Batteries and Supercapacitors Aging Ⅱ

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: closed (30 June 2023) | Viewed by 25095

Special Issue Editors


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Guest Editor
Department of the Ampère Laboratory, Claude Bernard University Lyon 1, 69100 Villeurbanne, France
Interests: characterization; modeling; reliability; aging and diagnosis of electric energy storage system (batteries, supercapacitors, capacitors)
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Guest Editor
Department of Planning, Gustave Eiffel University, 69500 Bron, France
Interests: lithium-ion batteries; battery aging; battery characterization and modeling; electric vehicles; energy storage systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This issue is a continuation of the previous successful Special Issue “Batteries and Supercapacitors Aging”.

Electrochemical energy storage is a key element of systems in a wide range of sectors, such as electro-mobility, portable devices, or renewable energy. The energy storage systems (ESS) considered here are batteries, supercapacitors, or hybrid components such lithium-ion capacitors. The durability of ESS determines the total cost of ownership and the global impacts (life cycle) on a large portion of these applications and thus their viability. Understanding ESS aging is a key issue for optimizing their design and usage within their applications. Knowledge of ESS aging is also essential for improving their dependability (reliability, availability, maintainability, and safety).

In this Special Issue, we are looking for contributions helping to understand the aging mechanisms, modes, and factors for performing ESS diagnosis and prognosis, as well as innovative solutions for prolonging their lifespans.

Topics of interest include, but are not limited to:

  • Innovative measurement techniques of ESS aging;
  • ESS aging modeling;
  • ESS state-of-health (SOH) estimation;
  • ESS prognostic and health management;
  • Balancing circuits with consideration of the lifetime of ESS;
  • Energy management laws taking into account aging;
  • Influence of aging on cost and environmental impact of ESS;
  • Multi-objective optimization strategies of ESS including aging consideration;
  • Optimal sizing and design of ESS with respect to their lifetime.

Prof. Dr. Pascal Venet
Dr. Eduardo Redondo-Iglesias
Guest Editors

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Keywords

  • aging mechanisms
  • aging modeling
  • component reliability
  • lifecycle assessment
  • lifetime prediction
  • state of health
  • battery
  • supercapacitor
  • hybrid capacitor

Published Papers (4 papers)

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Research

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18 pages, 4761 KiB  
Article
Effect of Capacity Variation in Series-Connected Batteries on Aging
by Sang-Sun Yun and Seok-Cheol Kee
Batteries 2023, 9(1), 22; https://doi.org/10.3390/batteries9010022 - 28 Dec 2022
Cited by 2 | Viewed by 3362
Abstract
Batteries are used in various combinations in various fields. Research on single-cell batteries is well underway and is approaching a stabilization phase. However, problems caused by battery combinations are still insufficiently studied. The purpose of this study was to investigate the cause of [...] Read more.
Batteries are used in various combinations in various fields. Research on single-cell batteries is well underway and is approaching a stabilization phase. However, problems caused by battery combinations are still insufficiently studied. The purpose of this study was to investigate the cause of fires due to gradual damage in a large-capacity energy storage system (ESS). In the paper Energy Storage System Safety Operation Plan by Preventing Overcharge During Relaxation Time, which was based on the fact that most fires in large-capacity energy storage devices occurred during the diastolic period, it was proven that the inflow of compensation current due to a voltage imbalance in the cell was the cause. The total amount of compensation current is determined by the voltage deviation of the battery. Batteries connected in series have different rates of aging due to differences in their capacities. Thus, with use, the total amount of compensating current continues to increase until a fire occurs. In this study, by analyzing the effect of battery-capacity deviation on the aging of individual cells, it was confirmed that the capacity deviation increased as the battery was used, resulting in an increase in the total amount of compensation current. In addition, if a solution to the problem is presented and the proposed solution is applied, the allowable range of battery-capacity deviation will be widened. We used Matlab 2009a, assuming a real environment. Using Simulink, problems were identified through simulation, improvement measures were suggested, and the proposed method was verified via simulation. Full article
(This article belongs to the Special Issue Batteries and Supercapacitors Aging Ⅱ)
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19 pages, 4525 KiB  
Article
Online State-of-Health Estimation of Lithium-Ion Battery Based on Incremental Capacity Curve and BP Neural Network
by Hongye Lin, Longyun Kang, Di Xie, Jinqing Linghu and Jie Li
Batteries 2022, 8(4), 29; https://doi.org/10.3390/batteries8040029 - 23 Mar 2022
Cited by 29 | Viewed by 4372
Abstract
Lithium-ion batteries (LIBs) have been widely used in various fields. In order to ensure the safety of LIBs, it is necessary to accurately estimate of the state of health (SOH) of the LIBs. This paper proposes a SOH hybrid estimation method based on [...] Read more.
Lithium-ion batteries (LIBs) have been widely used in various fields. In order to ensure the safety of LIBs, it is necessary to accurately estimate of the state of health (SOH) of the LIBs. This paper proposes a SOH hybrid estimation method based on incremental capacity (IC) curve and back-propagation neural network (BPNN). The voltage and current data of the LIB during the constant current (CC) charging process are used to convert into IC curves. Taking into account the incompleteness of the actual charging process, this paper divides the IC curve into multiple voltage segments for SOH prediction. Corresponding BP neural network is established in multiple voltage segments. The experiment divides the LIBs into five groups to carry out the aging experiment under different discharge conditions. Aging experiment data are used to establish the non-linear relationship between the decline of SOH and the change of IC curve by BP neural network. Experimental results show that in all voltage segments, the maximum mean absolute error does not exceed 2%. The SOH estimation method proposed in this research makes it possible to embed the SOH estimation function in battery management system (BMS), and can realize high-precision SOH online estimation. Full article
(This article belongs to the Special Issue Batteries and Supercapacitors Aging Ⅱ)
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18 pages, 1543 KiB  
Article
Modelling Lithium-Ion Battery Ageing in Electric Vehicle Applications—Calendar and Cycling Ageing Combination Effects
by Eduardo Redondo-Iglesias, Pascal Venet and Serge Pelissier
Batteries 2020, 6(1), 14; https://doi.org/10.3390/batteries6010014 - 19 Feb 2020
Cited by 48 | Viewed by 14611
Abstract
Battery ageing is an important issue in e-mobility applications. The performance degradation of lithium-ion batteries has a strong influence on electric vehicles’ range and cost. Modelling capacity fade of lithium-ion batteries is not simple: many ageing mechanisms can exist and interact. Because calendar [...] Read more.
Battery ageing is an important issue in e-mobility applications. The performance degradation of lithium-ion batteries has a strong influence on electric vehicles’ range and cost. Modelling capacity fade of lithium-ion batteries is not simple: many ageing mechanisms can exist and interact. Because calendar and cycling ageings are not additive, a major challenge is to model battery ageing in applications where the combination of cycling and rest periods are variable as, for example, in the electric vehicle application. In this work, an original approach to capacity fade modelling based on the formulation of reaction rate of a two-step reaction is proposed. A simple but effective model is obtained: based on only two differential equations and seven parameters, it can reproduce the capacity evolution of lithium-ion cells subjected to cycling profiles similar to those found in electric vehicle applications. Full article
(This article belongs to the Special Issue Batteries and Supercapacitors Aging Ⅱ)
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Review

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29 pages, 2328 KiB  
Review
SOC, SOH and RUL Estimation for Supercapacitor Management System: Methods, Implementation Factors, Limitations and Future Research Improvements
by Afida Ayob, Shaheer Ansari, Molla Shahadat Hossain Lipu, Aini Hussain and Mohamad Hanif Md Saad
Batteries 2022, 8(10), 189; https://doi.org/10.3390/batteries8100189 - 17 Oct 2022
Cited by 5 | Viewed by 4412
Abstract
The development of a supercapacitor management system (SMS) for clean energy applications is crucial to addressing carbon emissions problems. Consequently, state of charge (SOC), state of health (SOH), and remaining useful life (RUL) for SMS must be developed to evaluate supercapacitor robustness and [...] Read more.
The development of a supercapacitor management system (SMS) for clean energy applications is crucial to addressing carbon emissions problems. Consequently, state of charge (SOC), state of health (SOH), and remaining useful life (RUL) for SMS must be developed to evaluate supercapacitor robustness and reliability for mitigating supercapacitor issues related to safety and economic loss. State estimation of SMS results in safe operation and eliminates undesirable event occurrences and malfunctions. However, state estimations of SMS are challenging and tedious, as SMS is subject to various internal and external factors such as internal degradation mechanism and environmental factors. This review presents a comprehensive discussion and analysis of model-based and data-driven-based techniques for SOC, SOH, and RUL estimations of SMS concerning outcomes, advantages, disadvantages, and research gaps. The work also investigates various key implementation factors such as a supercapacitor test bench platform, experiments, a supercapacitor cell, data pre-processing, data size, model operation, functions, hyperparameter adjustments, and computational capability. Several key limitations, challenges, and issues regarding SOC, SOH, and RUL estimations are outlined. Lastly, effective suggestions are outlined for future research improvements towards delivering accurate and effective SOC, SOH, and RUL estimations of SMS. Critical analysis and discussion would be useful for developing accurate SMS technology for state estimation of a supercapacitor with clean energy and high reliability, and will provide significant contributions towards reducing greenhouse gas (GHG) to achieve global collaboration and sustainable development goals (SDGs). Full article
(This article belongs to the Special Issue Batteries and Supercapacitors Aging Ⅱ)
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