Challenges and Innovations in High-Performance Li-Ion Batteries and Electric Vehicle Charging Infrastructure

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Power Electronics".

Deadline for manuscript submissions: 15 February 2026 | Viewed by 216

Special Issue Editors


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Guest Editor
Information Technology, Analytics, and Operations, Queen’s Business School, Queen’s University Belfast, Belfast BT7 1NN, UK
Interests: low-carbon transportation; electricity market; energy system management; transportation planning

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Guest Editor
School of Electrical and Power Engineering, Hohai University, Nanjing 211100, China
Interests: microgrids; energy management system; energy-use internet; electricity market
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Special Issue Information

Dear Colleagues,

The rapid growth of electric vehicles (EVs) and renewable energy systems has placed enhanced pressure on battery technologies and charging infrastructures. High-performance lithium-ion (Li-ion) batteries and intelligent charging networks are now pivotal components in sustainable transportation and low-carbon energy systems. However, challenges remain in terms of battery efficiency, safety, grid integration and market coordination.

This Special Issue aims to explore the latest advances and innovations in Li-ion battery technologies and EV charging infrastructure, with an emphasis on their interactions with transportation systems and electricity markets. Topics such as battery design, health management, fast-charging strategies, grid-aware scheduling and policy-driven adoption models are of particular interest. We welcome original research and review articles that contribute to our understanding and development of scalable, efficient and intelligent battery and charging solutions. Researchers, engineers and practitioners from both academia and industry are encouraged to share their insights regarding advancements in electric mobility and energy management.

The scope of this Special Issue includes, but is not limited to, the following topics:

  • Battery structure, chemistry, and thermal management;
  • Fast charging strategies and battery degradation modelling;
  • Battery health diagnostics, prognostics, and digital twins;
  • EV charging scheduling, routing, and user behaviour modelling;
  • Vehicle-to-grid (V2G) and grid-responsive charging systems;
  • Renewable energy integration and charging load balancing;
  • AI-based optimization for battery and charging systems;
  • Impact of EVs on electricity markets and power system stability;
  • Demand-side management and dynamic pricing mechanisms;
  • Techno-economic analysis of EV infrastructure and battery applications;
  • Interoperability and cybersecurity in EV charging networks;
  • Policy incentives, carbon reduction potential, and system-level planning.

Dr. Qiao Peng
Prof. Dr. Haochen Hua
Guest Editors

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Keywords

  • lithium-ion batteries
  • electric vehicle charging
  • battery health management
  • smart grid
  • electricity market
  • vehicle-to-grid
  • energy storage
  • sustainable transport
  • demand response
  • fast charging

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Published Papers (2 papers)

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Research

15 pages, 2112 KB  
Article
Decentralized Dispatch Strategy for Island Microgrid Clusters Based on Historical Similarity and Offline Training
by Junjian Wu, Jingliao Sun, Honglei Xi, Yeyun Xiang, Jian Meng and Longdong Wu
Electronics 2025, 14(21), 4239; https://doi.org/10.3390/electronics14214239 - 29 Oct 2025
Abstract
To address the issues of efficiency and real-time performance in power mutual assistance among island microgrid clusters, a two-stage decentralized dispatching optimization method combining similarity measurement with offline training and online optimization is proposed. Firstly, the power architecture of island microgrid clusters is [...] Read more.
To address the issues of efficiency and real-time performance in power mutual assistance among island microgrid clusters, a two-stage decentralized dispatching optimization method combining similarity measurement with offline training and online optimization is proposed. Firstly, the power architecture of island microgrid clusters is studied, and the mathematical model of island microgrid units is established. Secondly, a centralized offline training model for island microgrid clusters with economic cost and carbon emissions as indicators is constructed, and the output states of adjustable units under different natural resource conditions are stored. Then, in actual operation, each microgrid selects the most similar historical data through similarity measurement and uses the output of controllable units as the initial dispatching value for online optimization. Finally, the effectiveness of the proposed method is verified through a practical example, reducing the dependence of island microgrid clusters on long-distance communication between islands and the mainland. Full article
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22 pages, 2412 KB  
Article
Hierarchical Distributed Energy Interaction Management Strategy for Multi-Island Microgrids Based on the Alternating Direction Multiplier Method
by Jingliao Sun, Honglei Xi, Kai Yu, Yeyun Xiang, Hezuo Qu and Longdong Wu
Electronics 2025, 14(21), 4238; https://doi.org/10.3390/electronics14214238 - 29 Oct 2025
Abstract
The effective management of energy interactions in multi-island microgrid systems presents a significant challenge due to the geographical dispersion of islands. To address this, this paper proposes a hierarchical distributed optimization strategy based on the alternating direction method of multipliers (ADMM). The strategy [...] Read more.
The effective management of energy interactions in multi-island microgrid systems presents a significant challenge due to the geographical dispersion of islands. To address this, this paper proposes a hierarchical distributed optimization strategy based on the alternating direction method of multipliers (ADMM). The strategy features a two-layer architecture: the upper layer employs the ADMM to solve the system-level optimal power flow problem and generates distributed node marginal electricity prices (DLMPs) as clear economic coordination signals. The lower layer consists of individual island microgrids, which independently and in parallel solve their internal security-constrained economic dispatch (SCED) problems upon receiving the converged DLMP signals. This layered decoupling design functionally separates system-level coordination from microgrid-level optimization and enhances privacy protection by preventing the exposure of internal cost functions and operational constraints during upper-layer iterations. Case studies demonstrate that the proposed strategy reduces total operating costs by 10.3% compared to a centralized approach, while also significantly decreasing communication data volume by 83% and ensuring robust privacy protection. The algorithm exhibits good scalability with sublinear growth in iteration counts as the system scales, validating its effectiveness and practical potential for enhancing energy management in multi-island microgrid systems. Full article
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