Wireless Charging Infrastructure Research Articles

Optimizing wireless charging infrastructure placement using genetic algorithms

Electric vehicles (EVs) play a crucial role in tackling environmental issues in the transportation industry. The incorporation of effective charging infrastructure is crucial in promoting the broad acceptance of electric vehicles (EVs). This work investigates the optimization of the location of wireless charging infrastructure in urban contexts using genetic algorithms (GAs). The location data, which includes latitude and longitude coordinates, showed a wide range of spatial distributions that are ideal for deploying charging stations. These distributions display variances that are favorable for strategically placing the infrastructure. The examination of power consumption data revealed significant variations in energy demand across different sites, ranging from 180 kWh to 300 kWh. These differences indicate that each location has its own distinct energy needs. The population density statistics exhibited a spectrum of values, ranging from 600 individuals per square unit. The population density is 1200 persons per square kilometer. The abbreviation "km" refers to kilometers, which is used to indicate different levels of prospective electric vehicle (EV) users. In addition, the distance data provided information about the lengths between prospective locations for charging stations, which varied from 400 km to 1200 km. These distances had an impact on the concerns of connection and transmission efficiency. The research highlights the intricate nature of the elements that affect the ideal location of infrastructure, underlining the need for a methodical approach to optimization. Integrating these statistics provides a foundation for developing an objective function in the GA framework to optimize the location of charging infrastructure. The study's results provide valuable understanding of the many factors that influence the location of charging infrastructure. The goal is to promote the development of efficient and easily accessible electric vehicle charging networks in metropolitan areas.

Optimal Planning of Dynamic Wireless Charging Infrastructure for Electric Vehicles

Optimal Planning of Dynamic Wireless Charging Infrastructure for Electric Vehicles

Multi-Criteria Design of Electric Transit Bus Based on Wireless Charging Infrastructure: A Case Study of Real Road Map in Wakefield

In this paper, a new design strategy is developed for the Wireless Charging Electric Transit Bus (WCETB). The technology is innovative in that the battery in the bus is charged while it is moving over the charging infrastructure. In particular, an improved version of the Whale Optimization Algorithm (IWOA) is adopted for the WCETB system in the road map of Wakefield City, located in the United Kingdom. The main challenge in the WCETB is to select the power transmitter and battery size efficiently from an economical point of view. For this purpose, both factors are considered in the objective function to achieve the benefits of WCETBs from an energy perspective. Two analytical economic design optimization models are developed in this work. The first model is the real- environment model, which considers a WCETB system operating under typical traffic conditions characterized by vehicle interactions and inherent uncertainties. In this scenario, vehicle speeds vary with time, and specific traffic routes may encounter congestion. The second model concentrates on a WCETB system operating in a traffic-free environment with minimal vehicle interactions and uncertainties. The IWOA is implemented for the WCETB to operate in the real environment. Under traffic-free environment conditions, we utilize mathematical procedures and General Algebraic Modeling System (GAMS) software to solve the optimization problem. This approach not only allows us to comprehensively analyze the WCETB system’s behavior but also examine the interactions among different components of the objective function and constraints. Finally, a comprehensive numerical analysis under various conditions, including changes in the number of buses and increases in the length of routes, is conducted.

Round-Trip Wireless Charging Infrastructure for Heterogeneous Electric Vehicles on Highways: Modelling and Optimization

In this paper, we propose a new approach to dynamic wireless charging that allows electric vehicles to charge wirelessly while in motion in both lanes on highways. The challenge is to locate the charging infrastructure on a highway between origin O and destination S (round trip) with heterogeneous battery vehicles, where each type of vehicle requires its allocation of charging segments on the road. We aim to ensure that each type of vehicle can complete a round trip without running out of battery charge while minimizing the number of charging segments and inverters on the road by studying both lanes simultaneously. We model the problem mathematically and validate it using a CPLEX optimizer for limited instances. Finally, we solve the problem using a hybrid approach that combines genetic algorithms and local search techniques to balance diversification and intensification. We have significantly improved the results found in the literature by reducing the number of inverters, which are expensive components in the charging infrastructure. Our approach takes advantage of utilizing a single inverter for both lanes of the highway, leading to cost savings and improved efficiency.

Performance Analysis of Charging Infrastructure Sharing in UAV and EV-Involved Networks

Electric vehicles (EVs) and unmanned aerial vehicles (UAVs) show great potential in modern transportation and communication networks, respectively. However, with growing demands for such technologies, the limited energy infrastructure becomes the bottleneck for their future growth. It might be of high cost and low energy efficiency for all the operators to each have their own dedicated energy infrastructure, such as charging stations. In this paper, we analyze a wireless charging infrastructure sharing strategy in UAV and EV-involved networks. We consider a scenario where UAVs can charge in EV charging stations and pay for the sharing fee. On the EVs' side, sharing infrastructure can earn extra profit but their service quality, such as waiting time, might slightly reduce. On the UAVs' side, if renting EV charging stations can achieve an acceptable system performance, say high coverage probability, while considering the cost, they may not need to build their dedicated charging stations. In this case, we use tools from stochastic geometry to model the locations and propose an optimization problem that captures the aforementioned trade-offs between cost or profit and quality of service. Our numerical results show that sharing infrastructure slightly increases the waiting time of EVs, say within 5 min, but dramatically decreases the waiting time of drones, say more than 50 min, and deploying more charging stations do achieve better performances, but all these better performances are expected to cost more.

Simulated and real world tests to compare drivers performance in dynamic wireless technology perspective

The ongoing electrification of road vehicles needs to be supported by proper growth of charging infrastructure. In this context, dynamic wireless charging can provide a number of advantages, main being the possibility to extend vehicle range without increasing on board battery capacity, potentially reducing cost, mass, and tank-to-wheel energy consumption. The development of such solutions however poses various questions, including the acceptance and the capability of users in driving according to infrastructure characteristics; in particular, the misalignment reduction while a driver follows a straight path positively influence the charging efficiency in dynamic wireless technology. In this paper, authors describe a tests campaign to determine driving performances using both a simulator and a real world equipped vehicle. The research question of this paper is to assess and quantify differences between the two approaches. To reach this objective, in a first phase, data have been collected through a driving simulator (i.e. a full car body mounted on a parallel linked kinematics with a large screen, proposing a virtual city scenario), and in a second phase through a car equipped with a camera. As a post processing phase, statistical tools have been used to describe driving performance indexes and related impacts on wireless charging infrastructure by determining the secondary voltage on the vehicle. Data coming from the activity will be functional to be used by scenario analyst to develop characterization tests only with a simulative approach to decrease costs.

Machine Learning for Relaying Topology: Optimization of IoT Networks With Energy Harvesting

In this paper, we examine Internet of Things (IoT) systems related to smart cities, smart factories, connected cars, etc. To support such systems in a wide area with low power consumption, energy harvesting technology utilizing wireless charging infrastructure is necessary for the longevity of networks. Considering that the position and amount of energy charged for each device could be unbalanced according to the distribution of nodes and energy sources, maximizing the minimum throughput among all nodes has become an NP-hard challenging issue. To overcome this challenge, we propose a machine learning based relaying topology algorithm with a novel backward-pass rate assessment method to present proper learning direction and an iterative balancing time slot allocation algorithm which can utilize a node with sufficient energy as the relay. To validate our proposed scheme, we conducted simulations on our established system model; thus, we confirm that the proposed scheme is stable and superior to conventional schemes.

Optimal Deployment of Wireless Charging Infrastructure for Electric Tram with Dual Operation Policy

The wireless charging electric tram system is presently receiving attention as an eco-friendly means of transportation. The conventional electric tram system has a similar advantage in regards to environmental pollution, but it has several problems that are caused by the overhead power supply line. The battery-type electric tram system should be considered carefully, because the battery itself is an environmentally harmful material. Therefore, the wireless charging electric tram system is regarded as an alternative means of transportation. The adequate battery capacity and the location of the wireless charging infrastructure are investigated in this study, which consider the dual operation policy, and the objective is to minimize the total investment cost. The variation of the battery capacity and the location of the wireless charging infrastructure are examined that compare Case 1, which involves the electric trams operating only in normal operations, and Case 2, which includes the electric trams operating in normal and express operations.

Sustainable Electric Personal Mobility: The Design of a Wireless Charging Infrastructure for Urban Tourism

Though new technologies have been applied in all industries, electric mobility technology using eco-friendly energy is drawing a great deal of attention. This research focuses on a personal electric mobility system for urban tourism. Some tourism sites such as Gyeongju, Korea, have broad spaces for tourists to walk around, but the public transportation system has been insufficiently developed due to economic reasons. Therefore, personal mobility technology such as electric scooters can be regarded as efficient alternatives. For the operation of electric scooters, a charging infrastructure is necessary. Generally, scooters can be charged via wires, but this research suggests an advanced electric personal mobility system based on wireless electric charging technology that can accommodate user convenience. A mathematical model-based optimization was adopted to derive an efficient design for a wireless charging infrastructure while minimizing total investment costs. By considering the type of tourists and their tour features, optimal locations and lengths of the static and dynamic wireless charging infrastructure are derived. By referring to this research, urban tourism can handle transportation issues from a sustainable point of view. Moreover, urban tourism will have a better chance of attracting tourists by conserving heritage sites and by facilitating outdoor activities with electric personal mobility.

Efficient Deployment Design of Wireless Charging Electric Tram System with Battery Management Policy

As an alternative to the environmental pollution problem of transportation means, the application of electric tram is considered in urban areas. However, due to the aesthetic problems occurs by the electric supply line for an electric tram, the wireless charging electric tram may be regarded as an alternative. It can be supplied electricity wirelessly from the wireless charging infrastructure installed on the railways even while moving. For a successful application, it is important to install and operate the overall systems with minimum investment cost. In this study, a mathematical model-based optimization technique, one of the methods of operations research, is adopted to derive the decision-making elements such as capacity and management of battery and allocation of the wireless charging infrastructure. Numerical example shows the optimal capacity and management of battery for a wireless charging electric tram and the ideal installation locations of the wireless charging infrastructures.

Analysis of Battery Reduction for an Improved Opportunistic Wireless-Charged Electric Bus

As an attractive alternative to the traditional plug-in charged electric vehicles (EVs), wireless-charged EVs have recently been in the spotlight. Opportunistically charged utilizing the wireless-charging infrastructure installed under the road at bus stops, an electric bus can have a smaller and lighter battery pack. In this paper, an improved opportunistic wireless-charging system (OWCS) for electric bus is introduced, which includes the opportunistic stationary wireless-charging system (OSWCS) and opportunistic hybrid wireless-charging system (OHWCS) consisting of stationary wireless-charging and dynamic wireless-charging. A general battery reduction model is established for the opportunistic wireless-charged electric bus (OWCEB). Two different battery-reduction models are built separately for OWCEB on account of the characteristics of OSWCS and OHWCS. Additionally, the cost saving models including the production cost saving, the operation cost saving and total cost saving are established. Then, the mathematical models are demonstrated with a numerical example intuitively. Furthermore, we analyze several parameters that influence the effectiveness of battery reduction due to the application of an opportunistic wireless-charging system on an electric bus. Finally, some points worth discussing in this work are performed.

Wireless charging and shared autonomous battery electric vehicles (W+SABEV): synergies that accelerate sustainable mobility and greenhouse gas emission reduction

Emerging technologies play important roles in shaping future mobility systems and impacting sustainability performance of the transportation sector in major economies, such as the United States of America (USA) and China. This study applies a life cycle framework to demonstrate and evaluate the synergies of the following four emerging transportation system technologies both qualitatively and quantitatively: (1) wireless charging; (2) shared mobility services; (3) autonomous driving; and (4) battery electric vehicles (BEV). The new concept of a wireless charging and shared autonomous battery electric vehicle (W+SABEV) system is introduced and modeled. First, an analytical framework is presented to assess the pros and cons of the W+SABEV system vs. a conventional plug-in charging BEV system, adhering to the principles of sustainable mobility and highlighting the impacts and dynamics of the disruptive technologies on the key parameters that define sustainable mobility. Second, a quantitative analysis presents the synergies of the four technologies by modeling a W+SABEV system and demonstrates that the combination of the four technologies can shorten the payback time of greenhouse gas (GHG) emission burdens for infrastructure and vehicles. Compared to a plug-in charging BEV system, a W+SABEV system pays back the additional GHG emission burdens of wireless charging infrastructure deployment within 5 years if the wireless charging utility factor (ratio of en route charging time vs. trip time) is above 19%.

System Design and Optimization of In-Route Wireless Charging Infrastructure for Shared Automated Electric Vehicles

Deploying shared automated electric vehicles (SAEVs) on current roadways in cities will significantly shape current transportation systems and make our urban mobility systems more efficient, convenient, and environmentally friendly. Utilizing wireless power transfer (WPT) technology to charge the SAEVs provides perfect fits for realizing a fully automated mobility system. However, the investment in wireless charging infrastructure (WCI) presents a critical barrier for commercializing and adopting this technology. The barrier can be cleared by realizing the proper design of the WPT system that maximizes the benefits and minimizes the cost of WCI at the same time. This paper introduces a system design optimization tool and methodology for WCI for serving fixed-route SAEVs in automated mobility districts (AMDs). The tool offers the capability of integrating driving data (simulated or collected from the real world), vehicle parameters (e.g., battery, motor, dimensions, and so on), and wireless charger characteristics (rate, locations, alignment, and so on) to generate energy and state-of-charge profiles for each vehicle, considering motoring, regenerative braking, and charging. Furthermore, the proposed tool incorporates a multi-objective optimization layer for searching the optimum design parameters based on predefined objectives and constraints. The proposed method was utilized to design the WCI for a hypothetical AMD scenario with four SAEVs. The outcomes show that implementing in-route wireless chargers at designated stops for the SAEVs with maximum power level has the potential to provide a charge sustaining operation with 52% reduction in the on-board battery and presents the most cost-effective solution. The proposed solution is assessed in comparison with other charging technologies, such as stationary WPT and dc fast charging, and it shows the most feasible option for an AMD network in terms of cost, convenience, and performance.

An efficient integration of the genetic algorithm and the reinforcement learning for optimal deployment of the wireless charging electric tram system

To solve the two main issues such as long charging down time and expensive battery price of the electric vehicle systems, lots of researchers have studied about advanced charging and battery technology. In same time, the new electric vehicle system which applying the wireless charging technology is also regarded as alternative means of transportation because it can reduce both the long charging down time and the battery capacity. It is called as the wireless charging electric vehicle system and it can be supplied the electricity wirelessly from the wireless charging infrastructure buried under the road. In the similar reason, the wireless charging electric tram system is received lots of interests as the alternative mass transportation in urban and suburb area. To derive the optimal decision-making elements such as the required minimum battery capacity and the allocation of the wireless charging infrastructure, which have a trade-off relationship, a mathematical model is developed. In addition, the genetic algorithm integrated with the reinforcement learning are proposed to generate an optimal solution. By assuming the several integration situations of them, an efficient integration level of the genetic algorithm and the reinforcement learning are examined.

Wireless charger deployment for an electric bus network: A multi-objective life cycle optimization

Deploying large-scale wireless charging infrastructure at bus stops to charge electric transit buses when loading and unloading passengers requires significant capital investment and brings environmental and energy burdens due to charger production and deployment. Optimal siting of wireless charging bus stops is key to reducing these burdens and enhancing the sustainability performance of a wireless charging bus fleet. This paper presents a novel multi-objective optimization model framework based on life cycle assessment (LCA) for siting wireless chargers in a multi-route electric bus system. Compared to previous studies, this multi-objective optimization framework evaluates not only the minimization of system-level costs, but also newly incorporates the objectives of minimizing life cycle greenhouse gas (GHG) emissions and energy consumption during the entire lifetime of a wireless charging bus system. The LCA-based optimization framework is more comprehensive than previous studies in that it encompasses not only the burdens associated with wireless charging infrastructure deployment, but also the benefits of electric bus battery downsizing and use-phase vehicle energy consumption reduction due to vehicle lightweighting, which are directly related to charger siting. The impact of charger siting at bus stops with different route utility and bus dwell time on battery life is also considered. To demonstrate the model application, the route information of the University of Michigan bus routes is used as a case study. Results from the baseline scenario show that the optimal siting strategies can help reduce life cycle costs, GHG, and energy by up to 13%, 8%, and 8%, respectively, compared to extreme cases of “no charger at any bus stop” and “chargers at every stop”. Further sensitivity analyses indicate that the optimization results are sensitive to the initial battery unit price ($/kWh), charging power rate (kW), charging infrastructure costs, and battery life estimation methods.

Efficient design of an operation profile for wireless charging electric tram systems

Abstract Recently, electric trams have been used as an eco-friendly mode of transport in urban areas. Of the various types of electric trams, this study deals with wireless charging electric trams. A wireless charging electric tram can draw electricity wirelessly from a wireless charging infrastructure installed on a railway line and store any excess electricity in its battery. Therefore, we can eliminate the power supply line of conventional electric trams and use smaller batteries than those necessary for battery-powered electric trams. Even when two wireless charging electric trams are operated for the same amount of time, their electricity consumption rates may differ depending on their speed, acceleration, and time spent at stations. Therefore, this study deals with the efficient design of an operation profile (velocity) for wireless charging electric trams that can minimize variations in the battery charge according to electricity consumption, considering the installed wireless charging infrastructure. Mathematical model–based optimization is adopted to derive an efficient design for the operation profile, and an appropriate solution procedure is proposed considering the features of the developed mathematical model. The proposed model and solution procedure are tested using data from Pangyo, Gyeonggi Province, Korea.

System Architecture and Mathematical Models of Electric Transit Bus System Utilizing Wireless Power Transfer Technology

We introduce a new type of electric transit bus (ETB) system that uses the innovative wireless power transfer technology developed by the Korea Advanced Institute of Technology (KAIST), which is called on-line electric vehicle (OLEV). In the ETB system, the wireless-charging infrastructure installed under the road charges the fleet of electric buses that are operative over that road. The technology is innovative in that the battery in the bus is charged while it is moving over the charging infrastructure. Unlike conventional electric vehicles, the OLEV-based ETB system is a road–vehicle integrated system. Since charging occurs while the vehicle is operational, the performance of the operation depends on the system integration of the vehicle and the road in which the charging infrastructure is embedded. In this paper, we qualitatively analyze the benefits of the OLEV-based ETB system from the energy logistics perspective. We then present two analytical economic design optimization models. The first model is for an ETB system operating in a “closed environment” with no traffic and no heavy vehicle interactions. The OLEV-based shuttle bus currently operating on the KAIST campus constitutes such a case. The second model is the “open environment model” and considers an ETB system operating in normal traffic conditions. We also present the result of numerical case studies for the optimization models. The goal of this paper is to present an innovative ETB system and a logical design framework for commercializing and deploying that system.

Electric Vehicles – Personal transportation for the future

Plug-in EVs are being heralded as a step towards acceptance of pure EV’s. However concerns remain regarding uptake by consumers, in terms of range anxiety and economic operation. These concerns are mitigated if energy can be transferred to the vehicle while it is moving, but there are implications in terms of the highway infrastructure required without restricting a vehicle’s freedom to move while providing power over air-gaps in excess of 200mm. Such flexibility brings with it problems that are not normally inherent in stationary systems where electronic alignment may be employed to control magnetic coupling. The system must cope with variances as seen by both the power supply and the EV, while ensuring that the power receiver is compatible with both stationary and roadway systems. This paper discusses recent developments in wireless charging infrastructure for EV’s and describes systems that may be suitable in future. As vehicles operate on the Grid they will constitute a substantial load that must be managed. A solution is presented here at little infrastructural cost such that the Grid, ‘on load’ has better power quality than ‘off-load’.