Vehicle tracking by automated machine-to-machine (M2M) communication over cellular wireless networks will be a key component in future "intelligent transport" systems in which existing transport infrastructure is used more efficiently, resulting in reduced traffic congestion, improved fuel efficiency, etc. Furthermore, as transport becomes electrified, M2M communication will enable the impact of electric vehicles (EVs) on the electricity distribution network to be managed. In this research, we have implemented a Raspberry Pi based data logging system (DLS) operating over a commercial 4G wireless network. The DLS sends trip information, such as state of charge (SoC) and location of EVs to a remote server at regular intervals. We also address the question of scalability of the wireless DLS, and report both numerical simulations and analytical results showing that up to 250 vehicles can be supported per base station before communication delays and blocking disrupt system operation. Lastly, we implement a neural-network-based intelligent decision-making system to utilise the M2M logged data for charge scheduling and load management of EVs in the power grid.
Table of Contents1. Introduction -- 2. Review of machine-to-machine communication -- 3. Raspberry Pi based data logging system -- 4. Scalability of vehicles -- 5. M2M communication with scheduling -- 6. ANN-controlled charge scheduling of EVs -- 7. Final conclusion and future work.
NotesBibliography: pages 63-69
Awarding InstitutionMacquarie University
Degree TypeThesis MRes
DegreeMRes, Macquarie University, Faculty of Science and Engineering, Department of Engineering
Department, Centre or SchoolDepartment of Engineering
Year of Award2016
Principal SupervisorGraham Town
RightsCopyright Sayidul Morsalin 2016.
Copyright disclaimer: http://mq.edu.au/library/copyright
Extent1 online resource (xviii, 69 pages) diagrams, graphs, tables