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<p>The rapid growth of electric vehicle (EV) adoption demands intelligent, secure, and resilient charging infrastructures capable of operating reliably under cyber-physical uncertainties. This paper designs a Composite Vulnerability Index (CVI)-based mitigation-aware control law for real-time, resilient management of multiple EV charging systems. The CVI quantifies the aggregated risk level of each EV’s charging context by fusing multiple factors such as cyber threats, communication latency, thermal stress, and power quality disturbances. The paper proposes a Digital-Twin enhanced Reinforcement Learning (RL) framework that integrates a Composite Vulnerability Index (CVI) with a mitigation-aware control law for large-scale multi-EV charging networks. A physics-informed EV Charging Digital Twin (DT) continuously predicts system evolution and identifies discrepancies using a learned residual model, enabling real-time characterization of operational uncertainty and cyber-attack impacts. The DT outputs both corrected state estimates and a risk cost that penalizes model uncertainty and elevated vulnerability levels. A Dreamer-V3 world-model-based RL agent uses these DT signals to learn an optimal control policy that adaptively allocates charging power, mitigates risks, and preserves grid safety under DDoS, MITM, spoofing, voltage instability, thermal overload, and communication delays. The proposed CVI integrates cyber, physical, and communication vulnerabilities into a unified state-aware metric, enabling coordinated power redistribution among multiple EV stations through a distributed primal-dual scheme. Simulation results with distributed EV charging stations demonstrate the system’s ability to ensure adaptive charging, equitable energy distribution, and successful convergence to target state-of-charge (SOC) levels while mitigating potential threats. The findings validate the CVI-based strategy as an effective framework for secure, scalable, and risk-aware EV energy management in next-generation smart grids.</p>

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Composite vulnerability index-based mitigation-aware control law for multiple electric vehicle charging system

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月29日

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  • Desh Deepak Sharma,
  • Rashmi Sharma

摘要

The rapid growth of electric vehicle (EV) adoption demands intelligent, secure, and resilient charging infrastructures capable of operating reliably under cyber-physical uncertainties. This paper designs a Composite Vulnerability Index (CVI)-based mitigation-aware control law for real-time, resilient management of multiple EV charging systems. The CVI quantifies the aggregated risk level of each EV’s charging context by fusing multiple factors such as cyber threats, communication latency, thermal stress, and power quality disturbances. The paper proposes a Digital-Twin enhanced Reinforcement Learning (RL) framework that integrates a Composite Vulnerability Index (CVI) with a mitigation-aware control law for large-scale multi-EV charging networks. A physics-informed EV Charging Digital Twin (DT) continuously predicts system evolution and identifies discrepancies using a learned residual model, enabling real-time characterization of operational uncertainty and cyber-attack impacts. The DT outputs both corrected state estimates and a risk cost that penalizes model uncertainty and elevated vulnerability levels. A Dreamer-V3 world-model-based RL agent uses these DT signals to learn an optimal control policy that adaptively allocates charging power, mitigates risks, and preserves grid safety under DDoS, MITM, spoofing, voltage instability, thermal overload, and communication delays. The proposed CVI integrates cyber, physical, and communication vulnerabilities into a unified state-aware metric, enabling coordinated power redistribution among multiple EV stations through a distributed primal-dual scheme. Simulation results with distributed EV charging stations demonstrate the system’s ability to ensure adaptive charging, equitable energy distribution, and successful convergence to target state-of-charge (SOC) levels while mitigating potential threats. The findings validate the CVI-based strategy as an effective framework for secure, scalable, and risk-aware EV energy management in next-generation smart grids.