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Batteries
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Physics-Informed Artificial Intelligence for Battery Energy Storage Systems
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Physics-Informed Artificial Intelligence for Battery Energy Storage Systems

A special issue of Batteries (ISSN 2313-0105). This special issue belongs to the section "Battery Performance, Ageing, Reliability and Safety".

Deadline for manuscript submissions: 30 December 2025 | Viewed by 4885

Special Issue Editors

Dr. Yanan Wang

E-Mail Website
Guest Editor
Department of Mechenical and Energy Engineering, Beijing University of Technology, Beijing 100021, China
Interests: energy intelligent management; AI for battery; fractional-order modeling
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Liping Chen

E-Mail Website
Guest Editor
School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China
Interests: battery energy management; fractional-order system
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yangquan Chen

grade E-Mail Website
Guest Editor
Department of Mechanical Engineering (ME), University of California, Merced, CA 95343, USA
Interests: data-driven modeling, learning, and optimization; control theory of fractional systems and their applications; distributed measurement and distributed control; signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In order to meet the demand of the energy revolution for carbon neutralization, batteries have become one of the most important components in all modes of electrified transportation and the supporting electric energy stations. The performance of a battery energy storage system affects the efficiency and safety of the operation of a power system significantly. Despite the widespread use of traditional modeling mechanisms and state estimation methods for battery energy storage systems, machine learning, physics-informed knowledge, and intelligent control have attracted increasing attention recently.

Therefore, this Special Issue is focused on recent advances in battery energy storage systems that address the above-mentioned aspects and go beyond the state of the art. Prospective authors are invited to submit original contributions/articles for review and possible publication in this Special Issue. Topics of interest include (but are not limited to) the following:

  • The design of BMSs (battery management systems);
  • Physics-informed and data-driven methods for battery modeling;
  • State estimation of battery energy storage systems;
  • Innovative methods of SOX (SOC, SOH, SOP, SOE, SOS, SOT) and other states estimations methodologies;
  • Intelligent battery management systems with advanced algorithms;
  • Battery safety management, including safety diagnostics, prognostics, and warning methods;
  • Battery life management, including degradation estimation, aging prediction, and health management;
  • Temperature control and thermal management of battery energy storage systems;
  • Methods for battery optimization, control, and balancing;
  • Energy management of advanced power systems.

Dr. Yanan Wang
Prof. Dr. Liping Chen
Prof. Dr. Yangquan Chen
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

  • machine learning
  • fractional-order modeling and control
  • intelligent management
  • energy storage systems
  • design of BMSs (battery management systems)
  • artificial intelligence for batteries
  • battery safety diagnostics
  • battery degradation estimation
  • physics-informed machine learning
  • data-driven modeling
  • edge–cloud collaboration
  • edge computing and cloud computing
  • digital twins
  • battery optimization and control
  • battery prognostics
  • battery pre-warning
  • cell balancing
  • battery lifetime monitoring

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

Published Papers (3 papers)

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Research

20 pages, 4665 KB  
Article
Remaining Useful Life Prediction of Lithium Batteries Based on Transfer Learning and Particle Filter Fusion
by Liping Chen, Xiaolong Liang, Jiyu Ding, Kun Qiu and Hongli Ma
Batteries 2025, 11(12), 459; https://doi.org/10.3390/batteries11120459 - 13 Dec 2025
Viewed by 218
Abstract
Accurately predicting the remaining useful life (RUL) of lithium-ion batteries is critical for anticipating battery failure and enabling effective health management. However, existing RUL prediction methods often suffer from several limitations, including the need for large volumes of training data, significant differences across [...] Read more.
Accurately predicting the remaining useful life (RUL) of lithium-ion batteries is critical for anticipating battery failure and enabling effective health management. However, existing RUL prediction methods often suffer from several limitations, including the need for large volumes of training data, significant differences across datasets, and insufficient accuracy in long-term forecasting, which hinder their applicability in real world scenarios. To address these challenges, this paper proposes a hybrid model that integrates transfer learning (TL) and particle filtering (PF) with the Mogrifier LSTM (MLSTM) network. Specifically, the model first employs a transfer learning-based Mogrifier LSTM (TL-MLSTM) to perform long-term prediction of battery capacity, thereby enhancing the model’s generalization ability to accommodate RUL prediction under varying operating conditions. Subsequently, the capacity predictions generated by TL-MLSTM are used as observations in the PF algorithm, which iteratively updates the battery state parameters and refines the capacity predictions, thereby further improving accuracy. The proposed model is validated using publicly available datasets comprising multiple types of batteries under various operational conditions. Experimental results demonstrate that the model achieves an average RMSE of 0.0199, MAPE of 0.5803%, MAE of 0.0167 and APE of 11 cycles across multiple test groups. Compared with standalone models or purely data-driven approaches, the proposed method exhibits significant advantages in robustness and accuracy for long-term capacity degradation prediction. Full article
(This article belongs to the Special Issue Physics-Informed Artificial Intelligence for Battery Energy Storage Systems)
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17 pages, 3915 KB  
Article
Research on Aging Evolution and Safety Characteristics of Lithium-Ion Batteries Cycling at Low Temperature
by Ruiheng Wang and Bing Xue
Batteries 2025, 11(11), 396; https://doi.org/10.3390/batteries11110396 - 27 Oct 2025
Viewed by 1534
Abstract
Complex operating conditions, such as low temperature, can affect the degradation and safety stability of lithium-ion batteries (LIBs). This paper conducts research on the aging evolution and safety characteristics of LIBs under low-temperature conditions (−20 °C), to reveal the change laws of battery [...] Read more.
Complex operating conditions, such as low temperature, can affect the degradation and safety stability of lithium-ion batteries (LIBs). This paper conducts research on the aging evolution and safety characteristics of LIBs under low-temperature conditions (−20 °C), to reveal the change laws of battery degradation and the trends of thermal parameters of aging LIBs. Cycling and charging/discharging experiments under low temperatures were conducted to collect realistic battery data. Various factors such as temperature, cycle number, charging/discharging rate, and depth of discharge/charge (DOD/DOC) are taken into consideration to test the battery cycling and thermal performance. With collected experimental results, basic electrical states of LIBs such as open-circuit voltage (OCV), internal resistance, and capacity are presented. Then, the capacity loss and internal resistance growth are also described and analyzed under various charge/discharge rates and DODs/DOCs. The experimental results show that low temperatures cause an almost 30% increase in polarization resistance, with nonlinear changes in total internal resistance. Moreover, the battery capacity and internal resistance also have extreme points with different charge/discharge rates under −20 °C, which may demonstrate that the charge/discharge rates of LIBs can be optimized under low temperature. Thermal runaway (TR) experiments were also conducted, and the self-heating rate and other indices are presented to show that an aging battery under low temperature still holds large energy to develop TR. The aging trends of LIBs under low temperatures are summarized, and battery safety is clarified to provide a reference for battery lifetime and safety management under low-temperature conditions. Full article
(This article belongs to the Special Issue Physics-Informed Artificial Intelligence for Battery Energy Storage Systems)
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15 pages, 2454 KB  
Article
Pulse-Driven Internal Resistance Dynamics Enable Dual-Function Lithium-Plating Diagnosis and Longevity Enhancement in V2G-Optimized Lithium-Ion Batteries
by Letong Li, Yanan Wang, Dongliang Guo, Xuebing Han, Hewu Wang, Lei Sun and Minggao Ouyang
Batteries 2025, 11(5), 200; https://doi.org/10.3390/batteries11050200 - 20 May 2025
Viewed by 1816
Abstract
The lithium-plating phenomenon induced by low-temperature fast charging of lithium-ion batteries severely compromises their performance and safety. However, current lithium-plating detection methods predominantly rely on complex hardware systems with insufficient sensitivity, presenting significant challenges for implementation in increasingly prevalent Vehicle-to-Grid (V2G) scenarios. This [...] Read more.
The lithium-plating phenomenon induced by low-temperature fast charging of lithium-ion batteries severely compromises their performance and safety. However, current lithium-plating detection methods predominantly rely on complex hardware systems with insufficient sensitivity, presenting significant challenges for implementation in increasingly prevalent Vehicle-to-Grid (V2G) scenarios. This study proposes a novel bidirectional pulse-current charging method designed to mitigate lithium plating and retard battery aging through intermittent pulse-current application. Experimental results verify a 30–50% reduction in capacity fade rate under fast charging conditions (≥0.5 C rates). Furthermore, by leveraging pulse-current characteristics, we reveal strong correlations between the evolution patterns of charge/discharge internal resistance and lithium plating. An in situ detection criterion requiring no additional hardware is established: the L-shaped decline of charging internal resistance under high-rate conditions coupled with the disappearance of defined reverse-hump curves in discharge resistance profiles serve as precise indicators of lithium-plating onset. Validation through SEM and relaxation voltage differential analysis confirms 100% detection accuracy. This methodology combines rapid detection capability, non-destructive nature, and compatibility with V2G applications, providing new perspectives for enhancing lithium-ion battery longevity and lithium-plating detection. Full article
(This article belongs to the Special Issue Physics-Informed Artificial Intelligence for Battery Energy Storage Systems)
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