3,818 research outputs found

    Deep Learning -Powered Computational Intelligence for Cyber-Attacks Detection and Mitigation in 5G-Enabled Electric Vehicle Charging Station

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    An electric vehicle charging station (EVCS) infrastructure is the backbone of transportation electrification. However, the EVCS has various cyber-attack vulnerabilities in software, hardware, supply chain, and incumbent legacy technologies such as network, communication, and control. Therefore, proactively monitoring, detecting, and defending against these attacks is very important. The state-of-the-art approaches are not agile and intelligent enough to detect, mitigate, and defend against various cyber-physical attacks in the EVCS system. To overcome these limitations, this dissertation primarily designs, develops, implements, and tests the data-driven deep learning-powered computational intelligence to detect and mitigate cyber-physical attacks at the network and physical layers of 5G-enabled EVCS infrastructure. Also, the 5G slicing application to ensure the security and service level agreement (SLA) in the EVCS ecosystem has been studied. Various cyber-attacks such as distributed denial of services (DDoS), False data injection (FDI), advanced persistent threats (APT), and ransomware attacks on the network in a standalone 5G-enabled EVCS environment have been considered. Mathematical models for the mentioned cyber-attacks have been developed. The impact of cyber-attacks on the EVCS operation has been analyzed. Various deep learning-powered intrusion detection systems have been proposed to detect attacks using local electrical and network fingerprints. Furthermore, a novel detection framework has been designed and developed to deal with ransomware threats in high-speed, high-dimensional, multimodal data and assets from eccentric stakeholders of the connected automated vehicle (CAV) ecosystem. To mitigate the adverse effects of cyber-attacks on EVCS controllers, novel data-driven digital clones based on Twin Delayed Deep Deterministic Policy Gradient (TD3) Deep Reinforcement Learning (DRL) has been developed. Also, various Bruteforce, Controller clones-based methods have been devised and tested to aid the defense and mitigation of the impact of the attacks of the EVCS operation. The performance of the proposed mitigation method has been compared with that of a benchmark Deep Deterministic Policy Gradient (DDPG)-based digital clones approach. Simulation results obtained from the Python, Matlab/Simulink, and NetSim software demonstrate that the cyber-attacks are disruptive and detrimental to the operation of EVCS. The proposed detection and mitigation methods are effective and perform better than the conventional and benchmark techniques for the 5G-enabled EVCS

    Real Time Predictive and Adaptive Hybrid Powertrain Control Development via Neuroevolution

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    The real-time application of powertrain-based predictive energy management (PrEM) brings the prospect of additional energy savings for hybrid powertrains. Torque split optimal control methodologies have been a focus in the automotive industry and academia for many years. Their real-time application in modern vehicles is, however, still lagging behind. While conventional exact and non-exact optimal control techniques such as Dynamic Programming and Model Predictive Control have been demonstrated, they suffer from the curse of dimensionality and quickly display limitations with high system complexity and highly stochastic environment operation. This paper demonstrates that Neuroevolution associated drive cycle classification algorithms can infer optimal control strategies for any system complexity and environment, hence streamlining and speeding up the control development process. Neuroevolution also circumvents the integration of low fidelity online plant models, further avoiding prohibitive embedded computing requirements and fidelity loss. This brings the prospect of optimal control to complex multi-physics system applications. The methodology presented here covers the development of the drive cycles used to train and validate the neurocontrollers and classifiers, as well as the application of the Neuroevolution process

    Software Defined Networks based Smart Grid Communication: A Comprehensive Survey

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    The current power grid is no longer a feasible solution due to ever-increasing user demand of electricity, old infrastructure, and reliability issues and thus require transformation to a better grid a.k.a., smart grid (SG). The key features that distinguish SG from the conventional electrical power grid are its capability to perform two-way communication, demand side management, and real time pricing. Despite all these advantages that SG will bring, there are certain issues which are specific to SG communication system. For instance, network management of current SG systems is complex, time consuming, and done manually. Moreover, SG communication (SGC) system is built on different vendor specific devices and protocols. Therefore, the current SG systems are not protocol independent, thus leading to interoperability issue. Software defined network (SDN) has been proposed to monitor and manage the communication networks globally. This article serves as a comprehensive survey on SDN-based SGC. In this article, we first discuss taxonomy of advantages of SDNbased SGC.We then discuss SDN-based SGC architectures, along with case studies. Our article provides an in-depth discussion on routing schemes for SDN-based SGC. We also provide detailed survey of security and privacy schemes applied to SDN-based SGC. We furthermore present challenges, open issues, and future research directions related to SDN-based SGC.Comment: Accepte

    Multi-Agent Deep Reinforcement Learning-Driven Mitigation of Adverse Effects of Cyber-Attacks on Electric Vehicle Charging Station

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    An electric vehicle charging station (EVCS) infrastructure is the backbone of transportation electrification. However, the EVCS has myriads of exploitable vulnerabilities in software, hardware, supply chain, and incumbent legacy technologies such as network, communication, and control. These standalone or networked EVCS open up large attack surfaces for the local or state-funded adversaries. The state-of-the-art approaches are not agile and intelligent enough to defend against and mitigate advanced persistent threats (APT). We propose the data-driven model-free distributed intelligence based on multiagent Deep Reinforcement Learning (MADRL)-- Twin Delayed Deep Deterministic Policy Gradient (TD3) -- that efficiently learns the control policy to mitigate the cyberattacks on the controllers of EVCS. Also, we have proposed two additional mitigation methods: the manual/Bruteforce mitigation and the controller clone-based mitigation. The attack model considers the APT designed to malfunction the duty cycles of the EVCS controllers with Type-I low-frequency attack and Type-II constant attack. The proposed model restores the EVCS operation under threat incidence in any/all controllers by correcting the control signals generated by the legacy controllers. Also, the TD3 algorithm provides higher granularity by learning nonlinear control policies as compared to the other two mitigation methods. Index Terms: Cyberattack, Deep Reinforcement Learning(DRL), Electric Vehicle Charging Station, Mitigation.Comment: Submitted to IEEE Transactions on Smart Grid

    An Approximate Feasibility Assessment of Electric Vehicles Adoption in Nigeria: Forecast 2030

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    Efforts toward building a sustainable future have underscored the importance of collective responsibility among state and non-state actors, corporations, and individuals to achieve climate goals. International initiatives, including the Sustainable Development Goals and the Paris Agreement, emphasize the need for immediate action from all stakeholders. This paper presents a feasibility assessment focused on the opportunities within Nigeria's Electric Vehicle Value Chain, aiming to enhance public understanding of the country's renewable energy sector. As petroleum currently fulfills over 95% of global transportation needs, energy companies must diversify their portfolios and integrate various renewable energy sources to transition toward a sustainable future. The shifting investor sentiment away from traditional fossil fuel industries further highlights the imperative of incorporating renewables. To facilitate significant progress in the renewable energy sector, it is vital to establish platforms that support the growth and diversification of industry players, with knowledge sharing playing a pivotal role. This feasibility assessment serves as an initial reference for individuals and businesses seeking technically and economically viable opportunities within the sector

    Framework for Electric Vehicles and Photovoltaic Synergies

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    Historically road transport has been exclusively dominated by petrol and diesel engines. Both alternatives are proved to be unsustainable due to their environmental impacts and the limited nature of their primary resources. Today’s transportation sector in the European Union (EU) accounts for 23% of CO2 emissions, 72% of which is being emitted by road transport. The European Union’s CO2 emission regulation for new cars, has come as a response to set emission performance limits for new passenger cars with the goal of establishing a road map change for automotive sector. Furthermore, the EU has set challenging targets to reduce greenhouse gas emissions by 40% in 2030 (relative to emissions in 1990) and for energy consumed to be generated at least with 27% from renewable sources in 2030. As regards energy efficiency, the 2030 framework also indicated that the cost-effective delivery of the greenhouse gas emissions reduction target for 2030 would require increased energy savings of the order of 27%. The renewable energy directive particularly identified: technological innovation, energy efficiency and contribution of renewable energy sources in transport sector as one of the most effective tools in reaching the expected targets in terms of sustainability and security of the supply. In such context it is obvious that reaching these challenges will be certainly depending on the rollout of Electric Vehicles (EV) as a mean of sustainable transport, higher penetration of distributed renewable energy sources. One consequential challenge will consist in accommodating such paradigm in the most cost-efficient fashion through active involvement of customer and better flexibility of the demand. This report highlights the current trends and expected evolution in the EU in term of electromobility, Photovoltaic (PV) systems and smart grids, with the aim of identifying mutual synergies aiming at enabling: energy efficiency, sustainable transport and higher share of renewable energy sources in the final energy mix. A technical conceptual architecture for integration of EV facilities and distributed generation sources in the context of smart grid is proposed to identify the predictable penetration limits of PV systems and EV users.JRC.F.3-Energy Security, Systems and Marke

    A Review of Active Management for Distribution Networks: Current Status and Future Development Trends

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    Driven by smart distribution technologies, by the widespread use of distributed generation sources, and by the injection of new loads, such as electric vehicles, distribution networks are evolving from passive to active. The integration of distributed generation, including renewable distributed generation changes the power flow of a distribution network from unidirectional to bi-directional. The adoption of electric vehicles makes the management of distribution networks even more challenging. As such, an active network management has to be fulfilled by taking advantage of the emerging techniques of control, monitoring, protection, and communication to assist distribution network operators in an optimal manner. This article presents a short review of recent advancements and identifies emerging technologies and future development trends to support active management of distribution networks
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