A hybrid CNN–BiLSTM–AT model optimized with enhanced whale optimization algorithm for remaining useful life forecasting of fuel cell

Author:

Quan Rui12ORCID,Zhang Jian12ORCID,Li Xuerong12ORCID,Guo Haifeng12ORCID,Chang Yufang12ORCID,Wan Hang12ORCID

Affiliation:

1. Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology 1 , Wuhan 430068, China

2. Hubei Engineering Research Center for Safety Monitoring of New Energy and Power Grid Equipment, Hubei University of Technology 2 , Wuhan 430068, China

Abstract

To further improve the remaining useful life forecasting accuracy of fuel cells using classic deep learning models, a convolutional neural network combining bidirectional long and short-term memory networks (BiLSTM) and attention mechanism (AT) is optimized with the enhanced whale optimization algorithm (EWOA). Singular spectrum analysis preprocesses the attenuation data to eliminate noise and enhance its effective information; the CNN–BiLSTM model extracts spatiotemporal features and learns historical and future information; AT further explores the spatiotemporal correlation; and EWOA optimizes its hyperparameters to reduce human intervention error. Results demonstrate that, compared with long and short-term memory, CNN–LSTM, CNN–BiLSTM, CNN–BiLSTM–AT, and CNN–BiLSTM–AT optimized with other algorithms, the CNN–BiLSTM–AT model optimized with EWOA achieves lower root mean square error, mean absolute error, mean absolute percentage error, and relative errors of 0.1951%–0.2059%, 0.1267%–0.1538%, 0.0319%–0.0366%, and 0.026%–0.036%, respectively, with different training data. Importantly, the proposed model still maintains good prediction robustness with over 40% of the missing data.

Funder

National Natural Science Foundation of China

Open Foundation of Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System

Publisher

AIP Publishing

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