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Batteries
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Advanced Intelligent Management Technologies of New Energy Batteries
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Advanced Intelligent Management Technologies of New Energy Batteries

A special issue of Batteries (ISSN 2313-0105). This special issue belongs to the section "Energy Storage System Aging, Diagnosis and Safety".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 879

Special Issue Editors

Dr. Sijia Yang

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Guest Editor
School of Intelligent Science and Technology, University of Science and Technology Beijing, Beijing 100083, China
Interests: evaluation of the state of new energy batteries; intelligent information processing; big data; machine learning
Prof. Dr. Zeyu Chen

E-Mail Website
Guest Editor
School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China
Interests: electric vehicle; lithium-ion battery
Special Issues, Collections and Topics in MDPI journals
Dr. Jichao Hong

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Guest Editor
School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: integrated design of new energy power storage systems; application of big data analytics; intelligent safety management throughout the entire lifecycle
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

New energy batteries are pivotal for sustainable energy storage solutions, from electric vehicles to grid storage. Their reliable and safe operation hinges on accurate state monitoring and prediction. However, challenges such as nonlinear degradation mechanisms, varied/extreme operating conditions, and limited on-board computing resources hinder the accuracy and robustness of existing methods. This Special Issue aims to gather cutting-edge research advances in the state monitoring and prediction of new energy batteries, with a focus on fundamental challenges and innovative solutions in sensor technology, data-driven algorithms, electrochemical modelling and hybrid methodologies, hoping to bridge the lab-scale innovations and on-board/grid-scale applications. We encourage submissions exploring both theoretical breakthroughs and empirical studies to foster a comprehensive understanding of battery behavior and predictive capabilities. The scope includes state estimation (state-of-charge, state-of-health, state-of-power, state-of-energy, state-of-safety, remaining useful life, etc.), fault diagnosis and their prediction/prognosis across diverse new energy battery chemistries (lithium-ion, sodium-ion, fuel cell, flow batteries, etc.).

Potential topics include, but are not limited to, the following:

  • Advanced SOC, SOH, SOP, SOE and SOS estimation algorithms.
  • Fault diagnosis and prognosis for new energy batteries.
  • Multi-physics coupling modeling for battery state estimation/prediction.
  • Novel sensing technologies and multi-sensor data fusion for new energy battery monitoring.
  • Physics-informed artificial intelligentmodels for battery state estimation/prediction.
  • Low-computational-cost algorithms for on-board battery management systems.

Dr. Sijia Yang
Prof. Dr. Zeyu Chen
Dr. Jichao Hong
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. Batteries is an international peer-reviewed open access monthly 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 2700 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

  • lithium-ion battery
  • fuel cells
  • lithium–sulfur battery
  • solid-state battery
  • flow battery
  • energy storage systems
  • electrical vehicles
  • battery management systems
  • SOX (SOC, SOH, SOP, SOE, SOS, SOT) estimation and prediction
  • lifetime prediction
  • battery safety diagnostics and prognostics
  • fault diagnosis
  • anomaly detection
  • artificial intelligence
  • physics-guided modeling

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

23 pages, 3020 KB  
Article
A State of Health Estimation Method for Lithium-Ion Battery Packs Using Two-Level Hierarchical Features and TCN–Transformer–SE
by Chaolong Zhang, Panfen Yin, Kaixin Cheng, Yupeng Wu, Min Xie, Guoqing Hua, Anxiang Wang and Kui Shao
Batteries 2026, 12(4), 123; https://doi.org/10.3390/batteries12040123 - 1 Apr 2026
Viewed by 285
Abstract
This study proposes a novel state of health (SOH) estimation method by extracting two-level hierarchical features linked to fundamental degradation mechanisms. At the module level, the length of the incremental power curve during constant current charging is extracted, capturing cumulative effects of subtle [...] Read more.
This study proposes a novel state of health (SOH) estimation method by extracting two-level hierarchical features linked to fundamental degradation mechanisms. At the module level, the length of the incremental power curve during constant current charging is extracted, capturing cumulative effects of subtle changes. At the cell level, a combined temperature-weighted voltage inconsistency curve is constructed. The state of charge (SOC) at its distinct knee point within the high-SOC range is a key indicator, signifying the accelerated failure stage where polarization and thermoelectric feedback intensify. This knee-point SOC quantitatively reflects the degree of SOH degradation, making it a valid feature for accurate SOH estimation. The proposed Temporal Convolutional Network–Transformer–Squeeze-and-Excitation (TCN–Transformer–SE) model assigns weights to these features via Squeeze-and-Excitation (SE) and uses Temporal Convolutional Network (TCN) and Transformer branches for parallel local and global temporal decisions. Aging experiments demonstrate the method’s superiority through multi-feature comparison, ablation studies, and benchmark evaluation, achieving a maximum mean absolute error (MAE) of 0.0031, a root mean square error (RMSE) of 0.0038, a coefficient of determination (R2) of 0.9937 and a mean absolute percentage error (MAPE) of 0.3820. The work provides a fusion estimation framework with enhanced interpretability grounded in electrochemical analysis. Full article
(This article belongs to the Special Issue Advanced Intelligent Management Technologies of New Energy Batteries)
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10 pages, 1121 KB  
Article
Research on the Active Safety Warning Technology of LIBs Thermal Runaway Based on FBG Sensing
by Yanli Miao, Xiao Tan, Chenying Li, Jianjun Liu, Ling Sa, Xiaohan Li, Zongjia Qiu and Zhichao Ding
Batteries 2026, 12(3), 110; https://doi.org/10.3390/batteries12030110 - 23 Mar 2026
Viewed by 291
Abstract
Lithium-ion batteries (LIBs) may experience thermal runaway (TR) under thermal abuse conditions, posing significant safety risks to energy storage systems, electric vehicles, and portable electronics. To ensure the safety of LIB-powered applications, developing an effective TR early warning method is crucial. This study [...] Read more.
Lithium-ion batteries (LIBs) may experience thermal runaway (TR) under thermal abuse conditions, posing significant safety risks to energy storage systems, electric vehicles, and portable electronics. To ensure the safety of LIB-powered applications, developing an effective TR early warning method is crucial. This study employs polyimide-coated femtosecond fiber Bragg grating (FBG) sensors to investigate TR characteristics in 18,650 LIBs (LiNi1/3Mn1/3Co1/3O2/graphite), including TR onset temperature determination and the evolution of temperature and radial strain at different states of charge (SOCs). Compared with existing studies, the polyimide-coated femtosecond FBGs employed here offer superior breakage resistance and high-temperature tolerance, enabling more precise temperature and strain measurements. For radial strain monitoring obtained during high-temperature-induced LIBs thermal runaway experiments, temperature compensation was achieved using polyimide-coated femtosecond FBG temperature sensors, yielding higher-accuracy strain evolution profiles. Experimental results demonstrate that the higher-SOC LIBs exhibit more severe TR eruptions, with 1.76× higher peak temperatures and 1.3× greater mass loss than low-SOC LIBs. The proposed scheme pioneers an new approach to effective active safety warning of LIBs thermal runaway. Full article
(This article belongs to the Special Issue Advanced Intelligent Management Technologies of New Energy Batteries)
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