Research on Energy Management of Hydrogen Fuel Cell Bus Based on Deep Reinforcement Learning Considering Velocity Control

Abstract

In the vehicle-to-everything scenario, the fuel cell bus can accurately obtain the surrounding traffic information, and quickly optimize the energy management problem while controlling its own safe and efficient driving. This paper proposes an energy management strategy (EMS) that considers speed control based on deep reinforcement learning (DRL) in complex traffic scenarios. Using SUMO simulation software (Version 1.15.0), a two-lane urban expressway is designed as a traffic scenario, and a hydrogen fuel cell bus speed control and energy management system is designed through the soft actor–critic (SAC) algorithm to effectively reduce the equivalent hydrogen consumption and fuel cell output power fluctuation while ensuring the safe, efficient and smooth driving of the vehicle. Compared with the SUMO–IDM car-following model, the average speed of vehicles is kept the same, and the average acceleration and acceleration change value decrease by 10.22% and 11.57% respectively. Compared with deep deterministic policy gradient (DDPG), the average speed is increased by 1.18%, and the average acceleration and acceleration change value are decreased by 4.82% and 5.31% respectively. In terms of energy management, the hydrogen consumption of SAC–OPT-based energy management strategy reaches 95.52% of that of the DP algorithm, and the fluctuation range is reduced by 32.65%. Compared with SAC strategy, the fluctuation amplitude is reduced by 15.29%, which effectively improves the durability of fuel cells.

The Sustainability Concept: A Review Focusing on Energy

“…Hydrogen fuel cell buses represent a promising avenue for sustainable urban transportation, but their energy efficiency is contingent on the effective management of power distribution and consumption. The study presented by Shen et al [48] delves into the utilization of deep reinforcement learning algorithms to enhance the energy efficiency of hydrogen fuel cell buses by dynamically adjusting their velocity profiles. As well evaluated nowadays, the energy supply has become more efficient with the use of time series forecasting [49] using hybrid models [50], classification (computer vision [51], convolutional neural networks [52], deep neural networks [53], multilayer perceptron, and k-nearest neighbors [54]), and Internet of Things (IoT) using embedded systems [55].…”