Data-driven energy management for electric vehicles using offline reinforcement learning

Energy management technologies have significant potential to optimize electric vehicle performance and support global energy sustainability. However, despite extensive research, their real-world application remains limited due to reliance on simulations, which often fail to bridge the gap between theory and practice. This study introduces a real-world data-driven energy management framework based on offline reinforcement learning. By leveraging electric vehicle operation data, the proposed approach eliminates the need for manually designed rules or reliance on high-fidelity simulations. It integrates seamlessly into existing frameworks, enhancing performance after deployment. The method is tested on fuel cell…

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  Award: 52172377