Research

EMC2: Efficient Mobility via Connectivity and Control


Within the Systems and Control discipline, we have pursued two main research tracks in the past few years: i) Cyberphysical Transportation Systems in which we are exploring the impact information technology and advanced computing can have on increasing energy efficiency and mobility of networked vehicles analytically and experimentally; and ii) Alternative Propulsion and Energy Storage Systems emphasizing systems engineering issues of renewable energy devices such as fuel cells, batteries, ultracapacitors and their integration into the next-generation hybrid electric cars and hybrid power systems.

1. Information Technology and Advanced Computing for Increasing Energy Efficiency and Mobility of Networked Vehicles:

[You can watch a presentation of some of our more recent work [here]

In the past few years we have been exploring the role that information technology can play in improving mobility and energy efficiency of vehicles. We have formulated algorithms that use preview information of terrain [1][2], traffic signal timing [3], [4], and traffic flow [5] for saving fuel and reducing emissions of modern vehicles with conventional or hybrid powertrains.

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 Wi-Fi or 3G 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, but our work has created wider interest and attracted additional sponsors and collaborators.

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Terrain Preview: Importance of terrain preview in energy management of hybrid vehicles has been a known but open problem that was not systematically addressed in the past. Recently our group, alongside with one or two European research teams, have published results that quantify the potential benefits attainable by terrain preview. Our perspective presented in [6] shows that utilizing digital maps will not only increase the fuel economy but can also help reduce battery’s charge cycle, which may help longevity of the battery. In [7] we present reduced computation methods that allow utilization of road preview in real-time. The digital map company, Intermap Technologies, has been our main industrial sponsor and collaborator ( Press Release) with additional support provided by NSF and ARC.


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Traffic Signal Preview: Our papers [8] and [9], to the best of our knowledge, are the first to propose use of preview information of signal phase and timing of multiple upcoming traffic signals for enhancing mobility and fuel economy of vehicles and shows the potential for significant energy savings. We have a patent on file, and have also created an iPhone App [see the video made by G. Mahler here] that suggests an eco-friendly speed to the driver based on future state of upcoming traffic signals, GPS location of the car, and a dynamic optimization algorithm that we have developed. We are now working with BMW Technology Offices, in MountainviewCalifornia and in Greenville, South Carolina to further enhance the algorithm and in incorporating it in the adaptive cruise control function of a car. With a team of mechanical engineering and computer science students and in collaboration with Clemson Computing, we hope to create a web-server that can broadcast traffic signal timings in real-time to any vehicle with Wi-Fi or 3G connectivity.


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Traffic Flow Preview: A step up from the work in [10] is our results in [11] in 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. Going beyond existing traffic prediction techniques, we are working on methods that predict future state of traffic and traffic signals probabilistically. By creating probabilistic traffic forecasting techniques, this research will enable predicting the chance of a future congestion or probability of a traffic light changing phase. These probabilistic predictions will be employed in optimal motion planning of individual vehicles for reducing their energy use and in optimal coordination of traffic control infrastructure for improving traffic flow. This is achieved by relying on real-time information from the vehicles and infrastructure, historical data, and computational power of a backend computing cluster. This work is important as it provides an alternative for managing congestion by relying mostly on information.

2. Alternative Propulsion and Energy Storage Systems:

We have been working on topics in the general area of renewable energy systems, based on the vision that renewable energy solutions will be pertinent to sustainable technological growth and that energy is going to play a central role in the global economy for the years to come. Our work spans: i) advanced propulsion and storage technologies, i.e. fuel cells and ultracapacitors, ii) novel optimization-based energy management techniques for hybrid powertrains, and iii) use of preview information for better energy utilization in hybrid vehicles.

Hybrid Powertrains: Since 2005 we have worked on a number of projects in the area of energy management of hybrid and plug-in hybrid vehicles. In a Ford Motor Company University Research Program project, awarded to us in 2007, our main focus has been on enhancing the energy management of hybrid powertrains by using systematic dynamic optimization techniques. The energy management of a hybrid vehicle is a complex problem due to strong nonlinearities, various constraints, and uncertainties. The energy management schemes in production are mostly rule-based relying on “if-then-else” logical rules and pre-calculated lookup tables. The optimality of such solutions is often unclear. On other hand, optimal solutions obtained using methods such as dynamic programming are often non-causal and too computationally demanding for real-time implementation. Our approach presented in [12] refined further in [14] employs moving horizon optimization (Model Predictive Control), and has evolved over more than three years of extremely hard work and under close monitoring of Ford scientists. The result is an algorithm based on fundamental control theoretical concepts that achieves close-to-optimal fuel economies and has the potential for real-time implementation. (See tutorial presentations on modeling and control of hybrid vehicles presented at the 2008 American Control Conference [here]).


Ultracapacitors: Ultracapacitors are high power density energy storage devices with capacitances in the order of hundreds of Farads, capable of releasing bursts of power in the order kilowatts. While their value is acknowledged as compared to modern batteries, few analytical and experimental studies have explored their true merits. Most existing studies have considered ultracapacitors for auxiliary energy storage along with batteries and fuel cells, including our paper [15]. Our paper [16] is the first, to our best knowledge, to suggest using ultracapacitors, stand-alone, for providing power boost in a mild hybrid powertrain. Our meticulously executed model-based simulations indicate that fuel savings up to 15% can be achieved by taking advantage of power boosts and energy recuperated during braking. More recently we have shown in [17] the benefits can be larger, up to 40%, for heavy trucks such as delivery vehicles with many stops and goes. This work was supported by Ford Motor Company. and ARC. which is now interested to explore these benefits experimentally.


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Fuel Cells: In continuation of the work in [18] and [19], we have worked on system architecture, power management techniques, and the power electronics needed for integrating fuel cells with ultracapacitors. Our experimental testbed includes a 1.2kW PEM fuel cell stack integrated with high-power density ultracapacitors using multiple power electronic devices and an advanced control system [see setup here]. We experienced several hurdles in selection of power electronic devices and in integration and control of the fuel cell and ultracapacitor. We hope to have addressed some of these issues in our paper [20] helping future researchers and practitioners. Our review paper [21] addresses the important issue of durability in fuel cells.


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