Parameter identification for electrochemical models of lithium-ion batteries using sensitivity analysis

被引：0

作者：

Dangwal, Chitra ^{[1
]}

Canova, Marcello ^{[1
]}

机构：

[1] Center for Automotive Research, The Ohio State University, Columbus, 43212, OH

来源：

ASME Letters in Dynamic Systems and Control | 2021年 / 1卷 / 04期

关键词：

Dynamics and control; Energy storage; Identification; Modeling; Optimization algorithms;

D O I：

10.1115/1.4050794

中图分类号：

学科分类号：

摘要：

Predicting the chemical and physical processes occurring in Lithium-ion cells with highfidelity electrochemical models is today a critical requirement to accelerate the design and optimization of battery packs for automotive and aerospace applications. One of the common issues associated with electrochemical models is the complexity of parameter identification, particularly when relying only on experimental data obtained via non-invasive techniques. This paper presents a novel approach to improve the common methods of parameter calibration that consists of matching the predicted terminal voltage to test data via optimization methods. The study is conducted for an nickel-manganese-cobalt (NMC)-graphite cell, modeled using a reduced-order Extended Single Particle Model (ESPM). The proposed approach relies on using a large-scale particle swarm optimization (PSO), modified by including a term that accounts for the parameter sensitivity information, such that the rate of convergence and robustness of the algorithm to obtain a consistent solution in the presence of uncertainties in the initial conditions are significantly improved. Copyright © 2021 by ASME.

引用

共 30 条

[1]

Doyle M., Fuller T. F., Newman J., Modeling of Galvanostatic Charge and Discharge of the Lithium/Polymer/Insertion Cell, J. Electrochem. Soc, 140, 6, pp. 1526-1533, (1993)

[2]

Bizeray A. M., Kim J.H., Duncan S.R., Howey D.A., Identifiability and Parameter Estimation of the Single Particle Lithium-Ion Battery Model, IEEE Trans Control Syst. Technol, 27, 6, pp. 1862-1877, (2018)

[3]

Chen C.-H., Planella F. B., O'regan K., Gastol D., Widanage W. D., Kendrick E., Development of Experimental Techniques for Parameterization of Multi-scale Lithium-Ion Battery Models, J. Electrochem. Soc, 167, 8, (2020)

[4]

Park S., Kato D., Gima Z., Klein R., Moura S., Optimal Experimental Design for Parameterization of an Electrochemical Lithium-Ion Battery Model, J. Electrochem. Soc, 165, 7, pp. A1309-A1323, (2018)

[5]

Forman J. C., Moura S. J., Stein J. L., Fathy H. K., Genetic Identification and Fisher Identifiability Analysis of the Doyle-Fuller-Newman Model From Experimental Cycling of a LiFePO4 Cell, J. Power Sources, 210, pp. 263-275, (2012)

[6]

Habte B. T., Jiang F., Effect of Microstructure Morphology on Li-Ion Battery Graphite Anode Performance: Electrochemical Impedance Spectroscopy Modeling and Analysis, Solid State Ionics, 314, pp. 81-91, (2018)

[7]

Tsai P.-C., Wen B., Wolfman M., Choe M.-J., Pan M. S., Su L., Thornton K., Cabana J., Chiang Y.-M., Single-Particle Measurements of Electrochemical Kinetics in NMC and NCA Cathodes for Li-Ion Batteries, Energy Environ. Sci, 11, 4, pp. 860-871, (2018)

[8]

Jin N., Danilov D. L., Van den Hof P. M. J., Donkers M. C. F., Parameter Estimation of an Electrochemistry-Based Lithium-Ion Battery Model Using a Two-Step Procedure and a Parameter Sensitivity Analysis, Int. J. Energy Res, 42, 7, pp. 2417-2430, (2018)

[9]

Schmidt A. P., Bitzer M., Imre A. W., Guzzella L., Experiment- Driven Electrochemical Modeling and Systematic Parameterization for a Lithium-Ion Battery Cell, J. Power Sources, 195, 15, pp. 5071-5080, (2010)

[10]

Zhang L., Lyu C., Hinds G., Wang L., Luo W., Zheng J., Ma K., Parameter Sensitivity Analysis of Cylindrical LiFePO4 Battery Performance Using Multi-Physics Modeling, J. Electrochem. Soc, 161, 5, pp. A762-A776, (2014)

← 1 2 3 →