Prediction-Based Fast Thermoelectric Generator Reconfiguration for Energy Harvesting from Vehicle Radiators
Hanchen Yang1, Feiyang Kang2, Caiwen Ding3, Ji Li4,a, Jaemin Kim5, Donkyu Baek6,d, Shahin Nazarian4,b, Xue Lin7, Paul Bogdan4,c and Naehyuck Chang6,e
1Beijing University of Posts and Telecommunications, Beijing, China
hcyang11@qq.com
2Zhejiang University, Hangzhou, China
fy.kang@outlook.com
3Syracuse University, Syracuse, NY, USA
cading@syr.edu
4University of Southern California, Los Angeles, CA, USA
ajli724@usc.edu
bshahin@usc.edu
cpbogdan@usc.edu
5Seoul National University, Seoul, Korea
jmkim@elpl.snu.ac.kr
6Korea Advanced Institute of Science and Technology, Daejeon, Korea
ddonkyu@cad4x.kaist.ac.kr
enaehyuck@cad4x.kaist.ac.kr
7Northeastern University, Boston, MA, USA
xue.lin@northeastern.edu
ABSTRACT
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.
