Design and Optimization of Wideband Multimode Piezoelectric MEMS Vibration Energy Harvesters

To enlarge operating frequency bandwidth of the multimode energy harvesters, nonlinearity characteristics has to be well presented by the system configuration. Therefore, the conventional optimization techniques, which are solely based on human observation, are highly difficult and somehow impossible. In this paper we propose an efficient optimization technique for automating the design of nonlinear piezoelectric MEMS energy harvesters based on Genetic Algorithm (GA) with minimum human efforts. In this regard, a MEMS piezoelectric harvester with capability of operating at multimode is proposed and a GA-based optimization methodology is utilized to shift its operational modes close to each other by optimizing device physical aspects. The experiments on post-optimization resonant frequencies show that our proposed optimization methodology is able to reduce the resonant frequencies by 13%, 10% and 9.5% for the first, second and third modes, respectively. In addition, the numerical simulation shows that our optimized energy harvester with a total chip area of 16-mm2 is able to maximally generate 655 mV, 80 mV and 572 mV at the first (153 Hz), second (168 Hz) and third (219 Hz) modes, respectively under 1 g vibration.