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Electronics
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Applications of Data Analytics and Artificial Intelligence in Electric Vehicles
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Applications of Data Analytics and Artificial Intelligence in Electric Vehicles

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: 15 September 2025 | Viewed by 6499

Special Issue Editors

Dr. Yitong Shang

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong 999077, China
Interests: electric vehicle; smart city; smart grids
Special Issues, Collections and Topics in MDPI journals
Dr. Junjie Zhong

E-Mail Website
Guest Editor
1. College of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410004, China
2. National Key Laboratory of Power Grid Disaster Prevention and Mitigation, Changsha 410004, China
Interests: vehicle-to-grid; integrated energy system; stochastic optimization; robust optimization; distributionally robust optimization; distributed optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Qiang Xing

E-Mail
Guest Editor
School of Automation and Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210042, China
Interests: vehicle-to-grid; smart grid; transportation electrification; deep reinforcement learning; graph reinforcement learning
Special Issues, Collections and Topics in MDPI journals
Dr. Shiwei Xie

E-Mail Website
Guest Editor
The Department of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China
Interests: electric vehicle; vehicle-to-grid; energy market; deep learning; game theory
Dr. Dongdong Zhang

E-Mail Website
Guest Editor
School of Electrical Engineering, Guangxi University, Nanning 530004, China
Interests: smart energy; artificial intelligence; renewable energy; smart grid; new power system; multi-carrier energy systems; integrated energy system
Special Issues, Collections and Topics in MDPI journals
Dr. Bo Wang

E-Mail Website
Guest Editor
The School of Electrical and Power Engineering, Hohai University, Nanjing, China
Interests: the planning and operation of the distribution network and microgrid; transactive energy; applications of optimization theory and machine learning

Special Issue Information

Dear Colleagues,

Electric vehicles are at the forefront of sustainable energy transportation. With the integration of sensors, communication equipment and data collection capabilities, electric vehicles can generate a large volume of data during processes such as grid interaction, intelligent transportation, battery management, etc. Therefore, the full utilization of this data to enhance the functionality, efficiency, and user experience of electric vehicles and even smart cities has become an important topic of research.

This Special Issue welcomes the submission of original research and review articles that advance our understanding and application of data analytics and artificial intelligence in various aspects of electric vehicles. The scope of this Special Issue thus includes, but is not limited to, the following:

  • Data-driven management of electric vehicle battery technologies;
  • Deep learning-assisted optimization of electric vehicle motor control strategies;
  • Electric vehicle load forecasting based on remote sensing and real-time data analytics;
  • Applications of deep learning and reinforcement learning in vehicle-to-grid operation;
  • Leveraging artificial intelligence and electric vehicles for multi-source energy integration and scheduling;
  • Big data analytics of urban logistics models for optimization of electric mobility services;
  • Roles of vehicle-to-everything (V2X) communications and artificial intelligence in intelligent transportation systems such as smart parking and public transport priority;
  • Cybersecurity and privacy-preserving techniques for connected and autonomous electric vehicles in artificial intelligence environments;
  • Machine learning and deep learning applications in electric vehicle performance monitoring and fault diagnosis;
  • Assessment of energy efficiency impacts, prospective analysis and policy implications of artificial intelligence adoption for sustainable mobility transition.

The aim of this Special Issue is to provide a platform for researchers and engineers to showcase the pioneering work of artificial intelligence in each of the above-mentioned areas of electric vehicle technology. It aims to advance state-of-the-art technology and accelerate the practical deployment of electric vehicles through the public dissemination of innovative ideas and solutions.

The papers in this Special Issue will supplement the existing literature in the following ways: 1) Integrate research progress in multiple interdisciplinary fields. 2) Enable researchers to understand the future development route of electric vehicles. 3) Address the use electric vehicles as a carrier, and study the prospects of applying artificial intelligence to promote transformation into a low-carbon economy.

Dr. Yitong Shang
Dr. Junjie Zhong
Dr. Qiang Xing
Dr. Shiwei Xie
Dr. Dongdong Zhang
Dr. Bo Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • electric vehicle
  • artificial intelligence
  • battery management
  • motor control
  • ev load forecasting
  • vehicle-to-grid
  • smart grid
  • intelligent transportation
  • sustainable city

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (6 papers)

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Research

20 pages, 2900 KiB  
Article
KAN–CNN: A Novel Framework for Electric Vehicle Load Forecasting with Enhanced Engineering Applicability and Simplified Neural Network Tuning
by Zhigang Pei, Zhiyuan Zhang, Jiaming Chen, Weikang Liu, Bailian Chen, Yanping Huang, Haofan Yang and Yijun Lu
Electronics 2025, 14(3), 414; https://doi.org/10.3390/electronics14030414 - 21 Jan 2025
Cited by 2 | Viewed by 844
Abstract
Electric Vehicle (EV) load forecasting is critical for optimizing resource allocation and ensuring the stability of modern energy systems. However, traditional machine learning models, predominantly based on Multi-Layer Perceptrons (MLPs), encounter substantial challenges in modeling the complex, nonlinear, and dynamic patterns inherent in [...] Read more.
Electric Vehicle (EV) load forecasting is critical for optimizing resource allocation and ensuring the stability of modern energy systems. However, traditional machine learning models, predominantly based on Multi-Layer Perceptrons (MLPs), encounter substantial challenges in modeling the complex, nonlinear, and dynamic patterns inherent in EV charging data, often leading to overfitting and high computational costs. To overcome these limitations, this study introduces KAN–CNN, a novel hybrid architecture that integrates Kolmogorov–Arnold Networks (KANs) into traditional machine learning frameworks, specifically Convolutional Neural Networks (CNNs). By combining the spatial feature extraction strength of CNNs with the adaptive nonlinearity of KAN, KAN–CNN achieves superior feature representation and modeling flexibility. The key innovations include bottleneck KAN convolutional layers for reducing parameter complexity, Self-Attention Kolmogorov–Arnold Network with Global Nonlinearity (Self-KAGN) Attention to enhance global dependency modeling, and Focal KAGN Modulation for dynamic feature refinement. Furthermore, regularization techniques such as L1/L2 penalties, dropout, and Gaussian noise injection are utilized to enhance the model’s robustness and generalization capability. When applied to EV load forecasting, KAN–CNN demonstrates prediction accuracy comparable to state-of-the-art methods while significantly reducing computational overhead and simplifying parameter tuning. This work bridges the gap between theoretical innovations and practical applications, offering a robust and efficient solution for dynamic energy system challenges. Full article
(This article belongs to the Special Issue Applications of Data Analytics and Artificial Intelligence in Electric Vehicles)
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16 pages, 2077 KiB  
Article
Distributed Data Privacy Protection via Collaborative Anomaly Detection
by Fei Zeng, Mingshen Wang, Yi Pan, Shukang Lv, Huiyu Miao, Huachun Han and Xiaodong Yuan
Electronics 2025, 14(2), 295; https://doi.org/10.3390/electronics14020295 - 13 Jan 2025
Viewed by 651
Abstract
Current anomaly detection methods for charging stations primarily rely on centralized network architectures with federated learning frameworks. However, the rapid increase in the number of charging stations and the expanding scale of these networks impose significant communication traffic loads. Consequently, it is essential [...] Read more.
Current anomaly detection methods for charging stations primarily rely on centralized network architectures with federated learning frameworks. However, the rapid increase in the number of charging stations and the expanding scale of these networks impose significant communication traffic loads. Consequently, it is essential to explore the relationship between data aggregation among charging stations and the anomaly detection accuracy at individual stations. In this paper, we address efficient anomaly detection in charging stations and propose a distributed anomaly detection algorithm powered by federated learning. To be specific, we introduce a distributed privacy-preserving data aggregation scheme, where a Transformer model is adopted to effectively smooth abnormal data fluctuations, minimizing disruptions to network aggregation nodes. Furthermore, we develop a distributed federated learning framework incorporating an efficient parameter update method without requiring prior knowledge or a central node. Compared with some existing detection solutions, the proposed approach significantly reduces communication bandwidth requirements while maintaining anomaly detection accuracy and mitigating data isolation issues. Extensive experiments demonstrate that the proposed algorithm not only achieves high accuracy in detecting anomalies in electric vehicle charging stations but also ensures robust user data privacy protection. Full article
(This article belongs to the Special Issue Applications of Data Analytics and Artificial Intelligence in Electric Vehicles)
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20 pages, 4737 KiB  
Article
Multi-Stage Hybrid Planning Method for Charging Stations Based on Graph Auto-Encoder
by Andrew Y. Wu, Juai Wu and Yui-yip Lau
Electronics 2025, 14(1), 114; https://doi.org/10.3390/electronics14010114 - 30 Dec 2024
Viewed by 1107
Abstract
To improve the operational efficiency of electric vehicle (EV) charging infrastructure, this paper proposes a multi-stage hybrid planning method for charging stations (CSs) based on graph auto-encoder (GAE). First, the network topology and dynamic interaction process of the coupled “Vehicle-Station-Network” system are characterized [...] Read more.
To improve the operational efficiency of electric vehicle (EV) charging infrastructure, this paper proposes a multi-stage hybrid planning method for charging stations (CSs) based on graph auto-encoder (GAE). First, the network topology and dynamic interaction process of the coupled “Vehicle-Station-Network” system are characterized as a graph-structured model. Second, in the first stage, a GAE-based deep neural network is used to learn the graph-structured model and identify and classify different charging station (CS) types for the network nodes of the coupled system topology. The candidate CS set is screened out, including fast-charging stations (FCSs), fast-medium-charging stations, medium-charging stations, and slow-charging stations. Then, in the second stage, the candidate CS set is re-optimized using a traditional swarm intelligence algorithm, considering the interests of multiple parties in CS construction. The optimal CS locations and charging pile configurations are determined. Finally, case studies are conducted within a practical traffic zone in Hong Kong, China. The existing CS planning methods rely on simulation topology, which makes it difficult to realize efficient collaboration of charging networks. However, the proposed scheme is based on the realistic geographical space and large-scale traffic topology. The scheme determines the station and pile configuration through multi-stage planning. With the help of an artificial intelligence (AI) algorithm, the user behavior characteristics are captured adaptively, and the distribution rule of established CSs is extracted to provide support for the planning of new CSs. The research results will help the power and transportation departments to reasonably plan charging facilities and promote the coordinated development of EV industry, energy, and transportation systems. Full article
(This article belongs to the Special Issue Applications of Data Analytics and Artificial Intelligence in Electric Vehicles)
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21 pages, 4868 KiB  
Article
Electric Vehicle Charging Guidance Strategy with Dual-Incentive Mechanisms for Charging and Discharging
by Shukang Lyu, Huiyu Miu, Xiaodong Yuan, Mingshen Wang, Fei Zeng and Yi Pan
Electronics 2024, 13(23), 4676; https://doi.org/10.3390/electronics13234676 - 27 Nov 2024
Cited by 1 | Viewed by 1000
Abstract
With the rapid development of electric vehicles (EVs) and charging facilities, EV charging guidance is currently mainly based on charging incentives. Without an in-depth exploration of the superimposed benefits to charging guidance caused by discharging incentives, it is difficult to maximize the benefits [...] Read more.
With the rapid development of electric vehicles (EVs) and charging facilities, EV charging guidance is currently mainly based on charging incentives. Without an in-depth exploration of the superimposed benefits to charging guidance caused by discharging incentives, it is difficult to maximize the benefits of charging station operators and to stimulate the enthusiasm of users to participate in the guidance. In this study, firstly, a traffic network model based on the Logit model is established, and the spatiotemporal distribution characteristics of EV users’ traveling demand based on the O-D matrix and the Monte Carlo Markov method are proposed. Secondly, we analyze the impact of charging and discharging incentive levels on users’ psychological responses to charging guidance. We assess battery degradation during irregular discharging processes of electric vehicles (EVs) while considering users’ personalized travel needs and anxiety levels. We propose a dual-incentive mechanism for charging and discharging to enhance users’ active participation in charging guidance. Then, we construct a model that incorporates users’ travel and waiting time costs, as well as the economic costs of charging and discharging. Subsequently, we consider the economic benefits for users under the discharging incentive mechanism and establish a user charging decision model based on prospect theory. Finally, considering the goal of maximizing the revenue of the charging station, a charging guidance strategy considering users’ participation in the charging and discharging incentive mechanism during the traveling process is proposed. The effectiveness of the EV charging guidance strategy under three different incentive scenarios is verified with comparative results. The proposed guidance strategy enhances operator revenue while taking user interests into account, achieving a 7% increase in operator revenue compared to a strategy that only considers charging incentives. This dual-incentive mechanism promotes operators’ enthusiasm for participating in vehicle-to-grid interactions. Full article
(This article belongs to the Special Issue Applications of Data Analytics and Artificial Intelligence in Electric Vehicles)
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21 pages, 4266 KiB  
Article
A Two-Stage Fault Localization Method for Active Distribution Networks Based on COA-SVM Model and Cosine Similarity
by Ruifeng Zhao, Jiangang Lu, Zhiwen Yu, Yuezhou Wu and Kailin Wang
Electronics 2024, 13(19), 3809; https://doi.org/10.3390/electronics13193809 - 26 Sep 2024
Viewed by 879
Abstract
To address the issues of low efficiency and poor noise immunity in traditional active distribution network (ADN) fault location methods based on swarm intelligent optimization algorithms, this paper proposes a two-stage fault location method utilizing the COA-SVM model and cosine similarity. First, this [...] Read more.
To address the issues of low efficiency and poor noise immunity in traditional active distribution network (ADN) fault location methods based on swarm intelligent optimization algorithms, this paper proposes a two-stage fault location method utilizing the COA-SVM model and cosine similarity. First, this paper constructs the fault signature database for the target distribution network by randomly simulating single- and multi-point faults using the fault current state equation. Next, this paper introduces the COA-SVM classification model, establishing the high-dimensional mapping relationship between the fault current direction matrix and the fault zones through model training. The well-trained COA-SVM classification model is used to identify the fault zones, which include the fault line segments. Finally, for each identified fault zone, this paper calculates the cosine similarity of the fault current direction information of adjacent line segments, accurately pinpointing the fault line segments by identifying mutation points of the cosine similarity. Using the modified IEEE 33 node test distribution network as an example, simulation results demonstrate that the proposed two-stage fault location method offers higher accuracy and resistance to signal interference compared to fault location methods based on swarm intelligence optimization algorithms. The COA-SVM classification model surpasses conventional models, achieving high accuracy and excellent noise resilience. It accurately identifies fault segments within the test distribution network with a remarkable 100% precision. Moreover, the accuracy of fault localization remains above 83% when the FTU encounters fewer than three abnormal signals. Full article
(This article belongs to the Special Issue Applications of Data Analytics and Artificial Intelligence in Electric Vehicles)
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19 pages, 1271 KiB  
Article
A Novel Areal Maintenance Strategy for Large-Scale Distributed Photovoltaic Maintenance
by Deyang Yin, Yuanyuan Zhu, Hao Qiang, Jianfeng Zheng and Zhenzhong Zhang
Electronics 2024, 13(18), 3593; https://doi.org/10.3390/electronics13183593 - 10 Sep 2024
Viewed by 628
Abstract
A smart grid is designed to enable the massive deployment and efficient use of distributed energy resources, including distributed photovoltaics (DPV). Due to the large number, wide distribution, and insufficient monitoring information of DPV stations, the pressure to maintain them has increased rapidly. [...] Read more.
A smart grid is designed to enable the massive deployment and efficient use of distributed energy resources, including distributed photovoltaics (DPV). Due to the large number, wide distribution, and insufficient monitoring information of DPV stations, the pressure to maintain them has increased rapidly. Furthermore, based on reports in the relevant literature, there is still a lack of efficient large-scale maintenance strategies for DPV stations at present, leading to the high maintenance costs and overall low efficiency of DPV stations. Therefore, this paper proposes a maintenance period decision model and an areal maintenance strategy. The implementation steps of the method are as follows: firstly, based on the reliability model and dust accumulation model of the DPV components, the maintenance period decision model is established for different numbers of DPV stations and different driving distances; secondly, the optimal maintenance period is determined by using the Monte Carlo method to calculate the average economic benefits of daily maintenance during different periods; then, an areal maintenance strategy is proposed to classify all the DPV stations into different areas optimally, where each area is maintained to reach the overall economic optimum for the DPV stations; finally, the validity and rationality of this strategy are verified with the case study of the DPV poverty alleviation project in Badong County, Hubei Province. The results indicate that compared with an independent maintenance strategy, the proposed strategy can decrease the maintenance cost by 10.38% per year, which will help promote the construction of the smart grid and the development of sustainable cities. The results prove that the method proposed in this paper can effectively reduce maintenance costs and improve maintenance efficiency. Full article
(This article belongs to the Special Issue Applications of Data Analytics and Artificial Intelligence in Electric Vehicles)
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