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Constrained Optimal Fuel Consumption of HEVs under Observational Noise

arXiv 2410.20913 ↗First posted 28 Oct 2024cs.LG cs.SY eess.SYcited by 0

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Abstract (arXiv)

In our prior work, we investigated the minimum fuel consumption of a hybrid electric vehicle (HEV) under a state-of-charge (SOC) balance constraint, assuming perfect SOC measurements and accurate reference speed profiles. The constrained optimal fuel consumption (COFC) problem was addressed using a constrained reinforcement learning (CRL) framework. However, in real-world scenarios, SOC readings are often corrupted by sensor noise, and reference speeds may deviate from actual driving conditions. To account for these imperfections, this study reformulates the COFC problem by explicitly incorporating observational noise in both SOC and reference speed. We adopt a robust CRL approach, where the noise is modeled as a uniform distribution, and employ a structured training procedure to ensure stability. The proposed method is evaluated through simulations on the Toyota Prius hybrid system (THS), using both the New European Driving Cycle (NEDC) and the Worldwide Harmonized Light Vehicles Test Cycle (WLTC). Results show that fuel consumption and SOC constraint satisfaction remain robust across varying noise levels. Furthermore, the analysis reveals that observational noise in SOC and speed can impact fuel consumption to different extents. To the best of our knowledge, this is the first study to explicitly examine how observational noise -- commonly encountered in dynamometer testing and predictive energy control (PEC) applications -- affects constrained optimal fuel consumption in HEVs.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 43 references that are in the map. First posted on arXiv 28 Oct 2024.

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References · 19

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7 more
  1. 2022Constrained Variational Policy Optimization for Safe Reinforcement Learningcited by 71
  2. 2022Efficient Adversarial Training without Attacking: Worst-Case-Aware Robust Reinforcement Learningcited by 43
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  5. 2022Robust Reinforcement Learning as a Stackelberg Game via Adaptively-Regularized Adversarial Trainingcited by 28
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  7. 2024Constrained Optimal Fuel Consumption of HEV: A Constrained Reinforcement Learning Approachcited by 1

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