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

2013 ◽  
Vol 448-453 ◽  
pp. 2872-2878
Author(s):  
Hong Fei Cao ◽  
Xin Jian Zhu ◽  
Hai Feng Shen ◽  
Meng Shao
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Average Power ◽  
Classification Problem ◽  
Vanadium Redox Flow Battery ◽  
Redox Flow Battery ◽  
Flow Battery ◽  
Capacity Loss ◽  
Vanadium Redox

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.

A Neural Network Based Method for Real-Time Measurement of Capacity and SOC of Vanadium Redox Flow Battery

2015 ◽  
Author(s):  
Hongfei Cao ◽  
Xinjian Zhu ◽  
Haifeng Shen ◽  
Meng Shao
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Probabilistic Neural Network ◽  
Average Power ◽  
Vanadium Redox Flow Battery ◽  
Redox Flow Battery ◽  
Flow Battery ◽  
Time Operation ◽  
Remaining Capacity ◽  
Vanadium Redox

The state of charge (SOC) of Vanadium Redox Flow Battery (VRFB) plays an important role in the operation and control of the Battery system. The value of SOC can be defined as the ratio of the remaining capacity to the rated capacity of the battery. Current measurement of SOC of VRFB is limited to one certain charge-discharge circulation so the rated capacity is known and can be regarded as a constant. However, during long time cycling, the capacity of VRFB will reduce gradually to a relatively low level so that the capacity of the battery cannot be seen as the constant value of rated capacity, which makes it difficult to measure the SOC accurately in real-time operation. This work presents a neural network based method of measuring the capacity and SOC for VRFB in real time. The capacity is firstly classified into three levels in terms of the loss degree by a Probabilistic Neural Network (PNN) using the values of the voltage per second and the average power of the cell stack in any period of the circulation. The values of capacity which fall within different levels are then given by different Back Propagation Neural Networks (BPNN) trained by the battery operation values in corresponding level. Finally, the SOC can be obtained by the calculated capacity. All the networks are validated by experimental data and the results indicate that the method is suitable for the measurement of VRFB capacity and SOC in the practical application.

scholarly journals Novel Approaches for Solving the Capacity Fade Problem during Operation of a Vanadium Redox Flow Battery

Batteries ◽  
2018 ◽  
Vol 4 (4) ◽  
pp. 48 ◽  
Author(s):  
Arjun Bhattarai ◽  
Purna Ghimire ◽  
Adam Whitehead ◽  
Rüdiger Schweiss ◽  
Günther Scherer ◽  
...  
Keyword(s):  
Cation Exchange ◽  
Large Scale ◽  
Vanadium Redox Flow Battery ◽  
Electrolyte Imbalance ◽  
Capacity Fade ◽  
Redox Flow Battery ◽  
Flow Battery ◽  
Safety Issues ◽  
Capacity Loss ◽  
Vanadium Redox

The vanadium redox flow battery (VRFB) is one of the most mature and commercially available electrochemical technologies for large-scale energy storage applications. The VRFB has unique advantages, such as separation of power and energy capacity, long lifetime (>20 years), stable performance under deep discharge cycling, few safety issues and easy recyclability. Despite these benefits, practical VRFB operation suffers from electrolyte imbalance, which is primarily due to the transfer of water and vanadium ions through the ion-exchange membranes. This can cause a cumulative capacity loss if the electrolytes are not rebalanced. In commercial systems, periodic complete or partial remixing of electrolyte is performed using a by-pass line. However, frequent mixing impacts the usable energy and requires extra hardware. To address this problem, research has focused on developing new membranes with higher selectivity and minimal crossover. In contrast, this study presents two alternative concepts to minimize capacity fade that would be of great practical benefit and are easy to implement: (1) introducing a hydraulic shunt between the electrolyte tanks and (2) having stacks containing both anion and cation exchange membranes. It will be shown that the hydraulic shunt is effective in passively resolving the continuous capacity loss without detrimentally influencing the energy efficiency. Similarly, the combination of anion and cation exchange membranes reduced the net electrolyte flux, reducing capacity loss. Both approaches work efficiently and passively to reduce capacity fade during operation of a flow battery system.

scholarly journals Neural Network Predictive Control for Vanadium Redox Flow Battery

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Hai-Feng Shen ◽  
Xin-Jian Zhu ◽  
Meng Shao ◽  
Hong-fei Cao
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Vanadium Redox Flow Battery ◽  
Redox Flow Battery ◽  
Flow Battery ◽  
Rbf Network ◽  
Gradient Descent Algorithm ◽  
Predictive Controller ◽  
Neural Network Predictive Control ◽  
Vanadium Redox

The vanadium redox flow battery (VRB) is a nonlinear system with unknown dynamics and disturbances. The flowrate of the electrolyte is an important control mechanism in the operation of a VRB system. Too low or too high flowrate is unfavorable for the safety and performance of VRB. This paper presents a neural network predictive control scheme to enhance the overall performance of the battery. A radial basis function (RBF) network is employed to approximate the dynamics of the VRB system. The genetic algorithm (GA) is used to obtain the optimum initial values of the RBF network parameters. The gradient descent algorithm is used to optimize the objective function of the predictive controller. Compared with the constant flowrate, the simulation results show that the flowrate optimized by neural network predictive controller can increase the power delivered by the battery during the discharge and decrease the power consumed during the charge.

Real-time monitoring of capacity loss for vanadium redox flow battery

2018 ◽  
Vol 390 ◽  
pp. 261-269 ◽  
Author(s):  
Zhongbao Wei ◽  
Arjun Bhattarai ◽  
Changfu Zou ◽  
Shujuan Meng ◽  
Tuti Mariana Lim ◽  
...  
Keyword(s):  
Real Time ◽  
Vanadium Redox Flow Battery ◽  
Redox Flow Battery ◽  
Real Time Monitoring ◽  
Flow Battery ◽  
Capacity Loss ◽  
Vanadium Redox
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