Electric Vehicles Energy Management, 2nd Volume

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closed (31 March 2024)

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31 May 2025

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Topic Information

Dear Colleagues,

The current Topic "Electric Vehicles Energy Management, 2^nd 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

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Batteries batteries	4.6	4.0	2015	22 Days	CHF 2700	Submit
Electronics electronics	2.6	5.3	2012	16.8 Days	CHF 2400	Submit
Energies energies	3.0	6.2	2008	17.5 Days	CHF 2600	Submit
Sustainability sustainability	3.3	6.8	2009	20 Days	CHF 2400	Submit
Vehicles vehicles	2.4	4.1	2019	24.7 Days	CHF 1600	Submit
World Electric Vehicle Journal wevj	2.6	4.5	2007	15.7 Days	CHF 1400	Submit

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Related Topic

Published Papers (4 papers)

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Journals

All Batteries Electronics Energies Sustainability Vehicles WEVJ

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19 pages, 1863 KiB  

Open AccessArticle

Dynamic Charging Optimization Algorithm for Electric Vehicles to Mitigate Grid Power Peaks

by Alain Aoun, Mehdi Adda, Adrian Ilinca, Mazen Ghandour and Hussein Ibrahim

World Electr. Veh. J. 2024, 15(7), 324; https://doi.org/10.3390/wevj15070324 - 21 Jul 2024

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Abstract

The rapid proliferation of electric vehicles (EVs) presents both opportunities and challenges for the electrical grid. While EVs offer a promising avenue for reducing greenhouse gas emissions and dependence on fossil fuels, their uncoordinated charging behavior can strain grid infrastructure, thus creating new [...] Read more.

The rapid proliferation of electric vehicles (EVs) presents both opportunities and challenges for the electrical grid. While EVs offer a promising avenue for reducing greenhouse gas emissions and dependence on fossil fuels, their uncoordinated charging behavior can strain grid infrastructure, thus creating new challenges for grid operators and EV owners equally. The uncoordinated nature of electric vehicle charging may lead to the emergence of new peak loads. Grid operators typically plan for peak demand periods and deploy resources accordingly to ensure grid stability. Uncoordinated EV charging can introduce unpredictability and variability into peak load patterns, making it more challenging for operators to manage peak loads effectively. This paper examines the implications of uncoordinated EV charging on the electric grid to address this challenge and proposes a novel dynamic optimization algorithm tailored to manage EV charging schedules efficiently, mitigating grid power peaks while ensuring user satisfaction and vehicle charging requirements. The proposed “Proof of Need” (PoN) charging algorithm aims to schedule the charging of EVs based on collected data such as the state of charge (SoC) of the EV’s battery, the charger power, the number of connected vehicles per household, the end-user’s preferences, and the local distribution substation’s capacity. The PoN algorithm calculates a priority index for each EV and coordinates the charging of all connected EVs at all times in a way that does not exceed the maximum allocated power capacity. The algorithm was tested under different scenarios, and the results offer a comparison of the charging power demand between an uncoordinated EV charging baseline scenario and the proposed coordinated charging model, proving the efficiency of our proposed algorithm, thus reducing the charging demand by 40.8% with no impact on the overall total charging time. Full article

(This article belongs to the Topic Electric Vehicles Energy Management, 2nd Volume)

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17 pages, 3138 KiB  

Open AccessArticle

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

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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  

Open AccessArticle

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

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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  

Open AccessArticle

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

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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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