This paper presents a practical human-computer interaction system for wheelchair motion through eye tracking and eye blink detection. In this system, the pupil in the eye image has been extracted after binarization, and the center of the pupil was localized to capture the trajectory of eye movement and determine the direction of eye gaze. Meanwhile, convolutional neural networks for feature extraction and classification of open-eye and closed-eye images have been built, and machine learning was performed by extracting features from multiple individual images of open-eye and closed-eye states for input to the system. As an application of this human-computer interaction control system, experimental validation was carried out on a modified wheelchair and the proposed method proved to be effective and reliable based on the experimental results.

All-vanadium flow batteries (VRFBs) are used in the field of energy storage due to their long service life and high safety. In order to further improve the charge-discharge performance of VRFB, this study mainly used the comparative evaluation of VRFB’s carbon fiber electrode compression ratio and electrolyte flow rate. The battery is charged and discharged under different current densities, different compression ratios, and different flow rates. The results show that increasing the compression ratio at different current densities can reduce the internal resistance of the battery, but an excessive compression ratio will accelerate the transfer of vanadium ions, increase the deviation of the electrolyte, and reduce the Coulombic efficiency of the battery. The performance of the battery tends to be balanced when the compression ratio is 30%. At the same time, in the case of the same compression ratio, increasing the flow rate of the electrolyte can reduce the internal reaction resistance of the battery. When the flow reaches a certain value, the influence on the internal resistance will be smaller.

The Carbon fiber electrode is a cathode material in all-vanadium flow battery. In order to further reduce the volume of the all-vanadium power storage system, further reduce the internal resistance of the carbon fiber electrode, increase the current density of the electrode, and achieve high electrical conductivity and large electrostatic capacitance are essential. Among them, the graphitization of the positive electrode material and the improvement of the specific surface area of the electrode surface also greatly affect the performance of the all-vanadium redox flow battery. Therefore, in this paper, carbon nanotubes (CNTs) with small diameter and large specific surface area are thermal plated on the surface of conventional carbon fibers, and the specific resistance can be reduced to almost half by increasing the specific surface area of the carbon fibers. The charge and discharge experiments of the all-vanadium flow battery prove that this method is very effective to improve the performance of the all-vanadium flow battery.