Data-driven degradation modeling and SOH prediction of Li-ion batteries

dc.contributor.authorPang, Bo
dc.contributor.authorChen, Li
dc.contributor.authorDong, Zuomin
dc.date.accessioned2022-11-04T18:38:42Z
dc.date.available2022-11-04T18:38:42Z
dc.date.copyright2022en_US
dc.date.issued2022
dc.description.abstractElectrified vehicles (EV) and marine vessels represent promising clean transportation solutions to reduce or eliminate petroleum fuel use, greenhouse gas emissions and air pollutants. The presently commonly used electric energy storage system (ESS) is based on lithium-ion batteries. These batteries are the electrified or hybridized powertrain’s most expensive component and show noticeable performance degradations under different use patterns. Therefore, battery life prediction models play a key role in realizing globally optimized EV design and energy control strategies. This research studies the data-driven modelling and prediction methods for Li-ion batteries’ performance degradation behaviour and the state of health (SOH) estimation. The research takes advantage of the increasingly available battery test and data to reduce prediction errors of the widely used semi-empirical modelling methods. Several data-driven modelling techniques have been applied, improved, and compared to identify their advantages and limitations. The data-driven approach and Kalman Filter (KF) algorithm are used to estimate and predict the degradation of the battery during operation. The combined algorithm of Gaussian Process Regression (GPR) and Extended Kalman Filter (EKF) showed higher accuracy than other algorithms.en_US
dc.description.reviewstatusRevieweden_US
dc.description.scholarlevelFacultyen_US
dc.identifier.citationPang, B., Chen, L., & Dong, Z. (2022). “Data-driven degradation modeling and SOH prediction of Li-ion batteries.” Energies, 15(15), 5580. https://doi.org/10.3390/en15155580en_US
dc.identifier.urihttps://doi.org/10.3390/en15155580
dc.identifier.urihttp://hdl.handle.net/1828/14399
dc.language.isoenen_US
dc.publisherEnergiesen_US
dc.subjectLi-ion batteriesen_US
dc.subjectperformance degradationen_US
dc.subjectdata-driven modellingen_US
dc.titleData-driven degradation modeling and SOH prediction of Li-ion batteriesen_US
dc.typeArticleen_US

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