Energy management with adaptive moving average filter and deep deterministic policy gradient reinforcement learning for fuel cell hybrid electric vehicles

Fuel cell hybrid electric vehicles (FCHEV) with battery (BAT) and supercapacitor (SC) advance in flexible configuration and high energy efficiency. However, the complex coupling relationship among various power sources poses a severe challenge to the design of the energy management system (EMS), including multi-degrees of freedom power allocation, fuel economy, and power sources lifespan of the FCHEV. This paper proposes an EMS based on a dual-layer power distribution structure. In the upper layer, adaptive moving average filter (AMAF) is designed to separate different frequency power, where the energy supply of the SC is managed to attenuate fluctuating power and simplifies the optimization problem and reduces computational costs. The lower layer is constructed by the deep deterministic policy gradient (DDPG) algorithm, where fuel cell system (FCS) hydrogen consumption and degradation rewards are designed to simultaneously enhance fuel efficiency and degradation performance by regulating the FCS real-time power variation. The proposed strategy has been evaluated regarding FCHEV fuel economy and FCS durability under combined driving cycle simulation, which shows AMAF + DDPG strategy reduces fuel consumption by 7.24 % and 1.3 %, also the degradation reduces by 0.04 % and 0.02 % compared with different EMS. Simulation results demonstrate that AMAF + DDPG optimizes the output characteristics of power sources.