Enhanced Whale Optimization Algorithm-Based CNN-BiGRU-AT Model for Aging Prediction of Fuel Cell
Quan Rui1, Cheng Gong1, Zhou Yulong1, Zhang Guoguang1, Quan Jin2
1. Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System Hubei University of Technology Wuhan 430068 China; 2. Wuhan Hyvitech Co. Ltd Wuhan 430000 China
Abstract：Fuel cells (FCs) are widely used due to their high energy conversion rate, low noise level and no pollutant emissions. However, the internal components of FCs will irreversibly degrade over time, and under certain complex operating conditions, their aging rate will limit their long-term applications. Therefore, accurate prediction of the remaining useful life (RUL) of FCs is essential to extend their service time, reduce operating costs and ensure their durability. Currently, RUL prediction for FCs is classified into model-based, data-driven and hybrid prediction methods. Model-based prediction methods use the physicochemical reactions inside the FCs to create models and make predictions, with the advantage of obtaining decay parameters to characterize the internal aging state, but it is difficult to establish an accurate mechanistic model for the complex physicochemical reactions inside the FCs. Data-driven approaches do not rely on mechanistic models and can learn from large amounts of experimental test data to make accurate RUL predictions. However, some traditional deep learning models, such as long short term memory (LSTM) neural networks, recurrent neural networks (RNN), and gated recurrent unit (GRU) have obvious limitations. They rely only on historical data to predict the RUL of FC without considering the before and after information of FC degradation. In addition, they cannot effectively exploit the spatial correlation existing in the test data, and the accuracy of RUL prediction needs to be further improved. The hybrid prediction method is to merge or fuse various models to make full use of the respective advantages of different models to improve the prediction accuracy. However, it relies on the establishment of an accurate model, and the accuracy and robustness of the model are difficult to guarantee due to the complexity of the degradation mechanism of the FCs, coupled with environmental disturbances and measurement noise. In addition, hybrid methods based on multiple data-driven approaches need to annotate a large amount of data, consume a lot of computational resources and time, and it is difficult to ensure the interpretability of the prediction results. To address these challenges, this paper proposed a hybrid model combining with convolutional neural network (CNN), bidirectional gated recurrent unit (BiGRU) and attention mechanism (AT) to further improve the RUL forecasting accuracy of FCs. Firstly, the FCs’ aging data recorded by the FCLAB Research Federation were preprocessed using singular spectrum analysis to eliminate noise and obtain effective information, the spatio-temporal features, historical and future information of FCs were extracted with CNN-BiGRU model, the spatio-temporal correlation was explored with AT, and the hyperparameters of the model were optimized with an enhanced whale optimization algorithm (EWOA) to reduce human intervention error. Subsequently, the root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and relative error (RE) were designed to evaluate the RUL forecasting accuracy of the proposed hybrid model by comparing with other deep learning models such as LSTM, CNN, GRU, CNN-LSTM, CNN-GRU, CNN-BiGRU, BiGRU-AT, CNN-BiGRU-AT, and CNN-BiGRU-AT optimized with bayesian optimization (BO), WOA, sparrow search algorithm (SSA), grey wolf optimization (GWO) and black widow optimization algorithm(BWOA). The following conclusions can be drawn from the results: (1) Compared with LSTM, CNN, GRU, CNN- LSTM, CNN-GRU, CNN-BiGRU, BiGRU-AT, CNN-BiGRU-AT, BO-CNN-BiGRU-AT, WOA-CNN-BiGRU-AT, SSA-CNN-BiGRU-AT, GWO-CNN-BiGRU-AT and BWO-CNN-BiGRU-AT, the proposed EWOA-optimized CNN-BiGRU-AT model has the smallest RMSE, MAE, MAPE and RE, which is 0.202 1%, 0.127 8%,0.033% and 0.027%, respectively. (2) The proposed model still maintains superior RUL prediction robustness with 60% missing data, the minimum RMSE, MAE, MAPE and RE are 0.387 9%, 0.255 9%, 0.081 1% and 0.32%, respectively. (3) Compared with the CNN, GRU, CNN-GRU, CNN-BiGRU and CNN-BiGRU models, the EWOA-optimized CNN-BiGRU-AT model can more accurately describe the twelve-, twenty-four-, and forty- eight-step aging curves of the FCs with higher reliability.
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