Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.6
4.8
Journals
Batteries
Special Issues
Intelligent Management and Sustainable Development of Lithium-Ion Batteries in Automotive...
share announcement

Intelligent Management and Sustainable Development of Lithium-Ion Batteries in Automotive Applications

A special issue of Batteries (ISSN 2313-0105). This special issue belongs to the section "Battery Modelling, Simulation, Management and Application".

Deadline for manuscript submissions: 25 November 2025 | Viewed by 1079

Special Issue Editor

Dr. Bin Ma

E-Mail Website
Guest Editor
College of Communication Engineering, Jilin University, Changchun 130012, China
Interests: deep learning-based state estimation and fault diagnosis for automotive batteries

Special Issue Information

Dear Colleagues,

As electric vehicles continue their rapid global adoption, lithium-ion batteries—the cornerstone energy storage technology—face critical challenges in operational efficiency optimization, lifespan extension, and sustainable lifecycle management. This Special Issue seeks to advance the frontiers of intelligent battery management through innovative integration of computational intelligence, distributed computing architectures, and data-driven analytics. We particularly welcome interdisciplinary studies that synergize artificial intelligence, physics-informed modeling, and advanced data science to achieve transformative breakthroughs in battery reliability, safety, and circular economy implementation.

In this context, we invite researchers from academia and industry to contribute original research articles, reviews, and case studies addressing, but not limited to, the following topics:

  1. Cloud-edge collaborative architectures for battery state estimation and parameter optimization;
  2. Physics-informed neural networks for cross-scale battery modeling;
  3. Transfer learning approaches for cross-chemistry state estimation generalization;
  4. Early fault warning systems based on multi-source data fusion;
  5. LLM-enhanced battery health monitoring and management systems;
  6. Vehicle energy management strategies considering battery aging suppression;
  7. Intelligent sorting and second-life applications of retired batteries.

We welcome contributions from experts in academia, industry, and policymaking to discuss cutting-edge technologies, standardization challenges, and policy implications for lithium-ion battery applications in vehicles.

Dr. Bin Ma
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. 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

  • lithium-ion batteries
  • cloud-edge collaboration
  • data-driven modeling
  • transfer learning
  • large language models
  • second-life applications
  • fault diagnosis
  • aging suppression

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

15 pages, 3176 KB  
Article
SoC Fusion Estimation Based on Neural Network Long and Short Time Series
by Bosong Zou, Wang Fu, Chunxia Yan, Qingshuang Zeng, Zheng Wang, Rong Wang, Wenlong Ding, Xianglong Chen and Qiuju Gao
Batteries 2025, 11(9), 336; https://doi.org/10.3390/batteries11090336 - 9 Sep 2025
Viewed by 694
Abstract
Accurate prediction of state-of-charge (SoC) is critical to ensure battery performance, extend lifetime and ensure safety. Data-driven methods for SoC prediction are highly adaptable and generalizable. However, the current method of estimating SoC using a single model suffers from the difficulty of accommodating [...] Read more.
Accurate prediction of state-of-charge (SoC) is critical to ensure battery performance, extend lifetime and ensure safety. Data-driven methods for SoC prediction are highly adaptable and generalizable. However, the current method of estimating SoC using a single model suffers from the difficulty of accommodating both global variations in the long time domain and local variations in the short time domain, which in turn leads to limited accuracy. Therefore, this paper proposes a dual-model fusion of Transformer and long short-term memory (LSTM) network for SoC estimation. Transformer and LSTM are used to capture the global change features of the battery in the long time domain and the local change features in the short time domain, respectively. First, we employ a single model to obtain separate SoC estimations for the long-term and short-term domains. Then, we fuse these long-term and short-term estimations using a neural network. Finally, we apply Kalman filtering to process the fused data and obtain the final SoC estimation. The proposed method is finally validated under different operating conditions and different temperatures, respectively. The results show that the root mean square error of the fused model is as low as 1.69%. This method can fully combine the advantages of LSTM for short-time sequences and Transformer for long-time sequence capture. The fused model is able to achieve satisfactory estimation accuracy under different temperatures and different working conditions with high accuracy and adaptability. Full article
(This article belongs to the Special Issue Intelligent Management and Sustainable Development of Lithium-Ion Batteries in Automotive Applications)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop