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Piezoelectric Energy Harvesters (PEHs) are typically employed to provideadditional source of energy for a sensing system. However, studies show that aPEH can be also used as a sensor to acquire information about the source ofvibration by analysing the produced voltage signal. This opens a possibility tocreate Simultaneous Energy Harvesting and Sensing (SEHS) system, where a singlepiece of hardware, a PEH, acts as both, a harvester and as a sensor. Thisraises a question if it is possible to design a bi-functional PEH device withoptimal harvesting and sensing performance. In this work, we propose abi-objective PEH design optimisation framework and show that there is atrade-off between energy harvesting efficiency and sensing accuracy within aPEH design space. The proposed framework is based on an extensive vibration(strain and acceleration) dataset collected from a real-world operationalcable-stayed bridge in New South Wales, Australia. The bridge acceleration datais used as an input for a PEH numerical model to simulate a voltage signal andestimate the amount of produced energy. The numerical PEH model is based on theKirchhoff-Love plate and isogeometric analysis. For sensing, convolutionalneural network AlexNet is trained to identify traffic speed labels from voltageCWT (Continuous Wavelet Transform) images. In order to improve computationalefficiency of the approach, a kriging metamodel is built and genetic algorithmis used as an optimisation method. The results are presented in the form ofPareto fronts in three design spaces.

On the Joint Optimization of Energy Harvesting and Sensing of Piezoelectric Energy Harvesters: Case Study of a Cable-Stayed Bridge