Topic Editors

1. Department of Electric Engineering and Energy Technology (ETEC), Mobility, Logistics and Automotive Technology Research Centre (MOBI), Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussel, Belgium
2. Flanders Make, 3001 Heverlee, Belgium
Department of Electric Engineering and Energy Technology (ETEC), Mobility, Logistics and Automotive Technology Research Centre (MOBI), Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussel, Belgium

Electric Vehicles Energy Management, 2nd Volume

Abstract submission deadline
closed (31 March 2024)
Manuscript submission deadline
31 May 2025
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2129

Topic Information

Dear Colleagues,

The current Topic "Electric Vehicles Energy Management, 2nd Volume" aims to build upon the achievements of its predecessor Topic, “Electric Vehicles Energy Management”, maintaining the continuity of advancement and delving deeper into the intricacies of managing energy in the realm of hybrid and electric vehicles. Continuing in this trajectory, the present Topic endeavors to foster interdisciplinary dialogue and collaboration among researchers, engineers, policymakers, and industry practitioners to bring together cutting-edge research, innovative technologies, and real-world insights, and aspires to catalyze advancements that drive the widespread integration of hybrid and electric vehicles into our transportation ecosystem.

In this context, we invite contributions that explore methodologies, tests, algorithms, models, and technologies aimed at optimizing energy utilization, enhancing vehicle performance, extending battery life, and improving overall system efficiency in electric and hybrid vehicles. Topics of interest include but are not limited to:

  • Energy-efficient powertrain design, sizing, and control.
  • Predictive energy management strategies.
  • Advanced battery management systems.
  • Life cycle assessments.
  • Minimized drivetrain cost/energy consumption/environmental impact.
  • Extended-range analyses and trajectory planning.
  • Vehicle-to-grid and grid-to-vehicle integration and protocols.
  • Connected and non-connected self-driving vehicle energy management.
  • Smart charging infrastructures.
  • Sustainable mobility solutions and policy frameworks.

We encourage researchers, practitioners, and stakeholders from academia, industry, and government agencies to contribute original research articles, review papers, and case studies that shed light on the latest advancements and best practices within the discipline.

Dr. Danial Karimi
Dr. Majid Vafaeipour
Topic Editors

Keywords

  • energy management
  • electric vehicles
  • extended range
  • energy storage
  • batteries
  • fuel economy
  • powertrain
  • power electronics
  • electrical machines and drives
  • modeling and design

 

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Batteries
batteries
4.6 4.0 2015 17.7 Days CHF 2700 Submit
Electronics
electronics
2.6 5.3 2012 15.6 Days CHF 2400 Submit
Energies
energies
3.0 6.2 2008 16.1 Days CHF 2600 Submit
Sustainability
sustainability
3.3 6.8 2009 18.8 Days CHF 2400 Submit
Vehicles
vehicles
2.4 4.1 2019 22.2 Days CHF 1600 Submit
World Electric Vehicle Journal
wevj
2.6 4.5 2007 14.1 Days CHF 1400 Submit

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Published Papers (3 papers)

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17 pages, 3138 KiB  
Article
Demand Time Series Prediction of Stacked Long Short-Term Memory Electric Vehicle Charging Stations Based on Fused Attention Mechanism
by Chengyu Yang, Han Zhou, Ximing Chen and Jiejun Huang
Energies 2024, 17(9), 2041; https://doi.org/10.3390/en17092041 - 25 Apr 2024
Viewed by 485
Abstract
The layout and configuration of urban infrastructure are essential for the orderly operation and healthy development of cities. With the promotion and popularization of new energy vehicles, the modeling and prediction of charging pile usage and allocation have garnered significant attention from governments [...] Read more.
The layout and configuration of urban infrastructure are essential for the orderly operation and healthy development of cities. With the promotion and popularization of new energy vehicles, the modeling and prediction of charging pile usage and allocation have garnered significant attention from governments and enterprises. Short-term demand forecasting for charging piles is crucial for their efficient operation. However, existing prediction models lack a discussion on the appropriate time window, resulting in limitations in station-level predictions. Recognizing the temporal nature of charging pile occupancy, this paper proposes a novel stacked-LSTM model called attention-SLSTM that integrates an attention mechanism to predict the charging demand of electric vehicles at the station level over the next few hours. To evaluate its performance, this paper compares it with several methods. The experimental results demonstrate that the attention-SLSTM model outperforms both LSTM and stacked-LSTM models. Deep learning methods generally outperform traditional time series forecasting methods. In the test set, MAE is 1.6860, RMSE is 2.5040, and MAPE is 9.7680%. Compared to the stacked-LSTM model, MAE and RMSE are reduced by 4.7%and 5%, respectively; while MAPE value decreases by 1.3%, making it superior to LSTM overall. Furthermore, subsequent experiments compare prediction performance among different charging stations, which confirms that the attention-SLSTM model exhibits excellent predictive capabilities within a six-step (2 h) window. Full article
(This article belongs to the Topic Electric Vehicles Energy Management, 2nd Volume)
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17 pages, 4318 KiB  
Article
Electric Vehicle Routing Problem with States of Charging Stations
by Gitae Kim
Sustainability 2024, 16(8), 3439; https://doi.org/10.3390/su16083439 - 19 Apr 2024
Viewed by 663
Abstract
This paper proposes an electric vehicle routing problem, considers the states of charging stations and suggests solution strategies. The charging of electric vehicles is a main issue in the field of electric vehicle routing. There are many studies that find the locations of [...] Read more.
This paper proposes an electric vehicle routing problem, considers the states of charging stations and suggests solution strategies. The charging of electric vehicles is a main issue in the field of electric vehicle routing. There are many studies that find the locations of charging stations, recharging functions for the batteries of vehicles, and so on. However, the state of charging stations significantly affects the routes of electric vehicles, which is not much explored. The states may include open or closed charging stations, occupied or empty charging slots, and so on. This paper investigates how the states of charging stations are estimated and how routing strategies are determined. We formulate a mixed integer programming model and suggest how to solve the problem with an exact method. Numerical examples provide the optimal routing strategies of electric vehicles for the changing environments regarding the states of charging stations. Full article
(This article belongs to the Topic Electric Vehicles Energy Management, 2nd Volume)
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13 pages, 1829 KiB  
Article
A Model for Electrifying Fire Ambulance Service Stations Considering Practical Service Data and Charging Strategies
by Yih-Her Yan, Rong-Ceng Leou and Chien-Chin Ko
Energies 2024, 17(6), 1445; https://doi.org/10.3390/en17061445 - 17 Mar 2024
Viewed by 616
Abstract
Due to concerns with air pollution and climate change, governments and transport operators around the world have engaged in transforming their fossil-fueled vehicles into electric vehicles (EVs). It is essential to build a model for the electrifying process to minimize the operation costs. [...] Read more.
Due to concerns with air pollution and climate change, governments and transport operators around the world have engaged in transforming their fossil-fueled vehicles into electric vehicles (EVs). It is essential to build a model for the electrifying process to minimize the operation costs. This paper presents a systematic analytical approach for the electrification of a fire ambulance service station. This approach begins with the selection of suitable EVs to replace the current service vehicles. Subsequently, an in-depth analysis is conducted to determine the practical utilization of EVs at the station. The model proposes two charging strategies: immediate charging upon an EVs’ return and smart charging. Based on the chosen EVs and charging strategies, a comprehensive assessment of the load profiles for the planned EV charging station is performed. In accordance with the load profiles, a mathematical model to minimize the infrastructure and operation costs of the charging station is proposed. Various pricing schemes are compared to identify the most efficient pricing scheme for the charging station, and economic analyses of the EVs and traditional ambulance vehicles are proposed in this paper. The test results indicate that the progressive pricing scheme is well suited for immediate charging strategies, whereas smart charging should opt for the time-of-use pricing scheme. Selecting the appropriate pricing scheme has the potential to significantly reduce electric energy costs. Full article
(This article belongs to the Topic Electric Vehicles Energy Management, 2nd Volume)
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