Personalized-Preference-Based Electric Vehicle Charging Recommendation Considering Photovoltaic Consumption: A Transfer Reinforcement Learning Method

The electrification of transportation has emerged as a notable trend due to advancements in battery technology and widespread adoption of renewable energy such as photovoltaics (PVs). Many countries have instituted policies aimed at expediting the penetration of electric vehicles (EVs). However, prolonged charging queues and the failure to adequately meet personalized preferences for charging price and time cost have significantly impacted the user experience, thereby impeding the broader adoption of EVs. Moreover, the potential of EVs to use the power from PV panels at charging stations (CSs) for lower charging tariffs through charging recommendation has not been fully explored. In this article, we present a charging recommendation method to optimize the drivers’ charging experience, offering three recommendation modes: time priority, price priority, and balanced to enhance the compliance level of recommendations. We also consider PV generation in the recommendations, enabling drivers to obtain lower charging tariffs while promoting PV consumption. We formulate the problem as a Markov decision process (MDP) and design a customized reinforcement learning (RL) method. Extensive simulations are conducted using the SUMO simulation platform. Results indicate that compared with existing methods, our method promotes the PV consumption ratio by 10.5% and effectively enhances the quality of experience (QoE), thereby increasing the recommendation compliance level by 17.4%.