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Thermoelectric generation (TEG) has increasingly drawn attention for beingenvironmentally friendly. A few researches have focused on improving TEGefficiency at the system level on vehicle radiators. The most recentreconfiguration algorithm shows improvement in performance but suffers frommajor drawback on computational time and energy overhead, and non-scalabilityin terms of array size and processing frequency. In this paper, we propose anovel TEG array reconfiguration algorithm that determines near-optimalconfiguration with an acceptable computational time. More precisely, with$O(N)$ time complexity, our prediction-based fast TEG reconfiguration algorithmenables all modules to work at or near their maximum power points (MPP).Additionally, we incorporate prediction methods to further reduce the runtimeand switching overhead during the reconfiguration process. Experimental resultspresent $30\%$ performance improvement, almost $100\times$ reduction onswitching overhead and $13\times$ enhancement on computational speed comparedto the baseline and prior work. The scalability of our algorithm makes itapplicable to larger scale systems such as industrial boilers and heatexchangers.

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