2017
17-19 September 2017 - 49th North American Power Symposium
OSU Intelligent Power Systems
Presenter: Mahesh Illindala
Associate Professor
Department of Electrical and Computer Engineering
2017
17-19 September 2017 - 49th North American Power Symposium
AcknowledgmentThe work presented is from a paper published in the Proceedings of 2017 North American Power Symposium (NAPS) entitled
“A Simple Method for Energy Optimization to Enhance Durability of Hybrid UAV Power Systems”
by Danielle Meyer, Richard Alexander, and Jiankang Wang.
2017
17-19 September 2017 - 49th North American Power Symposium
Outline• Background and Motivation
• System Description
• Proposed Method
• Analytical Model and Formulation
• Simulation Results
• Conclusion
2017
17-19 September 2017 - 49th North American Power Symposium
Background and Motivation
2017
17-19 September 2017 - 49th North American Power Symposium
System Description
Simple Representation of Hybrid UAV System
Sample Load Profile for Full UAV Mission
2017
17-19 September 2017 - 49th North American Power Symposium
Proposed Method
• Multifaceted solution• Algorithm – Optimizes battery capacity/engine output at each time step• Virtual UAV – Validates algorithm results in realistic, nonlinear system
• Closed loop system where user can provide algorithm alterations H
• All necessary complex constraints jointly considered
• Reduced complexity and computation time
2017
17-19 September 2017 - 49th North American Power Symposium
Analytical Model and Formulation
f(xk) is the optimal value of a mixed integer linear optimization problem
2017
17-19 September 2017 - 49th North American Power Symposium
Analytical Model and Formulation
SOC_1
Cost = ∞SOC Out of
Bounds
Cost = 0
SOC(1) = 1000
SOC(2) = 2000
SOC(3) = 0
Cost = ∞Engine Out of Bounds
Cost = ∞SOC Out
of Bounds
Cost = +1SOC & Engine in
Bounds
Cost = -1SOC & Engine
in Bounds
Optimal Path Found!
SOC_2,1 SOC_2,2 SOC_2,3
SOC_3,4 SOC_3,5 SOC_3,6 SOC_3,7
Cost = ∞Engine Out of Bounds
Load(2) = 1800Load(3) = 5000 (Discharge Required) SOC Lower Bound = 1000Engine Upper Bound = 4000
SOC(2) = 0SOC(2) = 1000
SOC(3) = 1000SOC(3) = 2000SOC(3) = ?SOC(3) = 1000
2017
17-19 September 2017 - 49th North American Power Symposium
Analytical Model and Formulation• Energy distribution decisions from algorithm are fed into controller
• Model subject to complicated aerial, electrical, and thermodynamics
Virtual UAV Model
• Separated payloads, enabling variable load characteristics
• Engine modeled using real component specifications
• Main battery sized according to longest “dash” period
2017
17-19 September 2017 - 49th North American Power Symposium
Simulation Results• Results from Virtual UAV
implementation closely followed DP simulation results
• Charges and stores just enough power for future requirements
• Fixed C-rate for initial results
• Voltage variability resulted in transients in engine output
2017
17-19 September 2017 - 49th North American Power Symposium
Conclusion
• Quick and accurate computations are required to achieve improved energy efficiency and reliability for lengthy UAV missions
• Existing methods have not considered importance of quick decisions that satisfy the highly nonlinear physical constraints
• Developed an integrated optimization method including mathematical programming and an virtual UAV simulation model
• Approach enables robust decisions to be made without extensive computation time
2017
17-19 September 2017 - 49th North American Power Symposium
Contact InformationAuthor Information
Danielle Meyer: [email protected]
Richard Alexander: [email protected]
Dr. Jiankang (J.K.) Wang: [email protected]
Department Information
OSU Department of Electrical and Computer Engineering
2015 Neil Ave, Columbus, OH 43210
URL: ece.osu.edu
2017
17-19 September 2017 - 49th North American Power Symposium
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2017
17-19 September 2017 - 49th North American Power Symposium
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[2] H. Zhang, C. Saudemont, B. Robyns, and M. Petit, “Comparison of technical features between a more electric aircraft and a hybrid electric vehicle,” in Vehicle Power and Propulsion Conference, 2008. VPPC’08. IEEE. IEEE, 2008, pp. 1–6.
[3] A. Aibinu, H. B. Salau, C. Akachukwu, and M. Nwohu, “Polygamy based genetic algorithm for unmanned aerial vehicle (uav) power optimization: A proposal,” in Electronics, Computer and Computation (ICECCO), 2014 11th International Conference on. IEEE, 2014, pp. 1–5.
[4] G. Steinmauer and L. Del Re, “Optimal control of dual power sources,” in Control Applications, 2001. (CCA’01). Proceedings of the 2001 IEEE International Conference on. IEEE, 2001, pp. 422–427.
[5] T. Nuesch, P. Elbert, M. Flankl, C. Onder, and L. Guzzella, “Convex optimization for the energy management of hybrid electric vehicles considering engine start and gearshift costs,” Energies, vol. 7, no. 2, pp. 834–856, 2014.
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