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Special Issue "Control and Optimization of Alternative-Energy Vehicles for Sustainable Transportation"

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: 31 March 2018

Special Issue Editors

Guest Editor
Prof. Dr. Xiaosong Hu

Department of Automotive Engineering, Chongqing University, China
Website | E-Mail
Interests: management and control of energy storage systems; optimal control ofalternative-energy vehicles forsustainable transportation; energy internet; vehicle–traffic–grid–home nexus; automated vehicles
Guest Editor
Dr. Chen Lv

Advanced Vehicle Engineering Centre, Cranfield University, UK
Website | E-Mail
Interests: energy conversion and management of electrified vehicles; energy-efficiency cyber-physical systems; advanced control of alternative-energy vehicles for sustainable transportation; automated vehicles

Special Issue Information

Dear Colleagues,

 

This Special Issue focuses on the control and optimization of alternative-energy vehicles with the aim of presenting this phenomenon through an integrated vision that may come from both specialized and from interdisciplinary articles. Received papers are expected to cover a wide range of topics: From the advanced energy management of alternative-energy vehicles to the analysis of the suitability and efficiency for the sustainable transportation; or from the detailed study of topology design and optimization of sustainable energy storage systems to their integrations within ITS and/or smart grid. Of course, the control and optimization of alternative-energy vehicles will be not limited to sustainable transportation: Mechatronics applications, cyber-physical systems, vehicular and energy internet will be dealt with the same attention. Papers selected for this Special Issue will be subject to a peer review procedure with the aim of rapid and wide dissemination of their contents.

 

Prof. Dr. Xiaosong Hu
Dr. Chen Lv
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


Keywords

  • Alternative-energy vehicles
  • control and optimization
  • energy conversion and storage
  • sustainable transportation

Published Papers (3 papers)

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Research

Open AccessArticle Multi-Objective Optimization of a Hybrid ESS Based on Optimal Energy Management Strategy for LHDs
Sustainability 2017, 9(10), 1874; doi:10.3390/su9101874 (registering DOI)
Received: 10 September 2017 / Revised: 13 October 2017 / Accepted: 13 October 2017 / Published: 21 October 2017
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Abstract
Energy storage systems (ESS) play an important role in the performance of mining vehicles. A hybrid ESS combining both batteries (BTs) and supercapacitors (SCs) is one of the most promising solutions. As a case study, this paper discusses the optimal hybrid ESS sizing
[...] Read more.
Energy storage systems (ESS) play an important role in the performance of mining vehicles. A hybrid ESS combining both batteries (BTs) and supercapacitors (SCs) is one of the most promising solutions. As a case study, this paper discusses the optimal hybrid ESS sizing and energy management strategy (EMS) of 14-ton underground load-haul-dump vehicles (LHDs). Three novel contributions are added to the relevant literature. First, a multi-objective optimization is formulated regarding energy consumption and the total cost of a hybrid ESS, which are the key factors of LHDs, and a battery capacity degradation model is used. During the process, dynamic programming (DP)-based EMS is employed to obtain the optimal energy consumption and hybrid ESS power profiles. Second, a 10-year life cycle cost model of a hybrid ESS for LHDs is established to calculate the total cost, including capital cost, operating cost, and replacement cost. According to the optimization results, three solutions chosen from the Pareto front are compared comprehensively, and the optimal one is selected. Finally, the optimal and battery-only options are compared quantitatively using the same objectives, and the hybrid ESS is found to be a more economical and efficient option. Full article
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Open AccessArticle Heuristic Optimization for the Energy Management and Race Strategy of a Solar Car
Sustainability 2017, 9(10), 1576; doi:10.3390/su9101576
Received: 7 July 2017 / Revised: 28 August 2017 / Accepted: 1 September 2017 / Published: 26 September 2017
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Abstract
Solar cars are known for their energy efficiency, and different races are designed to measure their performance under certain conditions. For these races, in addition to an efficient vehicle, a competition strategy is required to define the optimal speed, with the objective of
[...] Read more.
Solar cars are known for their energy efficiency, and different races are designed to measure their performance under certain conditions. For these races, in addition to an efficient vehicle, a competition strategy is required to define the optimal speed, with the objective of finishing the race in the shortest possible time using the energy available. Two heuristic optimization methods are implemented to solve this problem, a convergence and performance comparison of both methods is presented. A computational model of the race is developed, including energy input, consumption and storage systems. Based on this model, the different optimization methods are tested on the optimization of the World Solar Challenge 2015 race strategy under two different environmental conditions. A suitable method for solar car racing strategy is developed with the vehicle specifications taken as an independent input to permit the simulation of different solar or electric vehicles. Full article
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Open AccessArticle Situational Assessments Based on Uncertainty-Risk Awareness in Complex Traffic Scenarios
Sustainability 2017, 9(9), 1582; doi:10.3390/su9091582
Received: 17 August 2017 / Revised: 2 September 2017 / Accepted: 3 September 2017 / Published: 7 September 2017
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Abstract
Situational assessment (SA) is one of the key parts for the application of intelligent alternative-energy vehicles (IAVs) in the sustainable transportation. It helps IAVs understand and comprehend traffic environments better. In SA, it is crucial to be aware of uncertainty-risks, such as sensor
[...] Read more.
Situational assessment (SA) is one of the key parts for the application of intelligent alternative-energy vehicles (IAVs) in the sustainable transportation. It helps IAVs understand and comprehend traffic environments better. In SA, it is crucial to be aware of uncertainty-risks, such as sensor failure or communication loss. The objective of this study is to assess traffic situations considering uncertainty-risks, including environment predicting uncertainty. According to the stochastic environment model, collision probabilities between multiple vehicles are estimated based on integrated trajectory prediction under uncertainty, which combines the physics- and maneuver-based trajectory prediction models for accurate prediction results in the long term. The SA method considers the probabilities of collision at different predicting points, the masses, and relative speeds between the possible colliding objects. In addition, risks beyond the prediction horizon are considered with the proposition of infinite risk assessments (IRAs). This method is applied and proved to assess risks regarding unexpected obstacles in traffic, sensor failure or communication loss, and imperfect detections with different sensing accuracies of the environment. The results indicate that the SA method could evaluate traffic risks under uncertainty in the dynamic traffic environment. This could help IAVs’ plan motion trajectories and make high-level decisions in uncertain environments. Full article
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