Design and Development of Real-Time Optimal Energy Management System for Hybrid Electric Vehicles

This paper describes a strategy to develop an energy
management system (EMS) for a charge-sustaining power-split hybrid
electric vehicle. This kind of hybrid electric vehicles (HEVs) benefit
from the advantages of both parallel and series architecture. However,
it gets relatively more complicated to manage power flow between the
battery and the engine optimally. The applied strategy in this paper is
based on nonlinear model predictive control approach. First of all, an
appropriate control-oriented model which was accurate enough and
simple was derived. Towards utilization of this controller in real-time,
the problem was solved off-line for a vast area of reference signals
and initial conditions and stored the computed manipulated variables
inside look-up tables. Look-up tables take a little amount of memory.
Also, the computational load dramatically decreased, because to find
required manipulated variables the controller just needed a simple
interpolation between tables.

Authors:

• Masood Roohi
• Amir Taghavipour

Keywords:

• Hybrid electric vehicles
• energy management system
• nonlinear model predictive control
• real-time.

References:

[1] E. I. A. O. of Integrated Analysis and F. U. D. of Energy Washington,
“Annual energy outlook 2009 with projections to 2030,” no.
DOE/EIA-0383(2009), 2009.
[2] S. Onori, L. Serrao, and G. Rizzoni, “Hybrid electric vehicles:
Energy management strategies,” ser. SpringerBriefs in Electrical and
Computer Engineering. Springer London, 2015. (Online). Available:
https://books.google.com/books?id=HCY3CwAAQBAJ
[3] G. Paganelli, G. Ercole, A. Brahma, Y. Guezennec, and G. Rizzoni,
“General supervisory control policy for the energy optimization of
charge-sustaining hybrid electric vehicles,” vol. 22, no. 4. Elsevier,
2001, pp. 511–518.
[4] S. Fekri and F. Assadian, “Fast model predictive control and its
application to energy management of hybrid electric vehicles.” INTECH
Open Access Publisher, 2011.
[5] A. Chasse and A. Sciarretta, “Supervisory control of hybrid powertrains:
An experimental benchmark of offline optimization and online energy
management,” vol. 19, no. 11. Elsevier, 2011, pp. 1253–1265.
[6] A. Taghavipour, N. L. Azad, and J. McPhee, “Real-time predictive
control strategy for a plug-in hybrid electric powertrain,” vol. 29.
Elsevier, 2015, pp. 13–27.
[7] A. Alessio and A. Bemporad, “A survey on explicit model predictive
control,” in Nonlinear model predictive control. Springer, 2009, pp.
345–369.
[8] A. Taghavipour, N. L. Azad, and J. McPhee, “Multi-parametric energy
management system with reduced computational complexity for plug-in
hybrid electric vehicles,” in Control Conference (ECC), 2015 European.
IEEE, 2015, pp. 3377–3382.
[9] R. Haber, R. Bars, and U. Schmitz, “Predictive control in process
engineering: From the basics to the applications.” John Wiley & Sons,
2012.
[10] A. Sciarretta, M. Back, and L. Guzzella, “Optimal control of parallel
hybrid electric vehicles,” vol. 12, no. 3. IEEE, 2004, pp. 352–363.
[11] L. Guzzella, A. Sciarretta et al., “Vehicle propulsion systems,” vol. 1.
Springer, 2007.
[12] J. Liu, H. Peng, and Z. Filipi, “Modeling and analysis of the toyota
hybrid system,” vol. 200, 2005, p. 3.