Intelligently chosen interventions have potential to outperform the diode bridge in power conditioning

F. Liu, Y. Zhang, O. Dahlsten, F. Wang

Scientific Reports 9, 190 (2019)


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LQ placeholderCost function landscape. X-axis is period and Y-axis is delay, Z-axis is the cost function value.

Cost function landscape. X-axis is period and Y-axis is delay, Z-axis is the cost function value.

We probe the potential for intelligent intervention to enhance the power output of energy harvesters. We investigate general principles and a case study: a bi-resonant piezo electric harvester. We consider intelligent interventions via pre-programmed reversible energy-conserving operations. these include voltage bias ips and voltage phase shifts. These can be used to rectify voltages and to remove destructive interference. We choose the intervention type based on past data, using machine learning techniques. We nd that in important parameter regimes the resulting interventions can outperform diode-based intervention, which in contrast has a fundamental minimum power dissipation bound.

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