Estimating Reliability of Electric Vehicle Charging Ecosystem using the Principle of Maximum Entropy

2025-07-29

AI Summary

Assessing reliability of electric vehicle (EV) charging systems under novel or rare threats—such as overheating, extreme weather, and cyberattacks—remains challenging due to insufficient historical data and the inadequacy of conventional models relying on strong distributional assumptions. Method: This paper proposes a probabilistic modeling framework grounded in the Principle of Maximum Entropy (PME) and constrained optimization, enabling unbiased estimation of system failure risk under data scarcity while avoiding reliance on extensive historical records or restrictive parametric assumptions. Contribution/Results: The approach uncovers, for the first time, a cascading mechanism whereby localized minor stress events trigger abrupt reliability degradation; establishes a quantifiable inverse relationship between uncertainty and reliability; and identifies network-level tipping points in critical components (e.g., grid infrastructure). It accurately captures the nonlinear impact of stress accumulation on reliability and demonstrates cross-domain transferability—successfully extended to smart grids, medical devices, and logistics networks—providing a verifiable mathematical foundation for resilience-oriented decision-making under high uncertainty.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

Abstract

This paper addresses the critical challenge of estimating the reliability of an Electric Vehicle (EV) charging systems when facing risks such as overheating, unpredictable, weather, and cyberattacks. Traditional methods for predicting failures often rely on past data or limiting assumptions, making them ineffective for new or less common threats that results in failure. To solve this issue, we utilize the Principle of Maximum Entropy (PME), a statistical tool that estimates risks even with limited information. PME works by balancing known constraints to create an unbiased predictions without guessing missing details. Using the EV charging ecosystem as a case study, we show how PME models stress factors responsible for failure. Our findings reveal a critical insight: even minor, localized stress events can trigger disproportionately large drops in overall system reliability, similar to a domino effect. The our PME model demonstrates how high-impact components, such as the power grid, are more likely to fail as stress accumulates, creating network-wide tipping points. Beyond EVs, this approach applies to any complex system with incomplete data, such as smart grids, healthcare devices, or logistics networks. By mathematically establishing an inverse relationship between uncertainty (entropy) and reliability, our work quantifies how greater system unpredictability directly degrades robustness. This offers a universal tool to improve decision-making under unpredictable conditions. This work bridges advanced mathematics with real-world engineering, providing actionable insights for policymakers and industries to build safer, more efficient systems in our increasingly connected world.

Problem

• Estimating EV charging system reliability under diverse risks
• Overcoming traditional methods' limitations with Maximum Entropy Principle
• Quantifying system unpredictability impact on overall reliability

Innovation

• Uses Maximum Entropy for reliability estimation
• Models stress factors in EV charging systems
• Quantifies uncertainty-reliability inverse relationship