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Increasing energy demand throughout the world produces great environmental issues, therefore, renewable and clean energy sources, such as tidal energy, wind energy, solar energy and geothermal energy, are desirable request. Meanwhile, developing a new critical energy storage technology to balance the instantaneous energy supply and demand of arranged electric energy is urgent. Rechargeable flow batteries are solutions for storing electricity in form of chemical energy, containing positive and negative electrodes reserved in two separate containers, which have the advantages of low self—discharge and independent scaling of power, therefore considered as promising energy storage technologies. Ionic liquids (ILs) have been widely studied and used in energy storage devices, such as lithium ion battery, for their unique prospective properties. Herein, the key role of ILs and their applications in supporting electrolytes, separators and additives in flow batteries are highlighted in this review. The approaches and challenges in developing ILs supported flow batteries are discussed, and a significative overview of the opportunities of ILs promote flow batteries are finally provided, which is expected to help achieving further improvements in flow batteries. [ABSTRACT FROM AUTHOR]

Among various fault diagnosis methods, deep learning has shown state-of-the-art performance in processing mechanical big data. This paper investigates a reliable deep learning method known as autoencoder, which is most suitable for automatic feature extraction of fault signals. However, traditional autoencoders have two deficiencies: (1) the multi-layer structure of autoencoder has an internal covariate shift problem, which will cause great difficulty for the network training. (2) The application of autoencoder in the case of rotating speed fluctuation is not mature. To overcome the aforementioned deficiencies, batch normalization strategy is employed in every layer of the autoencoder network to obtain a steady distribution of activation values during training. It can regularize the network without parameter adjustment, and deal with the speed fluctuation problem perfectly. So, a new network named batch-normalized autoencoder is first proposed for intelligent fault diagnosis. The raw vibration signals are directly fed into the network and the extracted features are employed to train a softmax classifier for health state identification. A bearing and a gearbox data set are finally used to confirm the effectiveness of the proposed method. The results manifest that the proposed method can extract salient features from the raw signals and handle the fault diagnosis problem under the speed fluctuation problem. [ABSTRACT FROM AUTHOR]

Intelligent fault diagnosis methods are promising in dealing with mechanical big data owing to their efficiency in extracting representative features. However, there is always an undesirable shift variant property embedded in raw vibration signals, which hinders the direct use of raw signals in fault diagnosis networks. A convolutional neural network (CNN) is a widely used and efficient method to extract features in various fields for its excellent sparse connectivity, equivalent representation and weight sharing properties. However, raw CNN is time-consuming and has a marginal problem. Heuristically, we construct a fault diagnosis framework called adaptive overlapping CNN (AOCNN) to deal with one dimension (1D) raw vibration signals directly. The shift variant problem is dealt with by the adaptive convolutional layer and the root-mean-square (RMS) pooling layer, and the marginal problem embedded in the CNN is relieved by employing the overlapping layer. Meanwhile, the AOCNN is also characterized by adopting different convolutional strides and diverse activation functions in feature extraction network training and usage. Furthermore, sparse filtering is embedded into the AOCNN, and experiments on a bearing dataset and a gearbox dataset are conducted to verify the validity of the proposed method separately. When compared with other state-of-the-art methods this method reveals its superiority. [ABSTRACT FROM AUTHOR]