Classification of Capacity Loss Degree of Vanadium Redox Flow Battery Based on Probabilistic Neural Network

• Published in: Applied Mechanics and Materials Vol. 448-453; pp. 2872 - 2878
• Main Authors: Zhu, Xin Jian, Shao, Meng, Shen, Hai Feng, Cao, Hong Fei
• Format: Journal Article
• Language: English
• Published: Zurich Trans Tech Publications Ltd 01.01.2014
• Subjects: Classification; Electrodes; Electrolytes; Energy storage; Neural networks; Rechargeable batteries; Probabilistic Neural Network; Vanadium Redox Flow Battery; Capacity Loss; Classification
• ISBN: 3037859121; 9783037859124
• ISSN: 1660-9336; 1662-7482; 1662-7482
• DOI: 10.4028/www.scientific.net/AMM.448-453.2872

During long-time charge-discharge cycling, the capacity of the vanadium redox flow battery (VRFB) will reduce gradually. To recognize the capacity loss condition in the time of the operation process, a method for classification of the capacity loss degree based on Probabilistic Neural Network (PNN) is presented. The network inputs are the value of the voltage per second and the average power of the cell stack in any two minutes of the circulation. The network will give out three classes in form of three numbers to classify the capacity loss degree into different levels. The network is trained and validated by experimental data and the results show that the network is suitable for the classification problem of VRFB capacity loss and the method is useful to determine whether the capacity is sufficient and when to restore the cell capacity in real time.