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Language: English

Conflict of Interest: In relation to this article, we declare that there is no conflict of interest.

Publication history: Received October 29, 2021
Accepted January 27, 2022

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Sensitivity analysis for parameter classification of energy balance-integrated single particle model for battery cells

Changbeom Hong Se-Kyu Oh¹ Yeonsoo Kim^†

Department of Chemical Engineering, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 01897, Korea ¹Electrification Control Development Team 1, Hyundai Motor Company, Hwaesong 18280, Korea

Korean Journal of Chemical Engineering, June 2022, 39(6), 1396-1411(16), 10.1007/s11814-022-1081-8

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Abstract

With the increasing use of electric vehicles (EVs), there is a growing interest in the thermal management of EVs. In this study, we first reduced the computational complexity of single particle model (SPM) for the battery cell by introducing a 4th order approximation for Li-ion concentration in the solid phase. In addition, by integrating it with an energy balance, the constructed model can calculate the battery temperature along with the terminal voltage and state of charge. To develop a model compatible with the experimental data requires parameter estimation. However, the estimation accuracy for each parameter depends on its sensitivity. We investigated the influence of 16 parameters on the measured data under general experimental conditions (constant C-rate discharge) through simulations and sensitivity analysis. We classified the radius of the particle, total active surface areas, electrode maximum concentration, and a heat transfer coefficient as dominant parameters. When dominant parameters were estimated using the virtual experimental data, the percent error was smaller than 3.1%. For the parameters with minor influence, the estimation error was large even with the excellent agreement of the experimental data. We confirmed which parameter could be estimated using the C-rate experimental data accurately and which parameter should be estimated with additional experiments.

Keywords

Single Particle Model Battery Temperature Sensitivity Analysis Genetic Algorithm Parameter Estimation

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