Potential Power Output from Vehicle Suspension Energy Harvesting Given Bumpy and Random-Surfaced Roads

Author:

Guo Hengyu12ORCID,Zeng Weijun34ORCID,Egloff Dario5ORCID,Meng Fei6ORCID,Dahlsten Oscar62ORCID

Affiliation:

1. Department of Computer Science, The University of Hong Kong, Hong Kong SAR, China

2. Department of Physics, Shenzhen Institute for Quantum Science and Engineering, Southern University of Science and Technology, Xueyuan Boulevard, Nanshan District, Shenzhen 518055, China

3. School of Innovation and Entrepreneurship, Southern University of Science and Technology, Xueyuan Boulevard, Nanshan District, Shenzhen 518055, China

4. Low Temperature Laboratory, Department of Applied Physics, Aalto University School of Science, P.O. Box 15100, 00076 Aalto, Finland

5. Departamento de Física, Universidad de los Andes, Carrera 1 # 18A-12, Bogotá 111711, Colombia

6. Department of Physics, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong SAR, China

Abstract

The energy efficiency of vehicles is a crucial challenge relating to sustainable energy preservation and regeneration methods. Regenerative breaking has proven feasible, and there is interest in whether harvesting energy from a vehicle’s suspension is similarly feasible. We here provide methods for estimating the amount of power that can be regenerated from the suspension for given vehicle and road parameters. We show that a reasonable road model is a generalised Gaussian process known as AR(1). Using this model, we can derive the key equation used in the ISO 8608 standard for measuring road roughness, such that the AR(1) parameters can be related to the measured road roughness data. We find that the road roughness coefficient of ISO 8608 and the diffusion coefficient of the AR(1) road are equal up to a factor. We provide an analytical expression for the maximum amount of power that can be generated for given road and car parameters, derived via Fourier analysis. We further model harvesting from large bumps using Simulink. These results help to estimate the potential power output given the measured road data.

Funder

National Natural Science Foundation of China

City University of Hong Kong

Shenzhen Science Technology and Innovation Commission

Publisher

MDPI AG

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