A Realtime Intelligent Energy Management Strategy for Hybrid Electric Vehicle
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Abstract
In response to the growing demand for sustainable transportation, this research proposes a real-time intelligent energy management strategy for hybrid electric vehicles (HEVs) employing reinforcement learning. HEVs inherently possess complex energy distribution systems, necessitating dynamic and adaptive approaches to optimize their performance. The proposed strategy harnesses the capabilities of reinforcement learning algorithms to continuously learn and refine decision-making processes in real-time. By considering diverse factors such as current driving conditions, external influences like traffic patterns and terrain, and overarching energy efficiency objectives, the system adeptly adjusts the power distribution between the internal combustion engine and electric motor. This adaptability ensures optimal fuel efficiency and emissions reduction, contributing to a more sustainable and environmentally friendly driving experience.The significance of this research lies in its contribution to advancing intelligent energy management solutions in the realm of modern transportation. The real-time nature of the proposed strategy represents a substantial leap forward, offering a dynamic and responsive approach to the intricate challenges associated with HEV energy optimization. By leveraging reinforcement learning, the system learns from its interactions with the environment, enabling continual improvement and adaptation to evolving driving conditions. Ultimately, this research not only enhances the overall performance of hybrid electric vehicles but also underscores the potential for intelligent, self-learning systems to play a pivotal role in shaping the future of eco-conscious mobility.