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Processes
Special Issues
Applications of AI in Advanced Energy Storage Systems (AESS)
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Applications of AI in Advanced Energy Storage Systems (AESS)

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".

Deadline for manuscript submissions: closed (17 November 2023) | Viewed by 2613

Special Issue Editor

Dr. Ashkan Nazari

E-Mail Website
Guest Editor
Co-Founder and CEO, Impedyme Inc., Virginia Tech Alumni, Blacksburg, VA, USA
Interests: energy; AI; deep learning; cloud BMS; cloud-based intelligence systems analysis

Special Issue Information

Dear Colleagues,

Energy storage systems are critical components for the future of the automotive industry and for achieving the reliable and efficient integration of renewable energy sources into the power grid. However, the optimal design, control, and management of energy storage systems remains challenging due to the complex nature of energy storage systems and the dynamic nature of renewable energy sources.

The primary goal of this Special Issue is to create an opportunity to showcase the most recent research on the integration of energy storage with AI technologies. We encourage the submission of original research articles and reviews that explore theoretical and methodological technologies and those that provide a critical overview on the state-of-the-art of such technologies toward the enhancement of environmental sustainability, efficiency, safety, reliability, and increase the energy density of energy storage systems through AI-driven new battery chemistries and materials development.

The topics of interest include, but are not limited to:

  • The development and implementation of advanced battery management systems (BMS) that integrate artificial intelligence (AI) and machine learning (ML) algorithms.
  • State estimation and predictive control of energy storage systems using AI techniques.
  • Lifetime prediction and early detection of hazardous events in energy storage systems using AI-enabled prognostics and health management.
  • Thermal management systems for energy storage systems with AI-based design and control strategies.
  • Novel energy storage materials and topologies with AI-driven design and optimization.
  • Modeling, simulation, and optimization of energy storage systems using AI and big data analytics.
  • Advanced energy management systems for energy storage, including AI-driven charging/discharging strategies.
  • Safety and reliability evaluation of energy storage systems with AI-enabled data analytics.
  • AI and big data analytics for energy trading and management in microgrids with energy storage.

Submission Guidelines:
We welcome original research papers, review articles, and case studies that address the application of AI and big data analytics in advanced energy storage systems. Submissions should be tightly linked to the application of AI and big data in energy storage systems. Manuscripts should be prepared according to the journal's guidelines and submitted through the journal's online submission system. All submissions will undergo a rigorous peer-review process.

Dr. Ashkan Nazari
Guest Editor

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. Processes 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 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

  • energy storage systems
  • AI
  • machine learning
  • advanced battery management systems
  • renewable energy

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

Published Papers (1 paper)

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Research

17 pages, 10755 KiB  
Article
Remaining Useful Life Prediction for Lithium-Ion Batteries Based on a Hybrid Deep Learning Model
by Chao Chen, Jie Wei and Zhenhua Li
Processes 2023, 11(8), 2333; https://doi.org/10.3390/pr11082333 - 3 Aug 2023
Cited by 10 | Viewed by 2195
Abstract
Lithium-ion batteries are widely utilized in various fields, including aerospace, new energy vehicles, energy storage systems, medical equipment, and security equipment, due to their high energy density, extended lifespan, and lightweight design. Precisely predicting the remaining useful life (RUL) of lithium batteries is [...] Read more.
Lithium-ion batteries are widely utilized in various fields, including aerospace, new energy vehicles, energy storage systems, medical equipment, and security equipment, due to their high energy density, extended lifespan, and lightweight design. Precisely predicting the remaining useful life (RUL) of lithium batteries is crucial for ensuring the safe use of a device. In order to solve the problems of unstable prediction accuracy and difficultly modeling lithium-ion battery RUL with previous methods, this paper combines a channel attention (CA) mechanism and long short-term memory networks (LSTM) to propose a new hybrid CA-LSTM lithium-ion battery RUL prediction model. By incorporating a CA mechanism, the utilization of local features in situations where data are limited can be improved. Additionally, the CA mechanism can effectively mitigate the impact of battery capacity rebound on the model during lithium-ion battery charging and discharging cycles. In order to ensure the full validity of the experiments, this paper utilized the National Aeronautics and Space Administration (NASA) and the University of Maryland Center for Advanced Life Cycle Engineering (CALCE) lithium-ion battery datasets and different prediction starting points for model validation. The experimental results demonstrated that the hybrid CA-LSTM lithium-ion battery RUL prediction model proposed in this paper exhibited a strong predictive performance and was minimally influenced by the prediction starting point. Full article
(This article belongs to the Special Issue Applications of AI in Advanced Energy Storage Systems (AESS))
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