Long short-term memory deep learning model for predicting the dynamic performance of automotive PEMFC system
Bowen Wang,
Zijun Yang,
Mingxi Ji,
Jing Shan,
Meng Ni,
Zhongjun Hou,
Jun Cai,
Xin Gu,
Xinjie Yuan,
Zhichao Gong,
Qing Du,
Yan Yin,
Kui Jiao
Affiliations
Bowen Wang
State Key Laboratory of Engines, Tianjin University, 135 Yaguan Rd, Tianjin 300350, China; Department of Building and Real Estate, Research Institute for Sustainable Urban Development (RISUD), Research Institute for Smart Energy (RISE), The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China; National Industry-Education Platform of Energy Storage, Tianjin University, Tianjin, China
Zijun Yang
State Key Laboratory of Engines, Tianjin University, 135 Yaguan Rd, Tianjin 300350, China
Mingxi Ji
Birmingham Centre for Energy Storage, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
Jing Shan
Shanghai Hydrogen Propulsion Technology Co., Ltd., Unit 10, BLDG 17, Innovation Park, Lane 56, Antuo Rd. Jiading, Shanghai China
Meng Ni
Department of Building and Real Estate, Research Institute for Sustainable Urban Development (RISUD), Research Institute for Smart Energy (RISE), The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China; Corresponding authors.
Zhongjun Hou
Shanghai Hydrogen Propulsion Technology Co., Ltd., Unit 10, BLDG 17, Innovation Park, Lane 56, Antuo Rd. Jiading, Shanghai China; Corresponding authors.
Jun Cai
Shanghai Hydrogen Propulsion Technology Co., Ltd., Unit 10, BLDG 17, Innovation Park, Lane 56, Antuo Rd. Jiading, Shanghai China
Xin Gu
Shanghai Hydrogen Propulsion Technology Co., Ltd., Unit 10, BLDG 17, Innovation Park, Lane 56, Antuo Rd. Jiading, Shanghai China
Xinjie Yuan
Shanghai Hydrogen Propulsion Technology Co., Ltd., Unit 10, BLDG 17, Innovation Park, Lane 56, Antuo Rd. Jiading, Shanghai China
Zhichao Gong
State Key Laboratory of Engines, Tianjin University, 135 Yaguan Rd, Tianjin 300350, China
Qing Du
State Key Laboratory of Engines, Tianjin University, 135 Yaguan Rd, Tianjin 300350, China; National Industry-Education Platform of Energy Storage, Tianjin University, Tianjin, China
Yan Yin
State Key Laboratory of Engines, Tianjin University, 135 Yaguan Rd, Tianjin 300350, China; National Industry-Education Platform of Energy Storage, Tianjin University, Tianjin, China
Kui Jiao
State Key Laboratory of Engines, Tianjin University, 135 Yaguan Rd, Tianjin 300350, China; National Industry-Education Platform of Energy Storage, Tianjin University, Tianjin, China; Corresponding authors.
As a high efficiency hydrogen-to-power device, proton exchange membrane fuel cell (PEMFC) attracts much attention, especially for the automotive applications. Real-time prediction of output voltage and area specific resistance (ASR) via the on-board model is critical to monitor the health state of the automotive PEMFC stack. In this study, we use a transient PEMFC system model for dynamic process simulation of PEMFC to generate the dataset, and a long short-term memory (LSTM) deep learning model is developed to predict the dynamic performance of PEMFC. The results show that the developed LSTM deep learning model has much better performance than other models. A sensitivity analysis on the input features is performed, and three insensitive features are removed, that could slightly improve the prediction accuracy and significantly reduce the data volume. The neural structure, sequence duration, and sampling frequency are optimized. We find that the optimal sequence data duration for predicting ASR is 5 s or 20 s, and that for predicting output voltage is 40 s. The sampling frequency can be reduced from 10 Hz to 0.5 Hz and 0.25 Hz, which slightly affects the prediction accuracy, but obviously reduces the data volume and computation amount.
