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Batteries
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Artificial Intelligence and Batteries: AI-Powered Innovations in Battery Technology
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Artificial Intelligence and Batteries: AI-Powered Innovations in Battery Technology

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: 15 October 2025 | Viewed by 8772

Special Issue Editors

Dr. Truong Minh Ngoc Bui

E-Mail Website
Guest Editor
Energy Innovation Centre, WMG, University of Warwick, Coventry CV4 7AL, UK
Interests: EV/HEV dynamic modelling; control and simulation; vehicle supervisory control; battery energy storage; energy management systems; battery management systems; vehicle-to-grid; smart grids
Special Issues, Collections and Topics in MDPI journals
Dr. Truong Quang Dinh

E-Mail Website
Guest Editor
WMG, University of Warwick, Coventry CV4 7AL, UK
Interests: lithium-ion batteries; battery thermal management; mechatronics; control theories and applications; modelling; management and control of energy systems; advanced propulsion systems
Special Issues, Collections and Topics in MDPI journals
Dr. Mona Faraji Niri

E-Mail Website
Guest Editor
Energy Innovation Centre, WMG, University of Warwick, Coventry CV4 7AL, UK
Interests: lithium-ion batteries; battery manufacturing; battery management; machine learning; electric vehicle powertrains

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) techniques, including machine learning, neural networks, and optimization algorithms, are being leveraged to address key challenges in battery technology, and this Special Issue explores the intersection of AI and batteries, aiming to enhance battery performance, lifespan, and safety. By integrating AI, advancements are made in battery efficiency, charging strategies, and energy storage applications across various sectors, including electric vehicles, renewable energy systems, and portable electronics. The Special Issue delves into the synergy between AI methodologies and battery research, encouraging innovation and propelling the evolution of smart, sustainable energy solutions. The research areas include battery monitoring, prediction, balancing, maintenance, fault detection, functional safety, and optimization of battery control and management, all empowered by AI support.

This Special Issue aims to gather together high-quality paper reviews and research articles within the topic of AI and battery research and applications. We encourage researchers from various fields within the journal’s scope to contribute their papers highlighting the latest research and developments in their research field, or to invite relevant experts and colleagues to do so. Topics of interest for this Special Issue include, but are not limited to:

  • State-of-the-art technologies and new developments for battery applications;
  • Advances in AI and battery research and applications;
  • Artificial intelligence in battery management and control;
  • Advanced battery state estimation: state-of-charge (SOC), state-of-health (SOH), state-of-power (SOP), state-of-function (SOF), remaining discharge energy (RDE), degradation;
  • Battery diagnostic and prognostic functions;
  • Battery balancing control features, topologies, and integration;
  • Advances in battery system thermal management;
  • Functional safety in batteries.

Dr. Truong Minh Ngoc Bui
Dr. Truong Quang Dinh
Dr. Mona Faraji Niri
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • artificial intelligence
  • neural networks
  • machine learning
  • state-of-charge (SOC)
  • state-of-health (SOH)
  • state-of-power (SOP)
  • state-of-function (SOF)
  • remaining discharge energy (RDE)
  • battery degradation
  • diagnostic and prognostic
  • battery balancing
  • battery thermal management

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

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20 pages, 4254 KiB  
Article
An AI-Driven Particle Filter Technology for Battery System State Estimation and RUL Prediction
by Mohamed Ahwiadi and Wilson Wang
Batteries 2024, 10(12), 437; https://doi.org/10.3390/batteries10120437 - 8 Dec 2024
Cited by 1 | Viewed by 1054
Abstract
The increasing demand for reliable and safe Lithium-ion (Li-ion) batteries requires more accurate estimation of state of health (SOH) and remaining useful life (RUL) prediction. However, the inherent complexity and non-linear dynamics of Li-ion batteries present specific challenges to traditional methods of SOH [...] Read more.
The increasing demand for reliable and safe Lithium-ion (Li-ion) batteries requires more accurate estimation of state of health (SOH) and remaining useful life (RUL) prediction. However, the inherent complexity and non-linear dynamics of Li-ion batteries present specific challenges to traditional methods of SOH modeling. Although particle filter (PF) techniques can handle nonlinear dynamics, they still face challenges, including particle degeneracy and loss of diversity, that reduce their ability to effectively model the nonlinear degradation mechanisms of batteries. To tackle these limitations, this paper presents a novel artificial intelligence-driven PF (AI-PF) technology for battery health modeling and prognosis. The main contributions of the AI-PF technique are as follows: (1) A novel dynamic sample degeneracy detection method is proposed to provide real-time assessment of particle weights so as to promptly identify degeneracy and improve computational efficiency. (2) An adaptive crossover and mutation strategy is proposed to reallocate low-weight particles and maintain particle diversity to improve modeling and RUL forecasting accuracy. The effectiveness of the AI-PF framework is validated through systematic evaluations carried out using benchmark models and well-recognized battery datasets. Full article
(This article belongs to the Special Issue Artificial Intelligence and Batteries: AI-Powered Innovations in Battery Technology)
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18 pages, 6199 KiB  
Article
In Operando Health Monitoring for Lithium-Ion Batteries in Electric Propulsion Using Deep Learning
by Jaya Vikeswara Rao Vajja, Alexey Serov, Meghana Sudarshan, Mahavir Singh and Vikas Tomar
Batteries 2024, 10(10), 355; https://doi.org/10.3390/batteries10100355 - 11 Oct 2024
Cited by 1 | Viewed by 1532
Abstract
Battery management systems (BMSs) play a vital role in understanding battery performance under extreme conditions such as high C-rate testing, where rapid charge or discharge is applied to batteries. This study presents a novel BMS tailored for continuous monitoring, transmission, and storage of [...] Read more.
Battery management systems (BMSs) play a vital role in understanding battery performance under extreme conditions such as high C-rate testing, where rapid charge or discharge is applied to batteries. This study presents a novel BMS tailored for continuous monitoring, transmission, and storage of essential parameters such as voltage, current, and temperature in an NCA 18650 4S lithium-ion battery (LIB) pack during high C-rate testing. By incorporating deep learning, our BMS monitors external battery parameters and predicts LIB’s health in terms of discharge capacity. Two experiments were conducted: a static experiment to validate the functionality of BMS, and an in operando experiment on an electrically propelled vehicle to assess real-world performance under high C-rate abuse testing with vibration. It was found that the external surface temperatures peaked at 55 °C during in operando flight, which was higher than that during static testing. During testing, the deep learning capacity estimation algorithm detected a mean capacity deviation of 0.04 Ah, showing an accurate state of health (SOH) by predicting the capacity of the battery. Our BMS demonstrated effective data collection and predictive capabilities, mirroring real-world conditions during abuse testing. Full article
(This article belongs to the Special Issue Artificial Intelligence and Batteries: AI-Powered Innovations in Battery Technology)
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Review

Jump to: Research

29 pages, 3624 KiB  
Review
Battery Health Monitoring and Remaining Useful Life Prediction Techniques: A Review of Technologies
by Mohamed Ahwiadi and Wilson Wang
Batteries 2025, 11(1), 31; https://doi.org/10.3390/batteries11010031 - 17 Jan 2025
Cited by 3 | Viewed by 2583
Abstract
Lithium-ion (Li-ion) batteries have become essential in modern industries and domestic applications due to their high energy density and efficiency. However, they experience gradual degradation over time, which presents significant challenges in maintaining optimal battery performance and increases the risk of unexpected system [...] Read more.
Lithium-ion (Li-ion) batteries have become essential in modern industries and domestic applications due to their high energy density and efficiency. However, they experience gradual degradation over time, which presents significant challenges in maintaining optimal battery performance and increases the risk of unexpected system failures. To ensure the reliability and longevity of Li-ion batteries in applications, various methods have been proposed for battery health monitoring and remaining useful life (RUL) prediction. This paper provides a comprehensive review and analysis of the primary approaches employed for battery health monitoring and RUL estimation under the categories of model-based, data-driven, and hybrid methods. Generally speaking, model-based methods use physical or electrochemical models to simulate battery behaviour, which offers valuable insights into the principles that govern battery degradation. Data-driven techniques leverage historical data, AI, and machine learning algorithms to identify degradation trends and predict RUL, which can provide flexible and adaptive solutions. Hybrid approaches integrate multiple methods to enhance predictive accuracy by combining the physical insights of model-based methods with the statistical and analytical strengths of data-driven techniques. This paper thoroughly evaluates these methodologies, focusing on recent advancements along with their respective strengths and limitations. By consolidating current findings and highlighting potential pathways for advancement, this review paper serves as a foundational resource for researchers and practitioners working to advance battery health monitoring and RUL prediction methods across both academic and industrial fields. Full article
(This article belongs to the Special Issue Artificial Intelligence and Batteries: AI-Powered Innovations in Battery Technology)
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32 pages, 2547 KiB  
Review
Perturbation-Based Battery Impedance Characterization Methods: From the Laboratory to Practical Implementation
by Chuanxin Fan, Xinxiang Tian and Chunfei Gu
Batteries 2024, 10(12), 414; https://doi.org/10.3390/batteries10120414 - 27 Nov 2024
Cited by 3 | Viewed by 1718
Abstract
To guarantee the secure and effective long-term functionality of lithium-ion batteries, vital functions, including lifespan estimation, condition assessment, and fault identification within battery management systems, are necessary. Battery impedance is a crucial indicator for assessing battery health and longevity, serving as an important [...] Read more.
To guarantee the secure and effective long-term functionality of lithium-ion batteries, vital functions, including lifespan estimation, condition assessment, and fault identification within battery management systems, are necessary. Battery impedance is a crucial indicator for assessing battery health and longevity, serving as an important reference in battery state evaluation. This study offers a comprehensive review of the characterization and applications of impedance spectroscopy. It highlights the increasing attention paid to broadband perturbation signals for impedance measurements, which promotes impedance characterization methods from laboratory to practical implementation. The impact of varying impedance characteristics on distinct cell states and their utilization is further examined. The discussion encompasses the challenges and opportunities for future research on onboard battery management system characterizations. Full article
(This article belongs to the Special Issue Artificial Intelligence and Batteries: AI-Powered Innovations in Battery Technology)
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