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Advanced Control and AI Methods for Future Battery Diagnostics and...
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Advanced Control and AI Methods for Future Battery Diagnostics and Prognostics

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: 15 August 2026 | Viewed by 1037

Special Issue Editors

Dr. Junzhe Shi

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, University of California, Berkeley, CA 94720, USA
Interests: energy systems; reinforcement learning; dynamic control; optimization
Dr. Shengyu Tao

E-Mail Website
Guest Editor
Department of Electrical Engineering, Chalmers University of Technology, 412 96 Gothenburg, Sweden
Interests: batteries; energy systems; AI for science; AI for sustainability; controls
Special Issues, Collections and Topics in MDPI journals
Dr. Wenxue Liu

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
Interests: lithium-ion batteries; battery management; electrified vehicles
Special Issues, Collections and Topics in MDPI journals
Dr. Xingjun Li

E-Mail Website
Guest Editor
Energy Department, Aalborg University, 9220 Aalborg, Denmark
Interests: battery electrochemical modeling; AI-based state estimation and lifetime prediction; battery fault diagnosis

Special Issue Information

Dear Colleagues,

(1) The rapid expansion of battery applications, ranging from electric vehicles and renewable energy storage to grid stabilization, has made battery diagnostics and prognostics a critical research frontier. The accurate, efficient, and real-time evaluation of battery states—such as state of charge (SOC), state of health (SOH), and state of safety (SOS)—is essential for ensuring the reliability, safety, and sustainability of future energy systems. However, traditional model-based and data-driven approaches face significant challenges in coping with the increasing complexity, heterogeneity, and aging dynamics of modern battery systems.

Recent advances in control theory and artificial intelligence (AI) offer transformative opportunities to enhance battery management across its lifecycle. Advanced control strategies can provide adaptive and robust system optimization under uncertainty, while AI methods, including machine learning, deep learning, and physics-informed models, enable predictive insights and decision-making from complex, high-dimensional battery data. The synergy of advanced control and AI thus holds great promise for enabling next-generation battery diagnostics and prognostics.

(2) This Special Issue aims to provide an international platform for researchers and practitioners to present cutting-edge developments at the intersection of advanced control methods and AI-driven techniques for battery health assessment, lifetime prediction, anomaly detection, and operational optimization.

The scope of this Special Issue aligns with the journal’s mission to advance theoretical innovation, technological development, and practical applications in the field of energy storage and management. We particularly welcome interdisciplinary works that integrate control, AI, electrochemistry, and system engineering to address critical challenges in battery diagnostics and prognostics.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

  • Advanced control strategies (e.g., model predictive control, adaptive control, robust control) for battery management systems;
  • AI-enhanced battery state estimation, SOH/SOC/SOS/RUL/degradation trajectory/anomaly prediction, and failure prognosis;
  • Physics-informed machine learning and hybrid modeling approaches for batteries;
  • Data generation, augmentation, and synthetic battery datasets for training AI models;
  • Optimal charging/discharging strategies based on predictive diagnostics;
  • Real-time implementation and embedded system development for AI-based battery monitoring;
  • Diagnostics and prognostics for second-life and recycled batteries;
  • Benchmarking studies and validation frameworks for battery diagnostics algorithms.

We look forward to receiving your contributions.

Dr. Junzhe Shi
Dr. Shengyu Tao
Dr. Wenxue Liu
Dr. Xingjun 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. Electronics 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 2400 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

  • batteries
  • controls
  • AI
  • modeling

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Published Papers (1 paper)

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Research

31 pages, 13729 KB  
Article
Stage-Wise SOH Prediction Using an Improved Random Forest Regression Algorithm
by Wei Xiao, Jun Jia, Wensheng Gao, Haibo Li, Hong Xu, Weidong Zhong and Ke He
Electronics 2026, 15(2), 287; https://doi.org/10.3390/electronics15020287 - 8 Jan 2026
Viewed by 548
Abstract
In complex energy storage operating scenarios, batteries seldom undergo complete charge–discharge cycles required for periodic capacity calibration. Methods based on accelerated aging experiments can indicate possible aging paths; however, due to uncertainties like changing operating conditions, environmental variations, and manufacturing inconsistencies, the degradation [...] Read more.
In complex energy storage operating scenarios, batteries seldom undergo complete charge–discharge cycles required for periodic capacity calibration. Methods based on accelerated aging experiments can indicate possible aging paths; however, due to uncertainties like changing operating conditions, environmental variations, and manufacturing inconsistencies, the degradation information obtained from such experiments may not be applicable to the entire lifecycle. To address this, we developed a stage-wise state-of-health (SOH) prediction approach that combined offline training with online updating. During the offline training phase, multiple single-cell experiments were conducted under various combinations of depth of discharge (DOD) and C-rate. Multi-dimensional health features (HFs) were extracted, and an accelerated aging probability pAA was defined. Based on the correlation statistics between HFs, kHF, the SOH, and pAA, all cells in the dataset were divided into general early, middle, and late aging stages. For each stage, cells were further classified by their longevity (long, medium, and short), and multiple models were trained offline for each category. The results show that models trained on cells following similar aging paths achieve significantly better performance than a model trained on all data combined. Meanwhile, HF optimization was performed via a three-step process: an initial screening based on expert knowledge, a second screening using Spearman correlation coefficients, and an automatic feature importance ranking using a random forest regression (RFR) model. The proposed method is innovative in the following ways: (1) The stage-wise multi-model strategy significantly improves the SOH prediction accuracy across the entire lifecycle, maintaining the mean absolute percentage error (MAPE) within 1%. (2) The improved model provides uncertainty quantification, issuing a warning signal at least 50 cycles before the onset of accelerated aging. (3) The analysis of feature importance from the model outputs allows the indirect identification of the primary aging mechanisms at different stages. (4) The model is robust against missing or low-quality HFs. If certain features cannot be obtained or are of poor quality, the prediction process does not fail. Full article
(This article belongs to the Special Issue Advanced Control and AI Methods for Future Battery Diagnostics and Prognostics)
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