Data-Driven Degradation Modeling and SOH Prediction of Li-Ion Batteries

Bo Pang, Li Chen and Zuomin Dong
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Bo Pang: Department of Mechanical Engineering, Institute for Integrated Energy System, University of Victoria, Victoria, BC V8W 2Y2, Canada
Li Chen: Department of Mechanical Engineering, Institute for Integrated Energy System, University of Victoria, Victoria, BC V8W 2Y2, Canada
Zuomin Dong: Department of Mechanical Engineering, Institute for Integrated Energy System, University of Victoria, Victoria, BC V8W 2Y2, Canada

Energies, 2022, vol. 15, issue 15, 1-12

Abstract: Electrified 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.

Keywords: Li-ion batteries; performance degradation; data-driven modelling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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