TY - GEN
T1 - Small scale mobile energy management system using Raspberry Pi and Python
AU - Ball, David
AU - Naik, Nitin
AU - Jenkins, Paul
PY - 2018/6/18
Y1 - 2018/6/18
N2 - This paper presents the design and implementation of a small scale mobile Energy Management System (EMS), for the optimized use of different energy sources, resulting in the most appropriate energy source being selected to charge a bank of batteries, which are being used to provide DC power to an emergency aid deployment in a remote location. The mobile EMS, utilizes a Raspberry Pi, a popular small scale computing device, with some peripheral devices attached and Python. The experimental simulation suggests a small emergency aid team deployment to a disaster area where local infrastructure may not be available, is possible. This proposed mobile EMS solution is small, lightweight, highly transportable and can be connected to any mobile device, and employed in any nomadic infrastructure for disaster management or for any other general purpose use.
AB - This paper presents the design and implementation of a small scale mobile Energy Management System (EMS), for the optimized use of different energy sources, resulting in the most appropriate energy source being selected to charge a bank of batteries, which are being used to provide DC power to an emergency aid deployment in a remote location. The mobile EMS, utilizes a Raspberry Pi, a popular small scale computing device, with some peripheral devices attached and Python. The experimental simulation suggests a small emergency aid team deployment to a disaster area where local infrastructure may not be available, is possible. This proposed mobile EMS solution is small, lightweight, highly transportable and can be connected to any mobile device, and employed in any nomadic infrastructure for disaster management or for any other general purpose use.
KW - EMS
KW - Energy Management System
KW - Power Management System
KW - Python
KW - Raspberry Pi
KW - Renewable Energy Sources
KW - RES
UR - http://www.scopus.com/inward/record.url?scp=85050156762&partnerID=8YFLogxK
UR - https://ieeexplore.ieee.org/document/8388631
U2 - 10.1109/ISSPIT.2017.8388631
DO - 10.1109/ISSPIT.2017.8388631
M3 - Conference publication
AN - SCOPUS:85050156762
T3 - 2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017
SP - 139
EP - 143
BT - 2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017
PB - IEEE
T2 - 17th IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017
Y2 - 18 December 2017 through 20 December 2017
ER -