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Fast Online-Implementable Sub-Optimal Energy Management of a Power-Split Hybrid Electric Vehicle

[+] Author Affiliations
Jian Dong, Rui Cheng, Zuomin Dong, Curran Crawford

University of Victoria, Victoria, BC, Canada

Paper No. DETC2013-13146, pp. V001T01A006; 11 pages
  • ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 1: 15th International Conference on Advanced Vehicle Technologies; 10th International Conference on Design Education; 7th International Conference on Micro- and Nanosystems
  • Portland, Oregon, USA, August 4–7, 2013
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5584-3
  • Copyright © 2013 by ASME


The current focus of HEV controller design is on the development of real-time implementable energy management strategies that can approximate the global optimal solution closely. In this work, the Toyota Prius power-split hybrid powertrain is used as a case study for developing online energy management strategy for hybrid electric vehicle.

The power-split hybrid powertrain combines the advantages of both the series and parallel hybrid powertrain and has been appealing to the auto-makers in the past years. The addition of two additional electric machines and a Planetary Gear Sets (PGS) allows more flexibility in terms of control at some cost of complexity.

A forward-looking dynamic model of the power-split powertrain system is developed and implemented in Simulink first. An optimal control problem is formulated, which is further reduced to an optimal control problem with a single-variable objective function and a single-state subject to both dynamic constraint and boundary constraint. The reduced optimal control problem is then solved by an on-line (real-time) implementable approach based on Pontryagin’s Minimum Principal (PMP), where the costate p is adapted based on SOC feedback.

Simulation results on standard driving cycles are compared using the proposed optimal control strategy and a rule-based control strategy. The results have shown significant improvement in fuel economy comparing to the baseline vehicle model, and the proposed online (real-time) PMP control algorithm with an adaptive costate p is very close to the optimal PMP solution with a constant costate. The proposed optimal control has a fast computation speed and calculates the optimal decision “dynamically” without the necessity of knowing future driving cycle information and can be practically implemented in real-time.

Copyright © 2013 by ASME



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