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Artificial Intelligence for Battery Health Monitoring and Lifetime Prediction
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Artificial Intelligence for Battery Health Monitoring and Lifetime Prediction

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: closed (24 October 2025) | Viewed by 2079

Special Issue Editors

Dr. Jiusi Zhang

E-Mail Website
Guest Editor
School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: intelligent fault diagnosis/prognosis/tolerance; Industrial big data and artificial intelligence; data-driven monitoring and optimization; intelligent operation and maintenance of complex industrial systems
Special Issues, Collections and Topics in MDPI journals
Dr. Tenglong Huang

E-Mail Website
Guest Editor
College of Mechanical and Electronic Engineering, Northwest Agriculture and Forestry University, Xianyang 712100, China
Interests: multi-objective planning and control of intelligent preview suspension; motion planning and dynamic control of intelligent vehicles; motion planning and fault-tolerant control of mobile robots; planning and control of multi-degree-of-freedom redundant robotic arms; smart agriculture and precision agriculture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid advancement of renewable energy systems and electrified transportation has intensified the demand for reliable, efficient, and sustainable energy storage solutions. Lithium-ion batteries (LIBs), a cornerstone of modern energy storage, require precise health monitoring and accurate lifetime prediction to ensure safety, longevity, and economic viability. However, the inherent complexity of battery degradation mechanisms—spanning electrochemical dynamics, material aging, and operational variability—poses significant challenges for traditional modeling approaches. This Special Issue aims to explore cutting-edge artificial intelligence (AI) methodologies that address these challenges, enabling breakthroughs in battery health management and prognostics.

This Special Issue is open to receiving the latest research on theoretical, methodological, and experimental advances in artificial intelligence for battery health monitoring and lifetime prediction. Topics of interest for this publication include, but are not limited to, the following:

  • The multi-sensory data analysis of batteries;
  • Accurate battery state-of-charge estimation approaches;
  • Model-based and data-driven battery state-of-health estimation approaches;
  • The prediction of remaining useful battery life and life extension approaches;
  • Intelligent approaches to battery health monitoring, diagnosis, and decisions;
  • AI-based applications in battery management systems.

Dr. Jiusi Zhang
Dr. Tenglong Huang
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 health monitoring
  • lifetime prediction
  • state of health
  • remaining useful life
  • artificial intelligence

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Published Papers (2 papers)

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Research

19 pages, 1609 KB  
Article
Instance-Based Transfer Learning-Improved Battery State-of-Health Estimation with Self-Attention Mechanism
by Renjun He, Chunxiao Wang, Chun Yin, Shang Yang, Yifan Wang, Yuanpeng Fang, Kai Chen and Jiusi Zhang
Energies 2025, 18(21), 5672; https://doi.org/10.3390/en18215672 - 29 Oct 2025
Viewed by 481
Abstract
Batteries’ state-of-health (SOH) estimation has attracted appealing attention in energy industrial systems. In conventional data-driven methods, the lack of target data and different source data can also lead to poor model training effect. To tackle this problem, this paper combines the instance-based transfer [...] Read more.
Batteries’ state-of-health (SOH) estimation has attracted appealing attention in energy industrial systems. In conventional data-driven methods, the lack of target data and different source data can also lead to poor model training effect. To tackle this problem, this paper combines the instance-based transfer (ITL) and interpretable self-attention mechanism (SAM) to integrate the fitting ability of long short-term memory (LSTM), which can improve the SOH estimation performance. ITL re-weights the temporal instance of a training set to give more impact of target-like data, which can relax the independent and identical distribution (IID) assumption. SAM method can enhance the estimation performance by re-weighting the spatial features, and be interpreted by detailed visualization. During the model training, the pre-trained multi-layer LSTM model is fine-tuned by target data to make full use of target information. The proposed method has outperformed other compared algorithms in transfer tasks, and has tested in real-world cross-domain conditions datasets. Full article
(This article belongs to the Special Issue Artificial Intelligence for Battery Health Monitoring and Lifetime Prediction)
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22 pages, 724 KB  
Article
State of Health Estimation for Batteries Based on a Dynamic Graph Pruning Neural Network with a Self-Attention Mechanism
by Xuanyuan Gu, Mu Liu and Jilun Tian
Energies 2025, 18(20), 5333; https://doi.org/10.3390/en18205333 - 10 Oct 2025
Viewed by 1202
Abstract
The accurate estimation of the state of health (SOH) of lithium-ion batteries is critical for ensuring the safety, reliability, and efficiency of modern energy storage systems. Traditional model-based and data-driven methods often struggle to capture complex spatiotemporal degradation patterns, leading to reduced accuracy [...] Read more.
The accurate estimation of the state of health (SOH) of lithium-ion batteries is critical for ensuring the safety, reliability, and efficiency of modern energy storage systems. Traditional model-based and data-driven methods often struggle to capture complex spatiotemporal degradation patterns, leading to reduced accuracy and robustness. To address these limitations, this paper proposes a novel dynamic graph pruning neural network with self-attention mechanism (DynaGPNN-SAM) for SOH estimation. The method transforms sequential battery features into graph-structured representations, enabling the explicit modeling of spatial dependencies among operational variables. A self-attention-guided pruning strategy is introduced to dynamically preserve informative nodes while filtering redundant ones, thereby enhancing interpretability and computational efficiency. The framework is validated on the NASA lithium-ion battery dataset, with extensive experiments and ablation studies demonstrating superior performance compared to conventional approaches. Results show that DynaGPNN-SAM achieves lower root mean square error (RMSE) and mean absolute error (MAE) values across multiple batteries, particularly excelling during rapid degradation phases. Overall, the proposed approach provides an accurate, robust, and scalable solution for real-world battery management systems. Full article
(This article belongs to the Special Issue Artificial Intelligence for Battery Health Monitoring and Lifetime Prediction)
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