Constrained Optimal Fuel Consumption of HEVs under Observational Noise

The article addresses the challenge of achieving optimal fuel consumption in hybrid electric vehicles (HEVs) when observational noise affects state-of-charge measurements. Building on prior research that employed a constrained reinforcement learning framework, the study emphasizes the importance of adapting control strategies to real-world conditions where sensor inaccuracies are prevalent. This adaptation is crucial because observational noise can degrade the performance of fuel optimization algorithms, potentially leading to suboptimal energy use. The constrained reinforcement learning approach provides a structured method to handle these uncertainties while maintaining operational constraints. By incorporating noise considerations into the learning process, the methodology aims to improve the robustness and reliability of fuel consumption optimization in HEVs. This work contributes to ongoing efforts in artificial intelligence to enhance vehicle efficiency under practical sensing limitations. Overall, the article highlights the intersection of AI techniques and automotive engineering challenges in the context of hybrid vehicle energy management.