State-space Modeling and Data Analysis of Electric Two-wheeler Performance Metrics in the American Market

Electric two-wheelers have emerged as a pivotal segment in the global EV revolution, especially in densely populated and urbanized regions where compact mobility solutions are in high demand. In the United States, while the adoption of four-wheeled electric vehicles has seen extensive research, the performance dynamics of electric two-wheelers remains underexplored. This research addresses this gap by developing regression-based state-space models to investigate key performance parameters. The study applies multiple regression models (linear, quadratic, cubic) to derive functional relationships between variables such as battery capacity, motor power, acceleration, range, and price. We aim to identify and quantify the interrelationships between key design and performance parameters, including battery capacity, motor power, acceleration, range, and base price. By employing regression-based state-space modeling with linear, quadratic, and cubic formulations, we extract functional patterns that shape the behavior and market positioning of these vehicles. Our data-centric methodology offers critical insights into how technical specifications influence affordability and adoption potential, particularly in the context of urban mobility. This work advances the broader discourse on electric vehicle innovation by spotlighting lightweight electric mobility tailored to American cityscapes. The findings have potential implications for manufacturers, policymakers, and urban planners seeking sustainable alternatives to car-centric infrastructure. As consumer interest in cost-effective and energy-efficient transport grows, understanding these relationships becomes essential for guiding future design and investment strategies.