Energy management for thermoelectric generators based on maximum power point and load power tracking. (1st December 2018)

• Record Type: Journal Article
• Authors: Wu, Shi-jun; Wang, Shuo; Yang, Can-jun; Xie, Ke-ren
• Is Part Of: Energy conversion and management. Volume 177(2018)
• Page Start: 55
• Page End: 63
• Publication Date: 2018-12-01
• Subjects: Energy management strategy -- Supercapacitor -- Load power tracking -- Fuzzy logic -- Maximum power point tracking; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2018.09.040
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: We presents an energy management for thermoelectric generators to handle different load conditions. The threshold control based on the bus voltage intervals is introduced for the energy management. The LPT based on fuzzy logic is applied when the system power demand is low. The MPPT is realized based on the open-circuit voltage method when system power demand is high. Abstract: Thermoelectric generators (TEGs) can be widely applied to directly convert thermal energy into electricity. Herein, we propose an energy management system for TEGs to achieve efficient energy storage and use. Specifically, we consider both the generally unstable power generation given the variable temperature difference between the hot and cold sides of TEGs and the variable load that requires the inclusion of a storage unit consisting of a battery and supercapacitor for improving the system performance. The supercapacitor mitigates energy fluctuations and reduces transients in the bus voltage, and the battery acts as a backup energy source, storing energy and delivering electricity when the load power demand is high. In addition, we propose a novel threshold control strategy based on the bus voltage intervals for the energy management system that incorporates maximum power point tracking (MPPT) and load power tracking (LPT) to maintain energy balance among the system units. MPPT aims to provide an efficient collection of thermal energy, and LPT based on fuzzy logic is used to maintain a stable bus voltage when the DC load power demand is low. The experimental results verify that the proposed LPT approach regulates the evaluated variations in DC load power with an average error of 3.5% and that the energy management properly operates under variable load power and battery state of charge.