Electric Vehicles Smart Charging: Strategies, Technologies, and Challenges

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Batteries batteries	4.8	6.6	2015	19.2 Days	CHF 2700	Submit
Electronics electronics	2.6	6.1	2012	16.4 Days	CHF 2400	Submit
Energies energies	3.2	7.3	2008	16.8 Days	CHF 2600	Submit
Eng eng	2.4	3.2	2020	18 Days	CHF 1400	Submit
Smart Cities smartcities	5.5	14.7	2018	25.2 Days	CHF 2000	Submit
Solar solar	-	4.3	2021	19.8 Days	CHF 1200	Submit
Vehicles vehicles	2.2	5.3	2019	21.4 Days	CHF 1800	Submit

32 pages, 2330 KB

Open AccessArticle

Multi-Domain Machine Learning Framework for Electric Vehicle Charging Prediction

by Hanan Thwany, Muhammad Alolaiwy and Mohamed Zohdy

Vehicles 2026, 8(5), 113; https://doi.org/10.3390/vehicles8050113 - 20 May 2026

Electric vehicle (EV) adoption is rising rapidly, creating growing challenges for charging infrastructure planning, energy demand management, and grid stability. However, most existing studies rely on single-domain data, such as behavioral charging sessions or station metadata, which limits their ability to capture the [...] Read more.

Electric vehicle (EV) adoption is rising rapidly, creating growing challenges for charging infrastructure planning, energy demand management, and grid stability. However, most existing studies rely on single-domain data, such as behavioral charging sessions or station metadata, which limits their ability to capture the joint effects of user behavior, charger characteristics, and market context. To address this gap, this study proposes a multi-domain machine learning framework for EV charger-type prediction by integrating behavioral, infrastructure, and market-level data. Behavioral charging logs are transformed into structured event-token sequences and modeled using XLM-RoBERTa (Cross-lingual Language Model–RoBERTa), which is used here as a transformer-based sequence encoder to capture long-range dependencies in charging behavior. Structured infrastructure and market features are modeled using LightGBM and TabNet. The study contributes a unified multi-domain framework, a systematic comparison of transformer and tabular-learning models, and a broader evaluation through ablation analysis, cross-validation, confusion matrix analysis, and confidence calibration. The results show that multi-domain fusion consistently improves performance over single-domain learning. XLM-RoBERTa achieved the best overall performance on the fused dataset, with 98.76% accuracy and 97.86% weighted F1-score, while TabNet demonstrated stronger calibration and deployment reliability. Full article

(This article belongs to the Topic Electric Vehicles Smart Charging: Strategies, Technologies, and Challenges)

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25 pages, 2864 KB

Open AccessArticle

V2G Optimization Strategy Based on the Cuckoo Optimization Algorithm from the Perspective of a Multi-Party Cooperative Game

by Zhuoqun Li, Xianglu Liu, Shi Qiu, Zhou Sun, Yi Wan, Yongliang Zhao, Fei Chen, Xu Zhang and Gangjun Gong

Energies 2026, 19(10), 2289; https://doi.org/10.3390/en19102289 - 9 May 2026

Viewed by 188

Abstract

This paper comprehensively considers the interest demands of three core stakeholders in V2G scenarios: electric vehicle (EV) users, the power grid, and electric vehicle aggregators (EVAs). EV users prioritize charging waiting time and queuing probability to improve travel experience; the power grid focuses [...] Read more.

This paper comprehensively considers the interest demands of three core stakeholders in V2G scenarios: electric vehicle (EV) users, the power grid, and electric vehicle aggregators (EVAs). EV users prioritize charging waiting time and queuing probability to improve travel experience; the power grid focuses on charging facility utilization and power supply reliability to maximize operational benefits; and the EVA concerns its own load level and charging/discharging pricing strategies to optimize operating income. A tripartite multi-objective optimization model for grid–EV–EVA-coordinated charging and discharging is constructed, and an improved multi-objective cuckoo search algorithm is proposed to solve the model. The algorithm integrates an iterative search process (initialization, Lévy flight search, nest abandonment and update) and a cooperative game process (iteration, convergence conditions, equilibrium implementation). Guided by the dominant strength law, the algorithm’s Pareto-optimal solution set is ranked. Finally, a V2G collaborative optimization strategy that balances the interests of all stakeholders is obtained, which can effectively reduce EV users’ charging waiting time, improve the utilization rate of grid charging facilities, and guarantee the static voltage stability of the distribution network. Full article

(This article belongs to the Topic Electric Vehicles Smart Charging: Strategies, Technologies, and Challenges)

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25 pages, 7214 KB

Open AccessArticle

Stress-Aware Stackelberg Pricing for Probabilistic Grid Impact Mitigation of Bidirectional EVs

by Amit Hasan Abir, Kazi N. Hasan, Asif Islam and Mohammad AlMuhaini

Smart Cities 2026, 9(5), 75; https://doi.org/10.3390/smartcities9050075 - 22 Apr 2026

Viewed by 539

Abstract

This paper presents an integrated techno–economic framework for coordinated grid-to-vehicle and vehicle-to-grid (G2V–V2G) operation in unbalanced distribution networks. A hardware-compatible bidirectional charger with nested AC/DC and DC/DC control loops, together with a rule-based energy management system (EMS), enables seamless mode transitions while enforcing [...] Read more.

This paper presents an integrated techno–economic framework for coordinated grid-to-vehicle and vehicle-to-grid (G2V–V2G) operation in unbalanced distribution networks. A hardware-compatible bidirectional charger with nested AC/DC and DC/DC control loops, together with a rule-based energy management system (EMS), enables seamless mode transitions while enforcing state-of-charge (SoC) and network constraints. A probabilistic Monte Carlo study on the IEEE 13-bus feeder shows that uncoordinated G2V charging induces adverse grid impacts such as voltage stress, line-ampacity violations, and transformer overloading, whereas EMS-driven V2G support improves voltage by 2–4%, reduces line loading by 15–25%, and lowers transformer stress by up to 10%. To align these technical benefits with economic incentives, a bi-level Stackelberg model is formulated where the utility updates locational energy prices based on combined voltage, line ampacity, transformer loading stress indices and EVs choose profit-maximizing nodes, modes and power levels. The interaction converges to a Stackelberg equilibrium with a clear win–win situation; the feeder’s average locational energy price falls entirely within the win–win region, yielding positive per-session profits for both the EV (≈$0.80) and the utility (≈$0.48) while reducing feeder stress. These results demonstrate that stress-aware locational pricing, combined with detailed converter-level control provides a technically robust and economically sustainable pathway for large-scale EV integration. Full article

(This article belongs to the Topic Electric Vehicles Smart Charging: Strategies, Technologies, and Challenges)

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26 pages, 819 KB

Open AccessArticle

From Hours to Milliseconds: Dual-Horizon Fault Prediction for Dynamic Wireless EV Charging via Digital Twin Integrated Deep Learning

by Mohammed Ahmed Mousa, Ali Sayghe, Salem Batiyah and Abdulrahman Husawi

Smart Cities 2026, 9(3), 43; https://doi.org/10.3390/smartcities9030043 - 26 Feb 2026

Viewed by 842

Abstract

Dynamic Wireless Power Transfer (DWPT) is emerging as critical smart city infrastructure for sustainable urban mobility, enabling electric vehicle charging while driving. However, DWPT introduces complex fault scenarios requiring intelligent monitoring. Existing fault diagnosis approaches for wireless power transfer systems face three key [...] Read more.

Dynamic Wireless Power Transfer (DWPT) is emerging as critical smart city infrastructure for sustainable urban mobility, enabling electric vehicle charging while driving. However, DWPT introduces complex fault scenarios requiring intelligent monitoring. Existing fault diagnosis approaches for wireless power transfer systems face three key complexities: (1) they are limited to static charging with only 2–4 fault categories, failing to address the time-varying coupling dynamics and segmented coil handover transients inherent in dynamic charging; (2) they lack integration with the host distribution grid, ignoring grid-side disturbances that propagate to charging stations; and (3) they offer only reactive detection without predictive capability for incipient fault management. This paper presents a deep neural network (DNN)-based fault diagnosis framework utilizing multi-station sensor fusion for DWPT systems integrated with the IEEE 13-bus distribution network to address these limitations. The system monitors 36 sensor features across three charging stations, employing feature-level concatenation with station-specific normalization for multi-station fusion, achieving 97.85% classification accuracy across eight fault types. Unlike static charging, the framework explicitly models time-varying coupling dynamics due to vehicle motion, including segmented coil handover effects. A digital twin provides dual-horizon prediction: long-term forecasting (24–72 h) for incipient faults and real-time detection under 50 ms for critical protection, with fault probability outputs and ranked fault lists enabling actionable maintenance decisions. The DNN outperforms SVM (92.45%), Random Forest (94.82%), and LSTM (96.54%) with statistical significance (

p < 0.001

), while maintaining model inference latency of 4.2 ms, suitable for edge deployment. Circuit-based analysis provides analytical justification for fault signatures, and practical parameter acquisition methods enable real-world implementation. Five case studies validate robustness across highway, urban, and grid disturbance scenarios with detection accuracies exceeding 95%. Full article

(This article belongs to the Topic Electric Vehicles Smart Charging: Strategies, Technologies, and Challenges)

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19 pages, 6405 KB

Open AccessArticle

Quick Identification of Single Open-Switch Faults in a Vienna Rectifier

by Qian Li, Yue Zhao, Xiaohui Li, Teng Ma and Fang Yao

Eng 2026, 7(2), 60; https://doi.org/10.3390/eng7020060 - 1 Feb 2026

Viewed by 465

Abstract

Three-leg AC-DC Vienna rectifiers are susceptible to single open-switch faults, which make DC-link voltage ripple and make three-leg input AC currents distorted and unbalanced. Thus, this paper presents a quick identification method for single open-switch faults based on three-leg fault currents and output [...] Read more.

Three-leg AC-DC Vienna rectifiers are susceptible to single open-switch faults, which make DC-link voltage ripple and make three-leg input AC currents distorted and unbalanced. Thus, this paper presents a quick identification method for single open-switch faults based on three-leg fault currents and output capacitors voltage difference. Fault-leg identification depended on zero-plateaus in the three-leg fault currents, whereas fault-side identification was dependent on reconstruction variables obtained through Clark transformation and phase shifting. In order to improve the reliability of the diagnosis system, the harmonic component of capacitor voltage difference is used to realize the missed diagnosis detection and adjust the time threshold automatically. This method requires no additional hardware and is easy to implement. Experimental results verify the effectiveness of this strategy. It is shown that the fault diagnosis method proposed in this paper has the advantages of fast diagnosis speed, high accuracy and good robustness. Full article

(This article belongs to the Topic Electric Vehicles Smart Charging: Strategies, Technologies, and Challenges)

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23 pages, 3451 KB

Open AccessArticle

Load Flexibilities from Charging Processes by Electric Vehicles at the Workplace: A Case Study in Southern Germany

by Ronald Opoku and Patrick Jochem

Energies 2026, 19(1), 42; https://doi.org/10.3390/en19010042 - 21 Dec 2025

Viewed by 762

Abstract

The workplace, as a promising location for Electric Vehicle Supply Equipment (EVSE), presents a particular challenge, as different user requirements (e.g., parking and charging durations) meet a spatially and quantitatively limited offer of EVSE. However, integrating electric vehicles synergistically into the energy system [...] Read more.

The workplace, as a promising location for Electric Vehicle Supply Equipment (EVSE), presents a particular challenge, as different user requirements (e.g., parking and charging durations) meet a spatially and quantitatively limited offer of EVSE. However, integrating electric vehicles synergistically into the energy system of the employer can increase the profitability of the system and, correspondingly, increase the number of EVSE. For this, a deep understanding of employees’ charging behavior is key. For providing some evidence of empirical charging patterns at the workplace, this work examined a dataset of 23.9 million observations on empirical charging processes at workplaces in 2023. To identify user groups, a probabilistic model (Gaussian Mixture Model) and a K-Means clustering approach were applied and the results compared. Eight groups were identified, including full-time and part-time employees, pool vehicle users, and opportunists. The group-specific probability distributions are used to publish a synthetic dataset of parking and charging patterns at workplaces. The openly provided dataset helps to identify the right composition of EVSE in the employee context and to optimize potential fields of action. Full article

(This article belongs to the Topic Electric Vehicles Smart Charging: Strategies, Technologies, and Challenges)

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25 pages, 8073 KB

Open AccessArticle

Maximum Efficiency Power Point Tracking in Reconfigurable S-LCC Compensated Wireless EV Charging Systems with Inherent CC and CV Modes Across Wide Operating Conditions

by Pabba Ramesh, Pongiannan Rakkiya Goundar Komarasamy, Ali ELrashidi, Mohammed Alruwaili and Narayanamoorthi Rajamanickam

Energies 2025, 18(18), 5031; https://doi.org/10.3390/en18185031 - 22 Sep 2025

Cited by 4 | Viewed by 1296

Abstract

The wireless charging of electric vehicles (EVs) has drawn much attention as it can ease the charging process under different charging situations and environmental conditions. However, power transfer rate and efficiency are the critical parameters for the wide adaptation of wireless charging systems. [...] Read more.

The wireless charging of electric vehicles (EVs) has drawn much attention as it can ease the charging process under different charging situations and environmental conditions. However, power transfer rate and efficiency are the critical parameters for the wide adaptation of wireless charging systems. Different investigations are presented in the literature that have aimed to improve power transfer efficiency and to maintain constant power at the load side. This paper introduces a Maximum Efficiency Point Tracking (MEPT) system designed specifically for a reconfigurable S-LCC compensated wireless charging system. The reconfigurable nature of the S-LCC system supports the constant current (CC) and constant voltage (CV) mode of operation by operating S-LCC and S-SP mode. The proposed system enhances power transfer efficiency under load fluctuations, coil misalignments, and a wide range of operating conditions. The developed S-LCC compensated system inherently maintains the power transfer rate constantly under a majority of load variations. Meanwhile, the inclusion of the MEPT method with the S-LCC system provides stable and maximum output under different coupling and load variations. The proposed MEPT approach uses a feedback mechanism to track and maintain the maximum efficiency point by iteratively adjusting the DC-DC converter duty ratio and by monitoring load power. The proposed approach was designed and tested in a 3.3 kW laboratory scale prototype module at an operating frequency of 85 kHz. The simulation and hardware results show that the developed system provides stable maximum power under a wider range of load and coupling variations. Full article

(This article belongs to the Topic Electric Vehicles Smart Charging: Strategies, Technologies, and Challenges)

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23 pages, 2624 KB

Open AccessArticle

Scalable Data-Driven EV Charging Optimization Using HDBSCAN-LP for Real-Time Pricing Load Management

by Mayank Saklani, Devender Kumar Saini, Monika Yadav and Pierluigi Siano

Smart Cities 2025, 8(4), 139; https://doi.org/10.3390/smartcities8040139 - 21 Aug 2025

Cited by 2 | Viewed by 2229

Abstract

The fast-changing scenario of the transportation industry due to the rapid adoption of electric vehicles (EVs) imposes significant challenges on power distribution networks. Challenges such as dynamic and concentrated charging loads necessitate intelligent demand-side management (DSM) strategies to ensure grid stability and cost [...] Read more.

The fast-changing scenario of the transportation industry due to the rapid adoption of electric vehicles (EVs) imposes significant challenges on power distribution networks. Challenges such as dynamic and concentrated charging loads necessitate intelligent demand-side management (DSM) strategies to ensure grid stability and cost efficiency. This study proposes a novel two-stage framework integrating Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) and linear programming (LP) to optimize EV charging loads across four operational scenarios: Summer Weekday, Summer Weekend, Winter Weekday, and Winter Weekend. Utilizing a dataset of 72,856 real-world charging sessions, the first stage employs HDBSCAN to segment charging behaviors into nine distinct clusters (Davies-Bouldin score: 0.355, noise fraction: 1.62%), capturing temporal, seasonal, and behavioral variability. The second stage applies linear programming optimization to redistribute loads under real-time pricing (RTP), minimizing operational costs and peak demand while adhering to grid constraints. Results demonstrate the load optimization by total peak reductions of 321.87–555.15 kWh (23.10–25.41%) and cost savings of $27.35–$50.71 (2.87–5.31%), with load factors improving by 14.29–17.14%. The framework’s scalability and adaptability make it a robust solution for smart grid integration, offering precise load management and economic benefits. Full article

(This article belongs to the Topic Electric Vehicles Smart Charging: Strategies, Technologies, and Challenges)

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27 pages, 5522 KB

Open AccessArticle

Integrated Vehicle-to-Building and Vehicle-to-Home Services for Residential and Worksite Microgrids

by Andrea Bonfiglio, Manuela Minetti, Riccardo Loggia, Lorenzo Frattale Mascioli, Andrea Golino, Cristina Moscatiello and Luigi Martirano

Smart Cities 2025, 8(3), 101; https://doi.org/10.3390/smartcities8030101 - 19 Jun 2025

Cited by 6 | Viewed by 2012

Abstract

The development of electric mobility offers new perspectives in the energy sector and improves resource efficiency and sustainability. This paper proposes a new strategy for synchronizing the energy requirements of home, commercial, and vehicle mobility, with a focus on the batteries of electric [...] Read more.

The development of electric mobility offers new perspectives in the energy sector and improves resource efficiency and sustainability. This paper proposes a new strategy for synchronizing the energy requirements of home, commercial, and vehicle mobility, with a focus on the batteries of electric cars. In particular, this paper describes the coordination between a battery management algorithm that optimally assigns its capacity so that at least a part is reserved for mobility and a vehicle-to-building (V2B) service algorithm that uses a share of EV battery energy to improve user participation in renewable energy exploitation at home and at work. The system offers the user the choice of always maintaining a minimum charge for mobility or providing more flexible use of energy for business needs while maintaining established vehicle autonomy. Suitable management at home and at work allows always charging the vehicle to the required level of charge with renewable power excess, highlighting how the cooperation of home and work charging may provide novel frameworks for a smarter and more sustainable integration of electric mobility, reducing energy consumption and providing more effective energy management. The effectiveness of the proposed solution is demonstrated in a realistic configuration with real data and an experimental setup. Full article

(This article belongs to the Topic Electric Vehicles Smart Charging: Strategies, Technologies, and Challenges)

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59 pages, 11235 KB

Open AccessEditor’s ChoiceReview

A Review of EV Adoption, Charging Standards, and Charging Infrastructure Growth in Europe and Italy

by Mahwish Memon and Claudio Rossi

Batteries 2025, 11(6), 229; https://doi.org/10.3390/batteries11060229 - 12 Jun 2025

Cited by 16 | Viewed by 14823

Abstract

This work analyzes the electric vehicle (EV) sales trends of plug-in hybrid electric vehicles (PHEVs) and battery electric vehicles (BEVs) and trends in the growth of Alternating Current (AC) and Direct Current (DC) charging infrastructure station scenarios in Europe and Italy. It offers [...] Read more.

This work analyzes the electric vehicle (EV) sales trends of plug-in hybrid electric vehicles (PHEVs) and battery electric vehicles (BEVs) and trends in the growth of Alternating Current (AC) and Direct Current (DC) charging infrastructure station scenarios in Europe and Italy. It offers a comprehensive view of market trends, technical developments, infrastructure development, and worldwide standardization initiatives for policymakers, researchers, and industry. A detailed classification of the charging technologies of EVs, i.e., conductive, wireless power transfer (WPT), battery swapping (BS), and different EV types, is presented. Finally, this work provides a comparative overview of charging standards and protocols, including the ones established by the Society of Automotive Engineers (SAE), International Electrotechnical Commission (IEC), and Standardization Administration of China (SAC), emphasizing interoperability and cross-border integration to accelerate the transition to clean transportation. Full article

(This article belongs to the Topic Electric Vehicles Smart Charging: Strategies, Technologies, and Challenges)

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17 pages, 3748 KB

Open AccessArticle

Research on Lithium-Ion Battery State of Health Prediction Based on XGBoost–ARIMA Joint Optimization

by Chen Fei, Zhuo Lu, Weiwei Jiang, Liang Zhao and Fan Zhang

Batteries 2025, 11(6), 207; https://doi.org/10.3390/batteries11060207 - 23 May 2025

Cited by 11 | Viewed by 3554

Abstract

Due to the complex electrochemical reactions within lithium-ion batteries and the uncertainties with respect to external environmental factors, accurately assessing their State of Health (SOH) remains a significant challenge. To improve the precision of SOH estimation, we propose an intelligent estimation approach that [...] Read more.

Due to the complex electrochemical reactions within lithium-ion batteries and the uncertainties with respect to external environmental factors, accurately assessing their State of Health (SOH) remains a significant challenge. To improve the precision of SOH estimation, we propose an intelligent estimation approach that integrates data visualization and advanced machine learning techniques. Initially, the battery data are visualized using matplotlib to extract key features such as temperature difference, voltage difference, and average voltage. Subsequently, an XGBoost-based model is constructed to perform the initial SOH estimation. To further enhance the estimation accuracy, we introduce the Autoregressive Integrated Moving Average Model (ARIMA) model for post-estimation correction, effectively refining the preliminary results. Experimental results demonstrate that the proposed XGBoost–ARIMA model outperforms traditional algorithms, including Linear Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN), not only in estimation accuracy but also in generalization capability, showing significant improvements over five other regression models. Full article

(This article belongs to the Topic Electric Vehicles Smart Charging: Strategies, Technologies, and Challenges)

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14 pages, 2421 KB

Open AccessArticle

Coordinated Optimization Method of Electric Buses and Voltage Source Converters for Improving the Absorption Capacity of New Energy Sources and Loads in Distribution Networks

by Yang Liu, Min Huang, Yujing Zhang, Lu Zhang, Wenbin Liu, Haidong Yu, Feng Wang and Lisheng Li

Energies 2025, 18(4), 832; https://doi.org/10.3390/en18040832 - 11 Feb 2025

Cited by 1 | Viewed by 1111

Abstract

The large-scale integration of renewable energy sources and new loads, such as distributed photovoltaics and electric vehicles, has resulted in frequent power quality issues within distribution networks. Traditional AC distribution networks lack the necessary flexibility and have limited capacity to accommodate these new [...] Read more.

The large-scale integration of renewable energy sources and new loads, such as distributed photovoltaics and electric vehicles, has resulted in frequent power quality issues within distribution networks. Traditional AC distribution networks lack the necessary flexibility and have limited capacity to accommodate these new energy sources and loads. Transforming the conventional distribution network into an AC-DC hybrid network using flexible interconnection devices like Voltage Source Converters can enhance the network’s flexibility, mitigating the power quality challenges arising from the integration of renewable energy and new loads. Electric buses, with their substantial capacity, mobility, and centralized management, offer potential as mobile energy storage. They can participate in the dispatching of the distribution network, thereby improving the network’s flexibility in power regulation. This paper proposes a coordinated optimization approach that integrates electric buses and VSCs for distribution network dispatch. This method enables electric buses to assist in power dispatch without interfering with their primary public transport duties, thus enhancing the network’s capacity to absorb new energy sources and loads. Firstly, considering the mobility characteristics of electric buses, a multi-layer stochastic Time–Space Network model is developed for bus dispatching. Secondly, an optimization model is constructed that accounts for the coordination of charging and discharging power between VSCs and electric buses, with the objective of minimizing the network losses in the distribution system. Finally, the proposed model is transformed into a second-order cone programming formulation, facilitating its solution through convex optimization techniques. The effectiveness of the proposed approach is demonstrated through a case study. Full article

(This article belongs to the Topic Electric Vehicles Smart Charging: Strategies, Technologies, and Challenges)

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44 pages, 5949 KB

Open AccessReview

Review of Authentication, Blockchain, Driver ID Systems, Economic Aspects, and Communication Technologies in DWC for EVs in Smart Cities Applications

by Narayanamoorthi Rajamanickam, Pradeep Vishnuram, Dominic Savio Abraham, Miroslava Gono, Petr Kacor and Tomas Mlcak

Smart Cities 2024, 7(6), 3121-3164; https://doi.org/10.3390/smartcities7060122 - 24 Oct 2024

Cited by 1 | Viewed by 3250

Abstract

The rapid advancement and adoption of electric vehicles (EVs) necessitate innovative solutions to address integration challenges in modern charging infrastructure. Dynamic wireless charging (DWC) is an innovative solution for powering electric vehicles (EVs) using multiple magnetic transmitters installed beneath the road and a [...] Read more.

The rapid advancement and adoption of electric vehicles (EVs) necessitate innovative solutions to address integration challenges in modern charging infrastructure. Dynamic wireless charging (DWC) is an innovative solution for powering electric vehicles (EVs) using multiple magnetic transmitters installed beneath the road and a receiver located on the underside of the EV. Dynamic charging offers a solution to the issue of range anxiety by allowing EVs to charge while in motion, thereby reducing the need for frequent stops. This manuscript reviews several pivotal areas critical to the future of EV DWC technology such as authentication techniques, blockchain applications, driver identification systems, economic aspects, and emerging communication technologies. Ensuring secure access to this charging infrastructure requires fast, lightweight authentication systems. Similarly, blockchain technology plays a critical role in enhancing the Internet of Vehicles (IoV) architecture by decentralizing and securing vehicular networks, thus improving privacy, security, and efficiency. Driver identification systems, crucial for EV safety and comfort, are analyzed. Additionally, the economic feasibility and impact of DWC are evaluated, providing essential insights into its potential effects on the EV ecosystem. The paper also emphasizes the need for quick and lightweight authentication systems to ensure secure access to DWC infrastructure and discusses how blockchain technology enhances the efficiency, security, and privacy of IoV networks. The importance of driver identification systems for comfort and safety is evaluated, and an economic study confirms the viability and potential benefits of DWC for the EV ecosystem. Full article

(This article belongs to the Topic Electric Vehicles Smart Charging: Strategies, Technologies, and Challenges)

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