A Novel Fading Memory Square Root UKF Algorithm for the High-precision State of Charge Estimation of High-power Lithium-ion Batteries

Abstract

The state-of-charge (SOC) is used to characterize the remaining capacity of power lithium-ion battery. Using the simplicity of Thevenin equivalent circuit model, a bidirectional online improvement model that distinguishes the charging and discharging process is proposed to characterize the state of lithiumion batteries, and the on-line parameter identification of the model is carried out by using the least square method of evolving memory. A novel square root unscented Kalman iterative algorithm based on the dynamic state-of -charge of the lithium battery is designed, and the SOC estimation effect of the combined dynamic estimation algorithm and the unscented Kalman algorithm (UKF) is compared. The dynamic stress test mode (DST) experiment was carried out on the ternary lithium-ion battery at 25℃. The simulation results show that the average error of the lithium-ion battery SOC estimation of the dynamic joint estimation algorithm and the unscented Kalman algorithm are 1.23% and 2.11% respectively. The experimental results show that the joint algorithm based on the square root unscented Kalman algorithm and the fading memory method has better tracking effect, and has higher SOC estimation accuracy and stability.

Novel co‐estimation strategy based on forgetting factor dual particle filter algorithm for the state of charge and state of health of the lithium‐ion battery

“…To facilitate the expansion of subsequent algorithms, the zero-state response of the observation equation above is firstly established, and then the s-domain equation is established by taking advantage of the Laplace transform. As shown in Equation (2).…”

“…With the continuous development of new energy vehicles, large-scale energy storage, special robots, and aerospace equipment, 1 lithium (Li)-ion batteries have become the most widely used energy storage mode at present. 2 However, the online monitoring and safety management of Li-ion batteries still face many challenges, 3 such as low robustness of state parameter reflection index and low accuracy of cycle life prediction algorithm. 4 Therefore, the state assessment of Li-ion batteries has become a hot topic and a significant research direction in the field of new energy.…”