Prediction-based fast thermoelectric generator reconfiguration for energy harvesting from vehicle radiators

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Thermoelectric generation (TEG) has increasingly drawn attention for being environmentally friendly. A few researches have focused on improving TEG efficiency at system level on vehicle radiators. The most recent reconfiguration algorithm shows improvement on 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 × reduction on switching overhead and 13 × 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.
Publisher
Institute of Electrical and Electronics Engineers Inc.
Issue Date
2018-03-19
Language
English
Citation

2018 Design, Automation and Test in Europe Conference and Exhibition, DATE 2018, pp.877 - 880

DOI
10.23919/DATE.2018.8342130
URI
http://hdl.handle.net/10203/269589
Appears in Collection
EE-Conference Papers(학술회의논문)
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