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Dear Colleagues,

Electric energy and hydrogen energy are some of the most promising renewable energy applications, and the development of hybrid electric vehicles, electric vehicles, and fuel cell vehicles has become a research hotspot. With the deepening of research, some emerging technologies can also be gradually applied in research related to new energy vehicles. This Special Issue is open to researchers, engineers, and students worldwide whose work focuses on new energy vehicle-related fields. Authors are welcome to publish original research papers describing theoretical development, system applications, and algorithm demonstrations. Potential topics include, but are not limited to: new energy vehicle technology; energy management strategies; association algorithms between new energy vehicles and traffic information; application of computer vision, reinforcement learning, deep learning, etc., in new energy vehicles; new energy vehicle control algorithms; development of motor control strategies in new energy vehicles; and battery management systems.

Dr. Feng Xiao
Dr. Shixin Song
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 submissions that pass pre-check are 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. Machines 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 2400 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

new energy vehicle technology
energy management strategies
association algorithms between new energy vehicles and traffic information
application of computer vision, reinforcement learning, deep learning, etc., in new energy vehicles
new energy vehicle control algorithms
development of motor control strategies in new energy vehicles
battery management systems.

Related Special Issue

Published Papers (1 paper)

Research

18 pages, 8602 KiB

Open AccessArticle

Effect of Variable-Nozzle-Turbocharger-Coupled Exhaust Gas Recirculation on Natural Gas Engine Emissions and Collaborative Optimization

by Kan Zhu, Diming Lou, Yunhua Zhang, Yedi Ren and Lanlan Fan

Machines 2024, 12(4), 260; https://doi.org/10.3390/machines12040260 - 15 Apr 2024

Viewed by 418

Abstract

Equivalent combustion natural gas engines typically utilize exhaust gas recirculation (EGR) systems to tackle their high thermal burden and NOx emissions. Variable nozzle turbochargers (VNT) can increase the engine intake and EGR rate simultaneously, resulting in NOx reduction while ensuring robust power performance. Using a VNT along with engine bench testing, the impact of VNT- and EGR-coordinated control on the performance and emissions of equivalent combustion natural gas engines was investigated under different operating conditions. Subsequently, multi-objective optimization was performed using a support vector machine. The results demonstrated that the use of VNTs in equivalent combustion natural gas engines could bolster the capacity to introduce EGR under several operative conditions and extend the scope of EGR regulation, thereby decreasing the engine’s thermal burden, improving fuel efficiency, and curbing emissions. Owing to the implementation of a multi-objective optimization method based on a support vector regression model and NSGA-II genetic algorithm, VNT and EGR control parameters could be optimized to slightly improve the economy and significantly reduce NOx emissions while maintaining the original engine power performance. At 20 operating points optimized for validation, brake-specific fuel consumption (BSFC) and NOx decreased by 0.94% and 47.0%, respectively, and CH₄ increased by 3.7%, on average. Full article

(This article belongs to the Special Issue Emerging Technologies in New Energy Vehicle, Volume II)

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