Robust Estimation for State-of-Charge and State-of-Health of Lithium-Ion Batteries Using Integral-Type Terminal Sliding-Mode Observers

Abstract

This paper investigates the real-time estimation on the state-of-charge (SoC) and state-of-health (SoH) of lithium-ion (Li-ion) batteries for the purpose of achieving reliable, safe, and efficient use of batteries. Three terminal sliding-mode observers (TSMOs) are designed; each observer is used to estimate one variable of a Li-ion cell for developing a real-time SoC estimation algorithm. To estimate the SoH, two additional TSMOs are subsequently presented. Finally, a set of complete estimation algorithms for SoC and SoH are formulated. The output injection signals of the proposed TSMOs are designed to be continuous. This can attenuate chattering that exists in the traditional sliding-mode observers and simplify the estimation algorithms. The main advantage of the proposed algorithms is eliminating the low-pass filter in the estimation algorithms. Therefore, higher estimation accuracy and faster response speed are obtained. The proposed methods are tested and evaluated using the acquired dynamic stress test and federal urban driving schedule test data, which demonstrate the effectiveness and feasibility.