Charging Navigation Research Articles

Deep Reinforcement Learning for EV Charging Navigation by Coordinating Smart Grid and Intelligent Transportation System

A coordinated operation of smart grid (SG) and intelligent transportation system (ITS) provides electric vehicle (EV) owners with a myriad of power and transportation network data for EV charging navigation. However, the optimal charging navigation would be a challenging task owing to the randomness of traffic conditions, charging prices and waiting time at EV charging station (EVCS). In this paper, we propose a deep reinforcement learning (DRL)-based EV charging navigation, aiming at minimizing the total travel time and the charging cost at EVCS. First, we utilize the deterministic shortest charging route model (DSCRM) to extract feature states out of collected stochastic data and then formulate EV charging navigation as a Markov Decision Process (MDP) with an unknown transition probability. The proposed DRL-based approach will approximate the solution, which can adaptively learn the optimal strategy without any prior knowledge of uncertainties. Case studies are carried out within a practical zone in Xi’an city, China. Numerous experimental results verity the effectiveness of the proposed approach and illustrate its adaptation to EV driver preferences. The coordination effect of SG and ITS on reducing the waiting time and the charging cost in EV charging navigations is also analyzed.

Joint planning of distributed generation and electric vehicle charging stations considering real-time charging navigation

With the popularity of intelligent mobile terminals, as well as the improvement in wireless communication technologies, the application of real-time charging navigation for electric vehicles (EVs) has met a booming development in recent years. Accordingly, more and more EV owners tend to make their charging decisions depending on the guidance from navigation systems, which to some extent transforms the EV charging demands into spatially dispatchable resources in the operation of distribution systems. To involve this spatially dispatchable characteristic at the planning stage of distribution systems, and deploy power devices in a cost-effective way, a comprehensive optimization model concerning the joint planning of distributed generators (DGs) and electric vehicle charging stations (EVCSs) is proposed in this paper from the perspective of a social planner. The proposed joint planning model is embedded with the spatial scheduling problem of EV charging demands, and focuses on the optimum of relevant social costs. To relieve the complexity of the optimization model, an exact second-order cone programming (SOCP) relaxation is utilized to transform the proposed model into the type of mixed integer second-order cone programming (MISOCP), which can be efficiently solved in polynomial time. Finally, a practical urban area coupled with the 33-bus distribution system is used as the test system to implement the proposed approach, and the corresponding allocation schemes as well as annualized social costs are minutely presented. The numerical comparison with traditional Voronoi diagram-based planning methods reveals the significant economic benefits achieved through considering real-time charging navigation technologies.

Optimal Charging Navigation Strategy Design for Rapid Charging Electric Vehicles

Electric vehicles (EVs) have become an efficient solution to making a transportation system environmentally friendly. However, as the number of EVs grows, the power demand from charging vehicles increases greatly. An unordered charging strategy for huge EVs affects the stability of a local power grid, especially during peak times. It becomes serious under the rapid charging mode, in which the EVs will be charged fully within a shorter time. In contrast to regular charging, the power quality (e.g.,voltages deviation, harmonic distortion) is affected when multiple EVs perform rapid charging at the same station simultaneously. To reduce the impacts on a power grid system caused by rapid charging, we propose an optimal EV rapid charging navigation strategy based on the internet of things network. The rapid charging price is designed based on the charging power regulation scheme. Both power grid operation and real-time traffic information are considered. The formulated objective of the navigation strategy is proposed to minimize the synthetic costs of EVs, including the traveling time and the charging costs. Simulation results demonstrate the effectiveness of the proposed strategy.

Electric Vehicles En-Route Charging Navigation Systems: Joint Charging and Routing Optimization

Widely recognized as an excellent solution of global warming and oil crisis, electric vehicles (EVs), however, suffer remarkable weakness, such as the limited cruise range, which can be partly addressed by introducing en-route charging navigation systems. Different from traditional navigation, which solves a shortest path problem, the en-route charging navigation resorts to a joint charging and routing optimization. In this brief, we formulate the en-route charging navigation in a dynamic programming setting in both a deterministic and a stochastic traffic network. Specifically, to relieve computational complexity in navigation systems, a simplified charge-control (SCC) algorithm is presented in the deterministic case, which can simplify the charging control decisions within an SCC set. In the stochastic case, an online state recursion algorithm is designed, which can provide an accurate navigation utilizing online information. Numerical simulation verifies the computing burden and accuracy of the proposed algorithms in a deterministic and a stochastic network.

Intelligent Vehicle-to-Vehicle Charging Navigation for Mobile Electric Vehicles via VANET-Based Communication

A direct vehicle-to-vehicle (V2V) charging scheme supplies flexible and fast energy exchange way for electric vehicles (EVs) without the supports of charging stations. Main technical challenges in cooperative V2V charging may include the efficient charging navigation structure designs with low communication loads and computational complexities, the decision-making intelligence for the selection of stopping locations to operate V2V charging services, and the optimal matching issue between charging EVs and discharging EVs. In this paper, to solve the above problems, we propose an intelligent V2V charging navigation strategy for a large number of mobile EVs. Specifically, by means of a hybrid vehicular ad-hoc networks (VANETs) based communication paradigm, we first study a mobile edge computing (MEC) based semi-centralized charging navigation framework to ensure the reliable communication and efficient charging coordination. Then, based on the derived charging models, we propose an effective local charging navigation scheme to adaptively select the optimal traveling route and appropriate stopping locations for mobile EVs via the designed Q-learning based algorithm. After that, an efficient global charging navigation mechanism is proposed to complete the best charging-discharging EV pair matching based on the constructed weighted bipartite graph. A series of simulation results and theoretical analyses are presented to demonstrate the feasibility and effectiveness of the proposed V2V charging navigation strategy.

Electric Vehicle Route Selection and Charging Navigation Strategy Based on Crowd Sensing

This paper has proposed an electric vehicle (EV) route selection and charging navigation optimization model, aiming to reduce EV users’ travel costs and improve the load level of the distribution system concerned. Moreover, with the aid of crowd sensing, a road velocity matrix acquisition and restoration algorithm is proposed. In addition, the waiting time at charging stations is addressed based on the queue theory. The formulated objective of the presented model is to minimize the EV users’ travel time, charging cost or the overall cost based on the time of use price mechanism, subject to a variety of technical constraints such as path selections, travel time, battery capacities, and charging or discharging constraints, etc. Case studies are carried out within a real-scale zone in a city where there are four charging stations and the IEEE 33-bus distribution system. The effects of real-time traffic information acquisition and different decision targets on EV users’ travel route and effects of charging or discharging of EVs on the load level of the distribution system are also analyzed. The simulation results have demonstrated the feasibility and effectiveness of the proposed approach.

Real-Time Charging Navigation of Electric Vehicles to Fast Charging Stations: A Hierarchical Game Approach

This paper proposes an integrated electric vehicle (EV) charging navigation framework, which takes into consideration the impacts from both the power system and transportation system. The proposed framework links the power system with transportation system through the charging navigation of massive EVs. It benefits the two systems by attracting EVs to charge at off-peak hours and saving the time of EV owners with real-time navigation. Based on the formulated framework, a hierarchical game approach is proposed in this paper to effectively navigate EVs to electric vehicle charging stations (EVCSs). At the upper level of the hierarchical game, a non-cooperative game is proposed to model the competition between EVCSs. Based on the pricing strategies obtained from the non-cooperative game, multiple evolutionary games are formulated at the lower level to evolve EVs’ strategies in choosing EVCSs. The simulation results show that the proposed integrated charging navigation approach is effective in improving both the reliability of the power distribution grid and economic profits of the charging stations.