Self-tuning stochastic resonance energy harvesting for rotating systems under modulated noise and its application to smart tires

Abstract

Abstract Energy harvesting from rotating system has been an influential topic for researchers over the past several years. In this paper, we propose an energy harvester for rotating systems under modulated noise excitations by taking advantage of self-tuning stochastic resonance with particular application to power smart tires. Compared to existing tire energy harvesters, it has larger power output and wider bandwidth. The former is achieved by stochastic resonance while the latter is by passively tuning the stochastic resonance frequency to track the time varying rotating speeds of the tire via a centrifugal stiffening effect; thus, the harvester maintains optimal power generation over a wide range of vehicle speed. It is an electromagnetic energy harvester consisting of an inward oriented rotating beam subjected to centrifugal force induced buckling. The compressive centrifugal force induces bistability to the harvester. The equation of motion is derived to investigate the effect of self-tuning. The tuning performance is verified by analysis of Kramers rate and signal-to-noise ratio (SNR). Numerical simulation is conducted to simulate the harvested power in a passenger car tire at different driving speeds. Maximum power of 45 mW is achieved in the simulation. The half-power bandwidth of the harvester is around 52–111 km/h (32 mph–70 mph), which corresponds to a typical speed range for a car in general roads and highways. To validate the simulation results, experiment is conducted. A rotating platform is built to mimic the tire rotation. Experiment results show good agreement with the numerical simulation with around 10% of errors, which proves the feasibility of the proposed harvester. A frequency sweep test also shows that the harvester works well in frequency-varying environments which are close to real driving conditions.