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 l...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.展开更多
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 pr...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.展开更多
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 of...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.展开更多
基金This project was funded by an industry-academia collaborative grant EPSRC EP/R511687/1 awarded by EPSRC&University of Edinburgh pro-gram Impact Acceleration Account(IAA)G.dos Reis acknowledges support from the Fundação para a Ciên-cia e a Tecnologia(Portuguese Foundation for Science and Technology)through the project UIDB/00297/2020(Centro de Matemática e Apli-cações CMA/FCT/UNL).
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.
基金This project was funded by an industry-academia collaborative grant EPSRC EP/R511687/1 awarded by EPSRC&University of Edin-burgh program Impact Acceleration Account(IAA).G.dos Reis acknowledges support from the Fundaç̃ao para a Cî𝑒ncia e a Tecnologia(Portuguese Foundation for Science and Technology,Por-tugal)through the project UIDB/00297/2020(Centro de Matemática e Aplicaç̃oes CMA/FCT/UNL).
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.
基金funded by an industry-academia collaborative grant EPSRC EP/R511687/1 awarded by Engineering and Physical Sciences Research Council(EPSRC)&University of Edinburgh United Kingdom program Impact Acceleration Account(IAA).G.dos Reis acknowledges support from the Fundaçao para a Ciencia e a Tecnologia(Portuguese Foundation for Science and Technology)Portugal through the project UIDB/00297/2020 and UIDP/00297/2020(Center for Mathematics and Applications,CMA/FCT/UNL Portugal)P.Dechent was supported by Bundesministerium für Bildung und Forschung Germany(BMBF 03XP0302C).
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.
