Prediction-Based Fast Thermoelectric Generator Reconfiguration for Energy Harvesting from Vehicle Radiators

TL;DR

This paper introduces a fast, prediction-based reconfiguration algorithm for thermoelectric generators in vehicle radiators, significantly improving efficiency, reducing overhead, and enhancing scalability for energy harvesting applications.

Contribution

It presents a novel $O(N)$ time complexity algorithm that achieves near-optimal TEG configuration with reduced computational and switching overhead, outperforming prior methods.

Findings

30% performance improvement

Almost 100x reduction in switching overhead

13x faster computational speed

Abstract

Thermoelectric generation (TEG) has increasingly drawn attention for being environmentally friendly. A few researches have focused on improving TEG efficiency at the system level on vehicle radiators. The most recent reconfiguration algorithm shows improvement in performance but suffers from major drawback on computational time and energy overhead, and non-scalability in terms of array size and processing frequency. In this paper, we propose a novel TEG array reconfiguration algorithm that determines near-optimal configuration with an acceptable computational time. More precisely, with $O(N)$ time complexity, our prediction-based fast TEG reconfiguration algorithm enables all modules to work at or near their maximum power points (MPP). Additionally, we incorporate prediction methods to further reduce the runtime and switching overhead during the reconfiguration process. Experimental results present $30\%$ performance improvement, almost $100\times$ reduction on switching overhead and $13\times$ enhancement on computational speed compared to the baseline and prior work. The scalability of our algorithm makes it applicable to larger scale systems such as industrial boilers and heat exchangers.