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

Bharat Khushalani; Reena Kollepara; Pallavi Katari; Priya Bavisetti; Deepanshu Balusu; Harshini Natakula

doi:doi:10.11648/j.ajtte.20251006.11

Research Article |

| Peer-Reviewed

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

Bharat Khushalani^*

, Reena Kollepara

, Pallavi Katari

, Priya Bavisetti

, Deepanshu Balusu

, Harshini Natakula

Published in American Journal of Traffic and Transportation Engineering (Volume 10, Issue 6)

Received: 24 June 2025 Accepted: 13 August 2025 Published: 9 December 2025

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Abstract

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.

Published in	American Journal of Traffic and Transportation Engineering (Volume 10, Issue 6)
DOI	10.11648/j.ajtte.20251006.11
Page(s)	135-149
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2025. Published by Science Publishing Group

Keywords

Electric Vehicles, Regression Analysis, Battery Capacity, Motor Power, Acceleration, Range, Pricing, Polynomial Models

References

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APA Style

Khushalani, B., Kollepara, R., Katari, P., Bavisetti, P., Balusu, D., et al. (2025). State-space Modeling and Data Analysis of Electric Two-wheeler Performance Metrics in the American Market. American Journal of Traffic and Transportation Engineering, 10(6), 135-149. https://doi.org/10.11648/j.ajtte.20251006.11

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ACS Style

Khushalani, B.; Kollepara, R.; Katari, P.; Bavisetti, P.; Balusu, D., et al. State-space Modeling and Data Analysis of Electric Two-wheeler Performance Metrics in the American Market. Am. J. Traffic Transp. Eng. 2025, 10(6), 135-149. doi: 10.11648/j.ajtte.20251006.11

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AMA Style

Khushalani B, Kollepara R, Katari P, Bavisetti P, Balusu D, et al. State-space Modeling and Data Analysis of Electric Two-wheeler Performance Metrics in the American Market. Am J Traffic Transp Eng. 2025;10(6):135-149. doi: 10.11648/j.ajtte.20251006.11

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@article{10.11648/j.ajtte.20251006.11,
  author = {Bharat Khushalani and Reena Kollepara and Pallavi Katari and Priya Bavisetti and Deepanshu Balusu and Harshini Natakula},
  title = {State-space Modeling and Data Analysis of Electric Two-wheeler Performance Metrics in the American Market},
  journal = {American Journal of Traffic and Transportation Engineering},
  volume = {10},
  number = {6},
  pages = {135-149},
  doi = {10.11648/j.ajtte.20251006.11},
  url = {https://doi.org/10.11648/j.ajtte.20251006.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtte.20251006.11},
  abstract = {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.},
 year = {2025}
}

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TY  - JOUR
T1  - State-space Modeling and Data Analysis of Electric Two-wheeler Performance Metrics in the American Market
AU  - Bharat Khushalani
AU  - Reena Kollepara
AU  - Pallavi Katari
AU  - Priya Bavisetti
AU  - Deepanshu Balusu
AU  - Harshini Natakula
Y1  - 2025/12/09
PY  - 2025
N1  - https://doi.org/10.11648/j.ajtte.20251006.11
DO  - 10.11648/j.ajtte.20251006.11
T2  - American Journal of Traffic and Transportation Engineering
JF  - American Journal of Traffic and Transportation Engineering
JO  - American Journal of Traffic and Transportation Engineering
SP  - 135
EP  - 149
PB  - Science Publishing Group
SN  - 2578-8604
UR  - https://doi.org/10.11648/j.ajtte.20251006.11
AB  - 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.
VL  - 10
IS  - 6
ER  -

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Author Information

Bharat Khushalani

Department of Artificial Intelligence, Shri Vishnu Engineering College for Women, Andhra Pradesh, India

Contact Email

http://orcid.org/0009-0008-1465-1433
Reena Kollepara

Department of Artificial Intelligence, Shri Vishnu Engineering College for Women, Andhra Pradesh, India

Contact Email

http://orcid.org/0009-0007-7255-0967
Pallavi Katari

Department of Artificial Intelligence, Shri Vishnu Engineering College for Women, Andhra Pradesh, India

Contact Email

http://orcid.org/0009-0002-7907-6712
Priya Bavisetti

Department of Artificial Intelligence, Shri Vishnu Engineering College for Women, Andhra Pradesh, India

Contact Email

http://orcid.org/0009-0006-7483-5694
Deepanshu Balusu

Department of Artificial Intelligence, Shri Vishnu Engineering College for Women, Andhra Pradesh, India

Contact Email

http://orcid.org/0009-0001-1653-7692
Harshini Natakula

Department of Artificial Intelligence, Shri Vishnu Engineering College for Women, Andhra Pradesh, India

Contact Email

http://orcid.org/0009-0006-0834-9321

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Plain Text BibTeX RIS

APA Style

Khushalani, B., Kollepara, R., Katari, P., Bavisetti, P., Balusu, D., et al. (2025). State-space Modeling and Data Analysis of Electric Two-wheeler Performance Metrics in the American Market. American Journal of Traffic and Transportation Engineering, 10(6), 135-149. https://doi.org/10.11648/j.ajtte.20251006.11

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ACS Style

Khushalani, B.; Kollepara, R.; Katari, P.; Bavisetti, P.; Balusu, D., et al. State-space Modeling and Data Analysis of Electric Two-wheeler Performance Metrics in the American Market. Am. J. Traffic Transp. Eng. 2025, 10(6), 135-149. doi: 10.11648/j.ajtte.20251006.11

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AMA Style

Khushalani B, Kollepara R, Katari P, Bavisetti P, Balusu D, et al. State-space Modeling and Data Analysis of Electric Two-wheeler Performance Metrics in the American Market. Am J Traffic Transp Eng. 2025;10(6):135-149. doi: 10.11648/j.ajtte.20251006.11

Copy | Download

@article{10.11648/j.ajtte.20251006.11,
  author = {Bharat Khushalani and Reena Kollepara and Pallavi Katari and Priya Bavisetti and Deepanshu Balusu and Harshini Natakula},
  title = {State-space Modeling and Data Analysis of Electric Two-wheeler Performance Metrics in the American Market},
  journal = {American Journal of Traffic and Transportation Engineering},
  volume = {10},
  number = {6},
  pages = {135-149},
  doi = {10.11648/j.ajtte.20251006.11},
  url = {https://doi.org/10.11648/j.ajtte.20251006.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtte.20251006.11},
  abstract = {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.},
 year = {2025}
}

Copy | Download

TY  - JOUR
T1  - State-space Modeling and Data Analysis of Electric Two-wheeler Performance Metrics in the American Market
AU  - Bharat Khushalani
AU  - Reena Kollepara
AU  - Pallavi Katari
AU  - Priya Bavisetti
AU  - Deepanshu Balusu
AU  - Harshini Natakula
Y1  - 2025/12/09
PY  - 2025
N1  - https://doi.org/10.11648/j.ajtte.20251006.11
DO  - 10.11648/j.ajtte.20251006.11
T2  - American Journal of Traffic and Transportation Engineering
JF  - American Journal of Traffic and Transportation Engineering
JO  - American Journal of Traffic and Transportation Engineering
SP  - 135
EP  - 149
PB  - Science Publishing Group
SN  - 2578-8604
UR  - https://doi.org/10.11648/j.ajtte.20251006.11
AB  - 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.
VL  - 10
IS  - 6
ER  -

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