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New Trends in Electric Vehicles
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New Trends in Electric Vehicles

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

Deadline for manuscript submissions: 30 September 2025 | Viewed by 2438

Special Issue Editor

Prof. Dr. Emilio Larrodé

E-Mail Website
Guest Editor
Department of Mechanical Engineering, School of Engineering and Architecture, University of Zaragoza, 50001 Zaragoza, Spain
Interests: vehicle design; sustainable transport systems; logistics engineering; transport operations; optimization of transportation system performance; decision-making in the optimization of transportation operations; technological competitiveness in the transport sector
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The unstoppable increase in the use of electric vehicles in our society and the need to achieve similar performances as current vehicles, is determining the application and development of new technologies, which improve the performances of electric vehicles. We can observe new trends not only in the design and configuration of vehicles, but in the application of new technologies that improve the effectiveness and sustainability of the use of these vehicles.

This special issue entitled "New trends in Electric Vehicles" invites you to write articles that try to show the new trends that are being used in the development of future electric vehicles, and that address, among other topics; systems and equipment for determining the available autonomy in real time, through the incorporation of sensors that allow the measurement of external and internal variables to determine vehicle consumption, trip planning and optimization systems based on the calculation of the expected consumption based on the route to be followed and the available autonomy, incorporation of new materials and new platform designs that reduce the weight of vehicles, better energy storage systems and possible combinations, alternative energy collection and generation systems, developments of elements that reduce rolling and aerodynamic losses, and incorporation of more efficient traction and motorization systems. In addition, will be of special interest those articles that present applications on automobiles, heavy duty vehicles as well as light vehicles, both for freight and passenger’s transport.

Prof. Dr. Emilio Larrodé
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

  • electric vehicle performances
  • autonomy estimation
  • instant consumption
  • energy losses
  • advanced light materials
  • vehicle measurement sensors

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Published Papers (2 papers)

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Research

22 pages, 7772 KiB  
Article
Driving Profile Optimization for Energy Management in the Formula Student Técnico Prototype
by Tomás R. Pires, João F. P. Fernandes and Paulo J. Costa Branco
Energies 2024, 17(24), 6313; https://doi.org/10.3390/en17246313 - 14 Dec 2024
Viewed by 1223
Abstract
This study addresses the challenge of optimizing energy management in the electric vehicle industry, specifically focusing on motorsport. It particularly targets optimizing energy management during an endurance event at the Formula Student competition. The research involves detailed simulation of a complete endurance event, [...] Read more.
This study addresses the challenge of optimizing energy management in the electric vehicle industry, specifically focusing on motorsport. It particularly targets optimizing energy management during an endurance event at the Formula Student competition. The research involves detailed simulation of a complete endurance event, including developing precise track and vehicle models and their application in real-time energy management of our motorsport vehicle. The primary objective is to develop an energy reference profile that optimizes point scoring following the event’s specific rules. The energy reference profile serves as a strategic guideline for energy consumption and its regeneration throughout the endurance event. What sets this study apart is its emphasis on the real-time feedback controller’s implementation in the Formula Student prototype, FST12, specifically during the endurance event. This controller dynamically regulates the inverter’s power output, ensuring the vehicle closely follows the pre-established energy reference profile. This real-time energy management approach enhances overall performance by optimizing energy utilization for maximum scoring potential. The developed distance estimation method presented an error of less than 0.7% compared to experimental measurements. The Formula Student prototype, FST12, underwent experimental validation on a real 20 km closed-loop track. Results showed that the optimized strategy can be implemented with less than 0.5% of error in energy consumption and 6.8% of error in the obtained competing points. Full article
(This article belongs to the Special Issue New Trends in Electric Vehicles)
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23 pages, 9494 KiB  
Article
A Model-Driven Approach for Estimating the Energy Performance of an Electric Vehicle Used as a Taxi in an Intermediate Andean City
by Jairo Castillo-Calderón, Daniel Cordero-Moreno and Emilio Larrodé Pellicer
Energies 2024, 17(23), 6053; https://doi.org/10.3390/en17236053 - 2 Dec 2024
Viewed by 677
Abstract
Regarding the decision to opt for vehicles with electric propulsion systems to achieve a sustainable future, much research has focused on the electrification of passenger cars, since this class of vehicles is the largest contributor of greenhouse gas emissions in the transportation sector. [...] Read more.
Regarding the decision to opt for vehicles with electric propulsion systems to achieve a sustainable future, much research has focused on the electrification of passenger cars, since this class of vehicles is the largest contributor of greenhouse gas emissions in the transportation sector. The purpose of this paper is to assess the energy performance of an electric vehicle used as a taxi in Loja, Ecuador, an intermediate Andean city, using a model-driven approach. Data acquisition was performed through the OBDII port of the KIA SOUL EV for 24 days and the variable mass of the vehicle was recorded as a function of the number of passengers; the effects of road gradient were also considered. The energy performance of the vehicle was simulated by developing an analytical model in MATLAB/Simulink. An average measured battery performance of 8.49 ± 1.4 km/kWh per day was obtained, where the actual energy regenerated was 31.2 ± 1.5%. To validate the proposed model, the results of the daily energy performance estimated with the simulation were compared with those measured in real driving conditions. The results demonstrated a Pearson correlation coefficient of 0.93, indicating a strong positive linear dependence between the variables. In addition, a coefficient of determination of 0.86 and a mean absolute percentage error of 3.35% were obtained, suggesting that the model has a satisfactory predictive capacity for energy performance. Full article
(This article belongs to the Special Issue New Trends in Electric Vehicles)
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