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您的检索式：作者名="Calum Strange"
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| 1 | Prediction of future capacity and internal resistance of Li-ion cells from one cycle of input data显示文摘There is a large demand for models able to predict the future capacity retention and internal resistance(IR)of Lithium-ion battery cells with as little testing as possible.We provide a data-centric model accurately predicting a cell’s entire capacity and IR trajectory from one single cycle of input data.This represents a significant reduction in the amount of input data needed over previous works.Our approach characterises the capacity and IR curve through a small number of key points,which,once predicted and interpolated,describe the full curve.With this approach the remaining useful life is predicted with an 8.6%mean absolute percentage error when the input-cycle is within the first 100 cycles. | Calum Strange Gonçalo dos Reis | 2021 | Energy and AI2021，5，3： | 0 |
| 2 | Lithium-ion battery data and where to find it显示文摘Lithium-ion batteries are fuelling the advancing renewable-energy based world.At the core of transformational developments in battery design,modelling and management is data.In this work,the datasets associated with lithium batteries in the public domain are summarised.We review the data by mode of experimental testing,giving particular attention to test variables and data provided.Alongside highlighted tools and platforms,over 30 datasets are reviewed. | Gonçalo dos Reis Calum Strange Mohit Yadav Shawn Li | 2021 | Energy and AI2021，5，3： | 0 |
| 3 | Automatic method for the estimation of li-ion degradation test sample sizesrequired to understand cell-to-cell variability显示文摘The testing of battery cells is a long and expensive process, and hence understanding how large a test set needsto be is very useful. This work proposes an automated methodology to estimate the smallest sample size ofcells required to capture the cell-to-cell variability seen in a larger population. We define cell-to-cell variationbased on the slopes of a linear regression model applied to capacity fade curves. Our methodology determinesa sample size which estimates this variability within user specified requirements on precision and confidence.The sample size is found using the distributional properties of the slopes under a normality assumption, andan implementation of the approach is available on GitHub.For the five datasets in the study, we find that a sample size of 8–10 cells (at a prespecified precision andconfidence) captures the cell-to-cell variability of the larger datasets. We show that prior testing knowledge canbe leveraged with machine learning models to operationally optimise the design of new cell-testing, leadingup to a 75% reduction in experimental costs. | Calum Strange Michael Allerhand Philipp Dechent Gonçalo dos Reis | 2022 | Energy and AI2022，9，3： | 0 |
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