{"id":43,"date":"2016-10-31T11:15:04","date_gmt":"2016-10-31T15:15:04","guid":{"rendered":"https:\/\/cecas.clemson.edu\/~avahidi\/?page_id=43"},"modified":"2023-10-21T02:38:55","modified_gmt":"2023-10-21T06:38:55","slug":"research","status":"publish","type":"page","link":"https:\/\/cecas.clemson.edu\/~avahidi\/research\/","title":{"rendered":"Research"},"content":{"rendered":"

EMC2: Efficient Mobility via Connectivity and Control<\/b><\/big><\/span><\/span><\/h2>\n
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Guided by a core background in Dynamic Systems and Control\u00a0 and more specifically in Optimal Control and Model Predictive Control Methods, our research focus over the past few years has been on : i) Connected and Automated Mobility<\/u>\u00a0<\/strong>and exploring the impact of vehicular connectivity and autonomy on increasing energy efficiency and mobility of vehicles. ii) Alternative Propulsion and Energy Storage Systems<\/u><\/strong> and integration, modeling, and control of renewable energy devices such as ultracapacitors, batteries, and fuel cells.<\/p>\n

1. Connected and Automated Mobility<\/span><\/p>\n

[You can watch a presentation of some of our more recent work [here<\/a>]\n<\/span><\/pre>\n
CAVs offer huge potentials for boosting road safety, capacity, and efficiency, because of their ability to process data from many more sources (e.g. V2X fused with on-board sensing) and their ability for more precise positioning and control than human drivers.In the past few years we have been exploring the role that information technology can play in improving mobility and energy efficiency of connected and automated vehicles. We have formulated algorithms that use preview information of terrain [J12]<\/a>,\u00a0[J15]<\/a>, traffic signal timing [C15]<\/a>, [J14]<\/a>, and traffic flow [C19]<\/a>\u00a0for saving fuel and reducing emissions of modern vehicles with conventional or hybrid powertrains. A recent review paper can be found here [J32<\/a>].<\/p>\n
The proposed solutions enable fuel saving and improve mobility relying mostly on software and information and with minimal hardware investments. This impacts not only the high-tech vehicles of the future but the current fleet with WLAN or cellluar connectivity. If successfully deployed, our methods can lead to dramatic reduction in CO2 emissions and total national fuel use with direct societal and economical impacts. Our main sponsor in this work is the National Science Foundation\u00a0<\/a><\/span>and Department of Energy<\/a> but our work has created wider interest and attracted additional sponsors and collaborators.<\/p>\n

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\nAnticipative Car Following and Lane Selection:<\/b> <\/span>A step up from the work in [J14]<\/a>\u00a0is our results in [C19]<\/a>\u00a0in which we propose predictive use of traffic flow for planning more fuel efficient trips. This is an ongoing and more challenging work, due to complexity of traffic prediction and dynamic nature of the traffic constraints imposed.\u00a0A recent Department of Energy grant has accelerated our progress in this field. We are developing perception and anticipative guidance schemes for connected autonomous vehicles (CAVs) and quantify their energy saving potential in large scale traffic microsimulations at different levels of technology penetration. More recent results here [J29<\/a>, J31<\/a>]. The following video is a highlight of this collaborative project with more details about the team here<\/a>.<\/p>\n