Lithium-ion battery degradation forecasting using data-driven time series models
Citation Link: https://doi.org/10.15480/882.16181
Publikationstyp
Conference Paper
Date Issued
2025-06
Sprache
English
Author(s)
Patel, Kishan
Salin, Athira Pulickakudy
TORE-DOI
Number in series
7-2025
Citation
44th International Conference on Ocean, Offshore and Arctic Engineering, OMAE 2025
Contribution to Conference
Publisher DOI
Publisher
American Society of Mechanical Engineers (ASME)
The maritime industry faces significant challenges as it adapts from a major carbon emitter to a low-emission sector, intending to eventually achieve zero emissions. This transition requires innovative solutions for both new and old vessels, lithium-ion batteries show promise in achieving these goals. Battery management systems improve reliability and safety by monitoring voltage, current, and temperature through sensors. These parameters enable the prediction of remaining usable life, allowing for prompt maintenance and replacement before failure occurs. Publicly accessible lithium battery datasets provide a useful starting point for predictive degradation model development. This study investigates time series modeling methodologies for lithium-ion battery degradation, utilizing NASA’s battery degradation dataset. Three models viz. Autoregressive, Autoregressive Integrated Moving Average, and its extension using seasonality parameters were developed. They were tested with four train/test ratios to predict the remaining useful life values and assess the accuracy of the predicted degradation curve against experimental results. From the results, it was observed that the Autoregressive Integrated Moving Average model had the least combined average Root Mean Square Error values, resulting in a good overall degradation curve fitting, whereas the Seasonal Autoregressive Integrated Moving Average model was able to predict the End of Life values more accurately.
Subjects
lithium-ion batteries
data driven time series model
AR
ARIMA
SARIMA
remaining useful life
DDC Class
330: Economics
380: Commerce, Communications, Transport
519: Applied Mathematics, Probabilities
623: Military Engineering and Marine Engineering
660: Chemistry; Chemical Engineering
Publication version
publishedVersion