Sliding Mode Observer based Co-Estimation of the Voltage and Temperature of Lithium-ion Batteries using an Electro-Thermal Model

Abstract

In the present era of transportation electrification, an integrated framework is developed in this paper for real-time estimation of the voltage and temperature in lithium-ion batteries (LIBs) based on a Sliding Mode Observer (SMO) approach. The study is based on a first-order electrical equivalent circuit model (1-RC model), augmented with thermal dynamics. The battery’s open circuit voltage (OCV) is modeled as a polynomial function of the state-of-charge (SOC), while the transient behavior is captured through a parallel RC network. For voltage estimation, the SMO is applied to accurately estimate the internal RC voltage drop, enabling the reconstruction of terminal voltage under dynamic current load profiles. In parallel, a thermal model is implemented to account for heat generation and dissipation, with a secondary SMO applied to enhance temperature estimation accuracy. Experimental data from Hybrid Pulse Power Characterization (HPPC) tests are utilized for validation and the performance is assessed by comparing measured and estimated values. The results demonstrate strong agreement between simulated and measured data, with reduced error margins—voltage and temperature Root Mean Squared Percentage Errors (RMSPEs) of 0.23% and 0.13% — confirming the accuracy, stability, and adaptability of the proposed SMO-based framework for advanced battery monitoring and state estimation applications.