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Advanced Battery Management Strategies

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "D2: Electrochem: Batteries, Fuel Cells, Capacitors".

Deadline for manuscript submissions: 5 December 2025 | Viewed by 370

Special Issue Editors


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Guest Editor
Department of Electrical Engineering, City University of Hong Kong, Hong Kong, China
Interests: power system; energy storage; artificial intelligence; battery aging modeling; energy management system

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Guest Editor
College of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Interests: novel power signal; lithium-ion batteries; non-contact voltage measurement

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Guest Editor
School of Mechanical Engineering, University of Houston, Houston, TX 77204, USA
Interests: energy storage; superconducting magnetic energy storage; flux pump; artificial intelligence

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Guest Editor
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore
Interests: energy storage planning and scheduling; microgrid energy management; intelligent optimization algorithm

Special Issue Information

Dear Colleagues,

Advanced battery management strategies are crucial for optimizing the efficiency, safety, reliability, and lifespan of batteries in various applications, including electric vehicles and renewable energy storage systems. With the growing global emphasis on sustainability, developing intelligent and optimized battery management systems (BMS) is crucial for enhancing energy storage performance and integrating with renewable energy sources. Innovations in battery diagnostics, state-of-health estimation, predictive control algorithms, and adaptive management techniques are significantly advancing battery technology, enabling the broader adoption of clean energy solutions.

This Special Issue invites original research and reviews focusing on advanced methods, algorithms, and technologies in battery management. Topics of interest include, but are not limited to:

  • Intelligent algorithms for battery state estimation and fault diagnostics;
  • Predictive and adaptive battery management systems;
  • Machine learning applications in battery health prognosis;
  • Optimization of battery management for electric vehicles and stationary storage;
  • Thermal management strategies for improved battery safety and performance;
  • Integration of advanced battery management with renewable energy systems;
  • Novel materials and sensors enhancing battery management accuracy.

Dr. Tianjing Wang
Prof. Dr. Ling Fu
Dr. Lingfeng Zhu
Dr. Xizhen Xue
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

  • battery management systems (BMS)
  • state-of-health estimation
  • state-of-charge estimation
  • predictive control algorithms
  • intelligent diagnostics
  • adaptive management techniques
  • battery prognostics
  • electric vehicle batteries
  • renewable energy integration
  • thermal management

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Published Papers (1 paper)

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Research

26 pages, 3954 KiB  
Article
Bi-Level Planning of Grid-Forming Energy Storage–Hydrogen Storage System Considering Inertia Response and Frequency Parameter Optimization
by Dongqi Huang, Pengwei Sun, Wenfeng Yao, Chang Liu, Hefeng Zhai and Yehao Gao
Energies 2025, 18(15), 3915; https://doi.org/10.3390/en18153915 - 23 Jul 2025
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Abstract
Energy storage plays an essential role in stabilizing fluctuations in renewable energy sources such as wind and solar, enabling surplus electricity retention, and delivering dynamic frequency regulation. However, relying solely on a single form of storage often proves insufficient due to constraints in [...] Read more.
Energy storage plays an essential role in stabilizing fluctuations in renewable energy sources such as wind and solar, enabling surplus electricity retention, and delivering dynamic frequency regulation. However, relying solely on a single form of storage often proves insufficient due to constraints in performance, capacity, and cost-effectiveness. To tackle frequency regulation challenges in remote desert-based renewable energy hubs—where traditional power infrastructure is unavailable—this study introduces a planning framework for an electro-hydrogen energy storage system with grid-forming capabilities, designed to supply both inertia and frequency response. At the system design stage, a direct current (DC) transmission network is modeled, integrating battery and hydrogen storage technologies. Using this configuration, the capacity settings for both grid-forming batteries and hydrogen units are optimized. This study then explores how hydrogen systems—comprising electrolyzers, storage tanks, and fuel cells—and grid-forming batteries contribute to inertial support. Virtual inertia models are established for each technology, enabling precise estimation of the total synthetic inertia provided. At the operational level, this study addresses stability concerns stemming from renewable generation variability by introducing three security indices. A joint optimization is performed for virtual inertia constants, which define the virtual inertia provided by energy storage systems to assist in frequency regulation, and primary frequency response parameters within the proposed storage scheme are optimized in this model. This enhances the frequency modulation potential of both systems and confirms the robustness of the proposed approach. Lastly, a real-world case study involving a 13 GW renewable energy base in Northwest China, connected via a ±10 GW HVDC export corridor, demonstrates the practical effectiveness of the optimization strategy and system configuration. Full article
(This article belongs to the Special Issue Advanced Battery Management Strategies)
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