In the cause of working out the challenge of remaining life prediction (RUL) of proton exchange membrane fuel cell (PEMFC) under dynamic operating conditions, this paper proposes a PEMFC RUL forecast technique based-on sparse autoencoder (SAE) and deep neural network (DNN). The method extracts the data set from the original experimental data at intervals periods of one hour to realize datum reconstruction. The Gaussian weighted moving average filter is used to smooth noisy data (voltage and current). The smoothed filtered power output signal of the stack is extracted as an aging indicator. The SAE is used to extract the prediction features automatically, and the DNN is applied to realize the RUL prediction. The proposed method is experimentally verified using 127369 experimental data. The effectiveness of the novel method is verified by three different training sets and test set configurations. The experimental results reveal that the novel approach has the best prediction effectiveness when the training set length is set to 500 hours. At this point, the prediction accuracy can reach 99.68%. The mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) are minimum values, which are 0.2035, 0.1121, and 0.3348, respectively. The superiority and effectiveness of the proposed approach are further validated by comparison with K-nearest neighbor and support vector regression machine. The proposed approach can be appropriate for the prediction of the RUL of PEMFC under dynamic conditions.

For solving the problem of sequence failure diagnosis of proton exchange membrane fuel cell (PEMFC) water management subsystem, this paper proposes a PEMFC failure diagnosis method of time series based on bidirectional long short-term memory network (BiLSTM) and t-distributed stochastic neighbour embedding (t-SNE). This approach adopts the normalization strategy to eliminate the influence caused by dimensional differences of different parameters. t-SNE is presented to decrease the dimensionality of normalized data to the estimate of intrinsic dimensionality to extract key characteristic variables. The width of the diagnostic window is set to transform the original single moment diagnosis problem into the fault diagnosis problem of multi-variable time series, which is more consistent with the time scale and physical evolution law of PEMFC water management fault generation. The 672 sets of training sets and 448 sets of test sets are learned and tested by BiLSTM. The experimental results show that the BiLSTM-tSNE method can realize the sequence fault diagnosis of the PEMFC water management subsystem with 96.88% diagnostic accuracy and 24 seconds of operation time. Compared with the conventional approach of multi-class support vector machine algorithm, the training accuracy and testing accuracy of the proposed method is improved by 15% and 16.88% respectively. The operation time of the presented approach is only about 1/28 of multi-class support vector machine algorithm.

The direct-quadrature (DQ) model, mirror-frequency impedance (MFI) model, and harmonic state-space (HSS) model are three main modeling methods for single-phase power electronic converter systems. These models are established under three different frameworks: the dq-frame, the mirror-frequency frame, and the extended harmonic domain frame. The purpose of this article is to demonstrate how to create a unified modeling framework and to provide an unambiguous relationship between the impedances in these three domains. The unified model framework facilitates flexible and scalable modeling by making different models to be easily interconnected with other systems. In addition, to simplify the stability analysis and impedance measurement process, the three modeling methods are reduced to first-order single-input and single-output (SISO) equivalent models, respectively, based on the model reduction technique. Finally, the impedance measurement and stability analysis results of electric multiple units (EMUs) accessed vehicle-grid system demonstrate the accuracy of the conversion and SISO equivalence technique.