Environmental issues, especially global warming due to the greenhouse effect, have become more and more critical in recent decades. As one potential candidate among different alternative “green energy” solutions for sustainable development, the proton exchange membrane fuel cell (PEMFC) has received extensive research attention for many years for energy and transportation applications. However, the relatively short lifespan of PEMFCs operating under non-steady-state conditions (for vehicles, for example) impedes its massive use. The accurate prediction of their aging mechanisms can thus help to design proper maintenance patterns of PEMFCs by providing foreseeable performance degradation information. In addition, the prediction could also help to avoid or mitigate the unwanted degradation of PEMFC systems during operation. In this paper, an advanced self-adaptive relevance vector machine (RVM) has been developed and demonstrated to predict the performance degradation of PEMFCs. In order to prove the versatility of proposed RVM method, the predictive results are experimentally validated using two different PEMFC stacks aging data under different operating patterns. Furthermore, the obtained results are compared with results from both classic support vector machine and original RVM methods in order to highlight the effectiveness of the proposed self-adaptive RVM method with a modified design matrix. A comparison between single-step-ahead and multiple-step-ahead predictions of the proposed method is also given and discussed. The results show that the proposed novel RVM method is powerful and effective for PEMFC degradation prediction.

In this paper, a novel degradation prediction model for proton-exchange-membrane fuel cell (PEMFC) performance is proposed based on a multiphysical aging model with particle filter (PF) and extrapolation approach. The proposed multiphysical aging model considers major internal physical aging phenomena of fuel cells, including fuel cell ohmic losses, reaction activity losses, and reactants mass transfer losses. Furthermore, in order to obtain accurate values of electrochemical activation losses under a variable load profile, a bisection solver is presented to solve the implicit Butler-Volmer equation. The proposed aging model is initialized at first by fitting the PEMFC polarization curve at the beginning of lifetime. During the prediction process, the aging dataset is then divided into two parts, learning and prediction phases. The PF framework is used to study the degradation characteristics and update the aging parameters during the learning phase. The suitable fitting curve functions are then selected to satisfy the degradation trends of trained aging parameters, and used to further extrapolate the future values of aging parameters in the prediction phase. By using these extrapolated aging parameters, the prediction results are thus obtained from the proposed aging model. Three experimental validations with different aging testing profiles have been performed. The results demonstrate the robustness and advantages of the proposed prediction method.

Proton exchange membrane fuel cell (PEMFC) degradation prediction is essential especially in transportation applications, since one of the major issues that hinder its worldwide commercialization is represented by its durability. However, due to the complex physical phenomena inside the fuel cell, which are usually strongly inter-coupled, the conventional semi-empirical model-based prognostics approach may fail to predict the aging phenomena under varies fuel cell operating conditions. In order to improve prognostics accuracy, this paper proposed a data-fusion approach to forecast the fuel cell performance based on long short-term memory (LSTM) recurrent neuron network (RNN) and auto-regressive integrated moving average (ARIMA) method. LSTM can efficiently make a prediction regarding a long-term physical degradation, whereas the fusion with ARIMA can effectively track the degradation tendency. In order to validate the performance of the proposed data-fusion approach, two different PEMFCs are tested for recording the aging experimental datasets. The forecasting results indicate that the proposed LSTM-ARIMA approach can accurately predict PEMFC degradation, which can be then used directly to optimize fuel cell performance implemented in transportation applications.

Proton exchange membrane fuel cells (PEMFCs) are considered as a potential candidate in the green-energy applications in the near future. Comparing with other energy options, the PEMFCs need only hydrogen and air during operation. Meanwhile, as a by-product during operation, water is produced. This energy-conversion process is 100% eco-friendly and completely unharmful to the environment. However, PEMFCs are vulnerable to the impurities of hydrogen or fluctuation of operational condition, which could cause the degradation of output performance over time during operation. Thus, the prediction of the performance degradation is critical to the PEMFC system. In this work, a novel PEMFC performance-forecasting model based on a modified relevance vector machine (RVM) has been proposed, followed by a comparison with the approach of classic support vector machine (SVM). First, the theoretical formulation of RVM is briefly introduced, then the implementation steps of RVM using the experimental aging data sets of PEMFC stack output voltage are presented. By considering the specific feature of aging data-prediction problem, an innovative modified RVM formulation is proposed. The results of proposed modified RVM method are analyzed and compared to the results of SVM. The results have demonstrated that the modified RVM can achieve better performance of prediction than SVM, especially in the cases with relatively small training data sets. This novel method based on modified RVM approach has been demonstrated to show its effectiveness on forecasting the performance degradation of PEMFCs.

The purpose of this work is to design a power allocation method (PAM) for multi-stack fuel cell system (MFCS). Due to the limitations of technology, large-scale application of proton exchange membrane fuel cell (PEMFC) in the field of rail transportation has not been implemented. Therefore, this study presents a MFCS to promote the use of PEMFCs in the high-power energy market. During the operation of the MFCS, since the output power of the FC varies dynamically with different operation conditions, the performance of each stack is usually inconsistent. A method for evaluating the degree of performance degradation (DOPD) of stacks is presented in this study. In addition, a fuel cell (FC) semi-empirical model is also adopted to simulate the effects of aging on the stacks' performance. In order to maintain the uniform performance of stacks and enhance the lifespan of the MFCS, a PAM that considers the DOPD of each stack is presented. Besides, in order to facilitate system expansion, this paper proposes a virtual droop control method to realize power splitting among the stacks. Also, a voltage control strategy is presented to compensate for the bus voltage drop caused by the droop control. The effectiveness and practicability of the presented power allocation method and MFCS are verified on the hardware-in-the-loop (HIL) test bench constructed by RT-LAB.