Gaussian process regression-based modelling of lithium-ion battery temperature-dependent open-circuit-voltage
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
Author(s)
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Detail(s)
Original language | English |
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Pages (from-to) | 1214-1216 |
Journal / Publication | Electronics Letters |
Volume | 53 |
Issue number | 17 |
Online published | 24 Jul 2017 |
Publication status | Published - 17 Aug 2017 |
Link(s)
Abstract
Open-circuit-voltage (OCV) plays a significant role in state-of-charge (SOC) estimation for lithium-ion batteries. The slight difference in OCV at various temperatures can result in a large deviation of SOC estimation. In this Letter, a novel model based on Gaussian process regression is proposed to describe the sophisticated relationship among the OCV, SOC, and temperature. To validate the effectiveness of the proposed model, a comprehensive comparison with widely considered benchmarking OCV models is conducted. Experiment results demonstrate the proposed model can provide the most accurate prediction of OCV compared with benchmarking models.
Citation Format(s)
Gaussian process regression-based modelling of lithium-ion battery temperature-dependent open-circuit-voltage. / Huang, C.; Wang, L.
In: Electronics Letters, Vol. 53, No. 17, 17.08.2017, p. 1214-1216.Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review