What does this research mean for the field?

An adaptive road-aware deep reinforcement learning framework coupled with predictive neural networks significantly reduces hydrogen consumption and improves charge sustainability in fuel cell hybrid electric vehicles compared to standard energy management strategies. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The aim is to optimize energy management in fuel cell hybrid electric vehicles using a predictive deep reinforcement learning framework.

June 9, 2026Open Access

Predictive Road-Aware Deep Reinforcement Learning for Energy Management of Fuel Cell Hybrid Electric Vehicles: A Real-World Tunisian Case Study

Key Points

The aim is to optimize energy management in fuel cell hybrid electric vehicles using a predictive deep reinforcement learning framework.
Utilized a 562 km real-world journey across Tunisia with varying driving conditions.
Employed a coupled architecture of deep reinforcement learning and a nonlinear autoregressive neural network.
Tested three reward formulations: baseline policy, battery-conservative policy, and adaptive road-aware policy.
The adaptive road-aware policy reduced hydrogen consumption by 8% compared to the deep deterministic policy gradient strategy.
It outperformed the equivalent consumption minimization strategy by 27.3% in hydrogen consumption during the long drive cycle.
In the combined generalization cycle, hydrogen consumption decreased by 6.64% relative to the baseline, confirming strong generalization.

Abstract

This study addresses the energy management problem of fuel cell hybrid electric vehicles operating over a 562 km real-world journey across Tunisia, encompassing heterogeneous driving conditions including urban centers, highways, and rest segments. The proposed framework preserves realistic battery dynamics through continuous state-of-charge tracking, where the final state of charge of each segment initializes the next one, with natural partial recovery during rest periods. A coupled architecture combining deep reinforcement learning and a nonlinear autoregressive neural network with exogenous inputs is used to forecast critical system variables and optimize real-time power split among the fuel cell, battery, and supercapacitor. Three reward formulations are investigated and compared: a baseline policy, a battery-conservative policy, and an adaptive road-aware policy. The results show that the adaptive road-aware strategy provides the best overall balance between hydrogen economy and charge sustainability. On the long drive cycle, the adaptive road-aware policy achieves the lowest hydrogen consumption, reducing hydrogen use by 8% relative to the deep deterministic policy gradient road-aware strategy and by 27.3% relative to the equivalent consumption minimization strategy, although its final state of charge is slightly lower than those two methods. On the combined generalization driving cycle, the adaptive road-aware policy reduces hydrogen consumption by 6.64% relative to the deep deterministic policy gradient baseline, while also outperforming the equivalent consumption minimization strategy by 3.4% in final state of charge and 26.5% in hydrogen consumption, confirming strong generalization to unseen driving conditions, which demonstrates that the proposed prediction-informed reinforcement learning framework is effective for both long-distance operation and broader real-world deployment.

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