Improved lumped electrical characteristic modeling and adaptive forgetting factor recursive least squares-linearized particle swarm optimization full-parameter identification strategy for lithium-ion batteries considering the hysteresis component effect

Abstract

Electric vehicles, as a new green mode of transportation, have put forward higher demand indicators for accurate modeling and efficient parameter identification strategies for lithium-ion batteries. In this paper, a lumped electrical characteristic model is constructed for lithium-ion batteries considering the hysteresis component effect based on a proposed adaptive forgetting factor recursive least squares-linearized particle swarm optimization (AFFRLS-LPSO) algorithm with strong working condition characterization capability for full parameter identification. First, to characterize the relationship accurately and intuitively between the external characteristics and the internal state quantities of the battery, the charging and discharging measurement information is captured, and the difference in the open circuit voltage (OCV) caused by the hysteresis phenomenon is resolved. Then, the complex working condition experiments is conducted, and through online inspection of experimental data, the fusion strategy concept is introduced to prevent the phenomenon of “filter saturation” and improve the ability of the algorithm to track the variable characteristic parameters of the battery. The full parameter identification results and terminal voltage tracking effects under different identification strategies are compared. Also, the consistency verification results of the adaptive parameter identification strategy under different working conditions are further analyzed. The experimental results show that the voltage tracking error of the model with an added hysteresis component is significantly smaller than the error without hysteresis. Furthermore, at an ambient temperature of 15 °C, the root mean squared error and mean absolute percentage error of the AFFRLS-LPSO algorithm are reduced by 0.0037 V and 0.300 %, respectively, under the dynamic stress test and Beijing bus dynamic stress test working conditions, and the consistency accuracy of the unconstrained parameter estimation is improved by 9.9 %. The fusion strategy provides a theoretical basis for real-time parameter identification models for lithium-ion batteries with high precision and adaptability for electric vehicles.