基于高斯过程回归补偿的燃料电池混合动力客车动力管理模型预测控制

Qiuyu Li, Hengzhao Yang
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引用次数: 0

摘要

以氢燃料电池(fc)为主要动力源、超级电容器(SCs)作为能量缓冲的燃料电池混合动力公交车(fcheb)可能是一种可行的电气化交通技术。提出了一种基于高斯过程回归补偿的模型预测控制方案(GPRC-MPC),用于FCHEB FC/SC混合储能系统的电源管理。为了提高线性MPC模型的精度,GPRC-MPC引入高斯过程回归对负载扰动误差和残差进行预测和补偿。仿真结果表明,GPRC-MPC在降低氢耗、维持SC SOC水平和缓解FC降解方面具有较好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Model Predictive Control with Gaussian Process Regression Compensation for Power Management in Fuel Cell Hybrid Electric Buses
Fuel cell hybrid electric buses (FCHEBs) using hydrogen fuel cells (FCs) as the main power source and supercapacitors (SCs) as the energy buffer may be a viable electrified transportation technology. This paper proposes a Model Predictive Control scheme with Gaussian Process Regression Compensation (GPRC-MPC) for power management in FCHEB FC/SC hybrid energy storage systems. To improve the accuracy of the linear MPC model, GPRC-MPC introduces Gaussian process regression to predict and compensate for the load disturbance error and the residual error. Simulation results show that GPRC-MPC performs better in reducing the hydrogen consumption, maintaining the SC SOC level, and alleviating the FC degradation.
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