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Journal articles on the topic 'Lithium-ion Battery Degradation'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

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1

Shi, Jian Ping. "Prediction study on the degeneration of lithium-ion battery based on fuzzy inference system." Modern Physics Letters B 31, no. 19-21 (2017): 1740083. http://dx.doi.org/10.1142/s0217984917400838.

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The degradation degree prediction of lithium-ion battery has been studied through experimental data. Characterization parameters on the degradation degree of lithium-ion battery were deduced under consideration of the internal and external factors. The analysis of discrete degree was proposed to depict the degradation degree for lithium-ion battery. Furthermore, based on fuzzy inference system (FIS), the predicted model of the degradation degree for lithium-ion battery was built and its output was defined as the degenerate coefficient [Formula: see text], [Formula: see text]. Finally, by learn

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2

Osara, Jude, and Michael Bryant. "A Thermodynamic Model for Lithium-Ion Battery Degradation: Application of the Degradation-Entropy Generation Theorem." Inventions 4, no. 2 (2019): 23. http://dx.doi.org/10.3390/inventions4020023.

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Presented is a lithium-ion battery degradation model, based on irreversible thermodynamics, which was experimentally verified, using commonly measured operational parameters. The methodology, applicable to all lithium-ion batteries of all chemistries and composition, combined fundamental thermodynamic principles, with the Degradation–Entropy Generation theorem, to relate instantaneous capacity fade (loss of useful charge-holding capacity) in the lithium-ion battery, to the irreversible entropy generated via the underlying dissipative physical processes responsible for battery degradation. Equa

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3

Wang, Lizhi, Yusheng Sun, Xiaohong Wang, Zhuo Wang, and Xuejiao Zhao. "Reliability Modeling Method for Lithium-ion Battery Packs Considering the Dependency of Cell Degradations Based on a Regression Model and Copulas." Materials 12, no. 7 (2019): 1054. http://dx.doi.org/10.3390/ma12071054.

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Lithium-ion batteries are widely used as basic power supplies and storage units for large-scale electric drive products such as electric vehicles. Their reliability is directly related to the life and safe operation of the electric drive products. In fact, the cells have a dependent relationship with the degradation process and they affect the degradation rate of the entire battery pack, thereby affecting its reliability. At present, most research focuses on the reliability of battery packs and assumes that their cells are independent of each other, which may cause the reliability of the evalu

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Wu, Yi, Youren Wang, Winco K. C. Yung, and Michael Pecht. "Ultrasonic Health Monitoring of Lithium-Ion Batteries." Electronics 8, no. 7 (2019): 751. http://dx.doi.org/10.3390/electronics8070751.

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Because of the complex physiochemical nature of the lithium-ion battery, it is difficult to identify the internal changes that lead to battery degradation and failure. This study develops an ultrasonic sensing technique for monitoring the commercial lithium-ion pouch cells and demonstrates this technique through experimental studies. Data fusion analysis is implemented using the ultrasonic sensing data to construct a new battery health indicator, thus extending the capabilities of traditional battery management systems. The combination of the ultrasonic sensing and data fusion approach is vali

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Guo, Liya, Daisy B. Thornton, Mohamed A. Koronfel, Ifan E. L. Stephens, and Mary P. Ryan. "Degradation in lithium ion battery current collectors." Journal of Physics: Energy 3, no. 3 (2021): 032015. http://dx.doi.org/10.1088/2515-7655/ac0c04.

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6

Perez, Aramis, Vanessa Quintero, Francisco Jaramillo, et al. "Characterization of the degradation process of lithium-ion batteries when discharged at different current rates." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 232, no. 8 (2018): 1075–89. http://dx.doi.org/10.1177/0959651818774481.

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The use of energy storage devices, such as lithium-ion batteries, has become popular in many different domains and applications. Hence, it is relatively easy to find literature associated with problems of battery state-of-charge estimation and energy autonomy prognostics. Despite this fact, the characterization of battery degradation processes is still a matter of ongoing research. Indeed, most battery degradation models solely consider operation under nominal (or strictly controlled) conditions, although actual operating profiles (including discharge current) may differ significantly from tho

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Sun, Yongquan, Saurabh Saxena, and Michael Pecht. "Derating Guidelines for Lithium-Ion Batteries." Energies 11, no. 12 (2018): 3295. http://dx.doi.org/10.3390/en11123295.

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Derating is widely applied to electronic components and products to ensure or extend their operational life for the targeted application. However, there are currently no derating guidelines for Li-ion batteries. This paper presents derating methodology and guidelines for Li-ion batteries using temperature, discharge C-rate, charge C-rate, charge cut-off current, charge cut-off voltage, and state of charge (SOC) stress factors to reduce the rate of capacity loss and extend battery calendar life and cycle life. Experimental battery degradation data from our testing and the literature have been r

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8

Zhang, X., J. Zhu, and E. Sahraei. "Degradation of battery separators under charge–discharge cycles." RSC Advances 7, no. 88 (2017): 56099–107. http://dx.doi.org/10.1039/c7ra11585g.

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9

Kim, Jae-Yeon, Jang-Hun Jo, and Jai-Won Byeon. "Ultrasonic monitoring performance degradation of lithium ion battery." Microelectronics Reliability 114 (November 2020): 113859. http://dx.doi.org/10.1016/j.microrel.2020.113859.

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10

Edge, Jacqueline S., Simon O’Kane, Ryan Prosser, et al. "Lithium ion battery degradation: what you need to know." Physical Chemistry Chemical Physics 23, no. 14 (2021): 8200–8221. http://dx.doi.org/10.1039/d1cp00359c.

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The expansion of lithium-ion batteries from consumer electronics to larger-scale transport and energy storage applications has made understanding the many mechanisms responsible for battery degradation increasingly important.

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11

Lin, Jie, and Minghua Wei. "Remaining useful life prediction of lithium-ion battery based on auto-regression and particle filter." International Journal of Intelligent Computing and Cybernetics 14, no. 2 (2021): 218–37. http://dx.doi.org/10.1108/ijicc-09-2020-0131.

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PurposeWith the rapid development and stable operated application of lithium-ion batteries used in uninterruptible power supply (UPS), the prediction of remaining useful life (RUL) for lithium-ion battery played an important role. More and more researchers paid more attentions on the reliability and safety for lithium-ion batteries based on prediction of RUL. The purpose of this paper is to predict the life of lithium-ion battery based on auto regression and particle filter method.Design/methodology/approachIn this paper, a simple and effective RUL prediction method based on the combination me

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Koleti, Upender Rao, Ashwin Rajan, Chaou Tan, Sanghamitra Moharana, Truong Quang Dinh, and James Marco. "A Study on the Influence of Lithium Plating on Battery Degradation." Energies 13, no. 13 (2020): 3458. http://dx.doi.org/10.3390/en13133458.

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Within Li-ion batteries, lithium plating is considered as one of the main reasons behind the capacity fade that occurs during low temperature and fast charging conditions. Previous studies indicate that plating is influenced by the levels of loss of lithium inventory (LLI) and the loss of active material (LAM) present in a battery. However, it is not clear from the literature on how lithium plating influences battery degradation in terms of LAM and LLI. Quantifying the undesirable impacts of lithium plating can help in understanding its impact on battery degradation and feedback effects of pre

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13

Wang, Zhuqing, Yangming Guo, and Cong Xu. "An HI Extraction Framework for Lithium-Ion Battery Prognostics Based on SAE-VMD." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 38, no. 4 (2020): 814–21. http://dx.doi.org/10.1051/jnwpu/20203840814.

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The signals of lithium-ion battery degradation are non-stationary and nonlinear. To adaptively extract the health indicator(HI) that can accurately represent the battery degradation characters and improve the prediction precision of battery remaining useful life (RUL), a stacked auto encoder-variational mode decomposition(SAE-VMD) based HI construction framework is proposed. Firstly, the stacked auto encoder(SAE) is used to reduce the noises of battery parameters and lower the data dimensionality and construct a syncretic HI that contains the battery degradation characters. Then the variationa

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14

Pang, Xiaoqiong, Rui Huang, Jie Wen, Yuanhao Shi, Jianfang Jia, and Jianchao Zeng. "A Lithium-ion Battery RUL Prediction Method Considering the Capacity Regeneration Phenomenon." Energies 12, no. 12 (2019): 2247. http://dx.doi.org/10.3390/en12122247.

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Prediction of Remaining Useful Life (RUL) of lithium-ion batteries plays a significant role in battery health management. Battery capacity is often chosen as the Health Indicator (HI) in research on lithium-ion battery RUL prediction. In the rest time of batteries, capacity will produce a certain degree of regeneration phenomenon, which exists in the use of each battery. Therefore, considering the capacity regeneration phenomenon in RUL prediction of lithium-ion batteries is helpful to improve the prediction performance of the model. In this paper, a novel method fusing the wavelet decompositi

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15

Wang, Dasong, Feng Yang, Lin Gan, and Yuliang Li. "Fuzzy Prediction of Power Lithium Ion Battery State of Function Based on the Fuzzy c-Means Clustering Algorithm." World Electric Vehicle Journal 10, no. 1 (2019): 1. http://dx.doi.org/10.3390/wevj10010001.

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Following the widespread and large-scale application of power lithium ion battery, State of Function (SOF) estimation technology of power lithium ion batteries has gained an increasing amount of attention from both scientists and engineers. During the lifetime of the power lithium ion battery, SOF reflects the maximum instantaneous output power of the battery. When discarded, it is able to show the degree of performance degradation of the power battery when also taken as a performance evaluation parameter. In this paper, the variables closely related to SOF have been selected to conduct the fu

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16

Xu, Peipei, Junqiu Li, Chao Sun, Guodong Yang, and Fengchun Sun. "Adaptive State-of-Charge Estimation for Lithium-Ion Batteries by Considering Capacity Degradation." Electronics 10, no. 2 (2021): 122. http://dx.doi.org/10.3390/electronics10020122.

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The accurate estimation of a lithium-ion battery’s state of charge (SOC) plays an important role in the operational safety and driving mileage improvement of electrical vehicles (EVs). The Adaptive Extended Kalman filter (AEKF) estimator is commonly used to estimate SOC; however, this method relies on the precise estimation of the battery’s model parameters and capacity. Furthermore, the actual capacity and battery parameters change in real time with the aging of the batteries. Therefore, to eliminate the influence of above-mentioned factors on SOC estimation, the main contributions of this pa

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17

Xu, Peipei, Junqiu Li, Chao Sun, Guodong Yang, and Fengchun Sun. "Adaptive State-of-Charge Estimation for Lithium-Ion Batteries by Considering Capacity Degradation." Electronics 10, no. 2 (2021): 122. http://dx.doi.org/10.3390/electronics10020122.

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The accurate estimation of a lithium-ion battery’s state of charge (SOC) plays an important role in the operational safety and driving mileage improvement of electrical vehicles (EVs). The Adaptive Extended Kalman filter (AEKF) estimator is commonly used to estimate SOC; however, this method relies on the precise estimation of the battery’s model parameters and capacity. Furthermore, the actual capacity and battery parameters change in real time with the aging of the batteries. Therefore, to eliminate the influence of above-mentioned factors on SOC estimation, the main contributions of this pa

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18

Sui, Xin, Maciej Świerczyński, Remus Teodorescu, and Daniel-Ioan Stroe. "The Degradation Behavior of LiFePO4/C Batteries during Long-Term Calendar Aging." Energies 14, no. 6 (2021): 1732. http://dx.doi.org/10.3390/en14061732.

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With widespread applications for lithium-ion batteries in energy storage systems, the performance degradation of the battery attracts more and more attention. Understanding the battery’s long-term aging characteristics is essential for the extension of the service lifetime of the battery and the safe operation of the system. In this paper, lithium iron phosphate (LiFePO4) batteries were subjected to long-term (i.e., 27–43 months) calendar aging under consideration of three stress factors (i.e., time, temperature and state-of-charge (SOC) level) impact. By means of capacity measurements and res

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19

Zhao, Lin, Yipeng Wang, and Jianhua Cheng. "A Hybrid Method for Remaining Useful Life Estimation of Lithium-Ion Battery with Regeneration Phenomena." Applied Sciences 9, no. 9 (2019): 1890. http://dx.doi.org/10.3390/app9091890.

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The lithium-ion battery has become the primary energy source of many electronic devices. Accurately forecasting the remaining useful life (RUL) of a battery plays an essential role in ensuring reliable operatioin of an electronic system. This paper investigates the lithium-ion battery RUL prediction problem with capacity regeneration phenomena. We aim to reduce the accumulation of the prediction error by integrating different capacity degradation models and thereby improve the prediction accuracy of the long-term RUL. To describe the degradation process more accurately, we decoupled the degrad

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20

Zhang, Yancheng, Chao-Yang Wang, and Xidong Tang. "Cycling degradation of an automotive LiFePO4 lithium-ion battery." Journal of Power Sources 196, no. 3 (2011): 1513–20. http://dx.doi.org/10.1016/j.jpowsour.2010.08.070.

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21

Ansean, David, Victor Manuel Garcia, Manuela Gonzalez, et al. "Lithium-Ion Battery Degradation Indicators Via Incremental Capacity Analysis." IEEE Transactions on Industry Applications 55, no. 3 (2019): 2992–3002. http://dx.doi.org/10.1109/tia.2019.2891213.

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22

Xu, Xin, Zhiguo Li, and Nan Chen. "A Hierarchical Model for Lithium-Ion Battery Degradation Prediction." IEEE Transactions on Reliability 65, no. 1 (2016): 310–25. http://dx.doi.org/10.1109/tr.2015.2451074.

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23

Smeu, Manuel, and Kevin Leung. "Electron leakage through heterogeneous LiF on lithium–metal battery anodes." Physical Chemistry Chemical Physics 23, no. 5 (2021): 3214–18. http://dx.doi.org/10.1039/d0cp06310j.

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24

Guo, Jia, Yaqi Li, Kjeld Pedersen, and Daniel-Ioan Stroe. "Lithium-Ion Battery Operation, Degradation, and Aging Mechanism in Electric Vehicles: An Overview." Energies 14, no. 17 (2021): 5220. http://dx.doi.org/10.3390/en14175220.

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Understanding the aging mechanism for lithium-ion batteries (LiBs) is crucial for optimizing the battery operation in real-life applications. This article gives a systematic description of the LiBs aging in real-life electric vehicle (EV) applications. First, the characteristics of the common EVs and the lithium-ion chemistries used in these applications are described. The battery operation in EVs is then classified into three modes: charging, standby, and driving, which are subsequently described. Finally, the aging behavior of LiBs in the actual charging, standby, and driving modes are revie

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25

Gailani, Ahmed, Maher Al-Greer, Michael Short, and Tracey Crosbie. "Degradation Cost Analysis of Li-Ion Batteries in the Capacity Market with Different Degradation Models." Electronics 9, no. 1 (2020): 90. http://dx.doi.org/10.3390/electronics9010090.

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Increased deployment of intermittent renewable energy plants raises concerns about energy security and energy affordability. Capacity markets (CMs) have been implemented to provide investment stability to generators and secure energy generation by reducing the number of shortage hours. The research presented in this paper contributes to answering the question of whether batteries can provide cost effective back up services for one year in this market. The analysis uses an equivalent circuit lithium ion battery model coupled with two degradation models (empirical and semi-empirical) to account

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Wang, Xiaohong, Shixiang Li, Lizhi Wang, Yaning Sun, and Zhongxing Wang. "Degradation and Dependence Analysis of a Lithium-Ion Battery Pack in the Unbalanced State." Energies 13, no. 22 (2020): 5934. http://dx.doi.org/10.3390/en13225934.

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Lithium-ion batteries are widely used in the energy field due to their high efficiency and clean characteristics. They provide more possibilities for electric vehicles, drones, and other applications, and they can provide the higher requirements necessary for the reliability of battery pack systems. However, it is easy for a battery pack to be unbalanced because of the dependence between the cells. The unbalanced state will make the degradation process more complex and cause abnormal discharge parameters, which brings challenges in the analysis of the state of health (SOH) of battery packs. In

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Yu, Bin, Tao Zhang, Tianyu Liu, and Lei Yao. "Reliability Evaluation and In-Orbit Residual Life Prediction for Satellite Lithium-Ion Batteries." Mathematical Problems in Engineering 2018 (December 6, 2018): 1–12. http://dx.doi.org/10.1155/2018/5918068.

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As a new type of secondary battery, lithium-ion battery is widely used in the aerospace industry with the advantages of long lifetime, high energy density and low pollution, etc. In this paper, we focus on the problem of offline and online life prediction for satellite lithium-ion batteries. Firstly, based on the NASA laboratory battery dataset, a Wiener process with time-scale transformation is used to capture battery capacity fading, and then the battery reliability point and interval estimation equation are derived, respectively. Secondly, by analyzing the charge and discharge profiles of t

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Susilo, Didik Djoko, Achmad Widodo, Toni Prahasto, and Muhammad Nizam. "State of Health Estimation of Lithium-Ion Batteries Based on Combination of Gaussian Distribution Data and Least Squares Support Vector Machines Regression." Materials Science Forum 929 (August 2018): 93–102. http://dx.doi.org/10.4028/www.scientific.net/msf.929.93.

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Lithium-ion batteries play a critical role in the reliability and safety of a system. Battery health monitoring and remaining useful life (RUL) prediction are needed to prevent catastrophic failure of the battery. The aim of this research is to develop a data-driven method to monitor the batteries state of health and predict their RUL by using the battery capacity degradation data. This paper also investigated the effect of prediction starting point to the RUL prediction error. One of the data-driven method drawbacks is the need of a large amount of data to obtain accurate prediction. This pap

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29

Li, Bei, and Xiaopeng Li. "Study on degenerate coefficient and degeneration evaluation of lithium-ion battery." Modern Physics Letters B 31, no. 19-21 (2017): 1740092. http://dx.doi.org/10.1142/s0217984917400929.

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Some characteristic parameters were epurated in this paper by analyzing internal and external factors of the degradation degree of lithium-ion battery. These characteristic parameters include open circuit voltage (OCV), state of charge (SOC) and ambient temperature. The degradation degree was evaluated by discrete degree of the array, which is composed of the above parameters. The epurated parameters were verified through adaptive neuro-fuzzy inference system (ANFIS) model building. The expression of degradation coefficient was finally determined. The simulation results show that the expressio

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30

Chandran, Venkatesan, Chandrashekhar K. Patil, Alagar Karthick, Dharmaraj Ganeshaperumal, Robbi Rahim, and Aritra Ghosh. "State of Charge Estimation of Lithium-Ion Battery for Electric Vehicles Using Machine Learning Algorithms." World Electric Vehicle Journal 12, no. 1 (2021): 38. http://dx.doi.org/10.3390/wevj12010038.

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The durability and reliability of battery management systems in electric vehicles to forecast the state of charge (SoC) is a tedious task. As the process of battery degradation is usually non-linear, it is extremely cumbersome work to predict SoC estimation with substantially less degradation. This paper presents the SoC estimation of lithium-ion battery systems using six machine learning algorithms for electric vehicles application. The employed algorithms are artificial neural network (ANN), support vector machine (SVM), linear regression (LR), Gaussian process regression (GPR), ensemble bag

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31

Gorman, Scott F., Tanveerkhan S. Pathan, and Emma Kendrick. "The ‘use-by date’ for lithium-ion battery components." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 377, no. 2152 (2019): 20180299. http://dx.doi.org/10.1098/rsta.2018.0299.

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Lithium-ion battery (LIB) manufacturing is based around the slurry tape casting of electrodes followed by the assembly of the dried electrodes into cells with a separator and electrolyte. Many aspects of the manufacturing process can affect the performance of a lithium-ion cell. One of the least understood aspects in academia is the effect of degradation of the materials during the manufacturing processes or the ‘shelf-life’ of the materials and components. Here, we discuss some of the time limitations and degradation issues observed during the manufacturing and testing of the components from

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Saxena, Saurabh, Darius Roman, Valentin Robu, David Flynn, and Michael Pecht. "Battery Stress Factor Ranking for Accelerated Degradation Test Planning Using Machine Learning." Energies 14, no. 3 (2021): 723. http://dx.doi.org/10.3390/en14030723.

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Lithium-ion batteries power numerous systems from consumer electronics to electric vehicles, and thus undergo qualification testing for degradation assessment prior to deployment. Qualification testing involves repeated charge–discharge operation of the batteries, which can take more than three months if subjected to 500 cycles at a C-rate of 0.5C. Accelerated degradation testing can be used to reduce extensive test time, but its application requires a careful selection of stress factors. To address this challenge, this study identifies and ranks stress factors in terms of their effects on bat

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33

Jia, Guan, and Wu. "A State of Health Estimation Framework for Lithium-Ion Batteries Using Transfer Components Analysis." Energies 12, no. 13 (2019): 2524. http://dx.doi.org/10.3390/en12132524.

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As different types of lithium batteries are increasingly employed in various devices, it is crucial to predict the state of health (SOH) of lithium batteries. There are plenty of methods for SOH estimation of a lithium-ion battery. However, existing technologies often have computational complexity. Furthermore, it is difficult to use least the previous 30% of data of the battery degradation process to predict the SOH variation of the entire degradation process. To address this problem, in this paper, the SOH of the target battery is estimated based on the transfer of different battery data set

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34

Xu, Bolun, Alexandre Oudalov, Andreas Ulbig, Goran Andersson, and Daniel S. Kirschen. "Modeling of Lithium-Ion Battery Degradation for Cell Life Assessment." IEEE Transactions on Smart Grid 9, no. 2 (2018): 1131–40. http://dx.doi.org/10.1109/tsg.2016.2578950.

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Stroe, Daniel‐Ioan, Maciej Swierczynski, Ana‐Irina Stroe, Søren Knudsen Kaer, and Remus Teodorescu. "Lithium‐ion battery power degradation modelling by electrochemical impedance spectroscopy." IET Renewable Power Generation 11, no. 9 (2017): 1136–41. http://dx.doi.org/10.1049/iet-rpg.2016.0958.

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Prosser, Ryan, Yatish Patel, and Gregory James Offer. "Lithium-Ion Battery Degradation Mode Diagnostics Using Heat Generation Profiles." ECS Meeting Abstracts MA2020-02, no. 62 (2020): 3175. http://dx.doi.org/10.1149/ma2020-02623175mtgabs.

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37

Chen, Hao, and Julia Shen. "A degradation-based sorting method for lithium-ion battery reuse." PLOS ONE 12, no. 10 (2017): e0185922. http://dx.doi.org/10.1371/journal.pone.0185922.

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Grolleau, Sébastien, Arnaud Delaille, and Hamid Gualous. "Predicting lithium-ion battery degradation for efficient design and management." World Electric Vehicle Journal 6, no. 3 (2013): 549–54. http://dx.doi.org/10.3390/wevj6030549.

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Zhang, Sihan, Md Sazzad Hosen, Theodoros Kalogiannis, Joeri Van Mierlo, and Maitane Berecibar. "State of Health Estimation of Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy and Backpropagation Neural Network." World Electric Vehicle Journal 12, no. 3 (2021): 156. http://dx.doi.org/10.3390/wevj12030156.

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The global electric vehicle (EV) is expanding enormously, foreseeing a 17.4% increase in compound annual growth rate (CAGR) by the end of 2027. The lithium-ion battery is considered as the most widely used battery technology in EV. The accurate and reliable diagnostic and prognostic of battery state guarantees the safe operation of EV and is crucial for durable electric vehicles. Research focusing on lithium-ion battery life degradation has grown more important in recent years. In this study, a model built for state of health (SoH) estimation for the LTO anode-based lithium-ion battery is pres

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40

Cortés-Arcos, Tomás, Rodolfo Dufo-López, and José L. Bernal-Agustín. "Estimating Degradation Costs for Non-Cyclic Usage of Lithium-Ion Batteries." Applied Sciences 10, no. 15 (2020): 5330. http://dx.doi.org/10.3390/app10155330.

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Estimating the degradation costs of lithium-ion batteries is essential to the designs of many systems because batteries are increasingly used in diverse applications. In this study, cyclic and calendar degradation models of lithium batteries were considered in optimization problems with randomized non-cyclic batteries use. Such models offer realistic results. Electrical, thermal, and degradation models were applied for lithium nickel cobalt manganese oxide (NMC) and lithium iron phosphate (LFP) technologies. Three possible strategies were identified to estimate degradation costs based on cell

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41

Jiang, Lin, Wei Ming Xian, Bin Long, and Hou Jun Wang. "Analysis of Data-Driven Prediction Algorithms for Lithium-Ion Batteries Remaining Useful Life." Advanced Materials Research 717 (July 2013): 390–95. http://dx.doi.org/10.4028/www.scientific.net/amr.717.390.

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As one of the most widely used energy storage systems, lithium-ion batteries are attracting more and more attention, and the estimation of lithium-ion batteries remaining useful life (RUL) becoming a critical problem. Generally, RUL can be predicted in two ways: physics of failure (PoF) method and data driven method. Due to the internal electro-chemical reactions are either inaccessible to sensors or hard to measure; the data-driven method is adopted because it does not require specific knowledge of material properties. In this paper, three data-driven algorithms, i.e., Support Vector Machine

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Zappen, Hendrik, Georg Fuchs, Alexander Gitis, and Dirk Sauer. "In-Operando Impedance Spectroscopy and Ultrasonic Measurements during High-Temperature Abuse Experiments on Lithium-Ion Batteries." Batteries 6, no. 2 (2020): 25. http://dx.doi.org/10.3390/batteries6020025.

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Lithium-Ion batteries are used in ever more demanding applications regarding operating range and safety requirements. This work presents a series of high-temperature abuse experiments on a nickel-manganese-cobalt oxide (NMC)/graphite lithium-ion battery cell, using advanced in-operando measurement techniques like fast impedance spectroscopy and ultrasonic waves, as well as strain-gauges. the presented results show, that by using these methods degradation effects at elevated temperature can be observed in real-time. These methods have the potential to be integrated into a battery management sys

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Al-Gabalawy, Mostafa, Nesreen S. Hosny, and Shimaa A. Hussien. "Lithium-Ion Battery Modeling Including Degradation Based on Single-Particle Approximations." Batteries 6, no. 3 (2020): 37. http://dx.doi.org/10.3390/batteries6030037.

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This paper introduces a physical–chemical model that governs the lithium ion (Li-ion) battery performance. It starts from the model of battery life and moves forward with simplifications based on the single-particle model (SPM), until arriving at a more simplified and computationally fast model. On the other hand, the implementation of this model is developed through MATLAB. The goal is to characterize an Li-ion cell and obtain its charging and discharging curves with different current rates and different cycle depths, as well as its transitory response. In addition, the results provided are r

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Wang, Xiaohong, Wenhui Fan, Shixiang Li, Xinjun Li, and Lizhi Wang. "SOH Estimation of Lithium-Ion Battery Pack Based on Integrated State Information from Cells." Applied Sciences 10, no. 19 (2020): 6637. http://dx.doi.org/10.3390/app10196637.

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Accompanied by the development of new energy resources, lithium-ion batteries have been used widely in various fields. Due to the significant influence of system performance, much attention has been paid to the accurate estimation and prediction about health status of lithium-ion batteries. In a battery pack, the structure connection causes sophisticated interaction between cells, or between the cells and the pack. Therefore, the degradation of any cell is the result of the deterioration of conjoint cells, and a rapid degradation speed for any individual cell can lead to the accelerated degrad

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Kwon, Soon-Jong, Sung-Eun Lee, Ji-Hun Lim, Jinhyeok Choi, and Jonghoon Kim. "Performance and Life Degradation Characteristics Analysis of NCM LIB for BESS." Electronics 7, no. 12 (2018): 406. http://dx.doi.org/10.3390/electronics7120406.

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The battery energy storage system (BESS) market is growing rapidly around the world. Lithium Nickel Cobalt Manganese Oxide (LiNiCoMnO2) is attracting attention due to its excellent energy density, high output power, and fast response characteristics. It is being extensively researched and is finding use in many applications, such as in electric vehicles (EV) and energy storage systems (ESS). The performance and lifetime characteristics of a battery change for varying Ni contents. The consideration of these characteristics of a battery allow for a more reliable battery management system (BMS) d

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Li, Lin, Alfredo Alan Flores Saldivar, Yun Bai, and Yun Li. "Battery Remaining Useful Life Prediction with Inheritance Particle Filtering." Energies 12, no. 14 (2019): 2784. http://dx.doi.org/10.3390/en12142784.

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Accurately forecasting a battery’s remaining useful life (RUL) plays an important role in the prognostics and health management of rechargeable batteries. An effective forecast is reported using a particle filter (PF), but it currently suffers from particle degeneracy and impoverishment deficiencies in RUL evaluations. In this paper, an inheritance PF is developed to predict lithium-ion battery RUL for the first time. A battery degradation model is first mapped onto a PF problem using the genetic algorithm (GA) framework. Then, a Lamarckian inheritance operator is designed to improve the light

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Li, Shuqing, Chuankun Ju, Jianliang Li, et al. "State-of-Charge Estimation of Lithium-Ion Batteries in the Battery Degradation Process Based on Recurrent Neural Network." Energies 14, no. 2 (2021): 306. http://dx.doi.org/10.3390/en14020306.

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Due to the rapidly increasing energy demand and the more serious environmental pollution problems, lithium-ion battery is more and more widely used as high-efficiency clean energy. State of Charge (SOC) representing the physical quantity of battery remaining energy is the most critical factor to ensure the stability and safety of lithium-ion battery. The novelty SOC estimation model, which is two recurrent neural networks with gated recurrent units combined with Coulomb counting method is proposed in this paper. The estimation model not only takes voltage, current, and temperature as input fea

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Li, Shuqing, Chuankun Ju, Jianliang Li, et al. "State-of-Charge Estimation of Lithium-Ion Batteries in the Battery Degradation Process Based on Recurrent Neural Network." Energies 14, no. 2 (2021): 306. http://dx.doi.org/10.3390/en14020306.

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Abstract:

Due to the rapidly increasing energy demand and the more serious environmental pollution problems, lithium-ion battery is more and more widely used as high-efficiency clean energy. State of Charge (SOC) representing the physical quantity of battery remaining energy is the most critical factor to ensure the stability and safety of lithium-ion battery. The novelty SOC estimation model, which is two recurrent neural networks with gated recurrent units combined with Coulomb counting method is proposed in this paper. The estimation model not only takes voltage, current, and temperature as input fea

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Zhang, Tao, Ningyuan Guo, Xiaoxia Sun, et al. "A Systematic Framework for State of Charge, State of Health and State of Power Co-Estimation of Lithium-Ion Battery in Electric Vehicles." Sustainability 13, no. 9 (2021): 5166. http://dx.doi.org/10.3390/su13095166.

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Due to its advantages of high voltage level, high specific energy, low self-discharging rate and relatively longer cycling life, the lithium-ion battery has been widely used in electric vehicles. To ensure safety and reduce degradation during the lithium-ion battery’s service life, precise estimation of its states like state of charge (SOC), capacity and peak power is indispensable. This paper proposes a systematic co-estimation framework for the lithium-ion battery in electric vehicle applications. First, a linearized equivalent circuit-based battery model, together with an affine projection

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Sieber, Tim, Jana Ducke, Anja Rietig, Thomas Langner, and Jörg Acker. "Recovery of Li(Ni0.33Mn0.33Co0.33)O2 from Lithium-Ion Battery Cathodes: Aspects of Degradation." Nanomaterials 9, no. 2 (2019): 246. http://dx.doi.org/10.3390/nano9020246.

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Nickel–manganese–cobalt oxides, with LiNi0.33Mn0.33Co0.33O2 (NMC) as the most prominent compound, are state-of-the-art cathode materials for lithium-ion batteries in electric vehicles. The growing market for electro mobility has led to a growing global demand for Li, Co, Ni, and Mn, making spent lithium-ion batteries a valuable secondary resource. Going forward, energy- and resource-inefficient pyrometallurgical and hydrometallurgical recycling strategies must be avoided. We presented an approach to recover NMC particles from spent lithium-ion battery cathodes while preserving their chemical a

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