Lithium-ion battery state of charge estimation based on a robust H_∞ filter

The state of charge (SOC) estimation is one of the core functions of the battery management system; it can play a significant role in the life cycle of electric vehicles. The SOC estimation method has attracted considerable research attention in recent years, particularly about improving estimation accuracy. However, most studies are limited by only focusing on known or fixed battery model parameters and not considering their temperature dependence. This indicates a need to explore how the lithium-ion battery temperature affects the model parameters, which leads to inaccurate SOC estimation. The principal objective of this study is to investigate the robust H_∞ filter-based method for the problem that temperature affects battery model parameters and thus leads to inaccurate SOC estimation. First, the second-order Thevenin equivalent circuit model with two parallel resistor–capacitor pairs is taken as the basic model of the lithium-ion battery. The influence of temperature on battery model parameters is modeled as an additive variable of the nominal resistance value and the total battery capacity, and the temperature change is considered an external disturbance of the system. Afterward, the sliding linear method is used to linearize this battery model; on this basis, a robust H_∞ filter for SOC estimation is designed using linear matrix inequality technology. Finally, the effectiveness of the proposed approach is verified using four different types of dynamic current load profiles (the BJDST-Beijing Dynamic Stress Test, FUDS-Federal Urban Driving Schedule, US06-US06 Highway Driving Schedule and BJDST-FUDS-US06 joint dynamic test) compared with the Kalman filter-based SOC estimation method. The simulation analysis results indicate that the proposed SOC estimation approach can realize a higher SOC estimation accuracy even if the model parameters vary with temperature, and it has good robustness to external disturbances.