Robust Monte Carlo Framework for Optimizing EV Fleet Maintenance: Balancing Stochastic Degradation and Lifecycle Cost
ID：120
Submission ID：125 View Protection：ATTENDEE
Updated Time：2025-11-03 11:46:59
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Oral Presentation
Start Time：2025-11-09 11:35 （Asia/Shanghai）
Duration：15min
Session：[S2] 2. Power electronics technology and application » [S2] 2.Power electronics technology and application
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Abstract
Electric vehicle (EV) battery degradation is a critical determinant of performance, cost, and longevity, particularly within diverse fleet operations. While extensive research exists on state of health (SoH) estimation and remaining useful life (RUL) prediction, few studies present a unified framework that explicitly connects stochastic degradation dynamics with comparative maintenance cost modeling across heterogeneous EV usage sectors. This paper addresses this gap by proposing a Monte Carlo-driven simulation framework that integrates an exponential SoH degradation model perturbed with Gaussian noise, analytical RUL forecasting, and economic models for both preventive and corrective maintenance. The simulations reveal pronounced degradation disparities across private EVs, taxis, and buses, with commercial fleets exhibiting accelerated aging. For instance, bus batteries reach the 80% SoH threshold in only 41.8 days, compared to 424.3 days for private EVs. The framework captures RUL variability under uncertainty, enabling robust predictive decision-making. A comparative strategy assessment demonstrates that preventive maintenance triggered at a SoH threshold of 0.85 consistently minimizes total lifecycle cost, achieving 49% – 57% savings over run-to-failure strategies. These findings offer actionable insights for fleet managers to optimize asset utilization, for original equipment manufacturers to design usage-specific battery packs, and for policymakers to formulate evidence-based fleet sustainability guidelines.
Keywords
Corrective maintenance, cost modeling, battery degradation, stochastic simulation, fleet management.
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