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Abstract

The optimization of power distribution in hybrid electric vehicles (HEVs) under unpredictable real-world driving conditions presents a significant challenge. The complexity arises from the myriad transient states that vehicles encounter, along with the necessity to maintain the State of Charge (SoC) amid stringent environmental regulations. This study aims to demonstrate the potential for enhancing power distribution strategies in real-world driving scenarios within HEV systems. To accurately reflect the operating conditions of actual HEVs, we utilized a Simulink model that mirrors the structure and performance of real vehicles. To increase the model’s validity and applicability, we trained it using real driving emissions (RDE) data instead of artificially generated driving cycles. Specifically, we employed the proximal policy optimization algorithm in an actor-critic framework to ensure training stability and data efficiency. Our proposed approach integrates nuanced control by segmenting power distribution into stages, considering engine usage and torque adjustment, thereby making the controller’s actions more descriptive, and interpretable. We demonstrated the effectiveness of this approach by comparing the results with rule-based methods and models trained solely on standardized driving profiles. The proposed method successfully maintained the SoC within a 1.4% deviation from the baseline value while optimizing fuel economy to 14.59 km/L for the Worldwide Harmonized Light Vehicles Test Procedure (WLTP) cycle. This study pioneers a comprehensive framework that combines advanced reinforcement learning techniques with sophisticated vehicle modeling using RDE data, offering a promising solution for optimizing HEV performance in diverse driving conditions.

Keywords

Hybrid electric vehicles power distribution reinforcement learning proximal policy optimization real driving emissions engine usage torque adjustment state of charge fuel efficiency

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Data-driven characterization of power distribution strategies in hybrid electric vehicles using the actor-critic method

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