Charging Navigation Research Articles

Electric Vehicle Integration in Coupled Power Distribution and Transportation Networks: A Review

Integrating electric vehicles (EVs) into the coupled power distribution network (PDN) and transportation network (TN) presents substantial challenges. This paper explores three key areas in EV integration: charging/discharging scheduling, charging navigation, and charging station planning. First, the paper discusses the features and importance of EV integrated traffic–power networks. Then, it examines key factors influencing EV strategy, such as user behavior, charging preferences, and battery performance. Next, the study establishes an EV charging and discharging model, with particular emphasis on the complexities introduced by factors such as pricing mechanisms and integration approaches. Furthermore, the charging navigation model and the role of real-time traffic information are discussed. Additionally, the paper highlights the importance of multi-type charging stations and the impact of uncertainty on charging station planning. The paper concludes by identifying significant challenges and potential opportunities for EV integration. Future research should focus on enhancing coupled network modeling, refining user behavior models, developing incentive pricing mechanisms, and advancing autonomous driving and automated charging technologies. Such efforts will be essential for achieving a sustainable and efficient EV ecosystem.

Fast Charging Navigation Strategy of EVs in Power-Transportation Networks: A Coupled Network Weighted Pricing Perspective

Fast Charging Navigation Strategy of EVs in Power-Transportation Networks: A Coupled Network Weighted Pricing Perspective

Flexible resource demand response scheduling strategy under 5G-V2X

Electric vehicles (EV) and 5 G base stations, functioning as flexible load resources, contribute significantly to the provision of ample reserve capacity for the power grid. The emergence of 5 G Vehicle-to-Everything (V2X) technology has fostered a coupling between EV and base stations, further enhancing their roles within the power grid. Building upon this foundation, this paper proposes a scheduling strategy for demand response in flexibility resources under the 5 G-V2X framework. Initially, the dispatchable load of EV is examined in conjunction with the traffic network. Subsequently, we consider the communication power consumption resulting from the connected vehicle model, enabling us to devise path planning and charging navigation strategies for EV. Lastly, we establish a scheduling decision model that aims to minimize both the operation costs of base stations and the variance in grid load. The simulation is validated using real-world road conditions in the primary urban area of a city. The findings illustrate that the proposed dispatching strategy not only achieves a mutually beneficial outcome for EV users and aggregators, without disrupting EV charging and discharging processes, but also enhances the capability of demand response, thereby ensuring reliable grid operation.

An intelligent partial charging navigation strategy for electric vehicles

ABSTRACT The rise of electric vehicles (EVs) has highlighted the issue of insufficient charging infrastructure, a major barrier to their widespread adoption. This problem is due to the limited capacity of existing charging networks and the performance constraints of EV batteries. Intelligent route planning, which optimizes travel time and energy use, could be a solution. Traditional studies on electric vehicle route planning oversimplify the charging process, assuming a linear relationship between charging level and time, neglecting its actual nonlinear nature. Additionally, these studies underestimate the impact of dynamically changing traffic conditions and charging station queue times on travel time. Integrating these factors into route planning can significantly reduce energy consumption and travel time. Our work proposes an intelligent partial charging strategy, relaxing full recharging restrictions, and fully considering the nonlinear battery characteristics, dynamic traffic conditions, and charging station queue times. Employing an improved genetic algorithm for route planning, our method demonstrates a dual advantage – reducing computational complexity and saving an average of 15% in travel time. Experimental results over a 400 km2 traffic road network validate the efficiency of our approach, outperforming existing methods facing similar challenges.

Electric vehicle charging navigation strategy in coupled smart grid and transportation network: A hierarchical reinforcement learning approach

Most of the existing electric vehicle (EV) charging navigation methods do not simultaneously take into account the electric vehicle charging destination optimization and path planning. Moreover, they are unable to provide online real-time decision-making under a variety of uncertain factors. To address these problems, this paper first establishes a bilevel stochastic optimization model for EV charging navigation considering various uncertainties, and then proposes an EV charging navigation method based on the hierarchical enhanced deep Q network (HEDQN) to solve the above stochastic optimization model in real-time. The proposed HEDQN contains two enhanced deep Q networks, which are utilized to optimize the charging destination and charging route path of EVs, respectively. Finally, the proposed method is simulated and validated in two urban transportation networks. The simulation results demonstrate that compared with the Dijkstra shortest path algorithm, single-layer deep reinforcement learning algorithm, and traditional hierarchical deep reinforcement learning algorithm, the proposed HEDQN algorithm can effectively reduce the total charging cost of electric vehicles and realize online real-time charging navigation of electric vehicles, that shows excellent generalization ability and scalability.

Smart and efficient EV charging navigation scheme in vehicular edge computing networks

With the increasing number of electric fast charging stations (FCSs) deployed along roadsides of both urban roads and highways, the long-distance travel of electric vehicles (EVs) becomes possible. The EV charging navigation scheme is significant for the quality of user experience. However, the variable conditions of both power grid and traffic networks make it a serious challenge. In this paper, we propose an efficient EV charging navigation scheme while considering both the electric and computation resource sharing. With the support of vehicular edge computing networks in intelligent transportation systems (ITSs), EVs perform both the flexible power load and edge computing nodes. When the traffic network in the established route starts to become congested, EVs can select to enter the nearest FCS. In addition to being supplemented by electric resources, EVs also benefit by sharing their own computing resource with FCSs. We formulate the EV charging navigation as a mixed integer programming problem, the EV moving route planning, FCS selection, and staying time in FCSs are optimized, to balance the relationships among the traveling time, traveling cost and reward. To address the influence caused by the randomness of traffic conditions and charging prices, a two-stage charging navigation algorithm combined with A∗\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$A^{*}$$\\end{document} algorithm and deep reinforcement learning (DRL) is proposed, with a novel designed reward function. Eventually, numerous experimental results show the effectiveness of the proposed schemes.

Dynamic impedance model based two‐stage customized charging–navigation strategy for electric vehicles

AbstractWith the continuously increasing penetration of electric vehicles (EVs), the mutual match between the distribution of charging resources and the spatial–temporal distribution of EV charging demands is becoming increasingly important. To address this, this paper proposes a novel two‐stage customized EV charging–navigation strategy. Building on previous research on the real‐time information from dynamic traffic networks, a personalized dynamic road impedance (PDRI) model is built to transform three main criteria (distance, time, and finance) affecting charging–navigation into comprehensive road impedance. In the first navigation stage, fast‐charging stations (FCSs) with the lowest overall objective are selected. In the second navigation stage, an improved Floyd–Warshall algorithm is utilized to identify the routes with the lowest personalized weight to the selected FCS in the PDRI model. Notably, the personalized preferences of EV drivers for the three primary criteria are considered in both stages of the navigation process. Finally, simulation results demonstrate a significant improvement in the degree of matching between charging navigation plans and drivers' personalized requirements, and a more balanced spatial–temporal distribution of EV charging demands among FCSs, which verifies the effectiveness of the proposed strategy.

Charging navigation strategy for electric vehicles considering empty-loading ratio and dynamic electricity price

While electric vehicle users enjoy the convenience of electrified transportation, their random driving characteristics and disorderly charging will cause traffic congestion and aggravate the peak valley difference of power grid load. An electric vehicle charging navigation strategy that takes into account the idle rate and dynamic tariff is proposed in this paper, which improves the load characteristics and reduce traffic congestion. Firstly, the electric vehicle charging navigation model with the integration of “four networks and four flows” is established. The interaction effect between the charging demand of electric vehicle users and the information network, traffic network, power grid is analyzed. Secondly, the multi-dimensional spatial and temporal characteristics of the empty-loading ratio of the electric taxi are studied. The optimal charging period for electric vehicle users is determined by the intelligent charging navigation terminal based on real-time road condition information. The path planning is carried out with the goal of minimizing the comprehensive cost of charging time and dynamic charging cost, and the electric taxi is guided to the optimal charging station to achieve orderly charging in space. Finally, taking the coupling system of traffic network and power grid in an urban area as an example, the proposed strategy is simulated and analyzed. The results show that this strategy can effectively balance the load of each charging station, improve the load characteristics of distribution network, which can effectively divert traffic flow, alleviate traffic congestion, and improve the operating income of electric taxi.

Pricing Based Charging Navigation Scheme for Highway Transportation to Enhance Renewable Generation Integration

The charging operation is rapidly becoming a key concern for highway charging station operation as the electric vehicle charging demand for highway traveling rises. This paper proposes a charging price-setting scheme for highway charging station operation, considering distributed renewable energy generators neighboring highway charging stations. For this purpose, a bi-level approach is proposed in this paper to model the conflicting objectives between the highway charging station operator and electric vehicles to optimize charging price and their routes. The upper level is designed to optimize the highway charging station operation's profit. The lower level characterizes the charging behavior of EVs via a time-space network model. To solve the proposed bi-level model, we adopt the column-and-constraint generation algorithm to obtain an optimal result. The performance of the proposed scheme is illustrated by numerical case studies based on an experimental highway system.

En-Route Electric Vehicles Charging Navigation Considering the Traffic-Flow-Dependent Energy Consumption

En-route charging navigation system is significant for electric vehicle (EV) owners to relieve the range anxiety. Considering the limited battery capacity of EVs, accurate battery energy consumption prediction is crucial for charging navigation in that it determines the maximum traveling distance. This article develops a comprehensive EV energy consumption prediction model and the impacts of the traffic flow on the motor efficiency and driving resistances have been thoroughly considered. Moreover, a stochastic traffic flow model is proposed to improve the energy consumption prediction precision, in which the related uncertainties, such as travel time, vehicle speed, etc., are captured. Then, a novel cost-effective en-route EV charging navigation problem is formulated as a two-stage stochastic optimization problem. In addition, the EV charging behavior and its interaction with the routing selection are characterized in the navigation problem. Finally, an improved Benders Algorithm is modified to obtain the global optimal solution of the proposed navigation model. The simulation verification is carried out in the actual road network of a certain city and the IEEE 33-node coupling system. Results demonstrate the feasibility and effectiveness of the proposed model and solution algorithm.

An Improved Charging Navigation Strategy of Electric Vehicles via Optimal Time-of-Use Pricing

Electric vehicles (EVs) have attracted worldwide attention and have been vigorously promoted by the government. However, users’ mileage anxiety and the intermittent charging load are still great challenges to the popularization of EVs. To tackle these problems, this paper proposes an improved charging navigation model, aiming at maximizing the benefits of multiple parties by setting time-of-use (TOU) price of fast charging stations (FCSs) to attract EVs to charge at off-peak hours and saving the costs of EVs with real-time navigation. It is modeled as a Stackelberg game, in which the FCSs operator and EVs are the leader and followers respectively. First, a rigorous and dedicated traffic simulation model, air conditioner energy consumption model and queuing model of EVs are established. Then, by researching the impact of prices on charging choices, an EV strategy including the selection of charging time, charging energy, charging station and routes is proposed to minimize EV’s cost. Based on EV charging response behavior, the optimal TOU pricing strategy for FCSs is formulated to maximize FCSs’ revenue. The simulation results confirm that the proposed approach is beneficial to EVs and FCSs, and can effectively reduce the peak-to-average ratio and the peak-valley difference of the power system.

Routing Optimization of Electric Vehicles for Charging With Event-Driven Pricing Strategy

With the increasing market penetration of electric vehicles (EVs), the charging behavior and driving characteristics of EVs have an increasing impact on the operation of power grids and traffic networks. Existing research on EV routing planning and charging navigation strategies mainly focuses on vehicle-road-network interactions, but the vehicle-to-vehicle interaction has rarely been considered, particularly in studying simultaneous charging requests. To investigate the interaction of multiple vehicles in routing planning and charging, a routing optimization of EVs for charging with an event-driven pricing strategy is proposed. The urban area of a city is taken as a case for numerical simulation, which demonstrates that the proposed strategy can not only alleviate the long-time queuing for EV fast charging but also improve the utilization rate of charging infrastructures. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article was inspired by the concerns of difficulties for electric vehicle (EV)’s fast charging and the imbalance of the utilization rate of charging facilities. Existing route optimization and charging navigation research are mainly applicable to static traffic networks, which cannot dynamically adjust driving routes and charging strategies with real-time traffic information. Besides, the mutual impact between vehicles is rarely considered in these works in routing planning. To resolve the shortcomings of existing models, a receding-horizon-based strategy that can be applied to dynamic traffic networks is proposed. In this article, various factors that the user is concerned about within the course of driving are converted into driving costs, through which each road section of traffic networks is assigned the corresponding values. Combined with the graph theory analysis method, the mathematical form of the dynamic traffic network is presented. Then, the article carefully plans and adjusts EV driving routes and charging strategies. Numerical results demonstrate that the proposed method can significantly increase the adoption of EV fast charging while alleviating unreasonable distributions of regional charging demand.

Reinforcement Learning-Based Multiple Constraint Electric Vehicle Charging Service Scheduling

The popularization of electric vehicles faces problems such as difficulty in charging, difficulty in selecting fast charging locations, and comprehensive consideration of multiple factors and vehicle interactions. With the increasingly mature application of navigation technology in vehicle-road coordination and other aspects, the proposal of an optimal dynamic charging method for electric fleets based on adaptive learning makes it possible for edge computing to process electric fleets to effectively execute the optimal route charging plan. We propose a method of electric vehicle charging service scheduling based on reinforcement learning. First, an intelligent transportation system is proposed, and on this basis a framework for the interaction between fast charging stations and electric vehicles is established. Subsequently, a dynamic travel time model for traffic sections was established. Based on the habits of electric vehicle owners, an electric vehicle charging navigation model and a reinforcement learning reward model were proposed. Finally, an electric vehicle charging navigation scheduling method is proposed to optimize the service resources of the fast charging stations in the area. The simulation results show that the method balances the charging load between stations, can effectively improve the charging efficiency of electric vehicles, and increases user satisfaction.

Hierarchical Voltage Control Strategy in Distribution Networks Considering Customized Charging Navigation of Electric Vehicles

This paper proposes a three-layer hierarchical voltage control strategy for distribution networks considering the customized charging navigation of electric vehicles (EVs). In the first layer, optimal power flow (OPF) is performed to determine the day-ahead dispatch of on-load tap changer (OLTC) and capacitor banks (CBs). The optimization problem is formulated as a mixed-integer second-order cone programming (MISOCP) which can be effectively solved. In the second layer, a customized charging navigation strategy is proposed to conduct the charging behaviors of EVs based on their own preferences. The novel preference modes are designed for different types of EV users to include not only the charge and time cost, but also the willingness to participate in voltage regulation service (VRS). The navigation problem is formulated as a mixed-integer linear programming (MILP), which is then solved by CPLEX solvers embedded with Dijkstra algorithm. In the third layer, charging stations measure local voltage and regulate the charging power of EVs to mitigate voltage violation. The charging selection and power allocation process are performed dynamically considering the mutual effect between the second and third layers. The economic compensation mechanism is also designed for both EV users and charging stations. The proposed approach is tested on the IEEE 33-bus distribution system coupled with a 24-bus transportation system, and simulation results verify the effectiveness both in charging navigation and mitigating voltage violation.

An intelligent charging navigation scheme for electric vehicles using a cloud computing platform

ABSTRACT Due to reasons of short range, long charging time, and insufficient information exchange between charging infrastructures, more attention needs to be paid to the charging problems of EVs. This work mainly focuses on designing an intelligent charging navigation method for EVs, building the logic framework, and internal structure framework. Then, battery SOC and energy consumption of EVs were predicted for the range, and queueing theory was used to calculate the charging queueing time. Furthermore, the historical trend method is used to predict road travel time. Finally, combined with the above data, an EV charging navigation model based on the shortest time was established and related algorithms were written to solve the model by using Dijkstra algorithm and an example is given to verify the feasibility of the intelligent charging navigation for EVs. About 24 minutes’ driving time and a total of 16.63 minutes were saved for the owner from charging decision to charging completion on a 50 km drive.

A Distributed EV Navigation Strategy Considering the Interaction Between Power System and Traffic Network

Increasing number of drivers are switching to electric vehicles (EV) for higher efficiency, lower environmental impact and less maintenance cost. To promote the EV revolution, charging issues of EVs needs to be well addressed. Since the traffic flow of the transportation network and the operating conditions of the power systems are time-varying, it is important to implement real-time charging navigation for EV drivers. In this paper, a novel navigation approach is proposed to search the fast charging station with the lowest overall objective, which consists both of the time consumption and the financial cost. The traffic condition and distribution system loading level are reflected by the time consumption on each road section and locational marginal price (LMP) at each fast charging station, respectively. The LMP can act as a signal to divert the EV load and thus relieve the traffic and power line congestions. The proposed navigation approach is based on the multi-agent system framework utilizing the distributed min/max- consensus and the biased min consensus algorithms. Simulations demonstrate the effectiveness of the proposed navigation approach in different scales of systems.

A Personalized Fast-Charging Navigation Strategy Based on Mutual Effect of Dynamic Queuing

Proper selection of electric vehicle (EV) path will be helpful to improving the travel efficiency of EV users and alleviate their difficulties in charging during peak hours. In this context, a personalized fast-charging navigation strategy based on mutual effect of dynamic queuing is proposed. First, an effective charging navigation system is presented to enable the integration of EV information terminals, fast-charging stations (FCSs), intelligent transportation system, and information processing center (IPC). Next, by incorporating the real-time mutual effect of dynamic queuing as well as considering the priority reservation cost of the EV drivers who reserve charging priority before others, a dynamic reservation-waiting queue model is established. After that, on the basis of the above model, the coupling relations of the criteria that may affect the charging experience are investigated for the first time and the charging navigation plans in IPC are optimized for diverse EVs. Finally, the validity of the method is verified by conducting the simulation under the participation of 2250 EVs in the urban areas of a city. Results show that the proposed charging strategy will increase charging satisfaction on average by more than 10.9% in all analyzed cases. Additionally, the navigation can save the charging cost of EV owners while also improve the operation efficiency of FCSs.

Price Incentive-Based Charging Navigation Strategy for Electric Vehicles

With rapid development of the electric vehicle (EV) industry, charging infrastructures are built fast. However, the unreasonable deployments with increasing EVs contribute to a long queuing time for charging demand of EVs, especially in the peak hours. How to navigate a specific EV to economically satisfy its charging demand, while relieve the traffic burden, is an urgent problem. To address that, a price incentive-based charging navigation strategy for EVs is proposed. Unlike previous charging navigation studies that mainly focus on the EVs-transportation-power systems modeling, it considers the spatial-temporal influence of EVs’ charging decision, especially the simultaneous charging requests. Specifically, the charging navigation framework with the collaborative working mode of EV-charging station-information exchange center-intelligent transportation system is established first. Following this, spatiotemporal distribution of the charging demand is obtained through the origin–destination analysis. After this, an event-driven dynamic queue model is constructed. It contributes to the modeling of the charging strategy, together with the proposed reservation opportunity cost mechanism. Finally, the simulation results indicate that the presented charging navigation strategy can not only reduce the EV's charging cost but also improve the utilization rate of charging facilities, which verify its effectiveness.

Dynamic Response Characteristics of Fast Charging Station-EVs on Interaction of Multiple Vehicles

In view of the existing problems that multiple vehicles interaction in the selection of fast charging stations for electric vehicles (EVs) and the equalizing the service capability by multiple stations game in station-EVs interaction, a dynamic response strategy of fast charging station-EVs considering interaction of multiple vehicles is proposed. According to this, the charging scheme of EVs and the dynamic service fee of charging stations are decided. Firstly, the charging guidance framework of station-EVs interaction is proposed to describe the information flow relationship for vehicle, station, road and intelligent transportation system (ITS). Secondly, in order to meet the diversified needs of car owners in charging selection, a charging navigation model is established. Considering the impact of dynamic path travel time, a dynamic path selection model of urban road network is established based on the road segment transmission model. Thirdly, in order to accurately analyze the interaction process between vehicles, a charging decision-making method is proposed considering the dynamic evolution of EVs, which reflects the station selection probability of different positions during driving. Fourthly, according to the queuing time of the charging station, the service fee of the charging station is dynamically adjusted to optimize the service capacity of the charging station, and the multi-agent stackelberg game model is established by combining the charging station selection of EVs with the dynamic service fee of charging station. Finally, Sioux Falls urban road network system is used as an example to analyze the path selection, dynamic decision of charging station selection and service fee, and station-EVs interaction strategy. The results show that this method improves the efficiency of electric vehicle charging station searching, guides EVs in the road network to charge orderly, balances the charging load between charging stations and optimizes the service capacity of charging station reasonably.

Route Planning and Charging Navigation Strategy for Electric Vehicles Based on Real-time Traffic Information and Grid Information

. Aiming at the characteristics of Electric Vehicles (EVs) combined with transportation and mobile load, and their charging and driving behaviors will interact with the traffic system and power grid system. On the basis of this, a route planning and charging navigation strategy for EVs based on real-time traffic information and grid information was presented in this paper. First, a dynamic traffic network model was established according to the time-varying feature of traffic information. And also, the road resistance model with road segment impedance and intersection node impedance was proposed depending on the attribute of urban roads. Furthermore, based on the establishment of traffic network model, distribution network model and single EV model, a multi-objective optimization function integrated with the road travel time, the charging station load and the quantity of vehicles entering into the station was determined, which was solved to recommend the optimal driving and charging paths by dynamic Dijkstra dynamic search algorithm. At last, the actual road network of a certain zone in Nanjing was selected as an example to analyze the spatial-temporal distribution of EV charging load, and evaluated the impact of its charging and driving on traffic network and distribution network. Simulation results demonstrate its effectiveness and feasibility.