Wireless Charging of Electric Vehicles Using Solar Road

<div class="section abstract"><div class="htmlview paragraph">Conventional charging systems for electric and plug-in hybrid vehicles currently use cables to connect to the grid. This methodology creates several disadvantages, including tampering, risk, depreciation and non-value added user efforts. Loose or faulty cables may also create a safety issue. Wireless charging for electric vehicles delivers both a simple, reliable and safe charging process. The system enhances consumer adoption and promotes the integration of electric vehicles into the automotive market. Increased access to the grid enables a higher level of flexibility for storage management, increasing battery longevity.</div><div class="htmlview paragraph">The power class of 3.7kW or less is an optimal choice for global standardization and implementation, due to the readily available power installations for potential customers throughout the world. One of the key features for wireless battery chargers are the inexpensive system costs, reduced content and light weight, easing vehicle integration.</div><div class="htmlview paragraph">This paper demonstrates a wireless charging design with minimal component content. It includes a car pickup coil with 300 mm side length and low volume and mass 1.5 dm<sup>3</sup> power interface electronics. After an overview of its hardware requirements, power transfer and efficiency benefits are presented, providing the anticipated horizontal and lateral deviations.</div><div class="htmlview paragraph">An intense magnetic field is required to transfer the target power at low volumes between the transfer units. This field heats up any metal object over the transfer coil, similar to an induction oven. Consequently, the system should be powered down whenever a metal object is detected in this area. A Foreign Object Detection (FOD) design has been developed to continuously monitor the critical high field area. Device testing results are also provided.</div><div class="htmlview paragraph">Field characteristics are verified alongside the vehicle, ensuring system safety for living beings; compliant with all applicable standards reference limits which is more challenging than the basic limits [<span class="xref">13</span>].</div></div>

This paper presents the design, simulation, and experimental validation of a constant current–constant voltage (CC–CV) wireless electric vehicle (EV) charging system utilizing Power Balance Control (PBC) in both primary-side (PS) and secondary-side (SS) converter configurations. The system was tested using a step-resistance load and a typical 72 V, 50 Ah lithium-ion NMC battery to assess control precision, dynamic behavior, and charging efficiency of the system. Experimental results demonstrate that both PS and SS converters employing PBC successfully achieve stable CC–CV operation. The SS converter achieved a peak efficiency of 82%, outperforming 77% from the PS converter. Regarding transient performance, the PBC significantly enhanced settling times over the conventional proportional–integral (PI) controller. Specifically, the settling time was reduced from 600 ms to 100 ms in the PS converter and from 400 ms to 200 ms in the SS converter, with minimal voltage overshoot. Power loss analysis indicated that the highest losses occurred in CC mode, particularly in the inductive power transfer (IPT) coils and high-frequency inverters. The SS control effectively minimized these losses by dynamically adjusting its input resistance to match the optimal load resistance. Overall, the proposed PBC approach, especially in the SS converter, demonstrates efficiency, faster dynamic response, and robust power regulation for next-generation wireless EV charging systems.

The economic viability of on-road wireless charging of electric vehicles (EVs) strongly depends on the choice of the inductive power transfer (IPT) system configuration (static or dynamic charging), charging power level and the percentage of road coverage of dynamic charging. In this paper, a case study is carried out to determine the expected investment costs involved in installing the on-road charging infrastructure for an electric bus fleet. Firstly, a generic methodology is described to determine the driving range of any EV (including electric buses) with any gross mass and frontal area. A dynamic power consumption model is developed for the EV, taking into account the rolling friction, acceleration, deceleration, aerodynamic drag, regenerative braking and Li-ion battery behavior. Based on the simulation results, the linear dependence of the battery state of charge (SoC) on the distance traveled is proven. Further, the impact of different IPT system parameters on driving range is incorporated. Economic implications of a combination of different IPT system parameters are explored for achieving the required driving range of 400 km, and the cost optimized solution is presented for the case study of an electric bus fleet. It is shown that the choice of charging power level and road coverage are interrelated in the economic context. The economic viability of reducing the capacity of the on-board battery as a trade-off between higher transport efficiency and larger on-road charging infrastructure is presented. Finally, important considerations, like the number of average running buses, scheduled stoppage time and on-board battery size, that make on-road charging an attractive option are explored. The cost break-up of various system components of the on-road charging scheme is estimated, and the final project cost and parameters are summarized. The specific cost of the wireless on-road charging system is found to be more expensive than the conventional trolley system at this point in time. With decreasing battery costs and a higher number of running buses, a more economically-viable system can be realized.

The usage of electric vehicles will contribute to a decrease in greenhouse gas emissions and rising fuel costs. To promote acceptance of electrified mobility, a variety of charging networks must be established in an approachable setting. A prospective substitute solution for charging electric vehicles (EVs) without any plug-in issues is wireless electric vehicle charging systems (WEVCS). This paper presents the surface of the wireless charging system flow from the start until the end of the line. The discussions take the consideration from the grid side until to the battery of the EVs including the battery management system (BMS). So, the application of WEVCS also is driven into the impact on the electric power grid. Then, the author also predicts the future of WEVCS based on potential technical advancements and already in effect policies, followed by some conclusions.

Bi-level programming model approach for electric vehicle charging stations considering user charging costs

The charging transaction data of electric vehicle (EV) users is crucial for studying charging market dynamics and formulating effective policies. However, due to factors such as the privacy of EV users and the complex coupling relationships between charging dealers, existing EV charging transaction datasets are plagued by issues such as incompleteness, significant bias, and a lack of real-time information. To address these issues, a real-time charging transaction dataset has been created, comprising 441,077 charging transactions collected from 13 charging stations in China over a 2-year period. The dataset includes detailed data of EV user charging transaction time, price and charging status, as well as the charging termination reasons and weather data for each charging session. This dataset offers references for identifying EV user behaviors and extracting charging fault factors from multiple aspects, supporting research applications in EV charging facility planning, EV charging and discharging management, and charging economic evaluation.

Wireless power transfer is a promising method to address the concerns over charging an electric vehicle. Since wireless charging stations operate without large cables or above-ground stations, they can be conveniently installed in public locations without the risk of vandalism or weather-inflicted damage, improving the lifespan of the electric vehicle charging station. In order for wireless charging stations to become widespread, possible health effects regarding exposure to the strong electromagnetic fields present during wireless power transfer must be investigated. This work examines, first, the potential human safety hazards, second, the electronic device interference, and, third, the thermal heating effects of wireless charging systems. A 3.3 kW wireless power transfer prototype was built in order to examine these effects. Changes in the wireless power transfer efficiency due to the coil misalignment were also investigated using an automated three-axis platform. Design considerations for electric vehicle wireless charging systems and safety recommendations are presented.

This research reviewed challenges in electric vehicles (EVs) charging during trips and proposes a novel powertrain architecture that is cost-effective and time-efficient for EVs to instantly increase their range without deviating from their route. The main objective is to study the feasibility and economic analysis of an additional battery for EV applications without disturbing the traction battery under unknown future charging requirement information about EVs, which helps reduce waiting time at charging stations, mitigate the problem of battery swapping, charging 100% through renewables, reduce the negative impact of EVs on the power grid. This study considers an add-on battery of 15kWh capacity offered as battery-as-a-service (BaaS), available at charging stations or delivered at remote locations for users. We conducted an economic analysis, comparing EVs with and without add-on batteries alongside traditional petrol and diesel vehicles. Based on the results obtained, it can be inferred that adopting this framework reduces the travel time by 55 minutes but increases the cost by US$ 0.176 compared to fast charging for a 400 km trip. We can conclude that EVs with main traction and add-on batteries provide a tradeoff between cost and time and are beneficial for both EV user and add-on battery charging and swapping station (ABCSS) operators.

Reasonable planning scheme of electric vehicle charging station directly affects the economic benefits of charging station operators, and also relates to the convenience and satisfaction of electric vehicle users. In order to balance the interests between charging station operators and electric vehicle users, and provide a reasonable basis for the number of charging piles, this paper studies the planning of electric vehicle charging stations. Firstly, the charging behavior of electric vehicles is analyzed, and the classical queuing theory is used to calculate the charging queuing index of electric vehicles to ensure the charging satisfaction of electric vehicle users. Secondly, the economic benefits of charging station operators and the time cost of users constitute the economic indicators of the system, and the planning model of electric vehicle charging station considering the efficiency of charging station utilization and user waiting time is proposed. Finally, the practicability and effectiveness of the proposed method are verified by 5-node charging station network.

Electric vehicles (EVs) charging infrastructure is directly related to the overall EV user experience and thus impacts the widespread adoption of EVs. Understanding key factors that affect EV users' charging experience is essential for building a robust and user friendly EV charging infrastructure. This study leverages about 17, 000 charging station (CS) reviews on Google Maps to explore EV user preferences for charging stations, employing ChatGPT 4.0 for aspect-based sentiment analysis. We identify twelve key aspects influencing user satisfaction, ranging from accessibility and reliability to amenities and pricing. Two distinct preference models are developed: a micro-level model focused on individual user satisfaction and a macro-level model capturing collective sentiment towards specific charging stations. Both models utilize the LightGBM algorithm for user preference prediction, achieving strong performance compared to other machine learning approaches. To further elucidate the impact of each aspect on user ratings, we employ SHAP (SHapley Additive exPlanations), a game-theoretic approach for interpreting machine learning models. Our findings highlight the significant impact of positive sentiment towards “amenities and location”, coupled with negative sentiment regarding “reliability and maintenance”, on overall user satisfaction. These insights offer actionable guidance to charging station operators, policymakers, and EV manufacturers, empowering them to enhance user experience and foster wider EV adoption.

Electric vehicles (EVs) are becoming a popular mode of transportation. However, one of the main concerns for EV and CNG owners is the availability and accessibility of charging stations. In this project, we present the development and implementation of an EV and CNG charging station finder web application. The app uses the Google Maps API to display the location of nearby pay stations and provide details such as connection type, availability and price. The app also allows users to filter charging and CNG stations according to their preferences and report charging stations as false or unserviceable. User testing is done to evaluate the usability and performance of the application. The results show that the app is easy to use and provides accurate and up-to date information on payment facilities In this system, the User is able to manage All his EVs inside the app plus he has search/book as lot in advance in the charging station. The User can also search an EV station based on nearby, city or kilometers. The System also provides a Roadmap if you enter source & destination with the charging stations on the way according to the kilometers entered. Admin will manage all the stations and slots. The need for developing infrastructure such as charging stations is undeniable. This EV Charging Station web application has been developed to help EV drivers locate available charging stations near them. After locating a charging station, users can also book a slot at the station to charge their vehicle. EV owners can also use this system to plan their trips more efficiently. Users simply need to specify the source and destination. Based on these two parameters, this system prepares a roadmap with all available charging stations along the journey.

Dynamic wireless charging of electric vehicles (EVs) aims at increasing EV range and reducing battery size and associated costs. Moreover, it minimizes the need for recharging stops, thus increasing travel comfort. As electrification of transport ultimately targets CO2 emissions reduction, increased utilization of renewable energy sources is likely to provide the means for reaching the decarbonization objectives. Demand-side management (DSM) enables the increased penetration of intermittent power sources by providing techniques that ensure grid stability. While complete frameworks for DSM deployment in static charging have been designed, these approaches must be re-evaluated with focus on the operational requirements for dynamic EV charging. In this paper, a review of DSM methods targeting static charging is made and one particular case of DSM is simulated in a dynamic charging environment in order to reveal critical aspects towards deployment.

The usage of Electric vehicle (EVs) has been exponentially growing due to its focus on eco-friendly means of transport, distributed charging platform and user dictated supporting infrastructures. The EVs are charged by the charging stations which equipped with Electric Vehicle Supply Equipment (EVSE) that contains Internet enabled computers. These systems are considered to be more important for controlling the function such as charging electric vehicles, authorization and smart connection to the local power grid using different wireless technologies such as green WIFI, Bluetooth and even 5 G. The cyber-attacks such as DoS and DDoS attacks can violate integrity, confidentiality and availability of the EVSE resources. Hence the intelligent Intrusion Detection System (IDS) is required to ensure the system for the robust and trustworthy deployment of EVSE resources. To meet the above challenge, this paper proposes new composite and intelligent system which contains the deep learning based IDS and high random chaotic generators to safeguard the data against the different cyber-attacks. The proposed IDS has been modelled based on Gated Recurrent Units (GRU) and counter measures are performed by adopting the Enhanced Chaotic Scroll attractor keys (ECSA). The contribution of this research paper is as follows: Novel Dataset Preparation for EVSE under different attack scenarios, Implementation of high accurate multi-objective accurate GRU based IDSs, Design of Enhanced Chaotic Countermeasure Encryption Schemes for the counterfeiting the attacks in Internet Enabled EVSE system. The extensive experimentation has been carried out into two important phases. In first phase algorithm centric metrics such as prediction accuracy, time of detection, whereas in second phase key centric metrics such as Number of Changing Pixel Rate (NPCR), Unified Averaged Changed Intensity (UACI), Key sensitivity and entropy are calculated and compared with the other existing methodologies. Results demonstrates that the proposed ensemble system has outperformed than the other methodologies and proves its strong place in designing the more secured Internet Enabled EVSE systems.

This paper develops a discrete-continuous model to examine the ownership and usage of electric vehicles in the household. The impact of the ownership and usage of ordinary vehicles is taken into consideration. 5766 stated preference data concerning purchasing electric vehicles in the Chukyo region in Japan are utilized as the research sample. The monthly mileages of ordinary and electric vehicles are measured by a Tobit model, respectively. The ordinary vehicle ownership is measured by an ordered probit model, while the electric vehicle ownership is measured by a binary probit model. Gibbs sampler algorithm is used to estimate four jointed equations. The result shows that there is a substitution effect between two types of vehicles in the ownership and usage. The price, capacity, range and charging rate in the gas station impact both the ownership and usage of electric vehicles. Meanwhile, charging time does not affect either the ownership or usage.

As the number of electric vehicles (EVs) is increasing, a balancing control is needed to decrease the charging delay for EV users and increase the overall number of charged EVs for a charging station (CS) service provider. This paper suggests the local pricing-based decentralized balancing control algorithm to balance the power demand and the number of EVs at each CS. The CSs choose the charging costs according to the power demand, and the probability of sending beacon message is controlled according to the queued ratio. When receiving the beacon messages, each EV chooses the CS with the lowest cost. Simulation results showed that the suggested algorithm could balance the parked pad ratio and the charging power ratio of multiple CSs. Furthermore, the number of completely charged EVs was increased and the quality of service was also increased due to the reduction of charging delay. In conclusion, the suggested algorithm can improve the overall performance at the local area from the perspective of both charging service provider and EV users.