A novel one dimensional convolutional neural network based data-driven vanadium redox flow battery modelling algorithm

Ran Li

doi:10.1016/j.est.2023.106767

A novel one dimensional convolutional neural network based data-driven vanadium redox flow battery modelling algorithm

2023-05-01

10

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AI

DOI：10.1016/j.est.2023.106767

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En 中文

power ;nore energy ]. For possess high good maintenance cost cost-effectiveness battery, applications with and states are electrochemical variables instead of electrical variables. Moreover, the usage of such models re. uires some electrochemical knowledge, which is an obstacle for electrical engineers. In recent years, VRB modelling for power system studies has been explored. For example, in 2015, a comprehensive electrical equivalent circuit model for system-level analysis was proposed [6]. The topology of proposed model is much simpler than the traditional electrochemical model. However, the prerequisite of accurate modelling is the knowledge of many interna] VRB parameters, such as battery cell dimensions, thermal hydraulic design, and effect of shunt current, which can be easily affected by manufacturing techniques and are subject to change in different operating environments. In 2016, an online model identiiica-tion method was to address the issue

Keyword:

vanadium redox flow battery

Modelling

One dimensional conventional neural network

Data-driven

A novel one dimensional convolutional neural network based data-driven vanadium redox flow battery modelling algorithm

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