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WEVJ
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Propulsion Systems of EVs 2.0
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Propulsion Systems of EVs 2.0

A special issue of World Electric Vehicle Journal (ISSN 2032-6653).

Deadline for manuscript submissions: 31 January 2025 | Viewed by 9968

Special Issue Editor

Prof. Dr. Paulo J. G. Pereirinha

E-Mail Website
Guest Editor
Coimbra Polytechnic—ISEC and INESC Coimbra, 3030-199 Coimbra, Portugal
Interests: electric vehicles; electrical machines; electromechanical drives (also finite elements and renewable energies)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Climate change and pollution are putting high pressure on finding more sustainable and effective transportation means, with several countries and cities announcing limitations to the circulation and sales of internal combustion engine vehicles in the near future. Electrically propelled vehicles, from pure electric to hybrid electric vehicles, either as private, public, or shared transport, are the most effective way of achieving these objectives. The electric vehicles available today have already reached a remarkable level of development in all their components, particularly in the last 10–12 years. Almost daily, new advances are being announced in energy storage systems and their components, electric machines, motor drives, hybrid electric systems, etc. Nevertheless, there is still much room for them to improve. This Special Issue of the World Electric Vehicle Journal is devoted to the last developments on the propulsion systems of EVs, including their components, for vehicles powered only by batteries, fuel cells, supercapacitors, or a combination of these, with electric machines or hybrids. More academic or more industrial technical development papers are sought. Extended versions of conference papers (with at least 50% different content and undergoing a new peer review process), focusing on the more technical aspects (methodologies, formulations, more results, etc.), are eligible and welcomed to this Special Issue.

Prof. Dr. Paulo J. G. Pereirinha
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. World Electric Vehicle Journal 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

  • electric vehicle
  • electrically propelled vehicles
  • hybrid electric vehicles
  • battery-powered vehicles
  • fuel cell vehicles
  • electric buses
  • propulsion systems
  • traction motor
  • electric machines for EVs
  • battery pack
  • batteries for electric vehicles
  • fuel cell systems
  • motor drives
  • power electronics for electric vehicles
  • multiple energy sources
  • energy management
  • drive train

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Related Special Issue

Published Papers (4 papers)

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Research

18 pages, 730 KiB  
Article
Electric Vehicle Battery Remanufacturing: Circular Economy Leadership and Workforce Development
by Bianca Ifeoma Chigbu, Fhulu H. Nekhwevha and Ikechukwu Umejesi
World Electr. Veh. J. 2024, 15(10), 441; https://doi.org/10.3390/wevj15100441 - 28 Sep 2024
Viewed by 1256
Abstract
Given the increasing momentum globally towards sustainable transportation, the remanufacturing of used electric vehicle lithium-ion batteries (EV LIBs) emerges as a critical opportunity to promote the principles of the circular economy. Existing research highlights the significance of remanufacturing in resource conservation and waste [...] Read more.
Given the increasing momentum globally towards sustainable transportation, the remanufacturing of used electric vehicle lithium-ion batteries (EV LIBs) emerges as a critical opportunity to promote the principles of the circular economy. Existing research highlights the significance of remanufacturing in resource conservation and waste reduction. Nevertheless, detailed insights into South Africa’s (SA’s) specific capabilities and strategic approaches in the context of used EV LIBs remain sparse. By utilizing in-depth interviews with fifteen key industry stakeholders and drawing on institutional theory, this qualitative study evaluates SA’s infrastructure, technical expertise, and regulatory frameworks in the EV LIB remanufacturing sector to address this gap. The findings reveal proactive strategies, including technical expertise, sustainable infrastructure, and robust regulatory frameworks aligned with global standards. This study proposes strategic initiatives like the Interdisciplinary Innovation Hub and Mobile Remanufacturing Labs, which are analytically derived from stakeholder insights and aim to predict potential pathways for workforce development, especially in rural areas. Innovative training programs, including the Virtual Reality Consortium, Circular Economy Institutes, and the Real-world Challenges Program, will ensure a skilled workforce committed to sustainability and circular economy principles. The conclusions highlight SA’s potential to become a leader in EV LIB remanufacturing by integrating circular economy principles, enhancing technical expertise, and fostering international collaboration. Full article
(This article belongs to the Special Issue Propulsion Systems of EVs 2.0)
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22 pages, 4441 KiB  
Article
Sizing of Autonomy Source Battery–Supercapacitor Vehicle with Power Required Analyses
by Juliana Lopes, José Antenor Pomilio and Paulo Augusto Valente Ferreira
World Electr. Veh. J. 2024, 15(3), 76; https://doi.org/10.3390/wevj15030076 - 20 Feb 2024
Cited by 1 | Viewed by 1849
Abstract
The combined use of batteries and supercapacitors is an alternative to reconcile the higher energy density of batteries with the high power density of supercapacitors. The optimal sizing of this assembly, especially with the minimization of mass, is one of the challenges of [...] Read more.
The combined use of batteries and supercapacitors is an alternative to reconcile the higher energy density of batteries with the high power density of supercapacitors. The optimal sizing of this assembly, especially with the minimization of mass, is one of the challenges of designing the power system of an electric vehicle. The condition of the unpredictability of the power demand determined by the vehicle driver must also be added, which must be met by the power system without exceeding safe operating limits for the devices. This article presents a methodology for minimizing the mass of the electrical energy storage system (ESS) that considers the various aspects mentioned and a variety of battery technologies and supercapacitor values. The resulting minimum mass dimensioning is verified by simulation for different driving cycles under conditions of maximum power demand. The system also includes a tertiary source, such as a fuel cell, responsible for the vehicle’s extended autonomy. In addition to sizing the ESS, the article also proposes a management strategy for the various sources to guarantee the vehicle’s expected performance while respecting each device’s operational limits. Full article
(This article belongs to the Special Issue Propulsion Systems of EVs 2.0)
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16 pages, 5532 KiB  
Article
Joint Estimation of State of Charge and State of Health of Lithium-Ion Batteries Based on Stacking Machine Learning Algorithm
by Yuqi Dong, Kexin Chen, Guiling Zhang and Ran Li
World Electr. Veh. J. 2024, 15(3), 75; https://doi.org/10.3390/wevj15030075 - 20 Feb 2024
Cited by 2 | Viewed by 1791
Abstract
Conducting online estimation studies of the SOH of lithium-ion batteries is indispensable for extending the cycle life of energy storage batteries. Data-driven methods are efficient, accurate, and do not depend on accurate battery models, which is an important direction for battery state estimation [...] Read more.
Conducting online estimation studies of the SOH of lithium-ion batteries is indispensable for extending the cycle life of energy storage batteries. Data-driven methods are efficient, accurate, and do not depend on accurate battery models, which is an important direction for battery state estimation research. However, the relationships between variables in lithium-ion battery datasets are mostly nonlinear, and a single data-driven algorithm is susceptible to a weak generalization ability affected by the dataset itself. Meanwhile, most of the related studies on battery health estimation are offline estimation, and the inability for online estimation is also a problem to be solved. In this study, an integrated learning method based on a stacking algorithm is proposed. In this study, the end voltage and discharge temperature were selected as the characteristics based on the sample data of NASA batteries, and the B0005 battery was used as the training set. After training on the dataset and parameter optimization using a Bayesian algorithm, the trained model was used to predict the SOH of B0007 and B0018 models. After comparative analysis, it was found that the prediction results obtained based on the proposed model not only have high accuracy and a short running time, but also have a strong generalization ability, which has a great potential to achieve online estimation. Full article
(This article belongs to the Special Issue Propulsion Systems of EVs 2.0)
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18 pages, 3096 KiB  
Article
Optimizing Electric Vehicle Battery Life: A Machine Learning Approach for Sustainable Transportation
by K. Karthick, S. Ravivarman and R. Priyanka
World Electr. Veh. J. 2024, 15(2), 60; https://doi.org/10.3390/wevj15020060 - 9 Feb 2024
Cited by 4 | Viewed by 4607
Abstract
Electric vehicles (EVs) are becoming increasingly popular, due to their beneficial environmental effects and low operating costs. However, one of the main challenges with EVs is their short battery life. This study presents a comprehensive approach for predicting the Remaining Useful Life (RUL) [...] Read more.
Electric vehicles (EVs) are becoming increasingly popular, due to their beneficial environmental effects and low operating costs. However, one of the main challenges with EVs is their short battery life. This study presents a comprehensive approach for predicting the Remaining Useful Life (RUL) of Nickel Manganese Cobalt-Lithium Cobalt Oxide (NMC-LCO) batteries. This research utilizes a dataset derived from the Hawaii Natural Energy Institute, encompassing 14 individual batteries subjected to over 1000 cycles under controlled conditions. A multi-step methodology is adopted, starting with data collection and preprocessing, followed by feature selection and outlier elimination. Machine learning models, including XGBoost, BaggingRegressor, LightGBM, CatBoost, and ExtraTreesRegressor, are employed to develop the RUL prediction model. Feature importance analysis aids in identifying critical parameters influencing battery health and lifespan. Statistical evaluations reveal no missing or duplicate data, and outlier removal enhances model accuracy. Notably, XGBoost emerged as the most effective algorithm, providing near-perfect predictions. This research underscores the significance of RUL prediction for enhancing battery lifecycle management, particularly in applications like electric vehicles, ensuring optimal resource utilization, cost efficiency, and environmental sustainability. Full article
(This article belongs to the Special Issue Propulsion Systems of EVs 2.0)
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