Time and Frequency Domain Health Indicators for Capacity Prediction of Lithium-ion Battery

Ma'd El-Dalahmeh, Prudhive Thummarapally, Maher Al-Greer, Mo'Ath El-Dalahmeh

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

Abstract

Predict the capacity of lithium-ion batteries with high accuracy is crucial to the reliability and safety of the system. Due to the complex nature and the nonlinear degradation phenomena of the lithium-ion battery, monitoring the battery's capacity is a challenging task. This paper proposes a machine learning model based on time and frequency domain health indicators to predict the capacity of lithium-ion battery cycled under different operational conditions. The time and frequency domain health indicators have been extracted from the measured voltage. The extracted features have been fed into extreme learning machine model to predict the capacity. This approach has been tested on 16 lithium-ion batteries cycled at many operational conditions from NASA. The results show that the proposed method can track the degradation from the extracted health indicators from both domains (time and frequency). The extreme learning model can effectively predict the capacity with a root mean square error of 1.3%.
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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