Online state of health and aging parameter estimation using a physics-based life model with a particle filter

Abstract

Degradation of lithium-ion batteries due to undesirable side reactions causes changes in the compounds and morphologies of electrode materials that lead to the loss of lithium ions, loss of active materials, consumption of electrolyte solvents, and increase of internal resistance, eventually resulting in capacity and power fade, thereby reducing battery life. In this paper, we propose an online estimator for the state of health (SOH) and aging parameters using a high-fidelity, reduced-order physics-based life model, which consists of a pseudo-two-dimensional model coupled with a degradation model of two types of side reactions—solid electrolyte interphase (SEI) layer formation and lithium plating—for the negative electrode, and a single particle model for the positive electrode to increase computational efficiency. Simultaneous SOH and aging parameter estimation are achieved by reformulating the model into a control-oriented recursive aging model and with the employment of a particle filter from real-time current and terminal voltage measurements, which enables the prediction of cell failure due to bulk and local aging mechanisms. Validations and battery-in-the-loop tests were conducted to illustrate the performance of the proposed estimation scheme at different temperatures, where the resulting errors of capacity and power fade estimation are within 3% and 4%, respectively.