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A Computationally Efficient Optimal Power Management for Power Split Hybrid Vehicle Based on Pontryagin’s Minimum Principle

[+] Author Affiliations
Masood Ghasemi, Xingyong Song

Texas A&M University, College Station, TX

Paper No. DSCC2017-5244, pp. V002T17A008; 6 pages
doi:10.1115/DSCC2017-5244
From:
  • ASME 2017 Dynamic Systems and Control Conference
  • Volume 2: Mechatronics; Estimation and Identification; Uncertain Systems and Robustness; Path Planning and Motion Control; Tracking Control Systems; Multi-Agent and Networked Systems; Manufacturing; Intelligent Transportation and Vehicles; Sensors and Actuators; Diagnostics and Detection; Unmanned, Ground and Surface Robotics; Motion and Vibration Control Applications
  • Tysons, Virginia, USA, October 11–13, 2017
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5828-8
  • Copyright © 2017 by ASME

abstract

The need for less fuel consumption urges effective powertrain management optimization in hybrid vehicles. In this study, we consider the real time power optimization problem of a power split hybrid vehicle. Assuming that the power on demand at the driveline can be predicted and known for each driving cycle, the powertrain management and optimization are conducted at the hybrid powertrain system’s level in a computationally efficient fashion. Specifically, we provide an analytical formulation of the powertrain optimization for the hybrid vehicle by using the Pontryagin’s minimum principle (PMP). By approximating the optimal instantaneous fuel consumption rate as a polynomial of the engine speed, we can formulate the optimization problem into a set of algebraic equations. In order to justify the applicability of the methodology for real-time implementations, we give directions on numerical iterative solutions for these algebraic equations. The analysis on the stability of the method is shown through statistical analysis. Finally, further simulations are given to confirm the efficacy and the robustness of the proposed optimal approach.

Copyright © 2017 by ASME

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