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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "E: Electric Vehicles".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 19049

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Special Issue Editor

Prof. Dr. Hai Wang

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Guest Editor

Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China
Interests: intelligent connected vehicles; machine vision; machine learning; deep learning

Special Issue Information

Dear Colleagues,

Intelligent Connected New Energy Vehicles (ICNEVs) have interdisciplinary applications, including vehicle engineering, energy engineering, artificial intelligence, mechanical systems, electric systems, electronic systems, automation and control, communication, etc. It is not only a key carrier of global strategy to build strength in transportation—it is also a strong pillar to support global development.

This Special Issue is open to researchers, engineers, and students worldwide, focusing on ICNEV-related fields. The authors are welcome to publish original research papers describing theory development, system applications, algorithm demonstrations, as well as including but not limited to:

New energy vehicle technology
Battery technology
Environment perception technology of intelligent vehicles
Vehicle localization and mapping technology of intelligent vehicles
Planning and control of intelligent vehicles
Simulation for intelligent connected new energy vehicles
V2X and cooperative driving
Sensors for intelligent connected new energy vehicles

Prof. Dr. Hai Wang
Guest Editor

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. Energies is an international peer-reviewed open access semimonthly 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 2600 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 vehicles
Energy saving
Artificial intelligence
Environment perception
Vehicle localization
Mapping and navigation
Planning and control
Driving strategy
Simulation
V2X
Cooperative driving
Sensors
Battery technology

Published Papers (5 papers)

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Research

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18 pages, 8893 KiB

Open AccessArticle

Charge Equalization System for an Electric Vehicle with a Solar Panel

by Darwin-Alexander Angamarca-Avendaño, Jonnathan-Francisco Saquicela-Moncayo, Byron-Humberto Capa-Carrillo and Juan-Carlos Cobos-Torres

Energies 2023, 16(8), 3360; https://doi.org/10.3390/en16083360 - 11 Apr 2023

Cited by 1 | Viewed by 1792

Abstract

Electric vehicles are environmentally friendly and more efficient than conventional combustion vehicles. However, from the point of view of energy vectors, they may use energy produced by less efficient and more polluting means. In this paper, an applicative methodology is used to develop a charging equalizer for an electric vehicle that makes it possible to efficiently use the energy produced by a 350 W photovoltaic panel to intelligently charge the five batteries of the vehicle. In addition, using a quantitative methodology, an analysis of the different physical and electrical parameters obtained by a series of sensors installed in the vehicle is presented, and the efficiency of the system is determined. Different routes were travelled within the city of Cuenca with and without the load equalization system, which made it possible to determine an increase in vehicle efficiency of up to 27.9%, equivalent to an additional travel distance of approximately 14.35 km. This is a promising result, since with small investments in solar panels and electronic materials, the performance of low-cost electric vehicles can be significantly improved. Full article

(This article belongs to the Special Issue Advanced Technologies in New Energy Vehicle)

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16 pages, 6344 KiB

Open AccessArticle

A Vehicle-to-Infrastructure beyond Visual Range Cooperative Perception Method Based on Heterogeneous Sensors

by Tong Luo, Long Chen, Tianyu Luan, Yang Li and Yicheng Li

Energies 2022, 15(21), 7956; https://doi.org/10.3390/en15217956 - 26 Oct 2022

Cited by 1 | Viewed by 1484

Abstract

With the development of autopilot, the performance of intelligent vehicles is constrained by their inability to perceive blind and beyond visual range areas. Vehicle-to-infrastructure cooperative perception has become an effective method for achieving reliable and higher-level autonomous driving. A vehicle-to-infrastructure cooperative beyond visual range and non-blind area method, based on heterogeneous sensors, was proposed in this study. Firstly, a feature map receptive field enhancement module with spatial dilated convolution module (SDCM), based on spatial dilated convolution, was proposed and embedded into the YOLOv4 algorithm. The YOLOv4-SDCM algorithm with SDCM module achieved a 1.65% mAP improvement in multi-object detection performance with the BDD100K test set. Moreover, the backbone of CenterPoint was improved with the addition of self-calibrated convolutions, coordinate attention, and residual structure. The proposed Centerpoint-FE (Feature Enhancement) algorithm achieved a 3.25% improvement in mAP with the ONCE data set. In this paper, a multi-object post-fusion matching method of heterogeneous sensors was designed to realize the vehicle-to-infrastructure cooperative beyond visual range. Experiments conducted at urban intersections without traffic lights demonstrated that the proposed method effectively resolved the problem of beyond visual range perception of intelligent vehicles. Full article

(This article belongs to the Special Issue Advanced Technologies in New Energy Vehicle)

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21 pages, 6492 KiB

Open AccessArticle

Accuracy Examination of the SDCM Augmentation System in Aerial Navigation

by Kamil Krasuski, Adam Ciećko, Mieczysław Bakuła and Grzegorz Grunwald

Energies 2022, 15(20), 7776; https://doi.org/10.3390/en15207776 - 20 Oct 2022

Cited by 2 | Viewed by 1320

Abstract

The paper presents a modified algorithm for determining the accuracy parameter of the system for differential corrections and monitoring (SDCM) navigation solution in air navigation. For this purpose, a solution to determine the resultant accuracy parameter was proposed by using two on-board global navigation satellite system (GNSS) receivers. The mathematical algorithm takes into account the calculation of a single point positioning accuracy for a given GNSS receiver and a weighting factor combining the position error values. The weighting factor was determined as a function of the number of tracked GNSS satellites used in the SDCM single point positioning solution. The resultant accuracy parameter was expressed in ellipsoidal coordinates BLh (B—latitude, L—longitude, h—ellipsoidal height). The study used GNSS kinematic data recorded by two on-board receivers: Trimble Alloy and Septentrio AsterRx2i, located in a Diamond DA 20-C1 aircraft. The test flight was performed near the city of Olsztyn in north-eastern Poland. Calculations and analyses were performed using RTKLIB software and the Scilab environment. On the basis of the performed tests, it was found that the proposed algorithm for SDCM system allows for improvement in the determination of the resultant accuracy value by 56–80% in relation to the results of position errors from a single GNSS receiver. Additionally, the proposed algorithm was tested for the European Geostationary Navigation Overlay Service (EGNOS) system, and in this case, the improvement in the accuracy parameter was even better and was in the range of 69–89%. The resulting SDCM and EGNOS positioning accuracy met the International Civil Aviation Organization (ICAO) certification requirements for SBAS systems in air navigation. The mathematical algorithm developed in this work was tested positively and can be implemented within the SBAS augmentation system in air navigation. Full article

(This article belongs to the Special Issue Advanced Technologies in New Energy Vehicle)

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17 pages, 7111 KiB

Open AccessArticle

Study on the Positioning Accuracy of the GNSS/INS System Supported by the RTK Receiver for Railway Measurements

by Mariusz Specht, Cezary Specht, Andrzej Stateczny, Paweł Burdziakowski, Paweł Dąbrowski and Oktawia Lewicka

Energies 2022, 15(11), 4094; https://doi.org/10.3390/en15114094 - 2 Jun 2022

Cited by 7 | Viewed by 2336

Abstract

Currently, the primary method for determining the object coordinates is positioning using Global Navigation Satellite Systems (GNSS) supported by Inertial Navigation Systems (INS). The main goal of this solution is to ensure high positioning availability, particularly when access to satellite signals is limited (in tunnels, areas with densely concentrated buildings and in forest areas). The aim of this article is to determine whether the GNSS/INS system supported by the RTK receiver is suitable for the implementation of selected geodetic and construction tasks in railway engineering, such as determining the place and extent of rail track deformations (1 cm (p = 0.95)), the process of a rapid stocktaking of existing rail tracks (3 cm (p = 0.95)) and for design and construction works (10 cm (p = 0.95)), as well as what the impact of various terrain obstacles have on the obtained positioning accuracy of the tested system. During the research, one INS was used, the Ekinox2-U by the SBG Systems, which was supported by the Real-Time Kinematic (RTK) receiver. GNSS/INS measurements were conducted on three representative sections varying in terms of terrain obstacles that limit the access to satellite signals during mobile railway measurements in Tricity (Poland). The acquired data allowed us to calculate the basic position accuracy measures that are commonly used in navigation and transport applications. On this basis, it was concluded that the Ekinox2-U system can satisfy the positioning accuracy requirements for rapid stocktaking of existing rail tracks (3 cm (p = 0.95)), as well as for design and construction works (10 cm (p = 0.95)). On the other hand, the system cannot be used to determine the place and extent of rail track deformations (1 cm (p = 0.95)). Full article

(This article belongs to the Special Issue Advanced Technologies in New Energy Vehicle)

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Review

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34 pages, 7331 KiB

Open AccessReview

Trends and Emerging Technologies for the Development of Electric Vehicles

by Tiande Mo, Yu Li, Kin-tak Lau, Chi Kin Poon, Yinghong Wu and Yang Luo

Energies 2022, 15(17), 6271; https://doi.org/10.3390/en15176271 - 28 Aug 2022

Cited by 36 | Viewed by 11032

Abstract

In response to severe environmental and energy crises, the world is increasingly focusing on electric vehicles (EVs) and related emerging technologies. Emerging technologies for EVs have great potential to accelerate the development of smart and sustainable transportation and help build future smart cities. This paper reviews new trends and emerging EV technologies, including wireless charging, smart power distribution, vehicle-to-home (V2H) and vehicle-to-grid (V2G) systems, connected vehicles, and autonomous driving. The opportunities, challenges, and prospects for emerging EV technologies are systematically discussed. The successful commercialization development cases of emerging EV technologies worldwide are provided. This review serves as a reference and guide for future technological development and commercialization of EVs and offers perspectives and recommendations on future smart transportation. Full article

(This article belongs to the Special Issue Advanced Technologies in New Energy Vehicle)

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