Modeling long-term capacity degradation of lithium-ion batteries

Johnen, Marcus; Pitzen, Simon; Kamps, Udo; Kateri, Maria; Dechent, Philipp; Sauer, Dirk Uwe

doi:10.1016/j.est.2020.102011

Cited by 32 publications

(9 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Those resolutions are then deemed a good trade-off to capture the impact of both storage cycling and the initial state of health update along the system lifetime. In the remaining of the paper, monthly stepwise In previous works (and similar to many research in the field of storage ageing), battery degradation was assessed with pre-defined SoC profiles, at a given C-rate, average values and depth of discharge [4], [5], [38]. In order to assess the loss of capacity under more realistic conditions, the EMS previously defined is used to generate various monthly SoC profiles under different conditions as follows:…”

Section: Preliminary Tests For the Simplification Of The Degradation ...mentioning

confidence: 99%

A storage degradation model of Li-ion batteries to integrate ageing effects in the optimal management and design of an isolated microgrid

et al. 2023

View full text Add to dashboard Cite

Section: Preliminary Tests For the Simplification Of The Degradation ...mentioning

confidence: 99%

A storage degradation model of Li-ion batteries to integrate ageing effects in the optimal management and design of an isolated microgrid

et al. 2023

View full text Add to dashboard Cite

“…Model-based approaches in predicting the cell performance as well as the battery cycle life are one of the key elements in optimizing the coating and, thus, in a further development of LIBs 20 – 26 . Here, reliable datasets of the cell performance are needed as input parameters or training data for data-driven modelling.…”

Section: Background and Summarymentioning

confidence: 99%

Reproducible long-term cycling data of Al2O3 coated LiNi0.70Co0.15Mn0.15O2 cathodes for lithium-ion batteries

Negi

Elm

2022

Sci Data

View full text Add to dashboard Cite

LiNixCoyMn1-x-yO2 (NCM) based cathodes for Li-ion batteries (LIBs) are of great interest due to their higher energy density and lower costs compared to conventional LiCoO2 based cathodes. However, NCM based cathodes suffer from instabilities of the cathode-electrolyte interface resulting in faster capacity fading during long-term cycling. Different NCM compositions along with different coatings have been developed to protect the interface. However, a detailed understanding why and how coatings work is still missing. Up to now, no state-of-the-art NCM or coating material have been agreed upon yet, making it difficult to benchmark the performance of the coating material. Undefined standards complicate the use of experimentally produced data for model-based studies, which are a key element in assessing the beneficial effect of coatings. In this work, we therefore describe reproducible long-term cycling data of NCM based cathodes with and without an Al2O3 based coating. The data set is provided to be used for parameter fitting and/or as training data to encourage the simulation of the performance of LIBs in model-based approaches.

show abstract

“…Analytical models proposed in the literature include numerous forms of relationships between capacity fade and number of cycles/time, such as linear (Belt, Utgikar, & Bloom, 2011), square-root (F. Yang, Song, Dong, & Tsui, 2019), power-law (Schmalstieg, Käbitz, Ecker, & Sauer, 2014;Han, Ouyang, Lu, & Li, 2014), exponential (X. Zhang, Miao, & Liu, 2017;Tang et al, 2019;Perez et al, 2018), polynomial (Micea, Ungurean, Cârstoiu, & Groza, 2011), sigmoid (Johnen et al, 2020) or a combination of these (Xing, Ma, Tsui, & Pecht, 2013). More complicated models also account for differences in C-rates and temperatures (Ji et al, 2020;Singh, Chen, Tan, & Huang, 2019).…”

Section: Empirical/analytical Modelsmentioning

confidence: 99%

Data-Driven Diagnostics and Prognostics for Modelling the State of Health of Maritime Battery Systems – a Review

Vanem¹,

Alnes

Lam³

2021

PHM_CONF

View full text Add to dashboard Cite

Battery systems are becoming an increasingly attractive alternative for powering ocean going ships, and the number of fully electric or hybrid ships relying on battery power for propulsion and maneuvering is growing. In order to ensure the safety of such electric ships, it is of paramount importance to monitor the available energy that can be stored in the batteries, and classification societies typically require that the state of health of the batteries can be verified by independent tests – annual capacity tests. However, this paper discusses data-driven diagnostics for state of health modelling for maritime battery systems based on operational sensor data collected from the batteries as an alternative approach. Thus, this paper presents a comprehensive review of different data-driven approaches to state of health modelling, and aims at giving an overview of current state of the art. Furthermore, the various methods for data-driven diagnostics are categorized in a few overall approaches with quite different properties and requirements with respect to data for training and from the operational phase. More than 300 papers have been reviewed, most of which are referred to in this paper. Moreover, some reflections and discussions on what types of approaches can be suitable for modelling and independent verification of state of health for maritime battery systems are presented.

show abstract

Modeling long-term capacity degradation of lithium-ion batteries

Cited by 32 publications

References 28 publications

A storage degradation model of Li-ion batteries to integrate ageing effects in the optimal management and design of an isolated microgrid

A storage degradation model of Li-ion batteries to integrate ageing effects in the optimal management and design of an isolated microgrid

Reproducible long-term cycling data of Al2O3 coated LiNi0.70Co0.15Mn0.15O2 cathodes for lithium-ion batteries

Data-Driven Diagnostics and Prognostics for Modelling the State of Health of Maritime Battery Systems – a Review

Contact Info

Product

Resources

About