Topic Editors

Institute for Engineering, Computing and Advanced Manufacturing, University of Cumbria, Lancaster LA1 3JD, UK
Aeronautical and Automotive Engineering, Loughborough University, Epinal Way, Loughborough LE11 3TU, UK
School of Engineering, University of Lincoln, Brayford Pool Lincoln, Lincoln LN6 7TS, UK

Low Carbon Energy in Transportation

Abstract submission deadline
closed (31 August 2023)
Manuscript submission deadline
closed (31 October 2023)
Viewed by
2616

Topic Information

Dear Colleagues,

We are inviting submissions to a Special Topic of Energies on the subject area of “Low Carbon Energy in Transportation” which covers a very broad technical scope from low-carbon aircraft, vehicles and ships, through mobile energy storage, energy vectors sources and fuels, multi-modal transport operations, scheduling and systems optimisation, to societal behavioural change, policy, energy economics and governance.

In multidisciplinary terms, the subject ranges include electronic, control and systems engineering, LC vehicle design, materials engineering and physics, energy transmission and conversion, computer and data science and AI, electrical machines and drives, operations research, energy economics and behavioural change, among others. Topics of interest for publication include, but are not limited to:

  • Novel Electrical Power Systems architectures and technologies;
  • Energy vectors, integration with renewables, power and energy dense machines, converters and energy storage;
  • Air, land and sea vehicles; electrical propulsion and actuation for land, sea and air vehicles;
  • Electrical Machines, Drives, Systems and Applications—AC and DC machines and drives;
  • Multiscale systems modelling; remote monitoring and diagnosis;
  • Power Electronic Systems—Converters and emerging technologies;
  • Modelling simulation and control, reliability and fault tolerance, safety critical operation;
  • Electrical Power Generation Systems—Modelling and simulation of electrical power systems;
  • Load management; power quality; distribution reliability; distributed and islanded power systems, sensor networks, communication and control;
  • Electrical Power Systems Modelling and Control—Modelling and control methodologies and applications;
  • Intelligent systems, optimization and advanced heuristics, adaptive systems, robust control;
  • Multi-modal transport operations, scheduling and optimisation.

Prof. Dr. Paul Stewart
Prof. Dr. Rui Chen
Prof. Dr. Chris Bingham
Topic Editors

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
C
carbon
4.1 - 2015 23.8 Days CHF 1600
Energies
energies
3.2 5.5 2008 16.1 Days CHF 2600
Environments
environments
3.7 5.9 2014 23.7 Days CHF 1800
Future Transportation
futuretransp
- - 2021 28.2 Days CHF 1000
Sustainability
sustainability
3.9 5.8 2009 18.8 Days CHF 2400

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Published Papers (2 papers)

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16 pages, 4710 KiB  
Article
Battery State-of-Health Evaluation for Roadside Energy Storage Systems in Electric Transportation
by Kailong Deng, Kaiyuan Shen, Zihao Dong, Zekai Liang, Lei Zhao, Ting Xu and Shunde Yin
Future Transp. 2023, 3(4), 1310-1325; https://doi.org/10.3390/futuretransp3040072 - 30 Nov 2023
Viewed by 723
Abstract
Battery health assessments are essential for roadside energy storage systems that facilitate electric transportation. This paper uses the samples from the charging and discharging data of the base station and the power station under different working conditions at different working hours and at [...] Read more.
Battery health assessments are essential for roadside energy storage systems that facilitate electric transportation. This paper uses the samples from the charging and discharging data of the base station and the power station under different working conditions at different working hours and at different temperatures to demonstrate the decay of the battery health of a roadside energy storage system under different cycles. In this paper, for the first time, the predicted state-of-health values are obtained by extracting the characteristic quantities affecting the battery health based on three indicators: the internal resistance, the rate of change of voltage, and the change of temperature. Data on state of health are clustered by K-Means, GMM, K-Means++ and divided into high, medium, and low levels. Using a comparison of the three methods, GMM clustering appears to be the best at reflecting the charging and discharging capacity of the battery. Full article
(This article belongs to the Topic Low Carbon Energy in Transportation)
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5 pages, 202 KiB  
Editorial
Climate Benefits Advocated by the Development of Sustainable Vehicles and Charging Infrastructures in the Transport Sector
by José A. Ventura
Energies 2023, 16(9), 3772; https://doi.org/10.3390/en16093772 - 28 Apr 2023
Viewed by 916
Abstract
Sustainable transportation refers to low vehicular greenhouse gas (GHG) emissions, energy efficient vehicles, and affordable modes of transportation, including electric and alternative fuel (AF) vehicles [...] Full article
(This article belongs to the Topic Low Carbon Energy in Transportation)
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