Robust adaptive sliding mode observer for core temperature and state of charge monitoring of Li-ion battery: A simulation study

Abstract

State of charge (SOC) and temperature monitoring of Li-ion batteries (LIBs) via battery management system (BMS) is crucial to ensure proper and secure performance of LIBs. Monitoring methods based on electrochemical models are highly accurate, which stems from their phenomenological nature; however, such multi-dimensional and rigorous models are computationally time-consuming and elaborate to implement. Consequently, a relatively accurate but more straight-forward estimation method with fast computational capabilities is needed in real-life BMS applications. In this work, an equivalent circuit model (ECM) together with a lumped thermal model has been considered as design model for an online adaptive sliding mode observer. This observer uses measurement of battery terminal voltage and surface temperature in order to estimate SOC and core temperature, both of which play decisive role in BMS applications. The proposed observer performance has been validated in constant current and urban dynamometer driving schedule (UDDS) load profile experiments, where inspection of results overwhelmingly indicates its utmost stability as well as robust performance in comparison with commonly used observers.