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

被引:4
作者
Wang, Guangqian [1 ]
Ding, Jiling [2 ]
Wang, Dongqing [1 ]
机构
[1] Qingdao Univ, Coll Elect Engn, Qingdao 266071, Peoples R China
[2] Jining Univ, Coll Math & Comp Applicat Technol, Qufu 273155, Peoples R China
基金
中国国家自然科学基金;
关键词
OF-CHARGE ESTIMATION; PARAMETER-ESTIMATION; SYSTEMS; STATE;
D O I
10.1149/1945-7111/acd354
中图分类号
O646 [电化学、电解、磁化学];
学科分类号
081704 ;
摘要
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 relation 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. Firstly, 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 (UDDS) profile and the Los Angeles 92 (LA92) 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.
引用
收藏
页数:8
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