A novel online energy management strategy for fuel cell vehicles based on improved random forest regression in multi road modes

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.