  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.
