Open Access
Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles
Volume 71, Number 2, March–April 2016
Article Number 30
Number of page(s) 14
Published online 31 March 2015
  • Sciarretta A., Guzzella L. (2007) Control of hybrid electric vehicles, IEEE Control Systems Magazine 27, 2, 60–70. [Google Scholar]
  • Sciarretta A., Back M., Guzzella L. (2004) Optimal control of parallel hybrid electric vehicles, IEEE Transactions on Control System Technology 12, 3, 352–363. [Google Scholar]
  • Musardo C., Rizzoni G., Staccia B. (2005) A-ECMS: An adaptive algorithm for hybrid electric vehicle energy management. IEEE Conference on Decision and Control, 2005 and 2005 European Control Conference, CDC-ECC ’05. IEEE. [Google Scholar]
  • Paganelli G., Ercole G., Brahma A., Guezennec Y., Rizzoni G. (2001) General supervisory control policy for the energy optimization of charge-sustaining hybrid electric vehicles, J. SAE Rev. 22, 511–518. [Google Scholar]
  • Serrao L., Onori S., Rizzoni G. (2009) ECMS as a realization of pontryagin’s minimum principle for HEV control. American Control Conference, June, pp. 3964–3969. [Google Scholar]
  • Pérez L.V., García G.O. (2010) State constrained optimal control applied to supervisory control in hybrid electric vehicles, Oil & Gas Science and Technology, Revue de l’Institut Français du Pétrole 65, 1, 191–201. [CrossRef] [EDP Sciences] [Google Scholar]
  • Geering H.P. (2007) Optimal control with engineering applications, Springer-Verlag. [Google Scholar]
  • Chiang A. (1992) Elements of dynamic optimization, McGraw-Hill. [Google Scholar]
  • Hartl R., Sheti S., Vickson G. (1995) A survey of the maximum principles for optimal control problems with state constraints, SIAM Review 37, 181–218. [Google Scholar]
  • Maurer H. (1977) On optimal control problems with bounded state variables and control appearing lineraly, SIAM J. Control and Optimization 15, 3, 345–362. [Google Scholar]
  • Delprat S., Guerra T.M., Paganelli G. (2001) Control strategy optimization for an hybrid parallel powertrain. Proc. of the American Control Conference 2001, pp. 1315–1320. [Google Scholar]
  • Delprat S., Guerra T.M., Rimaux J. (2002) Control strategies for hybrid vehicles: optimal control, Proc. of the IEEE 56th Vehicular Technology Conference, pp. 1681–1685. [CrossRef] [Google Scholar]
  • Delprat S., Lauber J., Guerra T.M., Rimaux J. (2004) Control of a parallel hybrid powertrain: Optimal control, IEEE Transactions on Vehicular Technology 53, 3, 872–881. [CrossRef] [Google Scholar]
  • Ascher U.M., Petzold L.R. (1992) Computer Methods for Ordinary Differential Equations and Differential-Algebraic Equations, SIAM. [Google Scholar]
  • Betts J.T. (2001) Practical Methods for Optimal Control Using Nonlinear Programming, SIAM. [Google Scholar]
  • Lentini M., Pereyra V. (1983) PASVA4: An O.D.E. boundary solver for problems with discontinuous interfaces and algebraic parameters, Mat. Aplic. Comp. 2, 103–118. [Google Scholar]
  • Pereyra V.L. (1979) Solución numérica de ecuaciones diferenciales con valores de frontera, Acta Científica Venezolana 30, 7–22. [Google Scholar]
  • Delprat S., Guerra T.M., Rimaux J. (2003). Control strategies for hybrid vehicles: synthesis and evaluation, Proc. of the IEEE 58th Vehicular Technology Conference, pp. 3246–3250. [Google Scholar]
  • Kermani S., Delprat S., Trigui R., Guerra T.M. (2008) Predictive management of hybrid vehicle, Proc. of the IEEE Vehicle Power and Propulsion Conference, Harbin, China, pp. 1–6. [Google Scholar]
  • Kermani S., Delprat S., Trigui R., Guerra T.M. (2009) Predictive control for HEV: experimental results, Proc. of the IEEE Vehicle Power and Propulsion Conference, Dearborn, MI, pp. 364–369. [Google Scholar]
  • Pérez L.V., de Angelo C.H., Pereyra V.L. (2013) Determinationof the adjoint state evolution for the efficient operation of a hybrid electric vehicle, Mathematical and Computer Modeling 57, 2257–2266. [CrossRef] [Google Scholar]
  • Rousseau G., Tran Q.H., Sinoquet D. (2008) SCOP: a sequential constraint-free optimal control problem algorithm, Chinese Control and Decision Conference, Yantai, China, pp. 273–278. [Google Scholar]
  • van Keulen T., Gillot Jan, de Jager B., Steinbuch M. (2014) Solution for state constrained optimal control problems applied to power split control for hybrid vehicles, Automatica 50, 187–192. [CrossRef] [MathSciNet] [Google Scholar]
  • Serrao L., Sciarretta A., Grondin O., Chasse A., Creff Y., di Domenico D., Pognant-Gros P., Querel C., Thibault L. (2013) Open issues in supervisory control of hybrid electric vehicles: a unified approach using optimal control methods, Oil Gas Science and Technology, Rev. IFP Energies nouvelles 68, 1, 23–33. [CrossRef] [EDP Sciences] [Google Scholar]
  • Brahma A., Guezennec Y., Rizzoni G. (2000). Optimal energy management in series hybrid vehicle, Proc. of the Amer. Control Conf, pp. 60–64. [Google Scholar]
  • Brahma A., Guezennec Y., Rizzoni G. (2000) Dynamic optimization of mechanical/electric power flow in parallel hybrid electric vehicles, Proc. of AVEC 2000, 5th. Intern. Symp. on Advanced Vehicle Control, Ann Arbor, Michigan. [Google Scholar]
  • Murtagh R., Saunders M. (1977) Minos user’s guide, report sol 77-9, Technical report, Department of Operations Research, Stanford University, Calif. [Google Scholar]
  • Murtagh R., Saunders M. (1978) Large scale linearly constrained optimization, Mathematical Programming 14, 41–72. [CrossRef] [Google Scholar]
  • Pérez L.V., Pilotta E.A. (2009) Optimal power split in a hybrid electric vehicle using direct transcription of an optimal control problem, Mathematics and Computers in Simulation 79, 6, 1959–1970. [CrossRef] [Google Scholar]
  • Pérez L.V., Bossio G.R., Moitre D., García G.O. (2006) Supervisory control of an hev using an inventory control approach, Latin American Applied Research 36, 93–100. [Google Scholar]

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