Adaptive treadmill based E-cycle design optimization: Achieving cost and convenience balance through grey relational analysis

Abstract

This study proposes a framework for optimizing treadmill-based e-cycle parameters, targeting both cost efficiency and user convenience. Using Grey Relational Analysis (GRA), four case studies were conducted to explore diverse user needs. Case 1 achieved an optimal balance with a 200 W hub motor, 14Ah battery, 100 W dynamo, and 4.5x gear ratio. Case 2 prioritized cost savings, adopting a minimal setup with a 2Ah battery and 60 W dynamo. Case 3 emphasized user convenience with higher energy storage and regeneration, while Case 4 analyzed performance across varying walking speeds. Results showed the system’s adaptability and highlighted the dynamo’s significant influence on performance. The findings demonstrate the framework’s potential to tailor e-cycle configurations based on user goals and activity levels. Future work will integrate machine learning for real-time control and perform life-cycle assessments to improve environmental sustainability, contributing to more efficient and eco-friendly personal mobility solutions.