Investigation and modeling of cyclic aging using a design of experiment with automotive grade lithium-ion cells

Stadler, Jochen; Krupp, Carsten; Ecker, Madeleine; Bandlow, Jochen; Spier, Bernd; Latz, Arnulf

doi:10.1016/j.jpowsour.2021.230952

Cited by 22 publications

(13 citation statements)

References 42 publications

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“…[ 8 ] for calendar aging and Stadler et al. [ 28 ] for cycle aging. They investigated the automotive lithium‐ion pouch‐cells described in Table 1 .…”

Section: Methodsmentioning

confidence: 99%

“…[26] Temperature (T), minimum and maximum state of charge (SoC min/max ), charging power (P ch ) and the ratio between charge depleting and sustaining driving mode (EV ratio ) are considered especially relevant. [15,19,22,27] Therefore, this work utilizes a statistically designed experiment previously published by Stadler et al [28] : The stress factors were varied systematically from a common center point (Figure S3, Supporting Information). This design is particularly well suited for data-driven modeling approaches.…”

Section: Cell Aging Datamentioning

confidence: 99%

“…[ 15 , 19 , 22 , 27 ] Therefore, this work utilizes a statistically designed experiment previously published by Stadler et al. [ 28 ] : The stress factors were varied systematically from a common center point (Figure S3 , Supporting Information). This design is particularly well suited for data‐driven modeling approaches.…”

Section: Data and Modeling Approaches For Lifetime Predictionmentioning

confidence: 99%

“…[ 33 ] and the cycle aging model inspired by Stadler et al. [ 28 ] are selected as best applicable (semi‐)empirical models. Extreme gradient boosting (see ref.…”

Section: Performance Comparison Of Modeling Approachesmentioning

confidence: 99%

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Machine Learning‐Based Lifetime Prediction of Lithium‐Ion Cells

et al. 2022

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Precise lifetime predictions for lithium‐ion cells are crucial for efficient battery development and thus enable profitable electric vehicles and a sustainable transformation towards zero‐emission mobility. However, limitations remain due to the complex degradation of lithium‐ion cells, strongly influenced by cell design as well as operating and storage conditions. To overcome them, a machine learning framework is developed based on symbolic regression via genetic programming. This evolutionary algorithm is capable of inferring physically interpretable models from cell aging data without requiring domain knowledge. This novel approach is compared against established approaches in case studies, which represent common tasks of lifetime prediction based on cycle and calendar aging data of 104 automotive lithium‐ion pouch‐cells. On average, predictive accuracy for extrapolations over storage time and energy throughput is increased by 38% and 13%, respectively. For predictions over other stress factors, error reductions of up to 77% are achieved. Furthermore, the evolutionary generated aging models meet requirements regarding applicability, generalizability, and interpretability. This highlights the potential of evolutionary algorithms to enhance cell aging predictions as well as insights.

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“…[ 8 ] for calendar aging and Stadler et al. [ 28 ] for cycle aging. They investigated the automotive lithium‐ion pouch‐cells described in Table 1 .…”

Section: Methodsmentioning

confidence: 99%

Section: Cell Aging Datamentioning

confidence: 99%

Section: Data and Modeling Approaches For Lifetime Predictionmentioning

confidence: 99%

“…[ 33 ] and the cycle aging model inspired by Stadler et al. [ 28 ] are selected as best applicable (semi‐)empirical models. Extreme gradient boosting (see ref.…”

Section: Performance Comparison Of Modeling Approachesmentioning

confidence: 99%

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Machine Learning‐Based Lifetime Prediction of Lithium‐Ion Cells

et al. 2022

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“…LIBs age over time while in or out of operation, often referred to battery cycle life (charge/discharge) and calendar life (shelved/stored), respectively. [5] During operation, the batteries undergo partial to complete charge/discharge cycles and several other operating conditions that lead to capacity decay and/or internal resistance (IR) increase. [6] The associated irreversible process results in continuous capacity decay, which eventually causes battery failure.…”

Section: Introductionmentioning

confidence: 99%

A Review on the Prediction of Health State and Serving Life of Lithium‐Ion Batteries

Zhong

Wang

Yang

et al. 2022

The Chemical Record

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The monitoring and prediction of the health status and the end of life of batteries during the actual operation plays a key role in the battery safety management. However, although many related studies have achieved exciting results, there are few systematic and comprehensive reviews on these prediction methods. In this paper, the current prediction models of remaining useful life of lithium‐ion batteries are divided into mechanism‐based models, semi‐empirical models and data‐driven models. Their advantages, technical obstacles, improvement methods and prediction performance are summarized, and the latest research results are shown by comparison. We highlight that the fusion models of convolution neural network, long short term memory network and so on, which have great practical application prospects because of their outstanding computing efficiency and strong modeling ability. Finally, we look forward to the future work in simplifying the model and improving its interpretability.

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Why Half‐Cell Samples Provide Limited Insight Into the Aging Mechanisms of Potassium Batteries

Jeschull,

Kataev,

Panasenko

et al. 2024

Advanced Energy Materials

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Photoelectron spectroscopy (PES) studies of solid electrolyte interphases (SEI) of cycled battery electrodes are mostly performed in half cell configurations (i.e., against metallic counter electrodes). In contrast to less reactive Li metal, problems arise in post‐Li systems, like K‐ion cells, where crosstalk phenomena strongly interfere with the surface layer formation process. This raises the question of whether surface analysis data from half cell experiments are still representative and transferable to corresponding full cells in post‐Li systems. Here the major differences between SEI layers formed on graphite electrodes are outlined in K‐ion half and full cells derived from an in‐depth surface analysis approach combining in‐house and (synchrotron‐based) hard X‐ray PES. This results highlight significant changes in SEI characteristics, both in terms of SEI layer thickness and gradual compositional changes across the interphase, when K‐metal (half cell) is replaced by a Prussian white positive electrode (full cell). Furthermore, the initial SEI layers formed on the first cycle are found to evolve and age differently upon further cycling, depending on the two cell configurations. This study stresses the additional complexity of studying post‐Li cells and the need to carefully design surface analysis experiments for meaningful material and electrode interphase characterization.

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Investigation and modeling of cyclic aging using a design of experiment with automotive grade lithium-ion cells

Cited by 22 publications

References 42 publications

Machine Learning‐Based Lifetime Prediction of Lithium‐Ion Cells

Machine Learning‐Based Lifetime Prediction of Lithium‐Ion Cells

A Review on the Prediction of Health State and Serving Life of Lithium‐Ion Batteries

Why Half‐Cell Samples Provide Limited Insight Into the Aging Mechanisms of Potassium Batteries

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