To provide efficient charging behavior decision-making for urban electric vehicles (EVs), this paper proposes a new platform for real-time EV charging navigation (EVCN) based on graph reinforcement learning (GRL). Considering the interaction of EVs with charging stations (CSs) and traffic networks, the navigation goal of the ‘Vehicle-Station-Network’ coupled system is to minimize the charging cost and traveling time of EV owners. Specifically, to realize data acquisition and decision-making output, we first characterize the EV charging and traveling behavior as the dynamic interaction process of graph-structured networks. A graph convolutional network (GCN) is used to extract the environment information required for EVCN, and the generated environment feature is fed into the underlying network of deep reinforcement learning (DRL), which can help the agent better understand massive graph-structured data. Then, the real-time navigation problem is duly formulated as a finite Markov decision process. A sequential scheduling pattern is built according to the sorting of EV charging urgency and solved by a Rainbow-based DRL algorithm. It achieves the sequential recommendation of CSs and planning of traveling routes for multiple EVs. Case studies are conducted within a practical zone in Nanjing, China. Simulation results verify the developed platform and the solving method.

This letter proposes a bilevel graph reinforcement learning method for electric vehicle (EV) fleet charging guidance, achieving collaborative optimization of the transportation-electrification coupled system. A dual-agent architecture is first constructed, where the upper-level is used for charging and the lower-level is used for routing. The EV traveling and charging behavior is characterized as a graph-structured interaction process. A graph attention network (GAT) is leveraged to extract the topology correlation and feature information. Then the extracted topology embedded with knowledge, as intermediate latent environment states, is fed into the underlying network of deep reinforcement learning (DRL). A DRL-based sequential scheduling pattern is developed to realize the guidance of multiple EVs. Extensive experimental results verify the superiority and adaptability of our proposed methodology.

To improve the operating efficiency and economic benefits, this article proposes a modified rainbow-based deep reinforcement learning (DRL) strategy to realize the charging station (CS) optimal scheduling. As the charging process is a real-time matching between electric vehicles ‘(EVs) charging demand and CS equipment resources, the CS charging scheduling problem is duly formulated as a finite Markov decision process (FMDP). Considering the multi-stakeholder interaction among EVs, CSs, and distribution networks (DNs), a comprehensive information perception model was constructed to extract the environmental state required by the agent. According to the random behavior characteristics of the EV charging arrival and departure times, the startup of the charging pile control module was regarded as the agent’s action space. To tackle this issue, the modified rainbow approach was utilized to develop a time-scale-based CS scheme to compensate for the resource requirements mismatch on the energy scale. Case studies were conducted within a CS integrated with the photovoltaic and energy storage system. The results reveal that the proposed method effectively reduces the CS operating cost and improves the new energy consumption.

To solve the problem, because of which conventional quick-charging strategies (CQCS) cannot meet the requirements of quick-charging for multiple types of electric vehicles (EV) on highways where vehicle inflow is excessive, this paper proposed a new quick-charging strategy (NQCS) for EVs: on the premise of not affecting those EVs being charged, the remaining power of the quick-charging pile with multiple power output interfaces is used to provide a synchronous charging service for EVs waiting in the queue. To verify the effectiveness of this strategy, a power distribution model of charging pile and a queuing model of charging station (CS) were constructed. In addition, based on an actual highway service area where vehicle inflow is excessive during the simulation period (0:00–24:00), charging situations of CQCS and NQCS were respectively simulated in a charging station (CS), with different number of chargers, by basic queuing algorithm and an improved queuing algorithm. The simulation results showed that when the relative EV inflow is excessive, compared to CQCS, NQCS not only can reduce user waiting time, charging time, and stay time, but also can improve the utilisation rate of charging infrastructure and service capacity of CS and reduce the queue length of CS. At the same time, NQCS can reduce the impact on the power grid. In addition, in NQCS, the on-demand power distribution method is more efficient than the average power distribution method. Therefore, NQCS is more suitable for quick-charging for multiple types of EVs on highways where vehicle inflow is excessive.

The rapid development of electric vehicles (EVs) has posed challenges to power grids and transportation networks. Accurately capturing the usage patterns of EV users is a prerequisite for EVs’ interaction with electrified transportation networks. Thus, this paper proposes a full data chain (FDC) driven model to mine EVs’ comprehensive characteristics. By collecting the data of 150 private electric vehicles (PREVs), 100 commercial electric vehicles (CEVs), and 50 official electric vehicles (OEVs) in Chongqing, China, the driving characteristics are firstly mined by the adoption of origin-destination (OD) distribution and driving portrait. Moreover, the charging characteristics are extracted based on the state recognition for data chains. Then, vehicle usage characteristics of different types of users are comprehensively described based on the density-based spatial clustering of applications with noise (DBSCAN). Finally, the results of EV user characteristics are analyzed, and the effectiveness of the proposed model is verified by regional charging load analysis and urban road traffic flow comparison. The findings provide a data source and user behavior model for the planning, operation, and control of power grids and transportation networks.