State of Health Estimation of Lithium-ion Batteries Based on Data-Driven Techniques

Ma'd El-Dalahmeh, Joseph Lillystone, Maher Al-Greer, Mo'Ath El-Dalahmeh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Accurate prediction of the state of health of lithium-ion batteries is essential to ensure safe and reliable operation with minimum maintenance cost. However, estimating the state of health of lithium-ion batteries is a challenge due to the complex and nonlinear characteristics of battery degradation during its lifetime. To this end, data-driven techniques are becoming a more attractive promising approach to provide accurate state of health estimation of the lithium-ion battery because of their simplicity and ability to cope with the nonlinearity dynamic behaviour of the lithium-ion battery. Therefore, this paper compares the performance of three data-driven algorithms: the nonlinear autoregressive neural network, convolutional neural network, and long short-term memory network in the state of health estimation. The performance of the proposed algorithms is analysed by using 16 lithium-ion cells cycled under various operating conditions from the NASA Ames Research center. The comparison results demonstrate that the long short-term memory network outperforms the other two methods with a maximum root mean square error of 0.50% and mean absolute error of 0.36%.
Original languageEnglish
Title of host publication56th International Universities Power Engineering Conference
PublisherIEEE
ISBN (Electronic)9781665443890
DOIs
Publication statusPublished - 2021
Event56th International Universities Power Engineering Conference - Middlesbrough, United Kingdom
Duration: 31 Aug 20213 Sep 2021
https://www.ieee-pes.org/meetings-and-conferences/conference-calendar/monthly-view/166-technically-cosponsored-by-pes/883-upec-2021

Conference

Conference56th International Universities Power Engineering Conference
Abbreviated titleUPEC
Country/TerritoryUnited Kingdom
CityMiddlesbrough
Period31/08/213/09/21
Internet address

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