Adaptability-Driven Multi-Objective Hardware Optimization for Vehicular Systems

Vehicular systems operate under diverse and often unpredictable operating conditions, necessitating robust adaptability to manage these uncertainties effectively. Traditionally, adaptability is linked to software capabilities, with hardware platforms frequently overlooked as variables for enhancement. This paper introduces a novel optimization framework aimed at improving the adaptability of vehicular systems during the hardware design phase. We develop an Adaptability Score and integrate it into a Multi-objective Optimization (MOO) framework, presenting its mathematical formulation and its application in hardware optimization. The efficacy of this framework is evaluated through its application to a real-world Hyperloop magnetic levitation system. Our results demonstrate that, compared to a baseline hardware configuration optimized without considering the Adaptability Score, our approach maintains multi-objective system performance across various anomalous scenarios by enhancing the system’s capability to adapt through improved hardware-software synergy.