The fast-growing electric vehicles (EVs) industry requires a well-designed recommendation system to locate charging stations while ensuring private data protection. This article proposes a secure cloudlet-based recommendation system for EVs. Unlike conventional methods where training a recommender model involves direct data sharing between data holders, our model utilizes a secure vertical federated learning technique, in which EVs data do not leave the platforms. To improve the efficiency of the model and to alleviate the communication-related concerns in our recommender model, cloudlet-based data aggregator(s) are used as a replacement for the existing centralized architectures. To enhance the security of our system, blockchain technology is incorporated to generate a trusted network of cloudlets that are responsible for transmitting the locally computed training parameters. The simulation results achieved from our proposed recommendation system show that the distribution of EVs over a designated area with charging stations is more optimal, and the proposed decentralized recommender with 10 cloudlets is 5.2 s quicker than a conventional centralized model.

Over the last few years, Electric Vehicles (EVs) have been gaining interest as a result of their ability to reduce vehicle emissions. Developing an intelligent system to manage EVs charging demands is one of the fundamental aspects of this technology to better adapt for all-purpose transportation utilization. It is necessary for EVs to be connected to the Smart Grid (SG) to communicate with charging stations and other energy resources in order to control charging schedules, while Artificial Intelligent (AI) techniques can be beneficial for improving the system, they can also raise security and privacy threats. In recent years, privacy preservation methodologies have been introduced to ensure data security. Federated Learning (FL) and blockchain technology are two emerging strategies to address information protection concerns. Therefore, in this article, a comprehensive literature review is proposed to analyze existing EVs energy management challenges and solutions and present potential future research directions for EVs charging/discharging coordination applications.

The Internet of Things (IoT) and the Electric Vehicle (EV) industry are critical to the rapid growth of consumer-centric Internet of Vehicles (IoV) services to facilitate 6G-enabled vehicle communication, which offers numerous advantages. Among the applications of the Internet of Vehicles (IoV), a recommendation system is introduced to identify nearby charging sources while preserving user privacy, a crucial aspect of the IoV framework security. Determining which charging sources to suggest to an EV (as consumer electronics) is a challenging issue, given the numerous potential recommendations available. This paper introduces a secure recommendation system for EV consumer electronics, considering both fixed and mobile charging locations, focusing on optimizing the well-being of EV consumers as well as owners. Unlike traditional methods that involve sharing data directly among data holders during model training, our model employs a secure vertical Federated Learning (FL) approach, ensuring that data from EVs and charging sources remains within their respective platforms. To enhance model efficiency and address communication-related concerns, we employ fog-based data aggregators with 6G network communication, responsible for transmitting locally computed training parameters instead of conventional centralized architectures. Simulation results from our recommended system show a more optimal distribution of EVs within designated areas.

Implementation of advanced intelligent deep learning techniques for Electric Vehicles (EVs) energy consumption analysis is obstructed by two main subjects. First, the problem of finding a very similar collection of data sets to the actual EVs energy usage in terms of feature space and data distribution. Second, training a retrained model from scratch requires a massive amount of computational power, however, this does not guarantee to catch rare events included in data sets. To mitigate the aforementioned concerns, this paper aims to present a model based on Deep Transfer Learning (DTL) between domain-variant data sets, to reduce the need for the existence of a vast amount of EVs data, including driving characteristics and patterns. Also, this model applies a distributed cooperative learning approach to identify highly-correlated energy consumption parameters by building the model on previously acquired knowledge from preceding learning phases in order to enhance the Artificial Intelligence (AI) accuracy level of the proposed energy management system.

The rapid expansion of Intelligent Transportation System (ITS) services depends on the Electric Vehicle (EV) and Mobile Charging Station (MCS) consumer electronics industry, as well as the intelligent Consumer Internet of Things (CIoT) platform. The functioning environment of MCSs is inherently dynamic, influenced by inconstant user preferences, energy demands, and charging service availability. Adapting to these changes in near-real-time while ensuring cost efficiency and fairness poses a notable challenge. These consumer electronic devices share data with third parties, so privacy is a critical concern. This paper presents a secure, optimized approach for enhancing the performance and accuracy of charging/discharging scheduling of MCSs within the CIoT network while protecting consumers’ data. This study aims to develop an optimization mechanism that enables decentralized learning with minimal data transfer while preserving user privacy by embedding Federated Learning (FL) as a security layer in our system. Also, it aims to maximize the potential profit of these stations while optimizing their daily operational efficiency. We propose a fog-edge communication to enhance communication in the decentralized FL-based network. Evaluating the result demonstrated enhanced profit maximization for MCSs operating within the CIoT network to fulfill as many energy requests from EVs as feasible while reducing self-charging expenses.

Electric vehicles are an integral part of transportation electrification and are increasingly embedded within smart-grid-integrated energy systems that support accessibility, efficiency, and reduced environmental impact. As electric vehicle adoption grows, new challenges emerge in intelligent vehicle control, energy management, load management, and EV integration into the smart grid. In response, this paper presents a comprehensive survey of electric vehicle systems covering market evolution, enabling technologies, operational performance, and the energy systems that underpin scalable electric mobility. The survey illustrates the need for real-time monitoring, control, and optimization while exploring advanced computational approaches in quantum computing and machine learning that can address these challenges. Finally, this work identifies open research challenges and future directions related to energy optimization, smart-grid integration, and intelligent load management to provide a unified perspective on electric vehicles as a key component of both intelligent vehicle systems and sustainable smart transportation.