Comparative Study on the Calendar Aging Behavior of Six Different Lithium-Ion Cell Chemistries in Terms of Parameter Variation

Geisbauer, Christian; Wöhrl, Katharina; Koch, Daniel; Wilhelm, Gudrun; Schneider, Gerhard; Schweiger, Hans−Georg

doi:10.3390/en14113358

Cited by 13 publications

(20 citation statements)

References 13 publications

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“…Geisbauer et al conducted a comparative study on the calendar aging behavior of five different cathode materials by relating the capacity decay to the resistance increase of full cells. It is found that NMC and NCA show a great resistance increase at high temperature and SOC, while LFP exhibits the best stability with little resistance increase at different conditions . Keil et al also studied the impacts of SOC and temperature on the calendar aging of three kinds of commercial LIBs with NMC, NCA, and LFP cathodes .…”

Section: Application Of Eis To Lib’s Aging Studymentioning

confidence: 99%

“…It is found that NMC and NCA show a great resistance increase at high temperature and SOC, while LFP exhibits the best stability with little resistance increase at different conditions. 113 Keil et al also studied the impacts of SOC and temperature on the calendar aging of three kinds of commercial LIBs with NMC, NCA, and LFP cathodes. 114 As shown in Figure 6c, the internal resistance of NMC and NCA increases slowly at low SOC (<60%) but rapidly enhances at high SOC, while the resistance of LFP shows little variation at different SOC.…”

Section: Eis Variations At Different Aging Conditionsmentioning

confidence: 99%

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Application of Electrochemical Impedance Spectroscopy to Degradation and Aging Research of Lithium-Ion Batteries

Peng

Wei³

et al. 2023

J. Phys. Chem. C

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An in-depth understanding of battery degradation and aging in-Operando not only plays a vital role in the design of battery managing systems but also helps to ensure safe use and manufacturing optimization of lithium-ion batteries (LIBs) in large-scale applications. Electrochemical impedance spectroscopy (EIS) is a nondestructive method which unravels electrode kinetic processes inside the batteries in different time domains, including charge-transfer reactions, interfacial evolutions, and mass diffusions. It has become a powerful diagnosis and pre/prognosis tool in battery aging research, as it provides important insight into the changes of internal electrochemical processes by correlating the impedance evolution to degradation mechanisms. This review gives a critical overview on rapidly developing impedance techniques for degradation and aging investigation of Li-ion batteries. The EIS variations of LIBs at different aging conditions of calendar aging and accelerated aging are systematically summarized. In addition, the working principles, data validation, and modeling methods, including equivalent circuit model (ECM), distribution of relaxation times (DRT), and transmission line model (TLM), of classical EIS and dynamic EIS are elaborately concluded. Finally, the challenges and perspectives of further application of EIS in the aging research of LIBs are presented.

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Section: Application Of Eis To Lib’s Aging Studymentioning

confidence: 99%

Section: Eis Variations At Different Aging Conditionsmentioning

confidence: 99%

Application of Electrochemical Impedance Spectroscopy to Degradation and Aging Research of Lithium-Ion Batteries

Peng

Wei³

et al. 2023

J. Phys. Chem. C

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“…The main technological challenge is connected with the reliability and safety of the most important components of electric vehicles: batteries, power electric convertors and electric motors [21]. A crucial question here is the battery technology and lifecycle [22][23][24][25], but environmental aspects of their production and recycling cannot be forgotten either [26,27]. From the point of view of electric transport functioning, a critical issue is the charging infrastructure [28,29].…”

Section: Literature Reviewmentioning

confidence: 99%

The Second Generation Electromobility in Polish Urban Public Transport: The Factors and Mechanisms of Spatial Development

et al. 2021

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One of the key challenges on the road to sustainable mobility is the development of low/zero emission urban public transport (UPT). This is crucial in order to meet environmental requirements aiming at reducing greenhouse gas (GHG) emission. In some countries (e.g., Poland) reduction of air pollution is also an important reason behind the implementation of low/zero emission UPT. The aim of this study is to investigate the factors and mechanisms influencing the development of modern electromobility in Polish UPT. We have examined all 242 UPT systems in the country in terms of the characteristics of the relevant urban municipalities, such as size, economic prosperity, level of human and social capital, development paths of urban public transport in the long term as well as the institutional context and proximity and connections to other cities with experience in electromobility. Classification and statistical methods are used based on a variety of approaches, as assigning a score to various preliminarily identified indicators or applying correlation between quantities to verify the formulated hypotheses. Our analysis demonstrates that electromobility adoption is the result of a combination of favourable economic, urban, social and technological characteristic features of a given city. Zero or low emission buses are more common in large cities which are highly positioned in urban hierarchy, economically sound and which are characterized by a well-developed tertiary economy as well as by high human capital. An additional factor that positively influences the implementation of electromobility—in particular at the very first stage—is proximity to the location of low emission bus producers. The leadership in modern electromobility can be understood as part of a broader, proactive development policy of the cities aimed at improving the quality of life of their residents. This is especially important in medium-sized towns where utilizing electric vehicles can be an instrument to maintain or even develop their role and status. The results of the article may provide a basis for creating sustainable urban policies, especially sustainable mobility and improving environmental quality.

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“…The recent studies conducted by Geisbauer et al, proved that despite Li-ion technology offering a wide range of chemistries, such as Nickle Manganese Cobalt Oxide (NMC), Nickle Cobalt Aluminum Oxide (NCA), Lithium Titanium Oxide (LTO), Lithium Cobalt Oxide (LCO) or Lithium Iron Phosphate (LFP), none of them were able to avoid the calendar ageing 7 . The study also clearly demonstrated high temperatures lead to even higher degradations 7 . These factors are important because, e.g.…”

Section: Introductionmentioning

confidence: 99%

Calendar ageing modelling using machine learning: an experimental investigation on lithium ion battery chemistries

Celen¹,

Ozcelik²,

Turgut³

et al. 2022

Open Res Europe

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Background: The phenomenon of calendar ageing continues to have an impact on battery systems worldwide by causing them to have undesirable operation life and performance. Predicting the degradation in the capacity can identify whether this phenomenon is occurring for a cell and pave the way for placing mechanisms that can circumvent this behaviour. Methods: In this study, the machine learning algorithms, Extreme Gradient Boosting (XGBoost) and artificial neural network (ANN) have been used to predict the calendar ageing data belonging to six types of cell chemistries namely, Lithium Cobalt Oxide, Lithium Iron Phosphate, Lithium Manganese Oxide, Lithium Titanium Oxide, Nickle Cobalt Aluminum Oxide and Nickle Manganese Cobalt Oxide. Results: Prediction results with overall Mean Absolute Percentage Error of 0.0126 have been obtained for XGBoost algorithm. Among these results, Nickle Cobalt Aluminum Oxide and Nickle Manganese Cobalt Oxide type cell chemistries stand out with their mean absolute percentage errors of 0.0035 and 0.0057 respectively. Also, algorithm fitting performance is relatively better for these chemistries at 100% state of charge and 60°C temperature compared to ANN results. ANN algorithm predicts with mean absolute error of approximately 0.0472 overall and 0.0238 and 0.03825 for Nickle Cobalt Aluminum Oxide and Nickle Manganese Cobalt Oxide. The fitting performance of ANN for Nickle Manganese Cobalt Oxide at 100% state of charge and 60°C temperature is especially poor compared to XGBoost. Conclusions: For an electric vehicle battery calendar ageing prediction application, XGBoost can establish itself as the primary choice more easily compared to ANN. The reason is XGBoost’s error rates and fitting performance are more usable for such application especially for Nickel Cobalt Aluminum Oxide and Nickel Manganese Cobalt Oxide chemistries, which are amongst the most demanded cell chemistries for electric vehicle battery packs.

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Comparative Study on the Calendar Aging Behavior of Six Different Lithium-Ion Cell Chemistries in Terms of Parameter Variation

Cited by 13 publications

References 13 publications

Application of Electrochemical Impedance Spectroscopy to Degradation and Aging Research of Lithium-Ion Batteries

Application of Electrochemical Impedance Spectroscopy to Degradation and Aging Research of Lithium-Ion Batteries

The Second Generation Electromobility in Polish Urban Public Transport: The Factors and Mechanisms of Spatial Development

Calendar ageing modelling using machine learning: an experimental investigation on lithium ion battery chemistries

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