An Efficient Design of an Energy Harvesting Backpack for Remote Applications

During past couple of decades, energy harvesting from human body motion has been explored extensively to provide electricity needed for energizing cell phones and other gadgets. In this paper, an optimized design of a suspended-load backpack with a mechanical motion rectifier (MMR) is presented. The Genetic Algorithm (GA) is used to optimize the system parameters for producing maximum output power with an 8-kg backpack, which is the lightest proposed in previous studies. A prototype of the backpack is fabricated based on the obtained optimal parameters, and experimental results for a normal walking speed show instant power of 11.57 Watts and average power of 4.37 Watts, which are one of the highest among all models in previous works. Finally, a modified dynamic model of the backpack is proposed based on an experimental model of the human shoulders displacement during walking to minimize the output power error between numerical simulation data and experimental test results.

Automated summary

This paper presents an optimized design of a suspended-load backpack with a mechanical motion rectifier (MMR) that harvests energy from human body motion. The Genetic Algorithm (GA) is used to optimize system parameters for maximum output power with a lightweight 8-kg backpack. Experimental results show high instant power and average power compared to previous works. Additionally, a modified dynamic model of the backpack is proposed based on experimental data to minimize output power error.