An adaptive fractional-order unscented Kalman filter for Li-ion batteries in the energy storage system.

Accurate estimation of the state of charge (SOC) can prolong the working life and enhance the safety of energy storage system. Considering the influence of noise and parameter changes in the operating environment, an adaptive fractional-order unscented Kalman filter algorithm is introduced to strengthen the accuracy of SOC estimation. To verify the effectiveness and robustness of the algorithm, the simulation is carried out under UDDS complex conditions. The experimental results indicate that the proposed algorithm has the highest SOC precision among the four algorithms, and the RMSE is 1.37%, indicating the superiority of the fractional-order modeling and the joint estimation algorithm. The online identification of full parameters can solve the shortcoming of the long time to obtain the open-circuit voltage in the experiment, and the adaptive filtering algorithm can overcome the problem of filtering divergence and improve the flexibility of SOC estimation