State-of-health estimation of lithium-ion batteries for electrified vehicles using a reduced-order electrochemical model

Abstract

The growing demand for Electric Vehicles (EVs) to operate reliably with ever-increasing driving ranges means that the lithium-ion batteries are more frequently working at their theoretical limits. Thus, it is essential that the Battery Management System (BMS) accurately monitors the internal states of the batteries with high precision and robustness. Although the Equivalent Circuit Model (ECM) is widely used in BMS, the Electrochemical Model (EM) outperforms the ECM in terms of inherent physics representation. In this paper, a novel coestimation scheme based on a fractional-order battery model is presented for simultaneous estimations of the internal resistance and capacity fade as State of Health (SOH) indicators. This approach avoids high computational cost due to a low number of calibration parameters, while maintaining high accuracy. Hence, it has great potential for BMS usage. First, the derivation of the fractional battery model from Partial Differential Equations (PDE) governing the Pseudo-Two-Dimensional model (P2D) is described. Next, the resistance estimation with an iterative model-based observer approach is developed to concurrently realize the adaptive estimation of the battery resistance and capacity. Finally, the effectiveness of the newly proposed approach is validated by experimental data considering different capacity aging levels, dynamic load profiles and initialization errors.