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Battery Safety and Smart Management

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: 10 July 2026 | Viewed by 700

Special Issue Editors

Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China
Interests: battery energy storage system; battery management system; battery modeling; wireless charging technology
Special Issues, Collections and Topics in MDPI journals
School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: battery management technology; lithium-ion battery modeling and simulation; heavy-duty electric vehicle technology

Special Issue Information

Dear Colleagues,

The rapid expansion of battery applications—from electric vehicles (EVs) to grid-scale energy storage—has intensified the demand for advanced safety and management strategies. As battery systems grow in complexity, integrating intelligent technologies becomes critical to prevent failures, enhance sustainability, and unlock the potential of the Intelligence Revolution in energy storage. With the continuous expansion of application scenarios, battery systems face multiple challenges, such as thermal runaway risks, cycle life degradation, and extreme environmental adaptability. These issues are not only related to the performance of batteries themselves but also directly influence the development of key technologies such as electric vehicles and large-scale energy storage. This Special Issue explores key strategies addressing safety, sustainability, and smart management, driving the evolution of battery technology, including, but not limited to, the following topics:

  1. Battery Safety Strategies for improved battery safety and performance;
  2. Intelligent algorithms for battery state estimation and fault diagnostics;
  3. Machine learning applications in battery health prognosis;
  4. Sustainable Battery Management: Deep learning models predict battery lifespan and voltage degradation using minimal data, enabling proactive maintenance and extending operational longevity.
  5. Intelligent Battery Management Systems (BMS), optimization of battery management for electric vehicles and stationary storage.

This Special Issue will bring together cutting-edge research from safety, sustainability, and smart management technology. The industry can mitigate risks, reduce environmental impact, and harness the full potential of energy storage, offering theoretical guidance and technical support for building safer and more reliable batteries and energy storage systems. We welcome innovative contributions from diverse disciplines.

Dr. Liye Wang
Dr. Yong Li
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 250 words) can be sent to the Editorial Office for assessment.

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 safety and test
  • smart battery management
  • battery performance evaluation and improvement
  • machine learning

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Published Papers (1 paper)

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Review

23 pages, 9255 KB  
Review
From Laboratory to Real-World Application: A Comprehensive Study on Battery State of Health Assessment Methods
by Chunxiao Ma, Liye Wang, Jinlong Wu, Chengyu Liu, Lifang Wang and Chenglin Liao
Energies 2026, 19(6), 1506; https://doi.org/10.3390/en19061506 - 18 Mar 2026
Viewed by 500
Abstract
Accurate state of health (SOH) assessment is the cornerstone for ensuring the safety, reliability, and lifecycle value prediction of electric vehicles. While extensive research has demonstrated the significant advantages of data-driven approaches in SOH evaluation, the vast majority of work still relies on [...] Read more.
Accurate state of health (SOH) assessment is the cornerstone for ensuring the safety, reliability, and lifecycle value prediction of electric vehicles. While extensive research has demonstrated the significant advantages of data-driven approaches in SOH evaluation, the vast majority of work still relies on standardized test data obtained under laboratory conditions. These ideal conditions, including complete charge–discharge cycles and constant temperatures, are often unattainable in real-world operation where EV batteries face highly irregular driving patterns, fragmented charging segments, and unpredictable environmental disturbances. This paper provides a comprehensive and systematic overview of data-driven SOH assessment based on real-vehicle data, aiming to address the current research gap in unified laboratory-to-vehicle transfer frameworks. This paper first reviews existing SOH evaluation methodologies and highlights the challenges encountered when transitioning to real-world vehicle data. It delves into core technical challenges and solutions across the entire real-world SOH assessment chain, closely examining the complex characteristics of real-world data. The paper thoroughly evaluates the role of cutting-edge paradigms including weakly supervised, self-supervised, and transfer learning in mitigating label scarcity. We summarize a unified evaluation framework tailored for real-world scenarios: Vehicles-Out, Time-Rolling, Domain-Stratified (VTDS). This framework aims to systematically assess models’ generalization limits and engineering deployability across vehicles, time, and operating conditions. This work provides systematic guidance for researchers and practitioners, advancing data-driven SOH evaluation methods from theoretical research to engineering applications. Full article
(This article belongs to the Special Issue Battery Safety and Smart Management)
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