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Prognostics of Battery Health and Faults

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "D2: Electrochem: Batteries, Fuel Cells, Capacitors".

Deadline for manuscript submissions: 20 February 2025 | Viewed by 10944

Special Issue Editors


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Guest Editor
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: parameters identification; state estimation; health and safety management
Special Issues, Collections and Topics in MDPI journals
School of Sustainability, Stanford University, Stanford, CA 94305, USA
Interests: battery modeling; lifetime prediction; battery control
School of Automotive Studies, Tongji University, Shanghai 201804, China
Interests: battery management; state estimation and prediction; optimization control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Lithium-ion batteries have become one of the most promising energy storage devices due to fast charge capability, high densities, and long cycle life. However, a lithium-ion battery is a complex electrochemical system; therefore, its performance and life gradually deteriorate during use. Battery degradation is caused by many different aspects, including physical (e.g., thermal and mechanical stress) and chemical (e.g., side reactions) mechanisms. Due to the complexity of battery degradation, battery health prediction is an extremely challenging task. Moreover, the faults in the battery system can accelerate battery degradation. These faults can be generally divided into battery faults, sensor faults, and actuator faults. Various fault detection and isolation methods have been proposed, but fault prognosis is not fully understood. Both battery degradation and battery system faults can cause battery failure. As one of the most expensive and essential components in electrified transportation systems and smart grids, battery failures can cause safety issues. Certain severe failures, such as thermal runaway, can lead to fire or even explosion. Therefore, the development of accurate health and fault prognostic technologies is becoming increasingly critical for safe and efficient battery management.

For this Special Issue on “Prognostics of Battery Health and Faults”, we warmly welcome the submission of comprehensive reviews and original research articles.

Dr. Zhongwei Deng
Dr. Le Xu
Dr. Bo Jiang
Guest Editors

Manuscript Submission Information

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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. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

  • energy storage system
  • electric vehicle
  • lithium-ion battery
  • health estimation/prediction
  • remaining useful life
  • degradation/failure mechanism
  • degradation trajectory prediction
  • remaining value assessment
  • fault diagnosis
  • early safety warning
  • internal short circuit
  • thermal runaway warning

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Published Papers (3 papers)

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Research

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21 pages, 7473 KiB  
Article
State of Health Estimations for Lithium-Ion Batteries Based on MSCNN
by Jiwei Wang, Hao Li, Chunling Wu, Yujun Shi, Linxuan Zhang and Yi An
Energies 2024, 17(17), 4220; https://doi.org/10.3390/en17174220 - 23 Aug 2024
Viewed by 607
Abstract
Lithium-ion batteries, essential components in new energy vehicles and energy storage stations, play a crucial role in health-status investigation and ensuring safe operation. To address challenges such as limited estimation accuracy and a weak generalization ability in conventional battery state of health (SOH) [...] Read more.
Lithium-ion batteries, essential components in new energy vehicles and energy storage stations, play a crucial role in health-status investigation and ensuring safe operation. To address challenges such as limited estimation accuracy and a weak generalization ability in conventional battery state of health (SOH) estimation methods, this study presents an integrated approach for SOH estimation that incorporates multiple health indicators and utilizes the multi-scale convolutional neural network (MSCNN) model. Initially, the aging characteristics of the battery are comprehensively analyzed, and then the health indicators are extracted from the charging data, including the temperature, time, current, voltage, etc., and the statistical transformation is performed. Subsequently, Pearson’s method is employed to analyze the correlation between these health indicators and identify those with strong correlations. A regression-prediction model based on the MSCNN model is then developed for estimating battery SOH. Finally, validation using a publicly available lithium-ion battery dataset demonstrates that, under similar operating conditions, the mean absolute error (MAE) for SOH estimation is less than 0.67%, the mean absolute percentage error (MAPE) is less than 0.37%, and the root mean square error (RMSE) is less than 0.74%. The MSCNN has good generalization for datasets with different working conditions. Full article
(This article belongs to the Special Issue Prognostics of Battery Health and Faults)
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16 pages, 5044 KiB  
Article
Coprecipitation Synthesis and Impedance Studies on Electrode Interface Characteristics of 0.5Li2MnO3·0.5Li(Ni0.44Mn0.44Co0.12)O2 Cathode Material
by Xing Zhao, Peng Wang, Yan Wang, Peipei Chao and Honglei Dong
Energies 2023, 16(16), 5919; https://doi.org/10.3390/en16165919 - 10 Aug 2023
Cited by 1 | Viewed by 1156
Abstract
The nanoscale 0.5Li2MnO3·0.5Li(Ni0.44Mn0.44Co0.12)O2 Li-manganese-rich electrode material was synthesized by the co-precipitate method, and its electrochemical properties were systematically analyzed, especially the electrochemical impedance spectroscopy. The failure of the electrode interface and the [...] Read more.
The nanoscale 0.5Li2MnO3·0.5Li(Ni0.44Mn0.44Co0.12)O2 Li-manganese-rich electrode material was synthesized by the co-precipitate method, and its electrochemical properties were systematically analyzed, especially the electrochemical impedance spectroscopy. The failure of the electrode interface and the structural transformation of the material at high potential are the main reasons for the deterioration of the Li-manganese-rich electrode, and high temperatures accelerate the deterioration. Based on the systematic analysis of the induced reactance change with electrode polarization potential, it is found that the induced reactance of a Li-manganese-rich electrode is not only related to the degree of delithiation/lithiation but also has a great relationship with the performance of the electrode/electrolyte interface. This conclusion is beneficial for the manufacturing of battery failure analysis by providing a theoretical basis for guidance. Full article
(This article belongs to the Special Issue Prognostics of Battery Health and Faults)
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Review

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20 pages, 3432 KiB  
Review
A Review on Battery Thermal Management for New Energy Vehicles
by Wenzhe Li, Youhang Zhou, Haonan Zhang and Xuan Tang
Energies 2023, 16(13), 4845; https://doi.org/10.3390/en16134845 - 21 Jun 2023
Cited by 20 | Viewed by 7782
Abstract
Lithium-ion batteries (LIBs) with relatively high energy density and power density are considered an important energy source for new energy vehicles (NEVs). However, LIBs are highly sensitive to temperature, which makes their thermal management challenging. Developing a high-performance battery thermal management system (BTMS) [...] Read more.
Lithium-ion batteries (LIBs) with relatively high energy density and power density are considered an important energy source for new energy vehicles (NEVs). However, LIBs are highly sensitive to temperature, which makes their thermal management challenging. Developing a high-performance battery thermal management system (BTMS) is crucial for the battery to retain high efficiency and security. Generally, the BTMS is divided into three categories based on the physical properties of the cooling medium, including phase change materials (PCMs), liquid, and air. This paper discusses the effect of temperature on the performance of individual batteries and battery systems, at first. Then, a systematic survey of the state-of-the-art BTMS is presented in terms of liquid-based, PCM-based, and air-based BTMS. To further utilize the heat source of the vehicle, the BTMS integrated with the vehicle thermal management system (VTMS) is discussed. Finally, the challenges and future prospects for BTMS with the ability to cut off the thermal runaway are discussed. The primary aim of this review is to offer some guidelines for the design of safe and effective BTMS for the battery pack of NEVs. Full article
(This article belongs to the Special Issue Prognostics of Battery Health and Faults)
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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: Advancing Urban Transport: Integration of Electric Buses with a Particular Focus on Energy Management and Battery Optimization
Authors: Szabolcs Kocsis Szürke; István Lakatos
Affiliation: Central Campus Győr, Széchenyi István University, H-9026 Győr, Hungary
Abstract: A critical aspect of local emission reduction is the electrification of local public transport. The introduction of electric buses for urban transport can significantly facilitate this. However, introducing new vehicles brings new problems: electric charging, new consumption standards, the relationship between distance traveled -temperatures and number of passengers, battery system status, etc. One of the aspects presented in this article was to collect and examine the factors affecting energy efficiency. The main aspects used to determine the actual consumption are external temperature factors, bus conditions, road conditions, the driver's ability, and driving style. By combining these values, a baseline of actual consumption can be established, and an energy balance can be calculated to identify energy-wasting locations and events. Facilitating selecting and assessing different routes, stopping points, and charging times. Furthermore, the approach helps to scale the bus battery system and select the correct local route (line). The second approach is to study and assess the battery systems of electric buses. An essential element of this approach is the cell-by-cell examination and analysis of the battery system, observing cells that appear to be weak. This is because, currently, even in electric vehicles, in most cases, the whole system is replaced (in the best case, only the faulty module) in case of a cell failure. In the case of a bus, where there are many more batteries in the system than in a road electric vehicle, such a replacement can be very costly and polluting. Therefore, a critical aspect was adopting a procedure to detect and localize possible faults.

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