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

By Calum Strange, Gonçalo dos Reis. 2021

76 citations recorded by the source corpus.

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 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.

Subfields: Automotive Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/4ce8f6b397d05ebe69c043aebf0df5fb60a2ecd9