A Hierarchical Identification Method for Lithium-Ion Battery SOC Based on the Hammerstein Model

Abstract

The two-input one-output Hammerstein model consists of two parallel nonlinear static blocks followed by a linear dynamic part. By using Hammerstein structure to map the relationship between a battery state of charge (SOC) and its terminal voltage/current, a hierarchical stochastic gradient algorithm is studied to estimate parameters of Hammerstein SOC model, so as to predict battery SOC. First, the Hammerstein model is transformed into a bilinear parameter system with the least number of required parameters. Then, a hierarchical stochastic gradient algorithm with a forgetting factor is used to update the two sets of model parameters of the bilinear parameter system, so as to realize SOC estimation. Furthermore, the experiment platform of lithium-ion battery was built and the data of the urban dynamometer driving schedule profile and the Los Angeles 92 profile were collected. Finally, the MATLAB simulation results show that the proposed parameter optimized method based Hammerstein model has the advantages of fast convergence speed and high SOC estimation accuracy.

Hammerstein Model Based Battery SOC Estimation Considering Temperature Variation

“…37,40,41 Recent studies on lithium-ion battery Hammerstein models include Liu's proposal of a SOC estimation model based on a key variable separation principle, 42 and Wang's suggestion of a SOC estimation model based on a recursive identification method. 43 However, these studies did not consider temperature factors.…”