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User regret psychology-driven electric vehicle charging navigation strategy based on deep reinforcement learning and transfer learning
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DOI：10.1016/j.ijepes.2025.111075.png)
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Abstract
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• A regret theory-based electric vehicle charging navigation model with multiple uncertainties is proposed. • Transfer learning-based method to estimate attributes of comparison strategies under uncertainty • Double deep Q-network enables fast and effective solving of the multi-uncertainty model. • Case studies show superior optimality, adaptability, and scalability of the proposed strategy.
Keywords:
Electric vehicle
Charging navigation strategy
Regret theory
Deep reinforcement learning
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