Optimized control of fuel cell hybrid electric vehicles based on model predictive control and adaptive soft actor critic algorithm in a car-following scenario

With the increasing demand for energy conservation and emission reduction, the fuel cell hybrid electric vehicle (FCHEV), as a new energy vehicle that includes two power sources—the fuel cell pack and the battery pack—will play an important role in future urban transportation. This article focuses on the car-following problem and studies the car-following optimization control strategy of FCHEV. A speed planning module based on model predictive control (MPC) and an energy management module based on adaptive soft actor critic (SAC) algorithm are designed to improve driving comfort and optimize vehicle’s energy consumption while ensuring safe car-following. Finally, simulation verification was conducted under the car-following conditions of Urban Dynamometer Driving Schedule (UDDS) and China light-duty vehicle test cycle (CLTC). Simulation results showed that under the car-following optimization control strategy proposed in this paper, the FCHEV can simultaneously balance safety car-following and driving comfort, achieving almost 95.18% and 91.02% of hydrogen economy of dynamic programming (DP) in UDDS and CLTC car-following conditions, respectively, while reducing hydrogen consumption by 38.05%, and 30.87% compared with the alternating direction method of multipliers (ADMMs). The real-time performance of the proposed car-following optimization strategy is compared and validated under typical car-following driving conditions as well.