Fuel cell vehicles (FCVs) offer a promising solution for achieving
environmentally friendly transportation and improving fuel economy. The energy
management strategy (EMS), as a critical technology for FCVs, faces significant
challenges of achieving a balanced coordination among the fuel economy, power
battery life, and durability of fuel cell across diverse environments. To
address these challenges, a learning-based EMS for fuel cell city buses
considering power source degradation is proposed. First, a fuel cell degradation
model and a power battery aging model from the literature are presented. Then,
based on the deep Q-network (DQN), four factors are incorporated into the reward
function, including comprehensive hydrogen consumption, fuel cell performance
degradation, power battery life degradation, and battery state of charge
deviation. The simulation results show that compared to the dynamic
programming–based EMS (DP-EMS), the proposed EMS improves the fuel cell
durability while approaching the control effectiveness of DP global
optimization. In comparison to the back-propagation-based EMS (BP-EMS), the
proposed EMS obtains a 0.37% reduction in the equivalent hydrogen consumption
and a 4.72% increase in effective Ah-throughput; the fuel cell performance
degradation reduces by 40.09%, balancing the degradation of fuel cell and power
battery while ensuring low energy consumption and improving the overall
performance of the system. Finally, the adaptability of the proposed strategy to
driving conditions is validated in this article.
