Multi-parameter theoretical analysis of wearable energy harvesting backpacks for performance enhancement

Abstract

Wearable energy harvesting technologies show a promising potential in IoT (Internet of Things) and human daily life because of their continuous power supply in place of traditional chemical batteries. However, the coupling effects between mechanical and electrical parameters, as well as human motion features, significantly complicate the performance of wearable energy harvesters. To address this issue, a multi-parameter theoretical analysis is conducted in this paper to improve the performance of an energy harvesting backpack composed of a spring, mass, electromagnetic motor, and rack-pinion-based power takeoff. The analytical equation of the average output power of the energy harvesting backpack is derived as a function of spring stiffness, external resistance, and structural and electrical damping. A comprehensive analytical analysis and numerical simulation are performed based on the average power equation to study the influence of carried mass and walking speed on the energy conversion performance. Experimental tests are implemented for different human subjects, various carried mass, spring stiffness, and electrical resistances to verify the analytical analysis. Theoretical and experimental results demonstrate that the optimal carried mass and external resistance for generating the maximum power output are determined by the total damping of the mechanical system and electrical circuit instead of resonance. Moreover, the sensitivity of power output to the human walking frequency and the carried mass can be reduced by sacrificing the peak output power. The results show that the optimal backpack with a carried mass of 12.95 kg can generate 4 W power at the walking speed of 5.6 km/h.