Investigation of the Effect of Electrical Current Variance on Thermoelectric Energy Harvesting
Source abstract
Abstract
The performance of thermoelectric modules for energy-harvesting applications is investigated, and a model is presented to predict module performance. Derived from energy conservation equations, the model predicts module performance by solving for the temperatures at both ends of the thermoelectric materials within a module. Unlike traditional methods, the model accounts for the effect of electrical current with respect to the load resistance by considering additional heat transfer by Joule heating and the Peltier effect. This establishes a nonlinear quadratic form of temperatures which can be solved by an iterative numerical solution. The model is extended to predict the performance of energy-harvesting systems, which may include connection of multiple thermoelectric modules in series to meet the necessary power requirements. However, a key issue with multiple module connection is the power reduction that arises when there are significant differences in module properties and/or the corresponding external conditions to which each individual module is exposed. Power reduction is thus investigated, as in some cases the overall power output for multiple modules can be less than the power output of a single module. For validation and comparison of the model, experimental support is provided for the case of two commercial thermoelectric modules connected in series. The model also provides optimum load resistances, and a system optimization of the number of modules for a designated heat sink to maximize power generation. The overarching goal of this work is to provide performance prediction and optimization considerations for actual thermoelectric energy-harvesting systems.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Advanced Thermoelectric Materials and Devices · Innovative Energy Harvesting Technologies · Energy Harvesting in Wireless Networks
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (21)
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