(Invited) Effect of Temperature on Lithium-Ion Battery Voltage Response and How to Model It in the Mechanistic Modeling Approach

Abstract

The ongoing usage of lithium-ion cells and packs in both stationary and mobile applications necessitates comprehensive tools and models to diagnose and prognose cell performance onboard. One of the main tasks of a battery management system is to infer the state of charge (SOC) and state of health (SOH) of batteries. SOC estimation is usually done by relating a measured relaxation voltage to a stored SOC versus open circuit voltage (OCV) curve. The latter is dependent on the cell SOH and its degradation path. Therefore, both an accurate OCV and relaxation measurement are necessary for state estimation and it is essential to consider how they vary with temperature to ensure accuracy.This study aims at an exploration of the impact of temperature on relaxation and cell’s kinetics and how the voltage response at different temperatures can be simulated using the mechanistic modeling framework by tuning two parameters, the ohmic resistance increase and the rate degradation factor. The mechanistic modeling framework is using electrode half-cell data to synthesize the terminal voltage of battery cells based on the individual electrode potentials. Although proven valid in the quantification of degradation modes, path dependence of aging and cell-to-cell variations, the application of this modeling framework has been restricted so far mostly to voltage profiles recorded at room temperature and low rates. To be used in deployed battery management systems, this limitation should preferably be overcome to enable on-board diagnostics from medium to high rates and at any temperature.