Citation: Pang, B.; Chen, L.; Dong, Z. Data-Driven Degradation Modeling and SOH Prediction of Li-Ion Batteries. Energies 2022, 15, 5580. https://doi.org/10.3390/en15155580 Academic Editor: Mogalahalli V. Reddy Received: 16 July 2022 Accepted: 29 July 2022 Published: 1 August 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). energies Article Data-Driven Degradation Modeling and SOH Prediction of Li-Ion Batteries Bo Pang * , Li Chen and Zuomin Dong * Department of Mechanical Engineering, Institute for Integrated Energy System, University of Victoria, Victoria, BC V8W 2Y2, Canada; chenli@uvic.ca * Correspondence: bopang@uvic.ca (B.P.); zdong@uvic.ca (Z.D.) 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 1. Introduction Today, the transportation sector contributes to about 30 percent of greenhouse gas (GHG) emissions and a significant amount of harmful air pollutants [1]. The vehicle electrification and hybridization rely on the electric energy storage system (ESS), based on lithium-ion (Li-ion) batteries, to serve as the sole source of propulsion energy for a battery electric vehicle (BEV) or an energy reservoir of a hybrid electric vehicle (HEV) to improve the operation condition and energy efficiency of the internal combustion engine (ICE). Li-ion batteries are thus critical for cleaning and decarbonizing transportation applications. The battery ESS contributes to about 1/3 to 1/2 the cost of a BEV [2]. Reduction in the costs of the battery ESS is the key to the commercial success and wide adoption of the zero pump- to-wheel (PTW) emission BEVs. An efficient means for reducing their lifecycle cost (LCC) is to extend their operating life by avoiding performance degrading and life-shortening operations. The main reason for the performance degradation of Li-ion batteries is that the electrodes are deformed and fractured due to the stress generated during the intercala- tion/extraction of Li-ions, causing short circuits and making the active electrode materials unable to store Li-ions. In general, the main consequences of battery performance degrada- tion include the capacity decay and impedance increment. The capacity decay is mostly due to the solid-electrolyte layer (SEI) formation on the anode and side reactions on the cathode, while the battery impedance can be affected by the material disordering and decomposition as well as the formation of SEI. Specifically, the main reasons for carbon-based anode deterioration are SEI formation and growth, corrosion of active carbons, lithium metal Energies 2022, 15, 5580. https://doi.org/10.3390/en15155580 https://www.mdpi.com/journal/energies