Evaluation on the Fuel Economy of Automated Vehicles with Data-Driven Simulation Method

Luo, Yueqi; Xiang, Dang; Zhang, Song; Wei-ming, Liang; Sun, Jian; Zhu, Liang

doi:10.1016/j.egyai.2021.100051

Cited by 7 publications

(5 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Along the same line, Zhang et al [33] reported an approximate average saving of fuel 10% for driving speeds 20 km/h to 40 km/h, calculated using the VSP model. Overall, while there are a few studies suggesting a potential increase in fuel consumption when AVs are in operation [16,34], the majority of research papers confirm that there will likely be a reduction in fuel consumption for single AV driving.…”

Section: Car-followingmentioning

confidence: 99%

“…In the domain of projecting future expected fuel consumption, the advent of AVs has introduced a large number of methodologies, ranging from simulation models [16][17][18], to surveys gauging public perceptions [19] and development of analytical frameworks [20,21]. While these approaches have provided valuable insights, the landscape remains marked by a scarcity of real-world applications.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Assessing the Paradox of Autonomous Vehicles: Promised Fuel Efficiency vs. Aggregate Fuel Consumption

Mohammed,

Horváth

2024

Energies

View full text Add to dashboard Cite

As autonomous vehicles (AVs) continue to evolve and approach widespread adoption in the near future, the touted benefits of improved fuel efficiency at an individual level come under scrutiny when considering the overall impact on fuel consumption. This research delves into the paradoxical relationship between the promising technology of AVs, their impact on traffic capacities, travel demand, and the subsequent influence on aggregate fuel consumption. While AVs have demonstrated enhanced fuel efficiency when considered as a singular mode of transportation, our study reveals a contrasting trend when scaled to a broader societal context. Through comprehensive analysis of the literature, we discovered that, at lower limits of energy savings achievable by a single AV, the overall fuel consumption increases by a staggering 42% compared to conventional human-driven vehicles. This counterintuitive outcome is a result of the aggregate effect of increased AV usage, leading to higher traffic volumes and travel demands. Conversely, at higher thresholds of energy savings by individual AVs, the percentage of fuel consumption increment diminishes, but remains notable. Even with advanced energy-saving features, the overall fuel quantity still experiences a substantial 30% increase compared to conventional vehicles when scaled up to widespread AV use. Our findings emphasize the importance of considering the holistic impact of AVs on transportation systems and energy consumption. As society transitions towards AV-dominated traffic, policymakers and stakeholders must address the challenges associated with increased travel demand, potential traffic congestion, and the resultant implications on fuel consumption.

show abstract

Section: Car-followingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing the Paradox of Autonomous Vehicles: Promised Fuel Efficiency vs. Aggregate Fuel Consumption

Mohammed,

Horváth

2024

Energies

View full text Add to dashboard Cite

show abstract

“…Recently, AI has received growing attention in all aspects of life, and “AI plus” shows great potential in solving complex problems − in chemical and electrochemical systems . Because a massive amount of data has been accumulated for porous structure characterization and subsequent variable distributions, AI methods can directly utilize these existing data and play a vital role in fuel cell R&D. , Lombardo et al conducted a comprehensive and critical review on the application of AI and machine learning (ML) to battery research and pointed to the enormous potential of AI in boosting the development of next-generation Li-ion batteries. Wang et al, Zhao et al, Ding et al, and Legala et al reviewed the application of the AI method in PEM fuel cells.…”

Section: Challenges and Future Prospectsmentioning

confidence: 99%

Porous Flow Field for Next-Generation Proton Exchange Membrane Fuel Cells: Materials, Characterization, Design, and Challenges

Zhang

Tao

et al. 2022

Chem. Rev.

View full text Add to dashboard Cite

Porous flow fields distribute fuel and oxygen for the electrochemical reactions of proton exchange membrane (PEM) fuel cells through their pore network instead of conventional flow channels. This type of flow fields has showed great promises in enhancing reactant supply, heat removal, and electrical conduction, reducing the concentration performance loss and improving operational stability for fuel cells. This review presents the research and development progress of porous flow fields with insights for next-generation PEM fuel cells of high power density (e.g., ∼9.0 kW L–1). Materials, fabrication methods, fundamentals, and fuel cell performance associated with porous flow fields are discussed in depth. Major challenges are described and explained, along with several future directions, including separated gas/liquid flow configurations, integrated porous structure, full morphology modeling, data-driven methods, and artificial intelligence-assisted design/optimization.

show abstract

“…Therefore, the testing and evaluation of autonomous driving functions have become an important foundation in the development of automated vehicles [3]. However, there is no complete standard or policy for reference for evaluation index systems for automated vehicle testing [4]. Qualitative evaluation methods exhibit a lack of subjectivity in determining the weight of evaluation indicators [5], while some quantitative evaluation methods do not consider the subjective will of decision makers in the evaluation process [6].…”

Section: Introduction 1motivationmentioning

confidence: 99%

An Evaluation Method for Automated Vehicles Combining Subjective and Objective Factors

Wang

et al. 2023

Machines

View full text Add to dashboard Cite

The rapid development of automated vehicle technology requires reasonable test scenarios and comprehensive evaluation methods. This paper proposes an evaluation method for automated vehicles combining subjective and objective factors. First, we propose a method for automatically generating test scenarios and for batch testing autonomous vehicles. Then, the use of the target layer, total index layer, and index layer of automated vehicles is proposed to establish a more comprehensive evaluation system for automated vehicles. Specifically, the analytic hierarchy process (AHP, subjective) and improved criteria importance though intercriteria correlation (CRITIC, objective) methods are used to determine the weight of the indicators, and a two-level fuzzy comprehensive (subjective and objective) evaluation method is adopted to comprehensively evaluate the performance of the automated vehicles. Finally, the effectiveness of the proposed evaluation method combining subjective and objective factors is verified through virtual simulations and real-world experiments. Through a combination of subjective and objective methods, improved results can be obtained for safety, efficiency, economy, intelligence, and comfort tests.

show abstract

Evaluation on the Fuel Economy of Automated Vehicles with Data-Driven Simulation Method

Cited by 7 publications

References 23 publications

Assessing the Paradox of Autonomous Vehicles: Promised Fuel Efficiency vs. Aggregate Fuel Consumption

Assessing the Paradox of Autonomous Vehicles: Promised Fuel Efficiency vs. Aggregate Fuel Consumption

Porous Flow Field for Next-Generation Proton Exchange Membrane Fuel Cells: Materials, Characterization, Design, and Challenges

An Evaluation Method for Automated Vehicles Combining Subjective and Objective Factors

Contact Info

Product

Resources

About