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
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.
