利用人工神经网络预测钒氧化还原液流电池的电压和过电位

IF 4.6 4区 化学 Q2 ELECTROCHEMISTRY
Joseba Martínez-López, Koldo Portal-Porras, U. Fernández-Gámiz, Eduardo Sánchez-Díez, Javier Olarte, Isak Jonsson
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引用次数: 0

摘要

本文探讨了训练有素的人工神经网络(ANN)在钒氧化还原液流电池性能预测中的新应用,并将其性能与二维数值模型进行了比较。目的是评估两个人工神经网络的能力,一个用于预测电池电位,另一个用于预测各种工作条件下的过电位。之前用实验数据验证过的二维模型被用来生成数据,以训练和测试 ANN。结果表明,在充电和放电模式下,第一个方差网络能精确预测不同充电状态和电流密度条件下的电池电压。负责过电位计算的第二个方差网络能准确预测整个细胞畴的过电位,而在电极膜和细胞畴边界等高梯度区域的置信度最低。此外,计算时间也大大缩短,使人工神经网络成为快速理解和优化 VRFB 的合适选择。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Voltage and Overpotential Prediction of Vanadium Redox Flow Batteries with Artificial Neural Networks
This article explores the novel application of a trained artificial neural network (ANN) in the prediction of vanadium redox flow battery behaviour and compares its performance with that of a two-dimensional numerical model. The aim is to evaluate the capability of two ANNs, one for predicting the cell potential and one for the overpotential under various operating conditions. The two-dimensional model, previously validated with experimental data, was used to generate data to train and test the ANNs. The results show that the first ANN precisely predicts the cell voltage under different states of charge and current density conditions in both the charge and discharge modes. The second ANN, which is responsible for the overpotential calculation, can accurately predict the overpotential across the cell domains, with the lowest confidence near high-gradient areas such as the electrode membrane and domain boundaries. Furthermore, the computational time is substantially reduced, making ANNs a suitable option for the fast understanding and optimisation of VRFBs.
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来源期刊
Batteries
Batteries Energy-Energy Engineering and Power Technology
CiteScore
4.00
自引率
15.00%
发文量
217
审稿时长
7 weeks
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