Single-Particle Model of Li-ion Battery - Model Calibration and Validation against Real Data in an Electric Vehicular Application

被引:1
作者
Munteanu, Iulian [1 ]
Bratcu, Antoneta Iuliana [1 ]
Thivel, Pierre-Xavier [2 ]
Bultel, Yann [2 ]
Georges, Didier [1 ]
Decaux, Celine [2 ]
机构
[1] Univ Grenoble Alpes, CNRS, Grenoble INP, GIPSA Lab, F-38000 Grenoble, France
[2] Univ Grenoble Alpes, Inst Engn & Management, Grenoble, France
关键词
partial-differential-equation model; electrochemical model; Single Particle Model (SPM); model reduction; errors in variables identification; parameter identification;
D O I
10.1016/j.ifacol.2024.07.454
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper investigates the problem of calibration and validation of a battery electrochemical model as a mandatory step towards accurate estimation of battery important variables, like state of charge (SoC) and state of health (SoH). Here, the Single Particle Model (SPM) is considered, which mathematically describes the battery internal governing phenomena by means of parabolic partial differential equations (PDEs), but whose parameters are notoriously difficult to measure or estimate. After suitable approximation of this model through a linear finite-dimensional model, a systematic procedure of SPM calibration is here proposed and validated against real data issued from battery cycling in an electric vehicular application, i.e., under standard driving cycle scenarios. In a novel approach of SoC estimation, the suitably calibrated SPM, together with measures of voltage and current, allow to analytically connect the internal spatially distributed ions' concentrations to the equlibrium concentration, which, at its turn, is an image of battery SoC. Results suggest that SPM can reliably predict the battery internal ions' concentrations and be further used for SoC accurate estimation. Copyright (c) 2024 The Authors.
引用
收藏
页码:23 / 30
页数:8
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