Mission Effectiveness Assessment of Solar-Powered Unmanned Surface Vehicles Considering Hardware States and Energy Levels

An improved availability–dependability–capability (ADC) assessment framework is proposed for evaluating the mission effectiveness of solar-powered unmanned surface vehicles (USVs) under the coupled effects of hardware state transitions and stochastic energy supply. Within the availability and dependability dimensions, a joint state space is constructed that couples hardware condition states with battery state of charge (SOC). A Monte Carlo simulation driven by real solar irradiance data is used to derive the initial probability distribution and the dynamic state transition matrix. In the capability dimension, a hierarchical index system is established, and a hierarchical Criteria Importance Through Intercriteria Correlation (CRITIC) method is introduced to achieve objective weighting that accounts for inter-index correlations across multiple levels. Case-study calculations for a typical task profile yield a baseline mission effectiveness of approximately 0.661934. Comparative analysis reveals that neglecting the dynamic evolution of the energy state overestimates effectiveness by 38.94%, whereas ignoring random hardware failures produces a deviation of roughly 9.48%. These findings indicate that, while energy-state dynamics impose the dominant constraint on mission performance, the contribution of hardware degradation remains substantial. The concurrent influence of both limiting factors confirms the necessity of an assessment framework that explicitly integrates hardware and energy states, as developed in this work.