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• Deep stochastic reinforcement learning based approach to address this issue of epistemic uncertainty in a midsize fuel cell hybrid electric vehicle. • The performance of the proposed approach is benchmarked against Double Deep Q-Network (DDQN), Power Follower Controller (PFC) and Fuzzy Logic Controller (FLC). • Using New York City cycle as a validation drive cycle, the approach improves fuel economy by 7.68%, 13.53%, and 10% compared to DDQN, PFC, and FLC, respectively. • The deep REINFORCE approach improves fuel economy by 5.31 %,9.78 %, and 9.93 % compared to DDQN, PFC, and FLC, respectively under another validation cycle, Amman cycle. • The proposed method has 38% less training time when compared to the DDQN approach. Fuel cell hybrid electric vehicles offer a promising solution for sustainable and environment friendly transportation, but they necessitate efficient energy management strategies (EMSs) to optimize their fuel economy. However, designing an optimal leaning-based EMS becomes challenging in the presence of limited training data. This paper presents a deep stochastic reinforcement learning based approach to address this issue of epistemic uncertainty in a midsize fuel cell hybrid electric vehicle. The approach introduces a deep REINFORCE framework with a deep neural network baseline and entropy regularization to develop a stochastic policy for EMS. The performance of the proposed approach is benchmarked against three EMSs: i) a state-of- art deep deterministic reinforcement learning technique called Double Deep Q-Network (DDQN), Power Follower Controller (PFC) and Fuzzy Logic Controller (FLC). Using New York City cycle as a validation drive cycle, the deep REINFORCE approach improves fuel economy by 7.68%, 13.53%, and 10% compared to DDQN, PFC, and FLC, respectively. The deep REINFORCE approach improves fuel economy by 5.31 %,9.78 %, and 9.93 % compared to DDQN, PFC, and FLC, respectively under another validation cycle, Amman cycle. Moreover, the training results show that the proposed algorithm reduces training time by 38% compared to the DDQN approach. The proposed deep REINFORCE-based EMS shows superiority not only in terms of fuel economy, but also in terms of dealing with epistemic uncertainty. [ABSTRACT FROM AUTHOR]

[Display omitted] • Explores RL-based EMS development in multi-power source vehicles. • Highlights general RL advantages over specific hybrid designs in EMS. • Discusses RL-EMS performance factors using PHEV and FCEV models. Reinforcement learning-based (RL-based) energy management strategy (EMS) is considered a promising solution for the energy management of electric vehicles with multiple power sources. Research and applications of reinforcement learning and deep reinforcement learning in energy management issues are emerging. However, previous studies have not systematically examined the essential elements of RL-based EMS. This paper presents a performance analysis of RL-based EMS in a Plug-in Hybrid Electric Vehicle (PHEV) and Fuel Cell Electric Vehicle (FCEV). The performance analysis is developed in four aspects: algorithm, perception and decision granularity, hyperparameters, and reward function. The results show that the Off-policy algorithm effectively develops a more fuel-efficient solution within the complete driving cycle compared with other algorithms. Improving the perception and decision granularity reduces the frequency of tabular-based policy updates but better balances battery power and fuel consumption. Setting a high initial SOC in training will effectively improve the performance of RL-based EMS. The construction of an equivalent energy loss reward function for RL-based EMS based on instantaneous State of Charge (SOC) variation should be approached with caution. This approach is highly sensitive to parameters and is more likely to lead to violations of SOC constraints. In contrast, an equivalent energy reward function based on overall SOC variation is a safer alternative. [ABSTRACT FROM AUTHOR]