2022 IEEE Transportation Electrification Conference &Amp; Expo (ITEC) 2022
DOI: 10.1109/itec53557.2022.9814022
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Enhancement of Stress Cycle-counting Algorithms for Li-ion Batteries by means of Fuzzy Logic

Abstract: The rainflow algorithm is one of the most commonly used tools for studying stress conditions of a wide variety of systems, including power electronics devices and electrochemical batteries. One of the main drawbacks of the algorithm is the trade-off between data compression and the loss of information when classifying the stress cycles into a finite amount of histogram bins. This paper proposes a novel approach for classifying the stress cycles by using fuzzy logic in order to reduce the quantization error of … Show more

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Cited by 4 publications
(2 citation statements)
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“…Our works bring improvements to the current landscape [34], [39], [40]. First, even though our test subjects are stationary batteries, our state-of-health inference approaches are designed so that they can be applied to either mobile or stationary systems, using both test-and operational-based data.…”
Section: Battery Degradation Analysis and Forecastmentioning
confidence: 99%
See 1 more Smart Citation
“…Our works bring improvements to the current landscape [34], [39], [40]. First, even though our test subjects are stationary batteries, our state-of-health inference approaches are designed so that they can be applied to either mobile or stationary systems, using both test-and operational-based data.…”
Section: Battery Degradation Analysis and Forecastmentioning
confidence: 99%
“…More specifically, one way to tackle the problems associated with this system topology is to upload only a selection of features extracted from the cell measurements (health indicators and cycling histograms) instead of the entire time series [39]. In this work, it was shown that the proposed method could compress several megabytes of data into fewer than 1 kB; hence, based on the table in Figure 2, this would lead to a significant operational cost reduction.…”
Section: Battery Degradation Analysis and Forecastmentioning
confidence: 99%