A Novel Approach for Accurate SOC Estimation of Lithium-Ion Electric Vehicle Batteries Using a (Q, S, R)-$γ$-Based...

Area of Science:

• Electrical Engineering
• Control Systems
• Battery Technology

Background:

• Accurate state-of-charge (SOC) estimation is crucial for electric vehicle (EV) battery management systems (BMSs).
• Model uncertainties and measurement noise hinder precise SOC estimation in lithium-ion batteries (LIBs).

Purpose of the Study:

• To develop a novel dissipativity-based observer for robust and stable SOC estimation in LIBs.
• To enhance the safety, performance, and lifespan of EV batteries through improved estimation accuracy.

Main Methods:

• A (Q, S, R)-γ-dissipativity theory framework is employed for observer design.
• Linear matrix inequality (LMI) and Lyapunov-Krasovskii functional (LKF) approaches are integrated.
• A one-resistor capacitor (1-RC) equivalent circuit model (ECM) is used for battery modeling and validated experimentally.

Main Results:

• The proposed observer demonstrates superior accuracy and robustness under uncertain and noisy conditions.
• Achieved root-mean-square errors (RMSEs) as low as 0.48% across four drive cycles (UDDS, US06, LA92, HWFET).
• Significant RMSE reductions of up to 88.93% compared to the adaptive unscented Kalman filter (AUKF), reaching a maximum accuracy of 99.23%.

Conclusions:

• The dissipativity-based observer provides a highly accurate and reliable method for SOC estimation in EVs.
• This advancement contributes to safer and more efficient operation of lithium-ion batteries in electric vehicles.
• The proposed method surpasses current state-of-the-art accuracy for SOC estimation.