An SOP Estimation Method for Lithium-ion Batteries Based on AOA-UKF

To address the high estimation errors in lithium-ion battery State of Power (SOP) estimation caused by environmental temperature, the highly nonlinear characteristics of lithium-ion batteries, and changes in battery State of Charge (SOC), this paper proposes a method that combines the Arithmetic Optimization Algorithm (AOA) and Unscented Kalman Filter (UKF) for SOC estimation, with SOP estimation under triple constraints. First, to improve the accuracy of SOC estimation, the AOA is introduced to optimize key parameters such as the system noise covariance matrix and measurement noise covariance matrix, based on the UKF. Then, by utilizing the definitions of battery SOP, peak power, and peak current, experiments on peak power at different times are conducted. Finally, using a second-order RC equivalent circuit model, an accurate SOP estimation is achieved under UDDS conditions by applying a multi-state joint constraint method based on battery SOC, terminal voltage, and the battery’s own current limits. The results are compared with SOP estimation under single constraints, such as terminal voltage or SOC. The findings demonstrate the accuracy of the proposed method and further enhance its reliability.