A new method for identifying the parameters of the Li-ion battery PNGV model

Q3 Engineering IFAC-PapersOnLine Pub Date : 2024-01-01 DOI:10.1016/j.ifacol.2024.07.453

M. Lagraoui , A. Nejmi

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Abstract

The article delves into modeling the Li-ion cell and the prerequisites for identifying system parameters. Initially, we construct a model that encapsulates the nonlinearity of our cell, employing a PNGV model encompassing dynamic parameters like state of charge (SOC), open-circuit voltage OCV, C_b, R₀, R₁ and C₁. Subsequent to this, pertinent battery discharge tests are conducted to discern the model parameters.

Commencing with establishing the relationship between state of charge (SOC) and the variable open-circuit voltage (OCV) using the least squares method, we progressed. Following determining thise relationship, cell discharge tests were employed to ascertain the values of R₀, R₁ and C₁. Once the various parameters of the PNGV Model are identified, validation of this established battery model is performed via experimental discharge tests. The resulting error indicates a mean absolute error (MAE) of 0.0176V for modeling, alongside a root mean square error (RMSE) of 0.0232V. These outcomes underscore the remarkable accuracy of our cell model.

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确定锂离子电池 PNGV 模型参数的新方法

文章深入探讨了锂离子电池建模和确定系统参数的先决条件。首先，我们构建了一个包含电池非线性特性的模型，采用了一个 PNGV 模型，其中包含动态参数，如充电状态 (SOC)、开路电压 OCV、Cb、R0、R1 和 C1。首先，我们使用最小二乘法建立了充电状态（SOC）与可变开路电压（OCV）之间的关系。在确定这种关系后，我们进行了电池放电测试，以确定 R0、R1 和 C1 的值。一旦确定了 PNGV 模型的各种参数，就可以通过实验放电测试来验证所建立的电池模型。结果误差表明，建模的平均绝对误差 (MAE) 为 0.0176V，均方根误差 (RMSE) 为 0.0232V。这些结果表明我们的电池模型非常准确。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IFAC-PapersOnLine Engineering-Control and Systems Engineering

CiteScore

1.70

自引率

0.00%

发文量

1122

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