Life prediction of fuel cells based on the LSTM-UPF hybrid method

doi:10.19799/j.cnki.2095-4239.2023.0705

Abstract

Abstract:

Life prediction for fuel cells is crucial to fuel cell health management, offering their operation and maintenance guidance. Advantages of a long short-term memory neural network (LSTM) and an unscented particle filter (UPF) algorithm are combined to enhance condition adaptability in life prediction and ensure accuracy. The proposed LSTM-UPF hybrid method is designed to predict fuel cell life under steady-state and quasidynamic conditions. Initially, experimental data used for model training is optimized and decomposed into high-frequency and low-frequency components using the discrete wavelet transform technique. The LSTM algorithm predicts these components, forecasting the long-term aging trend of fuel cells. Drift correction is then applied to refine the trend prediction results. The fuel cell's remaining useful life (RUL) is estimated using the UPF algorithm based on the long-term aging trend. Evaluation indexes, including prediction life end, life prediction error, confidence interval width, and RUL prediction error, are adopted to assess different life prediction methods. Comparative results demonstrate that the LSTM-UPF hybrid life prediction method yields RUL prediction errors of 4.1% and 3.4% for steady-state and quasidynamic conditions, respectively. Moreover, it exhibits more accurate RUL predictions, high-quality confidence intervals, and strong adaptability in both scenarios. This study enhances the accuracy and confidence level of fuel cell life predictions.

Key words: proton exchange membrane fuel cell, life prediction, long short-term memory neural network, unscented particle filter

CLC Number:

TK 91

Qiquan ZENG, Maji LUO, Yinlong YANG, Qingze HUANG. Life prediction of fuel cells based on the LSTM-UPF hybrid method[J]. Energy Storage Science and Technology, 2024, 13(3): 963-970.

Figures/Tables 11

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参量	控制范围
气体温度	20~80 ℃
气体湿度	0~100%
冷却液温度	20~80 ℃
冷却液流量	0~10 L/min
空气流量	0~100 L/min
氢气流量	0~30 L/min
气体压力	0~200 kPa

算法评估指标	PF方法	UPF方法	混合驱动方法
预测寿命终止点/h	686	771	802
预测寿命误差/h	106	21	10
置信区间宽度/h	96	5	11
RUL预测误差/％	43.8	8.7	4.1

算法评估指标	PF方法	UPF方法	混合驱动方法
预测寿命终止点/h	738	802	764
预测寿命误差/h	35	29	9
置信区间宽度/h	106	8	16
RUL预测误差/％	13.3	11.0	3.4

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