Gaussian process regression‐based modelling of lithium‐ion battery temperature‐dependent open‐circuit‐voltage | Zendy

Huang C. | Zendy; Wang L. | Zendy

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Gaussian process regression‐based modelling of lithium‐ion battery temperature‐dependent open‐circuit‐voltage

Author(s) -

Huang C.,

Wang L.

Publication year - 2017

Publication title -

electronics letters

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.375

H-Index - 146

ISSN - 1350-911X

DOI - 10.1049/el.2017.2136

Subject(s) - kriging , benchmarking , open circuit voltage , state of charge , voltage , gaussian process , battery (electricity) , gaussian , regression analysis , computer science , economic shortage , process (computing) , regression , electronic engineering , engineering , machine learning , statistics , electrical engineering , mathematics , chemistry , power (physics) , physics , business , operating system , marketing , quantum mechanics , computational chemistry , philosophy , government (linguistics) , linguistics

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.

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