A Time-Interleave-Based Power Management System with Maximum Power Extraction and Health Protection Algorithm for Multiple Microbial Fuel Cells for Internet of Things Smart Nodes

• Main Authors: Alfredo Costilla Reyes, Celal Erbay, Salvador Carreon-Bautista, Arum Han, Edgar Sánchez-Sinencio
• Format: Article
• Language: English
• Published: MDPI AG 2018-11-01
• Series: Applied Sciences
• Subjects: DC-DC power conversion; Internet of Things (IoT); microbial fuel cell array; power management system; remote monitoring; step-up converter; wastewater
• Online Access: https://www.mdpi.com/2076-3417/8/12/2404

Microbial Fuel Cell (MFC) technology is a novel Energy Harvesting (EH) source that can transform organic substrates in wastewater into electricity through a bioelectrochemical process. However, its limited output power available per liter is in the range of a few milliwatts, which results very limited to be used by an Internet of Things (IoT) smart node that could require power in the order of hundreds of milliwatts when in full operation. One way to reach a usable power output is to connect several MFCs in series or parallel; nevertheless, the high output characteristic resistance of MFCs and differences in output voltage from multiple MFCs, dramatically worsens its power efficiency for both series and parallel arrangements. In this paper, a Power Management System (PMS) is proposed to allow maximum power harvesting from multiple MFCs while providing a regulated output voltage. To enable a more efficient and reliable power-harvesting process from multiple MFCs that considers the biochemical limitations of the bacteria to extend its lifetime, a power ranking and MFC health-protection algorithm using an interleaved EH operation was implemented in a PIC24F16KA102 microcontroller. A power extraction sub-block of the system includes an ultra-low-power BQ25505 step-up DC-DC converter, which integrates Maximum Power Point Tracking (MPPT) capabilities. The maximum efficiency measured of the PMS was ~50.7%. The energy harvesting technique presented in this work was tested to power an internet-enabled temperature-sensing smart node.