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Energies
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Modeling, Diagnosis and Protection for Li-Ion Battery Energy Storage System
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Modeling, Diagnosis and Protection for Li-Ion Battery Energy Storage System

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "D: Energy Storage and Application".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 12656

Special Issue Editors

Prof. Dr. Bingxiang Sun
*
E-Mail Website
Guest Editor
National Active Distribution Network Technology Research Center (NANTEC), Beijing Jiaotong University, Beijing 100044, China
Interests: battery energy storage system; battery management system; time-frequency domain modeling; electrochemical model; virtual battery; low-temperature heating
* Leading Guest Editor of this Special Issue
Special Issues, Collections and Topics in MDPI journals
Dr. Liye Wang

E-Mail Website
Guest Editor
Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China
Interests: battery energy storage system; battery management system; battery modeling; wireless charging technology
Special Issues, Collections and Topics in MDPI journals
Dr. Linfeng Zheng

E-Mail Website
Guest Editor
Institute of Rail Transportation, Jinan University, Zhuhai 519070, China
Interests: battery management; embedded system; modeling and simulation; optimization and control; battery energy storage system
Special Issues, Collections and Topics in MDPI journals
Dr. Haijun Ruan

E-Mail Website
Guest Editor
Dyson School of Design Engineering, Imperial College London, London SW7 2BX, UK
Interests: lithium-ion batteries; battery management system; machine learning; optimization and control; energy storage system
Special Issues, Collections and Topics in MDPI journals
Dr. Dongsheng Ren

E-Mail
Guest Editor
Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China
Interests: battery energy storage system; thermal failure mechanism and multiscale modeling of lithium-ion battery/solid-state batteries
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Lithium-ion batteries are often connected in series and parallel to formulate a lithium-ion battery system (pack) for meeting the high-voltage and high-capacity requirements of energy storage systems. It is particularly important to accurately model the behaviors, estimate the states, diagnose the degradation and faults of the battery energy storage system, and further take necessary measures to prevent any potential safety hazards. However, with an appreciable number of batteries available worldwide, it is challenging to capture the performance, monitor the states, and identify the faults of each battery. Emerging techniques, such as big data mining, artificial intelligence, digital twin, and block chain, bring the potential of addressing these issues through developing new models, state estimation methods, and diagnostic approaches; thus, enabling a promising candidate able to achieve an early fault and safety warning. Furthermore, in order to allow for the application of battery energy storage systems to be safer and more reliable, increasing research has been aimed at optimizing the design and control technologies of battery systems. This Special Issue expects to explore research innovation within the battery system engineering challenge that incorporates modeling, state estimation, diagnostics, prognostics, control engineering, system design, and safety engineering; thus, promoting the mass commercialization and popularity of the Li-ion battery energy storage system. Manuscripts from cross-disciplinary fields, theoretical and practical studies and novel methods are strongly encouraged and welcome.

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

  • Accurate modeling and fast simulation of Li-ion battery systems;
  • Application of digital twin for lithium-ion battery systems;
  • The estimation of battery states, such as SOC, SOH, SOF, SOP, and temperature;
  • Fast charging and charging optimization methods;
  • Wireless charging technology;
  • Battery thermal management;
  • Design and application of battery virtualization equipment;
  • Reliability optimization techniques for lithium-ion battery systems;
  • State parameter prognosis and fault diagnosis of lithium-ion battery systems;
  • Thermal runaway and thermal failure mechanism;
  • Safety protection technology of lithium-ion battery pack;
  • Battery management system (BMS) optimization design technology.

Prof. Dr. Bingxiang Sun
Dr. Liye Wang
Dr. Linfeng Zheng
Dr. Haijun Ruan
Dr. Dongsheng Ren
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. Energies is an international peer-reviewed open access semimonthly 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 2600 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
  • modeling
  • digital twin
  • virtual battery
  • thermal management, state estimation and prognostics
  • faults diagnosis
  • safety protection
  • BMS

Related Special Issue

Published Papers (6 papers)

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Research

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14 pages, 5486 KiB  
Article
Experimental Investigation of Thermal Runaway Behavior and Hazards of a 1440 Ah LiFePO4 Battery Pack
by Hao Chen, Kai Yang, Youwei Liu, Mingjie Zhang, Hao Liu, Jialiang Liu, Zhanzhan Qu and Yilin Lai
Energies 2023, 16(8), 3398; https://doi.org/10.3390/en16083398 - 12 Apr 2023
Viewed by 1683
Abstract
The thermal runaway (TR) behavior and combustion hazards of lithium-ion battery (LIB) packs directly determine the implementation of firefighting and flame-retardants in energy storage systems. This work studied the TR propagation process and dangers of large-scale LIB packs by experimental methods. The LIB [...] Read more.
The thermal runaway (TR) behavior and combustion hazards of lithium-ion battery (LIB) packs directly determine the implementation of firefighting and flame-retardants in energy storage systems. This work studied the TR propagation process and dangers of large-scale LIB packs by experimental methods. The LIB pack consisted of twenty-four 60 Ah (192 Wh) LIBs with LiFePO4 (LFP) as the cathode material. Flame performance, temperature, smoke production, heat release rate (HRR), and mass loss were analyzed during the experiment. The results indicated that TR propagation of the LIB pack developed from the outside to the inside and from the middle to both sides. The development process could be divided into five stages corresponding to the combustion HRR peaks. In the initial stages, the main factor causing LFP battery TR under heating conditions was the external heat source. With the propagation of TR, heat conduction between batteries became the main factor. Hazard analysis found that the HRRmax of the LIB pack was 314 KW, more than eight times that of a single 60 Ah battery under heating conditions. The LIB pack had higher normalized mass loss and normalized THR (6.94 g/Ah and 187 KJ/Ah, respectively) than a single LFP battery. This study provides a reference for developing strategies to address TR propagation or firefighting in energy storage systems. Full article
(This article belongs to the Special Issue Modeling, Diagnosis and Protection for Li-Ion Battery Energy Storage System)
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14 pages, 12672 KiB  
Article
State of Health Estimation of Lithium-Ion Battery Based on Electrochemical Impedance Spectroscopy
by Maosong Fan, Mengmeng Geng, Kai Yang, Mingjie Zhang and Hao Liu
Energies 2023, 16(8), 3393; https://doi.org/10.3390/en16083393 - 12 Apr 2023
Cited by 8 | Viewed by 2085
Abstract
Energy storage is an important technical means to increase the consumption of renewable energy and reduce greenhouse gas emissions. Electrochemical energy storage, represented by lithium-ion batteries, has a promising developmental prospect. The performance of lithium-ion batteries continues to decline in the process of [...] Read more.
Energy storage is an important technical means to increase the consumption of renewable energy and reduce greenhouse gas emissions. Electrochemical energy storage, represented by lithium-ion batteries, has a promising developmental prospect. The performance of lithium-ion batteries continues to decline in the process of application, and the differences between batteries are increasing. Therefore, accurate estimation of the state of health (SOH) of batteries is the key to the safe and efficient operation of energy storage systems. In this paper, the electrochemical impedance spectroscopy (EIS) characteristics of Li-ion batteries under different states of charge and health were studied. Three groups of Li-ion battery impedance module values under different frequencies were selected as characteristic parameters, and the SOH estimation model of Li-ion batteries was built by using the support vector regression (SVR) algorithm. The results show that: the model with the second group of frequency-point combinations as characteristic parameters takes into account both accuracy and efficiency; the cumulative time of the characteristic frequency test and SOH evaluation of lithium-ion batteries is less than 10 s; and this technology has good engineering application value. Full article
(This article belongs to the Special Issue Modeling, Diagnosis and Protection for Li-Ion Battery Energy Storage System)
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13 pages, 2557 KiB  
Article
Lithium-Ion Battery Life Prediction Method under Thermal Gradient Conditions
by Dawei Song, Shiqian Wang, Li Di, Weijian Zhang, Qian Wang and Jing V. Wang
Energies 2023, 16(2), 767; https://doi.org/10.3390/en16020767 - 9 Jan 2023
Cited by 2 | Viewed by 1487
Abstract
Thermal gradient is inevitable in a lithium-ion battery pack because of uneven heat generation and dissipation, which will affect battery aging. In this paper, an experimental platform for a battery cycle aging test is built that can simulate practical thermal gradient conditions. Experimental [...] Read more.
Thermal gradient is inevitable in a lithium-ion battery pack because of uneven heat generation and dissipation, which will affect battery aging. In this paper, an experimental platform for a battery cycle aging test is built that can simulate practical thermal gradient conditions. Experimental results indicate a high nonlinear degree of battery degradation. Considering the nonlinearity of Li-ion batteries aging, the extreme learning machine (ELM), which has good learning and fitting ability for highly nonlinear, highly nonstationary, and time-varying data, is adopted for prediction. A battery life prediction model based on the sparrow search algorithm (SSA) is proposed in this paper to optimize the random weights and bias of the ELM network and verified by experimental data. The results show that compared with traditional ELM and back-propagation neural networks, the prediction results of ELM optimized by SSA have lower mean absolute error percentages and root mean square errors, indicating that the SSA-ELM model has higher prediction accuracy and better stability and has obvious advantages in processing data with a high nonlinear degree. Full article
(This article belongs to the Special Issue Modeling, Diagnosis and Protection for Li-Ion Battery Energy Storage System)
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21 pages, 7963 KiB  
Article
Virtual Battery Pack-Based Battery Management System Testing Framework
by Bingxiang Sun, Xinze Zhao, Xitian He, Haijun Ruan, Zhenlin Zhu and Xingzhen Zhou
Energies 2023, 16(2), 680; https://doi.org/10.3390/en16020680 - 6 Jan 2023
Cited by 3 | Viewed by 1955
Abstract
The battery management system (BMS) is a core component to ensure the efficient and safe operation of electric vehicles, and the practical evaluation of key BMS functions is thus of great importance. However, the testing of a BMS with actual battery packs suffers [...] Read more.
The battery management system (BMS) is a core component to ensure the efficient and safe operation of electric vehicles, and the practical evaluation of key BMS functions is thus of great importance. However, the testing of a BMS with actual battery packs suffers from a poor testing repeatability and a long status transition time due to the uncontrollable degradation of battery systems and testing environment variations. In this paper, to overcome this challenge, we propose an efficient BMS testing framework that uses virtual battery packs rather than actual ones, thus enabling a rapid and accurate evaluation of a BMSs key functions. A series-connected virtual battery pack model through leveraging Copula’s method is formulated to capture the dynamics and inconsistency of individual batteries in the pack. The developed lithium iron phosphate model features low computational efforts and is experimentally validated with different dynamical profiles, implying a high-precision virtual battery pack that is capable of reproducing the actual one. Furthermore, this framework includes a closed-loop testing platform, which can provide the state-of-charge/state-of-power references and thus automatically test and evaluate the states of the battery packs estimated from the BMS. Particularly, we consider the initial polarization that often exists in the batteries during the operation to accurately calibrate the available state-of-power benchmark of battery packs in the real world. The performed BMS testing results using the proposed framework illustrate that the tested BMS cannot adapt to the varied operation conditions, thus leading to high state estimation errors, which may result in the over-charge/discharge or over-temperature of the batteries. Therefore, this work highlights the value of effective BMS testing, providing the promising potential to achieve reliability and durability for battery systems. Full article
(This article belongs to the Special Issue Modeling, Diagnosis and Protection for Li-Ion Battery Energy Storage System)
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14 pages, 7258 KiB  
Article
Safety Characteristics of Lithium-Ion Batteries under Dynamic Impact Conditions
by Jinhua Shao, Chunjing Lin, Tao Yan, Chuang Qi and Yuanzhi Hu
Energies 2022, 15(23), 9148; https://doi.org/10.3390/en15239148 - 2 Dec 2022
Cited by 5 | Viewed by 1556
Abstract
With the rapid development of electric vehicles, the safety accidents caused by the damage and failure of lithium-ion batteries under mechanical load are increasing gradually, which increases the significance of collision safety in lithium-ion batteries. The failure threshold of the cell in a [...] Read more.
With the rapid development of electric vehicles, the safety accidents caused by the damage and failure of lithium-ion batteries under mechanical load are increasing gradually, which increases the significance of collision safety in lithium-ion batteries. The failure threshold of the cell in a free state is different from that of the cells in the module. Therefore, the safety characteristics of cells and modules under vertical dynamic impact conditions were studied in this paper. Lithium iron phosphate (LiFePO4) batteries and assembled 2-in-10 series modules with a 100% state of charge (SOC) were tested. Analyses included the voltage, temperature, and mechanical behavior of test samples under different impact loads, extrusion positions, and indenter shapes. The results showed that the damage behavior of a battery was closely related to the contact shape, contact area, and contact position. A smaller contact area led to greater deformation; moreover, the contact area being closer to the edge position meant greater deformation and weaker load-carrying capacity. The load-carrying capacity of the cell in a free state was weaker than that of the module, but the failure threshold of the cell in a free state was higher than that of the module. It can be concluded that the failure threshold of the cell cannot reflect the failure threshold of the module. Full article
(This article belongs to the Special Issue Modeling, Diagnosis and Protection for Li-Ion Battery Energy Storage System)
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Review

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37 pages, 6683 KiB  
Review
Review of Low-Temperature Performance, Modeling and Heating for Lithium-Ion Batteries
by Bingxiang Sun, Xianjie Qi, Donglin Song and Haijun Ruan
Energies 2023, 16(20), 7142; https://doi.org/10.3390/en16207142 - 19 Oct 2023
Viewed by 1921
Abstract
Lithium-ion batteries (LIBs) have the advantages of high energy/power densities, low self-discharge rate, and long cycle life, and thus are widely used in electric vehicles (EVs). However, at low temperatures, the peak power and available energy of LIBs drop sharply, with a high [...] Read more.
Lithium-ion batteries (LIBs) have the advantages of high energy/power densities, low self-discharge rate, and long cycle life, and thus are widely used in electric vehicles (EVs). However, at low temperatures, the peak power and available energy of LIBs drop sharply, with a high risk of lithium plating during charging. This poor performance significantly impacts the application of EVs in cold weather and dramatically limits the promotion of EVs in high-latitude regions. This challenge recently attracted much attention, especially investigating the performance decrease for LIBs at low temperatures, and exploring the solutions; however, limited reviews exist on this topic. Here, we thoroughly review the state-of-the-arts about battery performance decrease, modeling, and preheating, aiming to drive effective solutions for addressing the low-temperature challenge of LIBs. We outline the performance limitations of LIBs at low temperatures and quantify the significant changes in (dis)charging performance and resistance of LIBs at low temperatures. The various models considering low-temperature influencing factors are also tabulated and summarized, with the modeling improvement for describing low-temperature performance highlighted. Furthermore, we categorize the existing heating methods, and the metrics such as heating rate, energy consumption, and lifetime impact are highlighted to provide fundamental insights into the heating methods. Finally, the limits of current research on low-temperature LIBs are outlined, and an outlook on future research direction is provided. Full article
(This article belongs to the Special Issue Modeling, Diagnosis and Protection for Li-Ion Battery Energy Storage System)
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