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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: closed (31 March 2018)

Special Issue Editors

Guest Editor
Prof. Dr. Xiaosong Hu

Department of Automotive Engineering, Chongqing University, China
Website | E-Mail
Interests: Electrified vehicles; Alternative powertrains; Energy storage systems; Battery management; Vehicle-grid-home interactions; Energy management optimization
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 (8 papers)

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Research

Open AccessArticle Energy Management for Plug-In Hybrid Electric Vehicle Based on Adaptive Simplified-ECMS
Sustainability 2018, 10(6), 2060; https://doi.org/10.3390/su10062060
Received: 21 May 2018 / Revised: 12 June 2018 / Accepted: 12 June 2018 / Published: 17 June 2018
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Abstract
When searching for the optimal solution, Equivalent Consumption Minimum Strategy (ECMS) has to calculate and compare the total equivalent fuel rate of huge candidates covered all over the control domain for each time instant. Therefore, this strategy still has a heavy computation burden
[...] Read more.
When searching for the optimal solution, Equivalent Consumption Minimum Strategy (ECMS) has to calculate and compare the total equivalent fuel rate of huge candidates covered all over the control domain for each time instant. Therefore, this strategy still has a heavy computation burden problem; it is a challenge for ECMS to be implemented online for real-time control. To reduce ECMS’s calculation load, this paper proposes an adaptive Simplified-ECMS-based strategy for a parallel plug-in hybrid electric vehicle (PHEV). A convex piecewise function is applied to fit the total equivalent fuel rate with respect to the motor torque, which is the control variable. Then, the ECMS problem is simplified to calculate and compare only five candidates’ total equivalent fuel rate to determine the optimal torque distribution. Particle swarm optimization (PSO) algorithm is applied to optimize the equivalent factor, and the MAPs of this factor under different driving cycles, driving distances and initial SOC are obtained. Based on this, the adaptive Simplified-ECMS-based strategy is proposed. Simulations were performed, and the results show that the Simplified-ECMS-based strategy can obviously shorten the calculation time compared to ECMS-based strategy, and the adaptive Simplified-ECMS-based strategy can decrease fuel consumption of plug-in hybrid electric vehicle by 16.43% under the testing driving cycle, compared to CD-CS-based strategy. A road test on the prototype vehicle is conducted and the effectiveness of the Simplified-ECMS-based strategy is validated by the test data. Full article
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Open AccessArticle Design and Optimization of IPM Motor Considering Flux Weakening Capability and Vibration for Electric Vehicle Applications
Sustainability 2018, 10(5), 1533; https://doi.org/10.3390/su10051533
Received: 31 March 2018 / Revised: 25 April 2018 / Accepted: 27 April 2018 / Published: 11 May 2018
Cited by 1 | PDF Full-text (3322 KB) | HTML Full-text | XML Full-text
Abstract
As motor design is key to the development of electric vehicles (EVs) and hybrid EVs (HEVs), it has recently become the subject of considerable interest. Interior permanent magnet (IPM) motors offer advantages such as high torque density and high efficiency, benefiting from both
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As motor design is key to the development of electric vehicles (EVs) and hybrid EVs (HEVs), it has recently become the subject of considerable interest. Interior permanent magnet (IPM) motors offer advantages such as high torque density and high efficiency, benefiting from both permanent magnet (PM) torque and reluctance torque. However an obvious disadvantage of IPM motors is that operation at high speed involves difficulties in achieving the required flux-weakening capability and low vibration. This study focuses on optimizing the flux-weakening performance and reducing the vibration of an IPM motor for EVs. Firstly, flux-weakening capability, cogging torque, torque ripple, and radical vibration force are analyzed based on the mathematical model. Secondly, three kinds of motors are optimized by the genetic algorithm and analyzed, providing visible insights into the contribution of different rotor structures to the torque characteristics, efficiency, and extended speed range. Thirdly, a slotted rotor configuration is proposed to reduce the torque ripple and radical vibration force. The flux density distributions are discussed, explaining the principle that motors with slotted rotors and stator skew slots have smaller torque ripple and radical vibration force. Lastly, the design and optimization results have been validated against experiments. Full article
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Open AccessArticle Understanding the Emergence and Social Acceptance of Electric Vehicles as Next-Generation Models for the Automobile Industry
Sustainability 2018, 10(3), 662; https://doi.org/10.3390/su10030662
Received: 3 January 2018 / Revised: 26 February 2018 / Accepted: 27 February 2018 / Published: 1 March 2018
Cited by 1 | PDF Full-text (610 KB) | HTML Full-text | XML Full-text
Abstract
This study explores potential factors of drivers’ intentions to use electric vehicles and proposes an integrated adoption model. Results of a structural equation modeling analysis with 988 samples indicate that drivers’ intentions are predicted by one negative factor (cost) and three positive ones
[...] Read more.
This study explores potential factors of drivers’ intentions to use electric vehicles and proposes an integrated adoption model. Results of a structural equation modeling analysis with 988 samples indicate that drivers’ intentions are predicted by one negative factor (cost) and three positive ones (satisfaction, usefulness, and attitude). In addition, the total standardized effects of potential factors on the intention are computed. The current study also validates the original technology acceptance model. Based on the results of the current study, practical and academic implications with potential limitations are examined and presented. Full article
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Open AccessArticle Migrating towards Using Electric Vehicles in Campus-Proposed Methods for Fleet Optimization
Sustainability 2018, 10(2), 285; https://doi.org/10.3390/su10020285
Received: 28 September 2017 / Revised: 15 January 2018 / Accepted: 18 January 2018 / Published: 23 January 2018
Cited by 2 | PDF Full-text (2230 KB) | HTML Full-text | XML Full-text
Abstract
Managing a fleet efficiently to address demand within cost constraints is a challenge. Mismatched fleet size and demand can create suboptimal budget allocations and inconvenience users. To address this problem, many studies have been conducted around heterogeneous fleet optimization. That research has not
[...] Read more.
Managing a fleet efficiently to address demand within cost constraints is a challenge. Mismatched fleet size and demand can create suboptimal budget allocations and inconvenience users. To address this problem, many studies have been conducted around heterogeneous fleet optimization. That research has not included an examination of different vehicle types with travel distance constraints. This study focuses on optimizing the University of Tennessee (UT) motor pool which has a heterogeneous fleet that includes electric vehicles (EVs) with a travel distance and recharge time constraint. After assessing UT motor pool trip patterns as a case study, a queuing model was used to estimate the maximum number of each vehicle type needed to minimize the expected customer wait time to near zero. The break-even point is used for the optimization model to constrain the minimum number of years that electric vehicles should be operated under the no-subsidy assumption. The results show that the fleet has surplus vehicles. In addition to reducing the number of vehicles, total fleet costs could be minimized by using electric vehicles for all trips less than 100 miles. The models are flexible and can be applied and help fleet managers make decisions about fleet size and EV adoption. Full article
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Open AccessArticle Longitudinal Control for Mengshi Autonomous Vehicle via Gauss Cloud Model
Sustainability 2017, 9(12), 2259; https://doi.org/10.3390/su9122259
Received: 21 October 2017 / Revised: 29 November 2017 / Accepted: 29 November 2017 / Published: 6 December 2017
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Abstract
Dynamic robustness and stability control is a requirement for self-driving of autonomous vehicle. Longitudinal control technique of autonomous vehicle is basic theory and one key complex technique which must have the reliability and precision of vehicle controller. The longitudinal control technique is one
[...] Read more.
Dynamic robustness and stability control is a requirement for self-driving of autonomous vehicle. Longitudinal control technique of autonomous vehicle is basic theory and one key complex technique which must have the reliability and precision of vehicle controller. The longitudinal control technique is one of the foundations of the safety and stability of autonomous vehicle control. In our paper, we present a longitudinal control algorithm based on cloud model for Mengshi autonomous vehicle to ensure the dynamic stability and tracking performance of Mengshi autonomous vehicle. The longitudinal control algorithm mainly uses cloud model generator to control the acceleration of the autonomous vehicle to achieve the goal that controls the speed of Mengshi autonomous vehicle. The proposed longitudinal control algorithm based on cloud model is verified by real experiments on Highway driving scene. The experiments results of the acceleration and speed show that the algorithm is validity and stability. Full article
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Open AccessArticle Multi-Objective Optimization of a Hybrid ESS Based on Optimal Energy Management Strategy for LHDs
Sustainability 2017, 9(10), 1874; https://doi.org/10.3390/su9101874
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; https://doi.org/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; https://doi.org/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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