1	Eco-Vehicle Speed Control at Signalized Intersections using I2V Communication Dr. Hesham Rakha, Dr. Kyoungho Ahn & Raj Kishore Kamalanathsharma Center for Sustainable Mobility Virginia Tech Transportation Institute (VTTI), Blacksburg, VA E-mail: hrakha@vt.edu. Phone: +1-540-231-1505
2	Overview Introduction Literature Review Model Description Example Illustration Case Studies Eco-Vehicle Speed Control Application Conclusions & Recommendations
3	Introduction The research develops an eco-speed control system to reduce vehicle fuel consumption in the vicinity of signalized intersections.
4	Similar Research
5	Model Description Previous publications used a simplified objective function. Here, the system computes a “proposed time to reach intersection” using SPaT information Queued vehicle information Approaching vehicle information Computes a “proposed fuel-optimal trajectory” using Vehicle deceleration and acceleration models Microscopic fuel consumption models Roadway characteristics
6	Model Description
7	Model Logic Signal is currently GREEN Case 1: GREEN will continue so that vehicle can pass through at current speed. Case 2: GREEN will end soon but vehicle can legally pass through intersection during the green or yellow indication if it speeds up within speed limit. Case 3: GREEN will end soon and vehicle cannot pass during this phase. Signal is currently RED Case 4: RED will continue but vehicle needs to be delayed to receive GREEN indication. Case 5: RED will end soon so that vehicle will receive GREEN when it reaches stop-line at current speed.
8	Model Logic Cases 1,2, 3 and 5 are fairly simple Case 4 requires trajectory optimization every time step within detection zone. Min{fuel consumed} Subject to Fixed travel distance upstream. Fixed time to reach intersection. Variable speed at intersection. Vehicle acceleration characteristics downstream to accelerate back to initial speed.
9	Model Logic Speed trajectory at intersection is divided into: Upstream section (deceleration to achieve delay) & Downstream section (accelerate to original speed) Cruising section to maintain a constant distance of travel.
10	Deceleration Model Conserve x and t : and Combining them: Solving for v_a: For any v_a , x_r is given by: TTG = t seconds DTI = x meters Approach speed = v_a m/s Speed at signal = v_s m/s Delay required = ∆t seconds Veh. deceleration = d m/s² Cruising dist. = x_r m
11	Acceleration Model Rakha & Lucic Model [8] was used. Vehicle dynamics model. Acceleration = Resultant Force/mass Resultant Force = Tractive Force - Resistive Force
12	Fuel Consumption Model Virginia Tech Comprehensive Power-based Fuel Model (VT-CPFM) Type 1²¹. Based on instantaneous power Parameters α₀, α₁ and α₂ can be calibrated using EPA fuel economy ratings. Does not result in a bang-bang control Optimum acceleration is not necessarily full throttle acceleration
13	Example Illustration Simulation was conducted for different approach speeds considering the following parameters: TTG = t =14 s DTI = x = 200 m Approach speed = v_a = 20 m/s Delay required = ∆t = 4 s d_min = 0.82 m/s² (computed) d_max = 5.90 m/s² (limiting).
14	Example Illustration
15	Simulation Results
16	Case Studies Experiment repeated using various sets of Approach speeds Desired delay estimates Vehicle Types 80 cases simulated maintaining a constant DTI of 200 m.
17	Case Studies Four vehicles were tested: Vehicles selected were available at VTTI and thus were validated using field measurements
18	Sample Results (Fuel-consumption matrix)
19	Sample Results (fuel consumed in ml at 20% throttle) Results from two separate simulated cases are shown below (for 20% throttle) and are color coded according to fuel consumed.
20	Sample Results (deceleration in m/s² in optimum case)
21	Sample Results (% difference between worst case and best case)
22	MATLAB Application
23	MATLAB Application
24	MATLAB Application
25	Conclusions Presentation demonstrates that objective function Should not be simplified Need to include a fuel-consumption model Model should be robust Need to incorporate entire downstream and upstream maneuver. Fuel-optimum trajectory is case-specific and depends on many factors. Does not necessarily imply minimum deceleration level Potential savings for approaching vehicle: 53% for sedans and 65% & 80% for the R350 & Tahoe.
26	Conclusions Deceleration upstream is case-specific. Initial deceleration is proportional to approach speed. Initial deceleration is also proportional to required delay. Acceleration depends on Speed at intersection Function of deceleration level In-vehicle module demonstrated with MATLAB application. Accelerating at lowest throttle level Most fuel-optimal downstream action, but reduces discharge rate. Possible fuel savings is proportional to engine-size and approach speeds.
27	References S. C. Davis, S. W. Diegel, and R. G. Boundy, Transportation Energy Data Book, vol. 91. Oak Ridge, TN: , 2010, p. 385. A. Bandivadekar et al., On the road in 2035: Reducing transportation’s petroleum consumption and GHG emissions, no. July. 2008, p. 196. G. Wu, K. Boriboonsomsin, W.-B. Zhang, M. Li, and M. Barth, Energy and Emission Benefit Comparison of Stationary and In-Vehicle Advanced Driving Alert Systems, Transportation Research Record: Journal of the Transportation Research Board, vol. 2189, no. 1, pp. 98-106, Dec. 2010. B. Asadi and A. Vahidi, Predictive Cruise Control: Utilizing Upcoming Traffic Signal Information for Improving Fuel Economy and Reducing Trip Time, Control Systems Technology, IEEE Transactions, pp. 1-9, 2010. T. Tielert, M. Killat, H. Hartenstein, R. Luz, S. Hausberger, and T. Benz, The impact of traffic-light-to-vehicle communication on fuel consumption and emissions, in Internet of Things (IOT), 2010, 2010, pp. 1–8. K. J. Malakorn and B. Park, Assessment of mobility, energy, and environment impacts of IntelliDrive-based Cooperative Adaptive Cruise Control and Intelligent Traffic Signal control, in Sustainable Systems and Technology (ISSST), 2010 IEEE International Symposium, 2010, pp. 1–6.
28	References S. Mandava, K. Boriboonsomsin, and M. Barth, Arterial velocity planning based on traffic signal information under light traffic conditions, in Intelligent Transportation Systems, 2009. ITSC’09. 12th International IEEE Conference on Intelligent Transportation Systems., 2009, pp. 1–6. H. Rakha, M. Snare, and F. Dion, Vehicle dynamics model for estimating maximum light-duty vehicle acceleration levels, Transportation Research Record: Journal of the Transportation Research Board, vol. 1883, no. 1, pp. 40–49, Jan. 2004. H. A. Rakha, K. Ahn, K. Moran, B. Saerens, and E. V. D. Bulck, Virginia Tech Comprehensive Power-Based Fuel Consumption Model: Model development and testing, Transportation Research Part D: Transport and Environment, Jun. 2011.
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