基于实验测量数据和寿命预测模型的燃料电池空压机耐久性研究

IF 2.9 4区 工程技术 Q3 CHEMISTRY, PHYSICAL
Chaozheng Chang, Jianqin Fu, Peng Zhou
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引用次数: 0

摘要

空压机作为燃料电池系统的关键部件,对保证燃料电池系统的稳定性和可靠性起着至关重要的作用。为了进行耐久性研究,根据设计的试验剖面对燃料电池空气压缩机(FCAC)进行了5000小时的耐久性试验,该试验剖面反映了燃料电池汽车的实际运行条件。基于收集到的耐久性试验数据,分析了压缩机性能随时间的退化特征,建立了剩余使用寿命预测模型。耐久性试验结果表明,随着转速的增加,排气流量和压比的退化更为明显。这两个参数的操作范围分别下降了6.2%和11.1%。针对健康指标中的随机噪声干扰,提出了一种新的特征优化方法——移动中心支持向量回归算法(MC-SVR)。该方法在保持原始HI固有趋势的同时,有效地降低了干扰噪声,增强了其鲁棒性。将动态指数回归(DER)模型与MC-SVR方法相结合,建立了FCAC的RUL预测模型。与其他方法相比,使用MC-SVR方法训练的优化HI的RUL预测模型在MAE、MAPE、RMSE和CRA四个评价指标上的预测性能最好。这些都为FCACs的耐久性和热物理研究提供了有价值的见解和参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Durability Study of Fuel Cell Air Compressors Based on Experimental Measurement Data and Lifespan Prediction Models

As a key component of the fuel cell system, the air compressor plays a vital role in ensuring the stability and reliability of fuel cell systems. To conduct a durability study, a 5000-h durability test of the fuel cell air compressor (FCAC) was performed according to the designed test profile, which reflects the real-world operating conditions of fuel cell vehicles. Based on the collected durability test data, the performance degradation characteristics of the compressor over time were analyzed, and a remaining useful life (RUL) prediction model was developed. The durability test results show that as the rotational speed increases, the degradation of both the exhaust flow rate and pressure ratio becomes more pronounced. The operational range of these two parameters decreased by 6.2 % and 11.1 %, respectively. To mitigate stochastic noise interference in health indicators (HI), a novel feature optimization method called Moving Center SVR (MC-SVR) was proposed. This method effectively reduces the nuisance noise while preserving the inherent trend of the original HI, thereby enhancing its robustness. An RUL prediction model for the FCAC was established by integrating the dynamic exponential regression (DER) model with the MC-SVR method. Compared with other methods, the RUL prediction model trained with the optimized HI using the MC-SVR method achieved the best prediction performance across four evaluation metrics, namely MAE, MAPE, RMSE, and CRA. All of these provide valuable insights and references for the durability and thermophysics studies of FCACs.

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来源期刊
CiteScore
4.10
自引率
9.10%
发文量
179
审稿时长
5 months
期刊介绍: International Journal of Thermophysics serves as an international medium for the publication of papers in thermophysics, assisting both generators and users of thermophysical properties data. This distinguished journal publishes both experimental and theoretical papers on thermophysical properties of matter in the liquid, gaseous, and solid states (including soft matter, biofluids, and nano- and bio-materials), on instrumentation and techniques leading to their measurement, and on computer studies of model and related systems. Studies in all ranges of temperature, pressure, wavelength, and other relevant variables are included.
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