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Advances in Prognostics and Health Management for Battery Energy Storage...
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Advances in Prognostics and Health Management for Battery Energy Storage Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "D: Energy Storage and Application".

Deadline for manuscript submissions: 25 September 2026 | Viewed by 1887

Special Issue Editors

Dr. Da Li

E-Mail Website
Guest Editor
School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
Interests: battery prognostics and health management
Dr. Yang Qi

E-Mail Website
Guest Editor
School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
Interests: power electronics; more electric aircraft
Special Issues, Collections and Topics in MDPI journals
Dr. Jinhao Meng

E-Mail Website
Guest Editor
School of Electrical Engineering, Xi'an Jiaotong University, Xi’an 710049, China
Interests: battery management systems; battery state estimation; fast charging control; battery impedance measurement
Special Issues, Collections and Topics in MDPI journals
Dr. Junfu Li

E-Mail Website
Guest Editor
School of Automotive Engineering, Harbin Institute of Technology, Weihai 264209, China
Interests: power battery management system; power battery modeling; state estimation and health management; echelon utilization
Special Issues, Collections and Topics in MDPI journals
Dr. Qi Zhang

E-Mail Website
Guest Editor
School of Automotive Engineering, Harbin Institute of Technology, Weihai 264209, China
Interests: battery prognostics and health management

Special Issue Information

Dear Colleagues,

It is becoming increasingly evident that battery energy storage systems (BESSs) are a cornerstone of the transition towards electrified and sustainable technologies. Their critical role is rapidly expanding across a spectrum of modern applications, most notably in electric vehicles, electric aircraft, and electric ships, in which they are fundamental to propulsion, power management, and overall system reliability. This surge in demand for high-performance, safe, and durable batteries has accelerated research into advanced prognostics and health management (PHM) methodologies. The complexity of battery degradation mechanisms, influenced by varying operational profiles and environmental conditions, necessitates intelligent approaches that move beyond traditional monitoring.

This Special Issue aims to present and disseminate cutting-edge research and innovative developments in the field of PHM for battery energy storage systems. We seek contributions that address the challenges of state estimation, fault diagnosis, remaining useful life (RUL) prediction, and health-conscious management, with a particular emphasis on data-driven and artificial intelligence (AI) techniques. The integration of AI, including machine learning and deep learning models, is proving transformative, enabling the analysis of vast datasets for accurate state-of-health (SOH) assessment, early anomaly detection, and the prediction of long-term performance.

Topics of interest for publication include, but are not limited to:

  • AI and machine learning for battery state estimation (SOC, SOH, RUL);
  • Digital twin technologies for BESSs;
  • Advanced fault diagnosis and failure prognosis algorithms;
  • Data-driven and model-based fusion approaches for PHM;
  • Thermal runaway prediction and safety management;
  • Cloud-based and edge-computing solutions for BESS management;
  • Optimal charging strategies informed by health status;
  • Novel sensors and embedded monitoring systems;
  • Lifetime prediction and ageing mitigation techniques;
  • Other cross-disciplinary research in the field of batteries;
  • Other cross-disciplinary research in the field of PHM.

We look forward to receiving your high-quality contributions.

Dr. Da Li
Dr. Yang Qi
Dr. Jinhao Meng
Dr. Junfu Li
Dr. Qi Zhang
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
  • prognostics and health management
  • fault diagnosis
  • thermal runaway
  • machine learning

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Published Papers (3 papers)

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Research

28 pages, 1526 KB  
Article
Mechanism Analysis and Detection of Battery Nail Penetration Based on Dynamic Electrochemical Impedance Spectroscopy
by Yulin Luo, Zihao Zhang, Deshuai Sun, Facheng Wang, Qi Zhang and Dafang Wang
Energies 2026, 19(9), 2152; https://doi.org/10.3390/en19092152 - 29 Apr 2026
Viewed by 141
Abstract
To investigate the battery impedance variation after the occurrence of nail penetration, this paper adopts Dynamic Electrochemical Impedance Spectroscopy (DEIS) for real-time monitoring of the impedance changes of lithium-ion batteries during the nail penetration process. A piecewise multi-frequency superimposed sinusoidal excitation is designed, [...] Read more.
To investigate the battery impedance variation after the occurrence of nail penetration, this paper adopts Dynamic Electrochemical Impedance Spectroscopy (DEIS) for real-time monitoring of the impedance changes of lithium-ion batteries during the nail penetration process. A piecewise multi-frequency superimposed sinusoidal excitation is designed, which not only complies with the stability principle of battery testing but also ensures the signal-to-noise ratio of the excitation signal. By injecting the designed excitation signal into the operating battery and combining it with the rapid DEIS generation technology, the acquisition of DEIS data within the target frequency band in a short time is realized. Based on the obtained DEIS data, a fractional-order model is established and fitted for analysis before and after nail penetration. The results show that the steel nail introduces inductive reactance and impedance to the battery. Due to the parallel connection between the steel nail and the internal resistance of the battery, the overall impedance decreases, exhibiting a short-circuit state, and both the real and imaginary parts of the impedance experience an abrupt change at the moment of nail penetration. Considering the characteristic of abrupt impedance change of the battery after nail penetration, a battery nail penetration detection method based on DEIS is proposed. Considering the abrupt change characteristics of battery impedance after nail penetration, this paper proposes a battery nail penetration detection method based on DEIS. This method can effectively solve the problem of low sensitivity of traditional voltage monitoring methods in detecting nail penetration during battery operation. It has higher sensitivity and faster response speed compared with traditional methods, enabling online monitoring of battery states. Additionally, this paper also explores its potential application in real-world vehicles. Full article
(This article belongs to the Special Issue Advances in Prognostics and Health Management for Battery Energy Storage Systems)
24 pages, 8654 KB  
Article
Machine Learning-Based Lifetime Prediction of Lithium Batteries: A Comparative Assessment for Electric Vehicle Applications
by Abdelilah Hammou, Raffaele Petrone, Demba Diallo, Boubekeur Tala-Ighil, Philippe Makany Boussiengue, Hicham Chaoui and Hamid Gualous
Energies 2026, 19(5), 1203; https://doi.org/10.3390/en19051203 - 27 Feb 2026
Viewed by 790
Abstract
This paper evaluates and compares four data-driven methods (Gaussian Process Regression (GPR), echo state network (ESN), gated recurrent unit (GRU), and long short-term memory (LSTM)) for lithium-ion capacity prognostics adapted to electric vehicle conditions. This comparison aims to find the most efficient prognosis [...] Read more.
This paper evaluates and compares four data-driven methods (Gaussian Process Regression (GPR), echo state network (ESN), gated recurrent unit (GRU), and long short-term memory (LSTM)) for lithium-ion capacity prognostics adapted to electric vehicle conditions. This comparison aims to find the most efficient prognosis method considering two constraints: the limitation of computational power and the unavailability of on-board capacity measurement that requires full charge and discharge conditions. The machine learning models are trained using capacity values estimated under vehicle conditions. The ageing data is collected from cycling tests of two battery chemistries, Lithium Fer Phosphate (LFP) and Nickel Manganese Cobalt (NMC), with different ageing trends. The prognosis algorithms are tuned with three different percentages of the data, allowing for the evaluation of the methods at different ageing stages. The comparison and analysis of the results show that ESN outperforms other methods; it has the lowest prediction error (mean absolute percentage error less than 1.4% at initial ageing of the cells) and the shortest training time, making it the most appropriate method for automotive applications. Full article
(This article belongs to the Special Issue Advances in Prognostics and Health Management for Battery Energy Storage Systems)
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17 pages, 1621 KB  
Article
Reinforcement Learning-Based Optimization of Environmental Control Systems in Battery Energy Storage Rooms
by So-Yeon Park, Deun-Chan Kim and Jun-Ho Bang
Energies 2026, 19(2), 516; https://doi.org/10.3390/en19020516 - 20 Jan 2026
Viewed by 500
Abstract
This study proposes a reinforcement learning (RL)-based optimization framework for the environmental control system of battery rooms in Energy Storage Systems (ESS). Conventional rule-based air-conditioning strategies are unable to adapt to real-time temperature and humidity fluctuations, often leading to excessive energy consumption or [...] Read more.
This study proposes a reinforcement learning (RL)-based optimization framework for the environmental control system of battery rooms in Energy Storage Systems (ESS). Conventional rule-based air-conditioning strategies are unable to adapt to real-time temperature and humidity fluctuations, often leading to excessive energy consumption or insufficient thermal protection. To overcome these limitations, both value-based (DQN, Double DQN, Dueling DQN) and policy-based (Policy Gradient, PPO, TRPO) RL algorithms are implemented and systematically compared. The algorithms are trained and evaluated using one year of real ESS operational data and corresponding meteorological data sampled at 15-min intervals. Performance is assessed in terms of convergence speed, learning stability, and cooling-energy consumption. The experimental results show that the DQN algorithm reduces time-averaged cooling power consumption by 46.5% compared to conventional rule-based control, while maintaining temperature, humidity, and dew-point constraint violation rates below 1% throughout the testing period. Among the policy-based methods, the Policy Gradient algorithm demonstrates competitive energy-saving performance but requires longer training time and exhibits higher reward variance. These findings confirm that RL-based control can effectively adapt to dynamic environmental conditions, thereby improving both energy efficiency and operational safety in ESS battery rooms. The proposed framework offers a practical and scalable solution for intelligent thermal management in ESS facilities. Full article
(This article belongs to the Special Issue Advances in Prognostics and Health Management for Battery Energy Storage Systems)
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