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: 31 January 2025 | Viewed by 1760

Special Issue Editors


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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

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 (SOH), 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 (SOH)
  • 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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Published Papers (1 paper)

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Research

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
Viewed by 885
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
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