Gaussian process regression‐based modelling of lithium‐ion battery temperature‐dependent open‐circuit‐voltage. Issue 17 (1st August 2017)

• Record Type: Journal Article
• Authors: Huang, C.; Wang, L.
• Is Part Of: Electronics letters. Volume 53:Issue 17(2017)
• Page Start: 1214
• Page End: 1216
• Publication Date: 2017-08-01
• Subjects: lithium compounds -- secondary cells -- Gaussian processes -- regression analysis; open‐circuit‐voltage model -- SOC estimation -- state‐of‐charge estimation -- lithium‐ion battery temperature‐dependent OCV model -- Gaussian process regression‐based modelling; Electronics -- Periodicals; 621.381
• DOI: 10.1049/el.2017.2136
• Languages: English
• ISSNs: 0013-5194

Abstract

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.