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AI Solutions for Energy Management: Smart Grids and EV Charging
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AI Solutions for Energy Management: Smart Grids and EV Charging

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: 15 October 2025 | Viewed by 468

Special Issue Editors

Dr. Lujuan Dang

E-Mail Website
Guest Editor
National Key Laboratory of Human–Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, China
Interests: state estimation; machine learning; information-theoretic learning, and their applications in brain-computer interfaces (BCIs), robotics, and intelligent battery systems
Dr. Yuan Chen

E-Mail Website
Guest Editor
School of Artificial Intelligence, Anhui University, Hefei, China
Interests: battery modeling; states estimation; life prediction; fault diagnosis; battery management system
Dr. Wentao Ma

E-Mail Website
Guest Editor
School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China
Interests: deep learning; information theoretical learning; renewable energy power; forecast power; state estimation; energy storage management system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Energy management is crucial for sustainable development, with demand steadily rising due to global population growth and economic progress. As traditional energy resources dwindle, the challenge lies in efficient distribution.  Smart grids and Electric Vehicle (EV) charging systems introduce complexities that require advanced management tools. Decision-makers face uncertainties and multifaceted parameters, necessitating innovative solutions. 

This Special Issue focuses on the latest advancements in AI-driven solutions for energy management, particularly in the context of smart grids and EV charging. Topics cover the development and application of AI technologies to enhance energy efficiency, manage grid operations, optimize EV charging, and integrate renewable energy sources. The aim is to present cutting-edge research that addresses the complexities of modern energy systems, fostering more intelligent and sustainable energy management practices.

Dr. Lujuan Dang
Dr. Yuan Chen
Dr. Wentao Ma
Guest Editors

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

  • EV charging
  • smart grids
  • renewable energy sources

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Published Papers (2 papers)

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Research

17 pages, 2719 KiB  
Article
State of Health Prediction for Lithium-Ion Batteries Based on Gated Temporal Network Assisted by Improved Grasshopper Optimization
by Xiankun Wei, Silun Peng and Mingli Mo
Energies 2025, 18(14), 3856; https://doi.org/10.3390/en18143856 (registering DOI) - 20 Jul 2025
Abstract
Accurate SOH prediction provides a reliable reference for lithium-ion battery maintenance. However, novel algorithms are still needed because few studies have considered the correlations between monitored parameters in Euclidean space and non-Euclidean space at different time points. To address this challenge, a novel [...] Read more.
Accurate SOH prediction provides a reliable reference for lithium-ion battery maintenance. However, novel algorithms are still needed because few studies have considered the correlations between monitored parameters in Euclidean space and non-Euclidean space at different time points. To address this challenge, a novel gated-temporal network assisted by improved grasshopper optimization (IGOA-GGNN-TCN) is developed. In this model, features obtained from lithium-ion batteries are used to construct graph data based on cosine similarity. On this basis, the GGNN-TCN is employed to obtain the potential correlations between monitored parameters in Euclidean and non-Euclidean spaces. Furthermore, IGOA is introduced to overcome the issue of hyperparameter optimization for GGNN-TCN, improving the convergence speed and the local optimal problem. Competitive results on the Oxford dataset indicate that the SOH prediction performance of proposed IGOA-GGNN-TCN surpasses conventional methods, such as convolutional neural networks (CNNs) and gate recurrent unit (GRUs), achieving an R2 value greater than 0.99. The experimental results demonstrate that the proposed IGOA-GGNN-TCN framework offers a novel and effective approach for state-of-health (SOH) estimation in lithium-ion batteries. By integrating improved grasshopper optimization (IGOA) with hybrid graph-temporal modeling, the method achieves superior prediction accuracy compared to conventional techniques, providing a promising tool for battery management systems in real-world applications. Full article
(This article belongs to the Special Issue AI Solutions for Energy Management: Smart Grids and EV Charging)
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16 pages, 4237 KiB  
Article
Solid-State Circuit Breaker Topology Design Methodology for Smart DC Distribution Grids with Millisecond-Level Self-Healing Capability
by Baoquan Wei, Haoxiang Xiao, Hong Liu, Dongyu Li, Fangming Deng, Benren Pan and Zewen Li
Energies 2025, 18(14), 3613; https://doi.org/10.3390/en18143613 - 9 Jul 2025
Viewed by 229
Abstract
To address the challenges of prolonged current isolation times and high dependency on varistors in traditional flexible short-circuit fault isolation schemes for DC systems, this paper proposes a rapid fault isolation circuit design based on an adaptive solid-state circuit breaker (SSCB). By introducing [...] Read more.
To address the challenges of prolonged current isolation times and high dependency on varistors in traditional flexible short-circuit fault isolation schemes for DC systems, this paper proposes a rapid fault isolation circuit design based on an adaptive solid-state circuit breaker (SSCB). By introducing an adaptive current-limiting branch topology, the proposed solution reduces the risk of system oscillations induced by current-limiting inductors during normal operation and minimizes steady-state losses in the breaker. Upon fault occurrence, the current-limiting inductor is automatically activated to effectively suppress the transient current rise rate. An energy dissipation circuit (EDC) featuring a resistor as the primary energy absorber and an auxiliary varistor (MOV) for voltage clamping, alongside a snubber circuit, provides an independent path for inductor energy release after faults. This design significantly alleviates the impact of MOV capacity constraints on the fault isolation process compared to traditional schemes where the MOV is the primary energy sink. The proposed topology employs a symmetrical bridge structure compatible with both pole-to-pole and pole-to-ground fault scenarios. Parameter optimization ensures the IGBT voltage withstand capability and energy dissipation efficiency. Simulation and experimental results demonstrate that this scheme achieves fault isolation within 0.1 ms, reduces the maximum fault current-to-rated current ratio to 5.8, and exhibits significantly shorter isolation times compared to conventional approaches. This provides an effective solution for segment switches and tie switches in millisecond-level self-healing systems for both low-voltage (LVDC, e.g., 750 V/1500 V DC) and medium-voltage (MVDC, e.g., 10–35 kV DC) smart DC distribution grids, particularly in applications demanding ultra-fast fault isolation such as data centers, electric vehicle (EV) fast-charging parks, and shipboard power systems. Full article
(This article belongs to the Special Issue AI Solutions for Energy Management: Smart Grids and EV Charging)
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