A Novel Multi-Constraint Peak Power Prediction Method Combined with Online Model Parameter Identification and State-of-Charge Co-Estimation of Lithium-Ion Batteries

Abstract

The accuracy of the peak power is influenced by the accurate battery model, the results of the parameter identification, and the state of charge (SOC). First, to accurately predict the peak power of lithium-ion batteries, we propose an improved Thevenin model to describe the operating state of lithium-ion batteries by introducing model noise into the Thevenin model. Second, to achieve accurate online parameter identification, a forgetting factor recursive extended least squares (FFRELS) method is proposed to identify the parameters of the improved model. To optimize the effect of noise on SOC estimation, an improved adaptive extended Kalman filtering (AEKF) algorithm is proposed. Finally, to obtain higher accuracy of peak power estimation, a multi-constrained peak power prediction method based on state-recursive estimation is used in this paper. Experimental results show that the maximum error of the FFRELS algorithm under different working conditions is 34.35 mV, and the SOC estimation error of the improved AEKF algorithm is less than 0.53%. The improved multi-constraint peak power estimation algorithm has high estimation accuracy under two complex working conditions, and can accurately predict the power input and output capability of the battery.

High-Precision State of Charge Estimation of Urban-Road-Condition Lithium-Ion Batteries Based on Optimized High-Order Term Compensation-Adaptive Extended Kalman Filtering

“…It is necessary to improve the time variability of the forgetting factor. 41,42 The following recursive formula for adding the variable forgetting factor to the FFRLS is shown in 13 and 14.…”