Data-based fractional differential models for non-linear dynamic modeling of a lithium-ion battery

被引:37
作者
Jiang, Yunfeng [1 ]
Xia, Bing [2 ,3 ]
Zhao, Xin [1 ]
Truong Nguyen [3 ]
Mi, Chris [2 ]
de Callafon, Raymond A. [1 ]
机构
[1] Univ Calif San Diego, Dept Mech & Aerosp Engn, 9500 Gilman Dr, La Jolla, CA 92093 USA
[2] San Diego State Univ, Dept Elect & Comp Engn, 5500 Campanile Dr, San Diego, CA 92182 USA
[3] Univ Calif San Diego, Dept Elect & Comp Engn, 9500 Gilman Dr, La Jolla, CA 92093 USA
关键词
Battery management system (BMS); Fractional differential model (FDM); System identification; Least squares-based state-variable filter (LSSVF) method; Instrumental variable-based state-variable filter (IVSVF) method; INSTRUMENTAL VARIABLE METHODS; EQUIVALENT-CIRCUIT MODELS; BOX-JENKINS-MODELS; CHARGE ESTIMATION; PARAMETER-ESTIMATION; STATE; IDENTIFICATION; VEHICLES; UNCERTAINTY; PERFORMANCE;
D O I
10.1016/j.energy.2017.06.109
中图分类号
O414.1 [热力学];
学科分类号
摘要
This paper presents a battery model with non-integer order derivatives for modeling the dynamics of a lithium-ion battery over a large operating range. The non-integer or fractional differential model includes a constant phase element term to approximate the non-linear dynamical behavior of the battery. The proposed fractional differential model is an amalgamation of electrochemical impedance spectroscopy experimental data and standard 1-resistor-capacitor electrical circuit model. The standard least squares based state-variable filter identification method used for continuous-time system identification is used to estimate the model parameters and the fractional derivative coefficients of the proposed fractional differential model. For application of modeling fractional differential order battery dynamics, the continuous-time least squares-based state-variable filter parameter estimation approach is extended to an instrumental variable method to be robust to (non-white) noise perturbed output measurement. The model accuracy and model performance are validated on experimental data obtained from a lithium-ion battery and confirm that the proposed fractional differential model is able to accurately capture the battery dynamics over broad operating range. In comparison, the fractional differential model shows significant improvement on data prediction accuracy compared to a conventional integer model, making the fractional differential model suitable for monitoring battery dynamical behavior in a battery management system. (C) 2017 Elsevier Ltd. All rights reserved.
引用
收藏
页码:171 / 181
页数:11
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