Fu, C., Lv, Q., Tseng, M.-L., Wu, X. and Lim, M. K. (2022) A polynomial scale transformation and improved wiener process for a novel lithium-ion battery performance degradation model: remaining useful life performance. Journal of Ambient Intelligence and Humanized Computing, (doi: 10.1007/s12652-022-03883-0) (Early Online Publication)
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Abstract
This study contributes to propose a novel lithium-ion battery performance degradation model based on improved Wiener process. The aging of lithium-ion batteries brings potential hazards to the power system of electric vehicles, so the health status of lithium-ion batteries needs to be evaluated. First, a polynomial scale transformation model is established to scale the cycle number to transform the nonlinear Wiener process into linear Wiener process, and model parameters are estimated by the maximum likelihood functions. Second, a performance degradation model based on the improved Wiener process is constructed to estimate the remaining useful life (RUL) performance, in which the cumulative loss reaching the failure threshold is taken as the failure criterion. Finally, the proposed RUL estimation method is tested using data provided by NASA. The test results proved that the estimation errors of proposed model were controlled within 15%. The RUL estimation method proposed in this study provides a new way for the reliability evaluation of lithium-ion batteries and guarantees the safe operation of electric vehicle power system.
Item Type: | Articles |
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Status: | Early Online Publication |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Lim, Professor Ming |
Authors: | Fu, C., Lv, Q., Tseng, M.-L., Wu, X., and Lim, M. K. |
College/School: | College of Social Sciences > Adam Smith Business School > Management |
Journal Name: | Journal of Ambient Intelligence and Humanized Computing |
Publisher: | Springer |
ISSN: | 1868-5137 |
ISSN (Online): | 1868-5145 |
Published Online: | 03 June 2022 |
Copyright Holders: | Copyright © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
First Published: | First published in Journal of Ambient Intelligence and Humanized Computing 2022 |
Publisher Policy: | Reproduced in accordance with the publisher copyright policy |
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