Accelerated Aging Characterization of Lithium-ion Cells: Using Sensitivity Analysis to Identify the Stress Factors Relevant to Cyclic Aging

Gewald, Tanja; Candussio, Adrian; Wildfeuer, Leo; Lehmkuhl, Dirk; Hahn, Alexander; Lienkamp, Markus

doi:10.3390/batteries6010006

Cited by 22 publications

(10 citation statements)

References 44 publications

(71 reference statements)

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“…Cyclic aging corresponds to the impact of battery usage on the SOH, i.e. ageing due to charging and discharging (Gewald et al 2020).…”

Section: Lithium-ion Battery Components and Ageingmentioning

confidence: 99%

“…For example, a high SOC over 80% accelerates solid electrolyte interphase (SEI) growth (Barré et al 2013). Other stressors accelerating battery ageing are high charge and discharge C-rates 1 as well as a high ∆𝑆𝑂𝐶 (Gewald et al 2020;Marongiu, Roscher, and Sauer 2015). Known battery stressors are qualitatively displayed in Table 1.…”

Section: Lithium-ion Battery Components and Ageingmentioning

confidence: 99%

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State of Health Forecasting of Heterogeneous Lithium-ion Battery Types and Operation Enabled by Transfer Learning

Bülow¹,

Meisen

2022

PHME_CONF

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Due to the global transition to electromobility and the associated increased use of high-performance batteries, research is increasingly focused on estimating and forecasting the state of health (SOH) of lithium-ion batteries. Several data-intensive and well-performing methods for SOH forecasting have been introduced. However, these approaches are only reliable for new battery types, e.g., with a new cell chemistry, if a sufficient amount of training data is given, which is rarely the case. A promising approach is to transfer an established model of another battery type to the new battery type, using only a small amount of data of the new battery type. Such methods in machine learning are known as transfer learning. The usefulness and applicability of transfer learning and its underlying methods have been very successfully demonstrated in various fields, such as computer vision and natural language processing. Heterogeneity in battery systems, such as differences in rated capacity, cell cathode materials, as well as applied stress from use, necessitate transfer learning concepts for data-based battery SOH forecasting models. Hereby, the general electrochemical behavior of lithium-ion batteries, as a major common characteristic, supposedly provides an excellent starting point for a transfer learning approach for SOH forecasting models. In this paper, we present a transfer learning approach for SOH forecasting models using a multilayer perceptron (MLP). We apply and evaluate it on the method presented by von Bülow, Mentz, and Meisen (2021) using five battery datasets. In this regard, we investigate the optimal conditions and settings for the development of transfer learning with respect to suitable data from the target domain, as well as hyperparameters such as learning rate and frozen layers. We show that for the transfer of a SOH forecasting model to a new battery type it is more beneficial to have data of few old batteries, compared to data of many new batteries, especially in the case of superlinear degradation with knee points. Contrarily to computer vision freezing no layers is preferable in 95% of the experimental scenarios.

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“…Cyclic aging corresponds to the impact of battery usage on the SOH, i.e. ageing due to charging and discharging (Gewald et al 2020).…”

Section: Lithium-ion Battery Components and Ageingmentioning

confidence: 99%

Section: Lithium-ion Battery Components and Ageingmentioning

confidence: 99%

State of Health Forecasting of Heterogeneous Lithium-ion Battery Types and Operation Enabled by Transfer Learning

Bülow¹,

Meisen

2022

PHME_CONF

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“…3.4. Table 3.1 presents a literature revision based on the work developed by [57], where the importance of the different variables over the calendar ageing can be seen. The relevance of the stress factors on the calendar aging effects ∆C or ∆R is qualitatively classified as having no influence, minor influence, or strong influence.…”

Section: Calendar Ageingmentioning

confidence: 99%

“…The utilization of the battery is commonly measured by the DoD, representing the capacity variation along one cycle. Table 3.2 presents a literature revision based on the work developed by [57], where the importance of the different variables over the cycling ageing can be seen, with the same format as the one used for calendar ageing.…”

Section: Cycling Ageingmentioning

confidence: 99%

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Study and Analysis of the Behavior of LFP and NMC Electric Vehicle Batteries concerning their Ageing and their Integration into the Power Grid

Álvarez¹

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Esta tesis ha sido un camino largo de aprendizaje y crecimiento en casi todos los aspectos de mi vida, y no podía haber estado mejor acompañado.Gracias Jaime y Rosa, por la oportunidad, por vuestro esfuerzo, por vuestros valores y ética de trabajo, por todo lo que he aprendido de vosotros, por vuestra generosidad, por vuestra confianza y por facilitar al máximo el trabajo del día a día. Gracias sobre todo por decir que sí a una aventura que empezó con todas las dificultades del mundo, y en la que hemos ido superando todas y cada una de ellas. Hemos subido nuestro Tourmalet.Gracias a mis compañeros de tesis, Danny, Ebi y Hugo. Gracias por el apoyo y los ánimos, por los abrazos tras publicar un artículo y por las tardes en la pecera. Que la burocracia esté siempre de vuestro lado, que os lo habéis ganado.Gracias Marcos, por acogerme con los brazos abiertos y abrirme un mundo de conocimiento que me apasiona. Gracias por tu confianza, por todo lo que compartes, y por todo lo que aprendo a diario. Gracias también a Pablo, por tus consejos siempre acertados, por las sesiones de terapia post-práctica, y por quitarme el "miedo escénico" a la investigación.Gracias al maravilloso equipo uSEP (o Josep, o Giuseppe, o...). Marcos, Gustavo, Miguel, Jorge y Carlos, gracias por ayudarme a ser mejor investigador cada día y por hacer que, cada vez que hablo de trabajo, me salga una sonrisilla. Sois geniales, sobre todo haciendo barbacoas.Gracias a David, Mar, Fer, Rober y Rodolfo, por prestarme vuestra ayuda en el laboratorio cada vez que lo he necesitado y por aguantar estoicamente la "dulce melodía" de la bancada de baterías durante 8 horas diarias. Gracias también a todos los alumnos de TFG y TFM a los que he tutelado, por contribuir a la investigación de esta tesis, por aceptar mis limitaciones como tutor y por enseñarme a enseñar. Especialmente a Aitor, César, Iván, Guille, Cristina, y Carlos, aprender a comunicar y a enseñar con vosotros ha VII

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Field Data Analysis of a Commercial Vehicle Fleet in Relation to the Load of the HV Battery

Hadler¹,

Michalski²,

Schuler³

et al. 2022

Proceedings

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Accelerated Aging Characterization of Lithium-ion Cells: Using Sensitivity Analysis to Identify the Stress Factors Relevant to Cyclic Aging

Cited by 22 publications

References 44 publications

State of Health Forecasting of Heterogeneous Lithium-ion Battery Types and Operation Enabled by Transfer Learning

State of Health Forecasting of Heterogeneous Lithium-ion Battery Types and Operation Enabled by Transfer Learning

Study and Analysis of the Behavior of LFP and NMC Electric Vehicle Batteries concerning their Ageing and their Integration into the Power Grid

Field Data Analysis of a Commercial Vehicle Fleet in Relation to the Load of the HV Battery

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