Prediction of future capacity and internal resistance of Li-ion cells from one cycle of input data

Calum Strange, Goncalo Dos Reis

Research output: Contribution to journalArticlepeer-review

Abstract

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 datacentric 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, yield 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. Keywords: capacity degradation, internal resistance degradation, prediction of full degradation curve, knee and elbow-points, lithium-ion cells, machine learning, remaining-useful-life.
Original languageEnglish
Article number100097
Number of pages16
JournalEnergy and AI
Volume5
Early online date26 Jun 2021
DOIs
Publication statusE-pub ahead of print - 26 Jun 2021

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