Electric Vehicles Energy Management, 2nd Volume

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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	19.7 Days	CHF 2700	Submit
Electronics electronics	2.6	5.3	2012	16.4 Days	CHF 2400	Submit
Energies energies	3.0	6.2	2008	16.8 Days	CHF 2600	Submit
Sustainability sustainability	3.3	6.8	2009	19.7 Days	CHF 2400	Submit
Vehicles vehicles	2.4	4.1	2019	19.9 Days	CHF 1600	Submit
World Electric Vehicle Journal wevj	2.6	4.5	2007	16.2 Days	CHF 1400	Submit

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Related Topic

Published Papers (7 papers)

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All Batteries Electronics Energies Sustainability Vehicles WEVJ

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23 pages, 7213 KiB  

Open AccessArticle

Advanced Adaptive Rule-Based Energy Management for Hybrid Energy Storage Systems (HESSs) to Enhance the Driving Range of Electric Vehicles

by Chew Kuew Wai, Taha Sadeq and Lee Cheun Hau

Vehicles 2025, 7(1), 6; https://doi.org/10.3390/vehicles7010006 - 18 Jan 2025

Cited by 1 | Viewed by 959

Abstract

The energy storage system (ESS) plays a crucial role in electric vehicles (EVs), impacting their performance and efficiency. While batteries are the standard choice for energy storage, they come with drawbacks like low power density and limited life cycles, which can hinder pure [...] Read more.

The energy storage system (ESS) plays a crucial role in electric vehicles (EVs), impacting their performance and efficiency. While batteries are the standard choice for energy storage, they come with drawbacks like low power density and limited life cycles, which can hinder pure battery electric vehicles (PBEVs). To address these issues, a hybrid energy storage system (HESS) that combines a battery with a supercapacitor provides a more effective solution. The battery delivers consistent power, while the supercapacitor manages peak power demands and regenerative braking energy. This study proposes a new energy management strategy for the HESS, an advanced adaptive rule-based algorithm. The results of the standard rule-based and adaptive rule-based algorithms are used to verify the proposed control algorithm. The system was modeled in MATLAB/Simulink and evaluated across three driving cycles—UDDS, NYCC, and Japan1015—while varying states of charge for the supercapacitors. The findings indicate that the HESS significantly alleviates battery stress compared to a pure battery system, enhancing both efficiency and lifespan. Among the algorithms tested, the advanced adaptive rule-based algorithm yielded the best results, increasing the number of viable drive cycles. Full article

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

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49 pages, 4747 KiB  

Open AccessArticle

Electric Vehicle Traction Battery Recycling Decision-Making Considering Blockchain Technology in the Context of Capacitance Level Differential Demand

by Lijun Yang and Yi Wang

World Electr. Veh. J. 2024, 15(12), 561; https://doi.org/10.3390/wevj15120561 - 3 Dec 2024

Viewed by 1298

Abstract

In recent years, the rapid growth in electric vehicle ownership has resulted in a significant number of decommissioned traction batteries that will require recycling in the future. As consumer expectations for electric vehicle range continue to rise, the turnover of traction batteries has [...] Read more.

In recent years, the rapid growth in electric vehicle ownership has resulted in a significant number of decommissioned traction batteries that will require recycling in the future. As consumer expectations for electric vehicle range continue to rise, the turnover of traction batteries has accelerated substantially. Consequently, there is an urgent need for electric vehicle manufacturers to establish an efficient, recyclable supply chain for the return of end-of-life (EOL) electric vehicle (EV) traction batteries. In this paper, we investigate the closed-loop recycling supply chain for retired power batteries in electric vehicle manufacturers, taking into account blockchain technology and the high range preferences in the electric vehicle market, which are influenced by varying demand for different levels of electric vehicle capacitance. Blockchain, as a distributed and decentralized technology, offers features such as consensus mechanisms, traceability, and security, which have been effectively applied across various fields. In this study, we construct four models involving EV battery manufacturers, EV retailers, and battery comprehensive utilization (BCU) enterprises participating in the recycling process. Through the analysis of a Stackelberg response model, we find that (1) single-channel recycling is less efficient than dual-channel recycling models, a difference driven by the diversity of recycling channels and the variability in recycling markets; (2) Recycling models incorporating blockchain technology demonstrate superior performance compared to those that do not utilize blockchain technology, particularly when the intensity of recycling competition is below 0.76; (3) Traction batteries integrated with blockchain technology exhibit higher recycling rates when the optimization index is below 0.96. Electric vehicle battery manufacturers must evaluate the benefits and costs of adopting blockchain technology; (4) With lower recycling incentive levels and EV range preferences, the single-channel recycling model yields better returns than the other three recycling models. EV manufacturers can enhance overall battery supply chain revenues by establishing varying incentive levels based on market demand for different capacitance levels. Full article

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

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27 pages, 9360 KiB  

Open AccessArticle

Simulating EV Growth Scenarios in Jawa-Madura-Bali from 2024 to 2029: Balancing the Power Grid’s Supply and Demand

by Joshua Veli Tampubolon and Rinaldy Dalimi

World Electr. Veh. J. 2024, 15(8), 341; https://doi.org/10.3390/wevj15080341 - 29 Jul 2024

Viewed by 2048

Abstract

This study provides a comprehensive simulation for understanding the influence of EV growth and its external factors on grid stability and offers insights into effective management strategies. To manage the growth of battery-based electric vehicles (BEVs) in Indonesia and mitigate their impact on [...] Read more.

This study provides a comprehensive simulation for understanding the influence of EV growth and its external factors on grid stability and offers insights into effective management strategies. To manage the growth of battery-based electric vehicles (BEVs) in Indonesia and mitigate their impact on the power grid’s supply–demand equilibrium, regulatory adjustments and subsidies can be implemented by the government. The Jawa-Madura-Bali (Jamali) electrical system, as the largest in Indonesia, is challenged with accommodating the rising number of vehicles. Following an analysis of Jamali’s electricity supply using data from the National Electricity Company (RUPTL), simulations are constructed to model the grid’s demand side. Input variables such as Jamali’s population, the numbers of internal combustion engine (ICE) and electric vehicles, initial charging times (ICT), slow and fast charging ratios, and BEV charge load curves are simulated. Scenario variables, including supply capacity growth rate, vehicle population growth rate, subsidy impact on EV attractiveness, ICT, and fast charging ratio, are subsequently simulated for the 2024–2029 period. Four key simulation outcomes are identified. The best-case scenario (scenario 1776) achieves the highest EV growth with minimal grid disruption, resulting in a 45.38% EV percentage in 2029 and requiring an annual allocation of 492 billion rupiah to match supply with demand. The worst-case scenario leads to a 23.12% EV percentage, necessitating 47,566 billion rupiah for EV subsidies in 2029. Additionally, the most and least probable scenarios based on the literature research are evaluated. This novel simulation and its results provide insights into EV growth’s impact on the grid’s balance in one presidential term from 2024 to 2029, aiding the government in planning regulations and subsidies effectively. Full article

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

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18 pages, 1863 KiB  

Open AccessEditor’s ChoiceArticle

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

Cited by 6 | Viewed by 3913

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

Cited by 6 | Viewed by 1619

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

Cited by 2 | Viewed by 2560

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

Cited by 1 | Viewed by 1234

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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