Encyclopedia of Systems and Control

Living Edition
| Editors: John Baillieul, Tariq Samad

Powertrain Control for Hybrid-Electric and Electric Vehicles

Living reference work entry
DOI: https://doi.org/10.1007/978-1-4471-5102-9_75-1


Increasingly stringent fuel economy and emissions regulations have required the automotive industry to consider more fuel-efficient powertrains and alternative primary sources of transportation fuels. Powertrain electrification and hybridization have rapidly become part of the portfolio of all major automotive manufacturers, ranging from hybrid-electric, to plug-in hybrid-electric, to battery-electric vehicles, to hybrid-hydraulic and hybrid-mechanical solutions. The increased complexity of the powertrain systems associated with hybrid vehicles presents interesting control challenges and problems. This entry describes control problems associated with hybrid-electric vehicles (HEVs) and battery-electric vehicles (BEVs).

HEV Powertrains

An HEV powertrain contains at least two power sources: a primary engine – typically a combustion engine or a fuel cell fueled by a chemical fuel (in liquid or gaseous form) – and a secondary power source that makes use of a rechargeable...


Internal Combustion Engine Driving Cycle Battery Management System Vehicle Fuel Economy Equivalent Fuel Consumption 
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  1. Canova M, Guezennec Y, Yurkovich S (2009) On the control of engine start/stop dynamics in a hybrid electric vehicle. ASME J Dyn Syst Meas Control 131:061005CrossRefGoogle Scholar
  2. Chaturvedi NA, Klein R, Christensen J, Ahmed J, Kojic A (2010) Algorithms for advanced battery management systems. IEEE Control Syst Mag 30(2):49–68CrossRefMathSciNetGoogle Scholar
  3. Gong Q, Tulpule P, Midlam-Mohler S, Marano V, Rizzoni G (2011) The role of ITS in PHEV performance improvement. In: American control conference, San FranciscoGoogle Scholar
  4. Koprubasi K, Westervelt ER, Rizzoni G (2007) Toward the systematic design of controllers for smooth hybrid electric vehicle mode changes. In: Proceedings of the American control conference, AnchorageGoogle Scholar
  5. Miller JM (2004) Propulsion systems for hybrid vehicles. The Institution of Electrical Engineers, LondonCrossRefGoogle Scholar
  6. Musardo C, Rizzoni G, Guezennec Y, Staccia B (2005) A-ECMS: an adaptive algorithm for hybrid electric vehicle energy management. Eur J Control 11(4–5):509–524CrossRefMATHMathSciNetGoogle Scholar
  7. Onori S, Serrao L, Rizzoni G (2014) Energy management strategies for hybrid electric vehicles. Springer, BerlinGoogle Scholar
  8. 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. JSAE 22: 511–518CrossRefGoogle Scholar
  9. Rahn C, Wang C-Y (2013) Battery systems engineering. Wiley, New YorkCrossRefGoogle Scholar
  10. Rizzoni G, Peng H (2013) Hybrid and electric vehicles: the role of dynamics and control. ASME Dyn Syst Control Mag 1(1):10–17Google Scholar
  11. Rizzoni G, Guzzella L, Baumann B (1999) Unified modeling of hybrid-electric vehicle drivetrains. IEEE/ASME Trans Mechatron 4(3):246–257CrossRefGoogle Scholar
  12. Serrao L, Onori S, Rizzoni G (2009) ECMS as a realization of Pontryagin’s minimum principle for HEV control. In: Proceedings of the 2009 American control conference, PortlandGoogle Scholar
  13. Serrao L, Onori S, Rizzoni G (2011) A comparative analysis of energy management strategies for hybrid electric vehicles. ASME J Dyn Syst Meas Control 133:1–9CrossRefGoogle Scholar
  14. Tate ED, Grizzle JW, Peng H (2007) Shortest path stochastic control for hybrid electric vehicles. Int J Robust Nonlinear Control 18(14):1409–1429CrossRefMathSciNetGoogle Scholar
  15. Wei X, Rizzoni G (2004) Objective metrics of fuel economy, performance and driveability – a review. SAE Technical paper 2004-01-1338Google Scholar
  16. Wollaeger SK, Onori S, Di Cairano S, Filev D, Ozguner U, Rizzoni G (2012) Cloud-computing based velocity profile generation for minimum fuel consumption: a dynamic programming based solution. In: American control conference, Montreal, 27–29 June 2012Google Scholar

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© Springer-Verlag London 2014

Authors and Affiliations

  1. 1.Department of Mechanical and Aerospace EngineeringCenter for Automotive Research, The Ohio State UniversityColumbusUSA