On-board state of health estimation of Li-Ion batteries packs using incremental capacity analysis with principal components

Kuznietsov, Alexander; Happek, Tilman; Guefack, Frank Landry Tanenkeu

doi:10.1109/esars-itec.2018.8607760

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…[8] LiCO2 (probably) 100%-50% 2 types of small cells with 4 of each, full charge and discharge cycle for around 200-800 cycles depending on type [1] NiMH 50-0% possibly of life 2 small cells full charge and discharge cycle for around 85 cycles [27] Li Ion Oxford university data set [28] Up to 3600 charging cycles, looking for changes in the incremental capacity data as a function of probability [2] LiFePO4 4 small cells, full charge and discharge cycle for between 363 to 1549 cycles, undertaken during charging above 70% SOC [29] LiCoO2 100%-70% At least 7 small cells, discharge rates 10% to 90% DOD up to 4000+ cycles at 50% DOD [30] Lithium Ion 100%-70% small cells, 0.5C discharge cycle to 1000 cycles [12] Li(NiCoMn)1/ 3O2 100%-80% 6 small cells, 840 cycles undertaken at high temperature to speed aging [31] Li(NiCoAl)O2 Panasonic cylindrical 100%-about 60-70% 18 small cells, full charge and discharge cycle at cycle rates 0.5C, 1C and 2C and different temperatures to full DOD to up to 800 cycles [18] Lithium Ion 100%-80% 2 small groups of cells, between 2500 cycles one set with vibration [21] Li(NiCoAl)O2 cylindrical batteries 100%-80% (approx.) 0-100% DOD cycles at different charge rates (1C, 2C and 3.5C) and temperature (25oC and 40oC) to around 600 cycles.…”

Section: Fig 3 Svm Techniquesmentioning

confidence: 99%

Second Life Battery Life Cycle Estimation

Yang¹,

Beatty²,

Strickland³

et al. 2022

Preprint

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<p>There is increased talk about using second life batteries in applications. In first life applications, the batteries start from new and a range of life cycle estimation techniques are applied. However, it is not clear how second life batteries should be monitored compared to first life batteries. This paper investigated different algorithms from first life applications, for estimating and forecasting battery cell state of health in conjunction with capacity calculations using second life cells under long term durability testing. The paper looks at how close these models predict capacity fade based on a set of second life batteries that have been undertaking sweat testing over six different applications. The paper concludes that there are two methods that could be suitable candidates for predicting lifespan. One of these needed to be modified from the original.</p>

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Section: Fig 3 Svm Techniquesmentioning

confidence: 99%

Second Life Battery Life Cycle Estimation

Yang¹,

Beatty²,

Strickland³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…What these methods capture is the inner relationship between the extracted features with aging trend. 29 Methods such as incremental capacity analysis (ICA), 30 Gaussian process regression, 31 support vector machine, 32 and relevance vector regression 33 have been applied in SOH estimation and RUL prediction successfully. However, it is worth noting that the performance of above-mentioned methods is picky about the quality of extracted features, which costs computational efforts, and the resultant model may not be generalized.…”

Section: Introductionmentioning

confidence: 99%

State‐of‐health estimation and remaining useful life for lithium‐ion battery based on deep learning with Bayesian hyperparameter optimization

Kong

Wang

Ping

2021

Intl J of Energy Research

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Application of Digital Twin in Smart Battery Management Systems

Wang

Tian

et al. 2021

Chin. J. Mech. Eng.

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Lithium-ion batteries have always been a focus of research on new energy vehicles, however, their internal reactions are complex, and problems such as battery aging and safety have not been fully understood. In view of the research and preliminary application of the digital twin in complex systems such as aerospace, we will have the opportunity to use the digital twin to solve the bottleneck of current battery research. Firstly, this paper arranges the development history, basic concepts and key technologies of the digital twin, and summarizes current research methods and challenges in battery modeling, state estimation, remaining useful life prediction, battery safety and control. Furthermore, based on digital twin we describe the solutions for battery digital modeling, real-time state estimation, dynamic charging control, dynamic thermal management, and dynamic equalization control in the intelligent battery management system. We also give development opportunities for digital twin in the battery field. Finally we summarize the development trends and challenges of smart battery management.

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On-board state of health estimation of Li-Ion batteries packs using incremental capacity analysis with principal components

Cited by 4 publications

References 12 publications

Second Life Battery Life Cycle Estimation

Second Life Battery Life Cycle Estimation

State‐of‐health estimation and remaining useful life for lithium‐ion battery based on deep learning with Bayesian hyperparameter optimization

Application of Digital Twin in Smart Battery Management Systems

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