Flexible PV-cell modeling for energy harvesting in wearable IoT applications

Jaehyun Park, Hitesh Joshi, Hyung Gyu Lee, Sayfe Kiaei, Umit Ogras

Research output: Contribution to journalArticlepeer-review

39 Scopus citations


Wearable devices with sensing, processing and communication capabilities have become feasible with the advances in internet-of-things (IoT) and low power design technologies. Energy harvesting is extremely important for wearable IoT devices due to size and weight limitations of batteries. One of the most widely used energy harvesting sources is photovoltaic cell (PV-cell) owing to its simplicity and high output power. In particular, flexible PV-cells offer great potential for wearable applications. This paper models, for the first time, how bending a PV-cell significantly impacts the harvested energy. Furthermore, we derive an analytical model to quantify the harvested energy as a function of the radius of curvature. We validate the proposed model empirically using a commercial PV-cell under a wide range of bending scenarios, light intensities and elevation angles. Finally, we show that the proposed model can accelerate maximum power point tracking algorithms and increase the harvested energy by up to 25.0%.

Original languageEnglish (US)
Article number156
JournalACM Transactions on Embedded Computing Systems
Issue number5s
StatePublished - Sep 2017


  • Flexible hybrid electronics (FHE)
  • MPPT
  • PV-cell model
  • Power estimation
  • Wearable IoT devices

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture


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