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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 5434

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 (5 papers)

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Research

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16 pages, 804 KiB  
Article
A Deep Learning Approach for Online State of Health Estimation of Lithium-Ion Batteries Using Partial Constant Current Charging Curves
by Mano Schmitz and Julia Kowal
Batteries 2024, 10(6), 206; https://doi.org/10.3390/batteries10060206 - 14 Jun 2024
Viewed by 486
Abstract
The accurate state of health (SOH) estimation of lithium-ion batteries (LIBs) during operation is crucial to ensure optimal performance, prolonging battery life and preventing unexpected failure or safety hazards. This work presents a storage- and performance-optimised deep learning approach to estimate the capacity-based [...] Read more.
The accurate state of health (SOH) estimation of lithium-ion batteries (LIBs) during operation is crucial to ensure optimal performance, prolonging battery life and preventing unexpected failure or safety hazards. This work presents a storage- and performance-optimised deep learning approach to estimate the capacity-based SOH of LIBs using raw sensor data from partial charging curves under constant current condition. The proposed model is based on a combination of a one-dimensional convolutional and long short-term memory neural network, and processes time, voltage, and incremental capacity of partial charging curves as time series. The model is cross-validated on different ageing scenarios, reaching an overall MAE = 0.418% and RMSE = 0.531%, promising an accurate SOH estimation of LIBs under varying usage and environmental conditions in a real-world application. Full article
(This article belongs to the Special Issue Service Safety, Reliability, and Uncertainty Assessment of Lithium-Ion Battery)
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28 pages, 9374 KiB  
Article
Improved Thermal Management of Li-Ion Batteries with Phase-Change Materials and Metal Fins
by Pierluca Paciolla and Davide Papurello
Batteries 2024, 10(6), 190; https://doi.org/10.3390/batteries10060190 - 31 May 2024
Viewed by 538
Abstract
The continuing increase in pollutant emissions requires the use of alternative power sources. This includes the use of electric or hybrid vehicles whose energy storage system is based on batteries of various types, including lithium-ion batteries. The optimum operating temperature is between 15 [...] Read more.
The continuing increase in pollutant emissions requires the use of alternative power sources. This includes the use of electric or hybrid vehicles whose energy storage system is based on batteries of various types, including lithium-ion batteries. The optimum operating temperature is between 15 °C and 35 °C. Too high temperatures can lead to catastrophic phenomena such as thermal runaway. The thermal gradient within the system should not exceed 5 °C. An effective Battery Thermal Management System can mitigate this problem. This study analysed a lithium-ion battery with a bag structure. Temperature control was evaluated using a passive (low-cost) system with phase-change materials (PCMs). The material chosen was n-octadecane (paraffin) due to its thermophysical properties and market price. Four different cooling methods were analysed, including air, fins, pure PCM, and a mixed system of single cells and small battery packs. The results show that an undesirable temperature peak around 50 °C (323.15 K) can occur at hot spots. The best system for containing the temperature inside the battery pack is the PCM cooling system with fins. The optimum fin thickness is 1.5 mm. To contain the temperature inside the battery pack, the number of fins studied is 10, while the best temperature containment is achieved with n+ 1 plates, where n is the number of cells. Full article
(This article belongs to the Special Issue Service Safety, Reliability, and Uncertainty Assessment of Lithium-Ion Battery)
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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 - 1 Mar 2024
Viewed by 1464
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 1750
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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Review

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21 pages, 5186 KiB  
Review
Active Methods for the Equalization of a Serially Connected Lithium-Ion Battery Pack: A Review
by Longsheng Yuan, Tuo Ji and Lijun Zhang
Batteries 2024, 10(7), 239; https://doi.org/10.3390/batteries10070239 - 3 Jul 2024
Viewed by 416
Abstract
Traditional fuel vehicles are currently still the main means of transportation when people travel. It brings convenience to their travels, but it also causes energy shortages and environmental pollution. With the development of science and technology and the popularization of green environmental protection, [...] Read more.
Traditional fuel vehicles are currently still the main means of transportation when people travel. It brings convenience to their travels, but it also causes energy shortages and environmental pollution. With the development of science and technology and the popularization of green environmental protection, electric vehicles have gradually entered people’s lives, greatly alleviating these problems. As a power supply device for electric vehicles, the performance of batteries directly affects various indicators of vehicles. Due to their long lifespan and high energy density, lithium-ion batteries are now the preferred source of power for electric vehicles. However, due to various factors in the manufacturing and operation of lithium-ion batteries, there are often differences among individual cells. The power balance and performance of a battery pack are closely related. Thus, battery equalization is an important standard for a battery management system to work normally, and it is also one of the various battery management application problems. This paper reviews battery equalization systems and various active equalization circuits and summarizes the working principle and research progress of each active equalization circuit. Then, various active equalization circuits are analyzed and compared, and dynamic equalization for a second-life battery is introduced to enrich this review of equalization technology. Finally, the above contents are summarized and prospected. In order to obtain the best outcomes, different equalization circuits need to be chosen for various situations. 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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