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Batteries
Special Issues
Service Safety, Reliability, and Uncertainty Assessment of Lithium-Ion Battery
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Service Safety, Reliability, and Uncertainty Assessment of Lithium-Ion Battery

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: 25 October 2024 | Viewed by 3048

Special Issue Editors

Prof. Dr. Lijun Zhang

E-Mail Website
Guest Editor
1. National Center for Materials Service Safety, University of Science and Technology Beijing, Beijing 100083, China
2. Research Institute of Macro-Safety Science, University of Science and Technology Beijing, Beijing 100083, China
Interests: lithium-ion battery; safety assessment; fault diagnosis; prognostics and health management; digital twins
Dr. Zhimin Xi

E-Mail Website
Guest Editor
Department of Industrial and Systems Engineering, Rutgers University – New Brunswick, Piscataway, NJ 08854, USA
Interests: design for reliability and the application of reliable lithium-ion batteries

Special Issue Information

Dear Colleagues,

Recently, due to their wide temperature range, high energy density, low self-discharge rate, and long cycle life, lithium-ion batteries have been widely used in a variety of industries, such as transportation, electronics, portable mobile devices, and aerospace. However, the capacity degradation of lithium-ion batteries can lead to increased internal resistance, accelerated aging, and even a safety risk during long-term use and charge/discharge processing. Moreover, defects in the design stage, harsh service environment, and misuse can also cause serious degradation of the service performance of lithium-ion batteries. People are concerned about the risks associated with lithium-ion batteries, even though they bring convenience to mobile phones, electric bicycles, electric vehicles, and battery charging stations. Therefore, the service performance assessment of lithium-ion batteries has become a hot topic in theoretical research and engineering applications.

In this Special Issue, we welcome papers or reviews, including simulation studies and experimental studies, on the service performance of battery materials, battery cells, and battery packs, with a focus on safety, reliability, and uncertainty assessment. We specifically aim to address the reliability analysis and risk assessment of lithium-ion batteries in complex or real service environments, such as low temperature and vibration. Lithium-ion battery applications include electronic products, electric vehicles, charging stacks, public charging stations, and any industrial equipment that uses lithium-ion batteries. Furthermore, we also welcome articles on optimized design for lithium-ion battery service safety.

Prof. Dr. Lijun Zhang
Dr. Zhimin Xi
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

  • lithium-ion battery
  • performance assessment
  • safety
  • simulation
  • experiments
  • degradation
  • charge
  • discharge
  • modeling
  • reliability analysis
  • risk assessment
  • prediction
  • optimization

Published Papers (2 papers)

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Research

16 pages, 6519 KiB  
Article
Research on Inconsistency Evaluation of Retired Battery Systems in Real-World Vehicles
by Jiegang Wang, Kerui Li, Chi Zhang, Zhenpo Wang, Yangjie Zhou and Peng Liu
Batteries 2024, 10(3), 82; https://doi.org/10.3390/batteries10030082 - 01 Mar 2024
Viewed by 1067
Abstract
Inconsistency is a key factor triggering safety problems in battery packs. The inconsistency evaluation of retired batteries is of great significance to ensure the safe and stable operation of batteries during subsequent gradual use. This paper summaries the commonly used diagnostic methods for [...] Read more.
Inconsistency is a key factor triggering safety problems in battery packs. The inconsistency evaluation of retired batteries is of great significance to ensure the safe and stable operation of batteries during subsequent gradual use. This paper summaries the commonly used diagnostic methods for battery inconsistency assessment. The local outlier factor (LOF) algorithm and the improved Shannon entropy (ImEn) algorithm are selected for validation based on the individual voltage data from real-world vehicles. Then, a comprehensive inconsistency evaluation strategy for retired batteries with many levels and indicators is established based on the three parameters of LOF, ImEn, and cell voltage range. Finally, the evaluation strategy is validated using two real-world vehicle samples of retired batteries. The results show that the proposed method can achieve the inconsistency evaluation of retired batteries quickly and effectively. Full article
(This article belongs to the Special Issue Service Safety, Reliability, and Uncertainty Assessment of Lithium-Ion Battery)
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22 pages, 10847 KiB  
Article
Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Iterative Transfer Learning and Mogrifier LSTM
by Zihan Li, Fang Bai, Hongfu Zuo and Ying Zhang
Batteries 2023, 9(9), 448; https://doi.org/10.3390/batteries9090448 - 31 Aug 2023
Cited by 2 | Viewed by 1411
Abstract
Lithium-ion battery health and remaining useful life (RUL) are essential indicators for reliable operation. Currently, most of the RUL prediction methods proposed for lithium-ion batteries use data-driven methods, but the length of training data limits data-driven strategies. To solve this problem and improve [...] Read more.
Lithium-ion battery health and remaining useful life (RUL) are essential indicators for reliable operation. Currently, most of the RUL prediction methods proposed for lithium-ion batteries use data-driven methods, but the length of training data limits data-driven strategies. To solve this problem and improve the safety and reliability of lithium-ion batteries, a Li-ion battery RUL prediction method based on iterative transfer learning (ITL) and Mogrifier long and short-term memory network (Mogrifier LSTM) is proposed. Firstly, the capacity degradation data in the source and target domain lithium battery historical lifetime experimental data are extracted, the sparrow search algorithm (SSA) optimizes the variational modal decomposition (VMD) parameters, and several intrinsic mode function (IMF) components are obtained by decomposing the historical capacity degradation data using the optimization-seeking parameters. The highly correlated IMF components are selected using the maximum information factor. Capacity sequence reconstruction is performed as the capacity degradation information of the characterized lithium battery, and the reconstructed capacity degradation information of the source domain battery is iteratively input into the Mogrifier LSTM to obtain the pre-training model; finally, the pre-training model is transferred to the target domain to construct the lithium battery RUL prediction model. The method’s effectiveness is verified using CALCE and NASA Li-ion battery datasets, and the results show that the ITL-Mogrifier LSTM model has higher accuracy and better robustness and stability than other prediction methods. Full article
(This article belongs to the Special Issue Service Safety, Reliability, and Uncertainty Assessment of Lithium-Ion Battery)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Reliable capacity estimation for aged battery with automated optimal network selection
Authors: Zhimin Xi
Affiliation: Department of Industrial and Systems Engineering, Rutgers University – New Brunswick, Piscataway, NJ 08854, United States
Abstract: (optional)

Title: Accurate battery remaining useful life estimation using the minimum amount of battery history information
Authors: Jinwoo Bae; Zhimin Xi
Affiliation: Department of Industrial and Systems Engineering, Rutgers University – New Brunswick, Piscataway, NJ 08854, United States
Abstract: (optional)

Title: Balance control for lithium battery pack based on neuro-fuzzy control
Authors: Tuo Ji; Lijun Zhang
Affiliation: National Center for Materials Service Safety, University of Science and Technology Beijing, Beijing 100083, China
Abstract: (optional)

Title: Reviews on active methods for equalization of serially connected lithium battery pack
Authors: Longsheng Yuan; Lijun Zhang
Affiliation: National Center for Materials Service Safety, University of Science and Technology Beijing, Beijing 100083, China
Abstract: (optional)

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