An effective energy management strategy (EMS) is crucial for ensuring the safe and efficient operation of fuel cell vehicles. This paper proposes an online EMS for the fuel cell/battery hybrid system based on an improved random forest (RF) algorithm. First, in order to enhance the environmental adaptability of EMS, a learning vector quantization classifier is utilized to identify the driving mode under different road conditions. In each driving mode, the optimal control sequence is derived using the Pontryagin’s minimum principle (PMP), forming a globally optimal training set based on the idea of minimizing fuel consumption. Then an online application solution based on the RF is proposed to learn the optimal control sequence and address the real-time limitation. Furthermore, A RF hyper-parameter optimization method based on the NSGA-II algorithm is proposed to improve the accuracy and robustness of the RF under different driving patterns. The simulation experiments under two test conditions showed that compared with PMP method, the proposed EMS reached 97.67% and 98.71% of benchmark in terms of hydrogen consumption, and fuel cell degree of aging was reduced by 5.9% and 4.9%. Hence, the proposed method is proved to be capable of reducing degradation and exhibiting good fuel economy.

中文翻译：

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多道路模式下基于改进随机森林回归的新型燃料电池汽车在线能量管理策略

有效的能源管理策略（EMS）对于确保燃料电池汽车安全高效的运行至关重要。本文提出了一种基于改进的随机森林（RF）算法的燃料电池/电池混合系统的在线EMS。首先，为了增强EMS的环境适应性，利用学习矢量量化分类器来识别不同路况下的驾驶模式。在每种驾驶模式下，利用庞特里亚金极小原理（PMP）推导出最优控制序列，形成基于油耗最小化思想的全局最优训练集。然后提出了一种基于RF的在线应用解决方案来学习最优控制序列并解决实时限制。此外，提出了一种基于NSGA-II算法的射频超参数优化方法，以提高不同驾驶模式下射频的准确性和鲁棒性。两种测试条件下的模拟实验表明，与PMP方法相比，所提出的EMS在氢耗方面分别达到基准的97.67%和98.71%，燃料电池老化程度降低了5.9%和4.9%。因此，所提出的方法被证明能够减少退化并表现出良好的燃油经济性。