Additionally, while integrating AI and machine learning (ML) into these systems can enhance operational efficiency and cybersecurity, the implementation process is resource-intensive6. The complex algorithms involved in forecasting energy demand and response require significant computational power7. These systems must also be scalable to accommodate the growing number of EVs and charging stations without sacrificing performance. Regulatory challenges arise due to differing standards and policies related to energy usage, data privacy, and AI applications across regions8. These hurdles make it more difficult to implement consistent solutions across various jurisdictions. Addressing these issues will enable the development of a more resilient, scalable, and secure energy infrastructure for electric mobility9.
In line with the proposed SC-CMP platform, recent literature has increasingly focused on advanced methods for intelligent EV-grid coordination and system-level optimization. For instance, Renhai et al. introduced a non-parametric kernel density estimation method for managing under-frequency load shedding under renewable and EV uncertainty, which aligns with our model’s emphasis on handling unpredictability through reinforcement learning. Panda et al. proposed a smart residential demand-side management framework using multi-objective optimization, reinforcing the relevance of scalable and context-aware control strategies similar to those embedded in SC-CMP. Varshney et al. explored stochastic modeling and queueing-theoretical approaches to optimize charging station behavior under infrastructure constraints — concepts that are echoed in SC-CMP’s design through queue-aware load balancing and latency-sensitive scheduling. Several studies have emphasized the importance of reliable and efficient charging architectures, the expansion of charging infrastructure and grid integration, and AI-integrated blockchain frameworks for optimizing demand response and load balancing, all of which intersect with SC-CMP’s secure, cloud-edge computational framework. The use of hybrid classifiers and ensemble techniques to enhance charging prediction accuracy further validates our system’s edge inference design. Additionally, recent work on solar-powered station scheduling, EV route optimization via bio-inspired algorithms, and coordinated charging of fleet vehicles in shared parking infrastructures provides opportunities for extending SC-CMP to broader mobility and microgrid environments. Studies on load impact mitigation, e-mobility business models, fast-charging infrastructure, and AI/ML-enabled cybersecurity support the scalability, economic feasibility, and resilience dimensions of our approach. Together, these contributions form a robust foundation upon which SC-CMP builds and differentiates itself by offering a unified, scalable, and real-time platform for predictive EV energy management in dynamic grid situations.
Recent research has proposed a variety of intelligent approaches to improve cybersecurity, resilience, and communication efficiency in modern IoT and vehicular systems. Sanjalawe et al. introduced a deep learning-driven multi-layered steganographic technique to enhance data security by embedding information within digital content. Elomda et al. developed a multi-layer blockchain security model that improves scalability and latency performance for decentralized systems. In the context of IoT-integrated cyber-physical infrastructures, Ramesh et al. proposed a satellite-based terminal authentication mechanism to enhance consumer data protection. Qaddos et al. presented a novel intrusion detection framework specifically designed to optimize IoT security layers. Mughaid et al. applied intelligent cybersecurity methods for protecting cloud-based IoT environments, while Maaz et al. combined hybrid deep learning techniques to improve threat detection and enhance resilience across distributed IoT networks. Almahadeen et al. employed an autoencoder-MLP hybrid model to detect cyber threats in financial systems, highlighting its effectiveness in high-sensitivity domains. Focusing on EV-specific vulnerabilities, Tanyıldız et al. utilized a generative adversarial network to detect cyberattacks in EV charging infrastructure by modeling the remaining useful life of system components. Mohammad et al. proposed an edge computing and reinforcement learning-based framework for intelligent task offloading in Internet of Vehicles (IoV) environments. Finally, Akhunzada et al. designed an AI-enabled threat intelligence framework tailored for autonomous vehicles, supporting real-time decision-making and attack mitigation in connected EV ecosystems.
By leveraging AI and ML in energy management, the overall efficiency of operations is improved, helping the grid become more resilient and sustainable. The adaptability and scalability of cloud-based continuous monitoring systems are critical in addressing the evolving needs of modern smart grids. The Scalable Cloud-Based Continuous Monitoring Platform (SC-CMP) stands out among alternative methods for its optimal combination of scalability, continuous monitoring, and high performance.
Dynamic pricing mechanisms and optimized charging schedules are areas where advanced AI techniques, particularly deep learning and reinforcement learning, have proven highly effective. These technologies enable EV charging systems to adjust pricing based on real-time grid conditions and user demand, while optimizing charging schedules to avoid peak load times.
Furthermore, the SC-CMP’s ability to adapt to evolving grid requirements ensures it remains a flexible and scalable solution for future energy needs. As the number of EVs and charging stations increases, SC-CMP’s scalable cloud-based infrastructure can expand without the need for significant hardware investments. This makes it a cost-effective solution for utilities that are preparing for the rapid growth of the electric mobility ecosystem. The platform’s real-time data processing capabilities allow it to monitor and manage even the most complex energy networks, providing a reliable foundation for long-term energy management strategies. Utilizing edge computing in conjunction with cloud services allows for reduced latency, supporting real-time decision-making at a local level. This architecture is especially beneficial in smart grids that incorporate Internet of Things (IoT) devices, enabling seamless integration of cloud infrastructure with AI and machine learning. Such a system allows for efficient monitoring and control of power distribution, ultimately enhancing the reliability and performance of the grid.
As these technologies evolve, they are set to play a pivotal role in the modernization of energy distribution networks. The energy and automotive industries are experiencing a transformative shift with the integration of EVs and emerging technologies like blockchain, cloud computing, and AI. These innovations facilitate more efficient energy management and transaction security in electric mobility ecosystems, offering a modernized approach to both energy generation and consumption. This shift is crucial as it not only supports the growing number of EVs but also ensures that energy grids remain resilient and capable of handling dynamic demand patterns. Shen et al. proposed a hybrid AI classification method (HAICM) specifically for scheduling EVs in Vehicle-to-Grid (V2G) networks powered by 5G technology. This method aims to enhance the coordination between EVs and the grid, ensuring efficient energy transfer and load balancing. By leveraging the high-speed, low-latency capabilities of 5G networks, the HAICM allows for more precise scheduling and real-time adjustments to energy distribution, further optimizing grid management in the context of increasing EV adoption. The technique improves scheduling efficiency, and trials with cross-validation show that it successfully identifies the target EVs.Sun, D et al., proposed an NS-EC-SG architecture based on 5G smart grid and edge computing that also introduced a hybrid AI method to predict the charging behaviour of electric vehicles. The technology enhances the user experience when it comes to charging EV and energy suppliers reduce costs associated with this process. Simulation results reveal better prediction accuracy and scheduling efficiency than what is currently available using state-of-the-art methods. Donald et al.’s study focused on integrating cloud computing and artificial intelligence (AI) into electric vehicle designs, operations as well as connections so as I-EVs can be improved. We have managed to achieve dynamic charging optimization, predictive maintenance, and intelligent fleet management through leveraging automation driven by artificial intelligence.
This allows us to improve performance, energy efficiency, and user experience, ultimately leading to the advancement of sustainable mobility. An AI-enabled blockchain-based solution for smart grid power management employing EVs was proposed by Wang, Z. and is called AEBIS. It uses federated learning and neural networks to reliably estimate power consumption (R = 0.938), supply consistent electricity, and lessen power fluctuations. With blockchain technology, communication is safe, transparent, and memory and latency expenses are low.Evaluating multiple ML techniques (DNN, KNN, LSTM, RF, SVM, DT), Mazhar et al., presented an ML-CMS for electric vehicles. Improving the smart city’s transport system’s dependability and sustainability, the LSTM model significantly decreases peak voltage, power losses, and load variations while keeping billing expenses to a minimum.
Short-term wind speed forecasting models based on learning have also been widely investigated for smart grid applications. Machine learning and hybrid techniques show better accuracy and adaptability when it comes to integrating renewable energy sources. This has to do with how we use AI to predict EV load. Table 1 shows the summary of related works.
In summary, SC-CMP combines the power of cloud computing, AI, and dynamic pricing strategies to deliver a comprehensive, forward-looking solution for managing the challenges posed by EV charging and grid integration. By leveraging advanced technologies such as deep learning and reinforcement learning, the platform ensures that both energy demand forecasting and consumption are handled effectively, promoting sustainability while maintaining grid stability. There are quite a few boundaries that these techniques have to conquer. Concerns about interoperability persist because standardised verbal exchange protocols are essential for the mixing of various systems and technology. Due to the delicate nature of the facts processed and the danger of cyberattacks, facts privateness and security are of the maximum importance. Given the constraints of the existing network architecture, it could be specifically tough to assure low-latency communication for actual-time applications. Concerns approximately scalability arise when the range of electrical motors and charging stations rises because of the high computing requirements of state-of-the-art AI and ML models. Furthermore, nearby variations in coverage and law can obstruct the standardisation of these technology. To address those challenges, people want to continuously innovate, place into effect strong cybersecurity measures, and create standardised frameworks that make smart EV charging and grid management structures greater efficient and secure, whereas additionally making them extra scalable and compliant with guidelines.
Contributions:
The structure of the remaining sections of this research paper is organized as follows: “Literature survey” explores the application of cloud computing, artificial intelligence (AI), and machine learning (ML) in optimizing intelligent electric vehicle (EV) charging and grid management. “Problem formulation” introduces the Scalable Cloud-Based Continuous Monitoring Platform (SC-CMP), providing a detailed overview of its functionality and advantages. In “Results and discussion”, the study presents a comprehensive analysis, comparing the SC-CMP with previous methods and offering insights into its improved efficiency. Finally, “Conclusion” summarizes the key findings and implications of the research.
