The complex dynamic behavior of nonlinear harvesters make it difficult to identify the optimum mechanical and electrical parameters for maximum output power, when compared to linear energy harvesting devices. In addition, the chaotic and multi-frequencies characteristics of responses under realistic human motion excitations provide additional challenges for enhancing the energy harvesting performance, such as the traditional frequency domain method being inappropriate for optimum resistance selection. This paper provides detailed numerical and experimental investigations into the influence of resistance on the efficiency of nonlinear energy harvesting from human motions. Numerical simulations under human motions indicate that optimum resistance of a nonlinear harvester can be attained to maximize the power output. Moreover, simulations of linear and nonlinear harvesters under harmonic excitations verify the effectiveness of frequency dominant method to obtain optimum resistance in the absence of a change in the dynamic behavior of the harvester. However, numerical simulations and experiments are the effective methods when the harvester shows complex dynamic characteristics. Experimental measurements of harvested power under different motion speeds and resistances are in agreement to the numerical analysis for the nonlinear harvester. The results demonstrate the effectiveness of the proposed resistance optimization method for nonlinear energy harvesting from human motions.
- Energy harvesting
- Human motion
- Optimum load resistance
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- Department of Mechanical Engineering - Professor
- Centre for Sustainable and Circular Technologies (CSCT)
- Centre for Nanoscience and Nanotechnology
- Institute for Mathematical Innovation (IMI)
- Faculty of Engineering and Design - Associate Dean (Research)
- Institute for Sustainability
- Centre for Integrated Materials, Processes & Structures (IMPS)
- EPSRC Centre for Doctoral Training in Advanced Automotive Propulsion Systems (AAPS CDT)
- Institute for Advanced Automotive Propulsion Systems (IAAPS)
Person: Research & Teaching, Core staff, Affiliate staff