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Batteries
Special Issues
Modeling, Reliability and Health Management of Lithium-Ion Batteries
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Modeling, Reliability and Health Management of Lithium-Ion Batteries

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: closed (31 January 2024) | Viewed by 17321

Special Issue Editors

Dr. Fei Feng

E-Mail Website
Guest Editor
School of Automation, Chongqing University, Chongqing 400044, China
Interests: industrial artificial intelligence; fault diagnosis; prognosis and health management
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Rui Ling

E-Mail Website
Guest Editor
School of Automation, Chongqing University, Chongqing 40044, China
Interests: energy management and control technology of energy storage system; robot control technology
Special Issues, Collections and Topics in MDPI journals
Dr. Yi Xie

E-Mail Website
Guest Editor
College of Mechanical and Vehicular Engineering, Chongqing University, Chongqing 400044, China
Interests: battery thermal management system; vehicle thermal management system
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Shunli Wang

E-Mail Website
Guest Editor
School of Information Engineering, Southwest University of Science and Technology, Mianyang 621000, China
Interests: lithium battery modeling; state estimation; battery balancing; battery management system; new energy system
Special Issues, Collections and Topics in MDPI journals
Dr. Jinhao Meng

E-Mail Website
Guest Editor
School of Electrical Engineering, Xi'an Jiaotong University, Xi’an 710049, China
Interests: battery management systems; battery state estimation; fast charging control; battery impedance measurement
Special Issues, Collections and Topics in MDPI journals
Dr. Jiale Xie

E-Mail Website
Guest Editor
Automation Department, North China Electric Power University, Baoding Campus, Baoding 071051, China
Interests: battery characteristic modeling; fault diagnosis; states estimation; thermal management; energy equilibrium
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the increasing attention paid to environmental issues, “carbon neutrality” has become one of the policy goals of many countries and regions. The development of new energy vehicles and battery energy storage is of great significance for the control of carbon emissions. As a key component of new energy vehicles and energy storage systems, battery life and cost directly affect the life and economy of the whole system. Therefore, how to improve the reliability, durability and economy of the whole life cycle of the battery system has become an urgent scientific and major engineering problem.

This Special Issue will focus on battery energy storage and its health management system. Papers are invited in all different areas of battery health management, as battery energy storage is a multidisciplinary topic that involves research areas such as electrochemistry, materials, control, electrical and mechanical issues, as well as economic and environmental aspects. Both theoretical and experimental works, and, especially, the combination of these, are welcome. In recent years, the diagnosis of battery thermal runaway has attracted attention and encouraged scientists to explore new methods for the diagnosis of new physical quantities of battery thermal runaway.

Topics of interest for publication include, but are not limited to, the following:

  • Multi-physical field battery modeling (electrical, thermal, force, gas, etc.);
  • Battery degradation mechanism and modeling;
  • Battery thermal runaway mechanism and modeling;
  • Battery numerical calculation and simulation technology;
  • Battery states estimation algorithm (SOC, SOH, SOP, SOE, etc.);
  • Battery thermal runaway and fault diagnosis;
  • Battery life prediction and health management;
  • Battery reliability evaluation methods;
  • Battery charging control strategy;
  • Battery balancing control strategy;
  • Battery thermal management control strategy.

Dr. Fei Feng
Prof. Dr. Rui Ling
Prof. Dr. Yi Xie
Prof. Dr. Shunli Wang
Dr. Jinhao Meng
Dr. Jiale Xie
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

  • battery modeling
  • battery reliability
  • battery fault diagnosis
  • battery control strategy
  • battery health management

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Related Special Issue

Published Papers (5 papers)

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Research

12 pages, 4328 KiB  
Article
A Novel Reaction Rate Parametrization Method for Lithium-Ion Battery Electrochemical Modelling
by Alain Goussian, Loïc Assaud, Issam Baghdadi, Cédric Nouillant and Sylvain Franger
Batteries 2024, 10(6), 205; https://doi.org/10.3390/batteries10060205 - 14 Jun 2024
Cited by 2 | Viewed by 2055
Abstract
To meet the ever-growing worldwide electric vehicle demand, the development of advanced generations of lithium-ion batteries is required. To this end, modelling is one of the pillars for the innovation process. However, modelling batteries containing a large number of different mechanisms occurring at [...] Read more.
To meet the ever-growing worldwide electric vehicle demand, the development of advanced generations of lithium-ion batteries is required. To this end, modelling is one of the pillars for the innovation process. However, modelling batteries containing a large number of different mechanisms occurring at different scales remains a field of research that does not provide consensus for each particular model or approach. Parametrization as part of the modelling process appears to be one of the issues when it comes to building a high-fidelity model of a target cell. In this paper, a particular parameter identification is therefore discussed. Indeed, even if Butler–Volmer is a well-known equation in the electrochemistry field, identification of its reaction rate constant or exchange current density parameters is lacking in the literature. Thus, we discuss the process described in the literature and propose a new protocol that expects to overcome certain difficulties whereas the hypothesis of calculation and measurement maintains high sensitivity. Full article
(This article belongs to the Special Issue Modeling, Reliability and Health Management of Lithium-Ion Batteries)
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22 pages, 6542 KiB  
Article
Equivalent Minimum Hydrogen Consumption of Fuzzy Control-Based Fuel Cells: Exploration of Energy Management Strategies for Ships
by Yubo Sun, Qianming Shang and Wanying Jiang
Batteries 2024, 10(2), 66; https://doi.org/10.3390/batteries10020066 - 18 Feb 2024
Cited by 4 | Viewed by 2756
Abstract
Aiming to solve the problems of insufficient dynamic responses, the large loss of energy storage life of a single power cell, and the large fluctuation in DC (direct current) bus voltage in fuel cell vessels, this study takes a certain type of fuel [...] Read more.
Aiming to solve the problems of insufficient dynamic responses, the large loss of energy storage life of a single power cell, and the large fluctuation in DC (direct current) bus voltage in fuel cell vessels, this study takes a certain type of fuel cell ferry as the research object and proposes an improved equivalent minimum hydrogen consumption energy management strategy, based on fuzzy logic control. First, a hybrid power system including a fuel cell, a lithium–iron–phosphate battery, and a supercapacitor is proposed, with the simulation of the power system of the modified mother ship. Second, a power system simulation model and a double-closed-loop PI (proportion integration) control model are established in MATLAB/Simulink to design the equivalent hydrogen consumption model and fuzzy logic control strategy. The simulation results show that, under the premise of meeting the load requirements, the control strategy designed in this paper improves the Li-ion battery’s power, the Li-ion battery’s SOC (state of charge), the bus voltage stability, and the equivalent hydrogen consumption significantly, compared with those before optimization, which improves the stability and economy of the power system and has certain practical engineering value. Full article
(This article belongs to the Special Issue Modeling, Reliability and Health Management of Lithium-Ion Batteries)
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20 pages, 4606 KiB  
Article
Co-Estimation of State-of-Charge and State-of-Health for High-Capacity Lithium-Ion Batteries
by Ran Xiong, Shunli Wang, Fei Feng, Chunmei Yu, Yongcun Fan, Wen Cao and Carlos Fernandez
Batteries 2023, 9(10), 509; https://doi.org/10.3390/batteries9100509 - 12 Oct 2023
Cited by 5 | Viewed by 2383
Abstract
To address the challenges of efficient state monitoring of lithium-ion batteries in electric vehicles, a co-estimation algorithm of state-of-charge (SOC) and state-of-health (SOH) is developed. The algorithm integrates techniques of adaptive recursive least squares and dual adaptive extended Kalman filtering to enhance robustness, [...] Read more.
To address the challenges of efficient state monitoring of lithium-ion batteries in electric vehicles, a co-estimation algorithm of state-of-charge (SOC) and state-of-health (SOH) is developed. The algorithm integrates techniques of adaptive recursive least squares and dual adaptive extended Kalman filtering to enhance robustness, mitigate data saturation, and reduce the impact of colored noise. At 25 °C, the algorithm is tested and verified under dynamic stress test (DST) and Beijing bus DST conditions. Under the Beijing bus DST condition, the algorithm achieves a mean absolute error (MAE) of 0.17% and a root mean square error (RMSE) of 0.19% for SOC estimation, with a convergence time of 4 s. Under the DST condition, the corresponding values are 0.05% for MAE, 0.07% for RMSE, and 5 s for convergence time. Moreover, in this research, the SOH is described as having internal resistance. Under the Beijing bus DST condition, the MAE and the RMSE of the estimated internal resistance of the proposed approach are 0.018% and 0.075%, with the corresponding values of 0.014% and 0.043% under the DST condition. The results of the experiments provide empirical evidence for the challenges associated with the efficacious estimation of SOC and SOH. Full article
(This article belongs to the Special Issue Modeling, Reliability and Health Management of Lithium-Ion Batteries)
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15 pages, 6628 KiB  
Article
Determination of Lithium-Ion Battery Capacity for Practical Applications
by Hrvoje Bašić, Vedran Bobanac and Hrvoje Pandžić
Batteries 2023, 9(9), 459; https://doi.org/10.3390/batteries9090459 - 11 Sep 2023
Cited by 4 | Viewed by 7166
Abstract
Batteries are becoming highly important in automotive and power system applications. The lithium-ion battery, as the fastest growing energy storage technology today, has its specificities, and requires a good understanding of the operating characteristics in order to use it in full capacity. One [...] Read more.
Batteries are becoming highly important in automotive and power system applications. The lithium-ion battery, as the fastest growing energy storage technology today, has its specificities, and requires a good understanding of the operating characteristics in order to use it in full capacity. One such specificity is the dependence of the one-way charging/discharging efficiency on the charging/discharging current. This paper proposes a novel method for the determination of battery capacity based on experimental testing. The proposed method defines battery energy capacity as the energy actually stored in the battery, while accounting for both the charging and discharging losses. The experiments include one-way efficiency determination based on multiple cycles conducted under different operational and ambient conditions, the goal of which is to acquire the charging/discharging energies. The measured energies are corrected for one-way efficiencies to obtain values actually stored in a battery during charging or actually extracted from the battery during discharging. The proposed method is tested in a laboratory and compared against two existing baseline methods at different ambient temperatures. The results indicate that the proposed method significantly outperforms the baseline methods in terms of the accuracy of the determined battery energy capacity and state-of-energy. The prime reason for the good performance of the proposed method is that it accounts for both the operational (efficiency) and the ambient (temperature) conditions. Full article
(This article belongs to the Special Issue Modeling, Reliability and Health Management of Lithium-Ion Batteries)
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14 pages, 2689 KiB  
Article
Statistical Modeling Procedures for Rapid Battery Pack Characterization
by Lucas Beslow, Shantanu Landore and Jae Wan Park
Batteries 2023, 9(9), 437; https://doi.org/10.3390/batteries9090437 - 26 Aug 2023
Cited by 2 | Viewed by 1951
Abstract
As lithium-ion battery (LIB) cells degrade over time and usage, it is crucial to understand their remaining capacity, also known as State of Health (SoH), and inconsistencies between cells in a pack, also known as cell-to-cell variation (CtCV), to appropriately operate and maintain [...] Read more.
As lithium-ion battery (LIB) cells degrade over time and usage, it is crucial to understand their remaining capacity, also known as State of Health (SoH), and inconsistencies between cells in a pack, also known as cell-to-cell variation (CtCV), to appropriately operate and maintain LIB packs. This study outlines efforts to model pack SoH and SoH CtCV of nickel-cobalt-aluminum (NCA) and lithium-iron-phosphate (LFP) battery packs consisting of four cells in series using pack-level voltage data. Using small training data sets and rapid testing procedures, partial least squares regression (PLS) models were built and achieved a mean absolute error of 0.38% and 1.43% pack SoH for the NCA and LFP packs, respectively. PLS models were also built that correctly categorized the packs as having low, medium, and high-ranked SoH CtCV 72.5% and 65% of the time for the NCA and LFP packs, respectively. This study further investigates the relationships between pack SoH, SoH CtCV, and the voltage response of the NCA and LFP packs. The slope of the discharge voltage response of the NCA packs was shown to have a strong correlation with pack dynamics and pack SoH, and the lowest SoH cell within the NCA packs was shown to dominate the dynamic response of the entire pack. Full article
(This article belongs to the Special Issue Modeling, Reliability and Health Management of Lithium-Ion Batteries)
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