vehicles [10], [11], [12], [13], [14], only a recent work [15] is known for carbon-aware truck operations. Note that energyefficient operation may not necessarily lead to a low carbon footprint [15].…”

Section: Introductionmentioning

confidence: 99%

Follow the Sun and Go with the Wind: Carbon Footprint Optimized Timely E-Truck Transportation

Lin

Chen

2023

Proceedings of the 14th ACM International Conference on Future Energy Systems

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Carbon footprint optimization (CFO) is important for sustainable heavy-duty e-truck transportation. We consider the CFO problem for timely transportation of e-trucks, where the truck travels from an origin to a destination across a national highway network subject to a deadline. The goal is to minimize the carbon footprint by orchestrating path planning, speed planning, and intermediary charging planning. We first show that it is NP-hard even just to find a feasible CFO solution.We then develop a (1 + ϵF , 1 + ϵ β ) bi-criteria approximation algorithm that achieves a carbon footprint within a ratio of (1+ϵF ) to the minimum with no deadline violation and at most a ratio of (1 + ϵ β ) battery capacity violation (for any positive ϵF and ϵ β ). Its time complexity is polynomial in the size of the highway network, 1/ϵF , and 1/ϵ β . Such algorithmic results are among the best possible unless P=NP. Simulation results based on real-world traces show that our scheme reduces up to 11% carbon footprint as compared to baseline alternatives considering only energy consumption but not carbon footprint.

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Conditions of Effective Application of Energy-Saving Programs for the Movement of Heavy Trucks on the Highway

Oliskevych

Danchuk

2023

Lecture Notes in Intelligent Transportation and Infrastructure

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Optimal Eco-Driving Control of Autonomous and Electric Trucks in Adaptation to Highway Topography: Energy Minimization and Battery Life Extension

Cited by 17 publications

References 47 publications

Enhancing Energy Efficiency in Connected Vehicles for Traffic Flow Optimization

Enhancing Energy Efficiency in Connected Vehicles for Traffic Flow Optimization

Follow the Sun and Go with the Wind: Carbon Footprint Optimized Timely E-Truck Transportation

Conditions of Effective Application of Energy-Saving Programs for the Movement of Heavy Trucks on the Highway

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