To enhance the accuracy of a lithium battery model and realize accurate state estimation of the lithium battery, a fractional-order electric model of the lithium battery was built, and the model parameters were identified using adaptive genetic algorithm. Based on the fractional-order electric model, the unscented Kalman filter was adopted to estimate the lithium battery’s state-of-charge (SOC) and state-of-health (SOH). Stimulation results show that, the established fractional- order electric model of the lithium battery can more accurately describe its dynamic characteristics during charging and discharging, and the accuracy of the proposed state estimation strategy was higher than that the conventional control strategy.

Fractional-order  /  lithium battery  /  parameter identification  /  state-of-charge (SOC)  /  unscented Kalman