Remaining useful life prediction of PEMFC systems based on the multi-input echo state network
Remaining useful life prediction of PEMFC systems based on the multi-input echo state network
复制标题
DOI：
10.1016/j.apenergy.2020.114791
复制
发表时间：
2020-05-01
期刊：
影响因子：
11.2
通讯作者：
Gao, Fei
中科院分区：
文献类型：
--
作者：
Hua, Zhiguang;Zheng, Zhixue;Gao, Fei
质子交换膜燃料电池（PEMFC）的耐久性是阻碍其大规模商业化应用的关键因素之一。数据驱动的预测方法旨在估计剩余使用寿命（RUL），而不需要对系统的物理现象有完整的了解。回声状态网络（Echo State network， ESN）作为递归神经网络的改进结构，在降低计算复杂度和加快收敛速度方面表现出了更好的性能。传统的预测方法仅利用前一个状态，如堆叠电压，进行预测。然而，当前的操作条件，如堆电流、堆温度和反应物（即氧和氢）的压力，在实践中也可能包含重要的降解信息。特别是堆栈电流是一个至关重要的运行参数，因为它通常被作为调度变量，它可以反映运行状况。与单输入单输出（SISO-ESN）结构相比，本文提出了多输入多输出ESN （MIMO-ESN）结构，以提高RUL的预测精度。利用堆电压、堆电流、堆温度和反应物压力等因素综合预测了堆压比。通过数学建模和参数设计，在实验室研制的1kw电功率试验台上验证了siso -回声状态网络和mimo -回声状态网络的预测性能。结果表明，无论在静态还是准动态工况下，MIMO-ESN方法的性能都优于sso - esn方法。
The limited durability is one of the key barriers of Proton Exchange Membrane Fuel Cell (PEMFC) to large-scale commercial applications. The data-driven prognostic method aims to estimate the Remaining Useful Life (RUL) without the need for complete knowledge about the system's physical phenomena. As an improved structure of the recurrent neural network, the Echo State Network (ESN) has demonstrated better performances, especially in reducing the computational complexity and accelerating the convergence rate. The traditional prognostic methods utilize only the previous state, e.g. stack voltage, for prediction. Nevertheless, the current operating conditions, such as stack current, stack temperature and the pressures of the reactants (i.e. oxygen and hydrogen) can also contain important degradation information in practice. Especially, the stack current is a crucial operating parameter, since it is normally taken as the scheduling variable and it could reflect the operating conditions. Compared with the single-input and single-output (SISO-ESN) structure, the ESN with multiple inputs and multiple outputs (MIMO-ESN) is proposed in this paper to improve the RUL prediction accuracy. Stack voltage, stack current, stack temperature and the pressures of the reactants are combinedly used to predict the RUL. After the mathematical modeling and the parameter designing, the prediction performance of SISO-ESN and MIMO-ESN are verified and compared on a 1 kW electrical power test bench developed in the laboratory. Results show that the MIMO-ESN method has a better performance than the SISO-ESN method under both static and quasi-dynamic operating conditions.