Abstract
Plug-in hybrid electric vehicle (PHEV) powertrain design is a complex process that requires an approach which enables the simultaneous integration of component design and powertrain control strategy decisions. Combined optimal design and control (co-design) methods are generally used to support this design paradigm. Although several PHEV powertrain co-design problems have been explored in the past, there have been no studies that have simultaneously addressed the impact of performance criteria such as acceleration performance and all-electric range (AER) along with predefined duty cycles on component design and hybrid mode (non-AER) supervisory control strategies. This is problematic as these performance criteria tend to strongly affect component sizing, which in turn can affect the supervisory control strategy in such a way that a non-performance-based co-design solution may become suboptimal. Therefore, this research addresses these issues by solving a comprehensive PHEV powertrain co-design performance study using a co-design method known as multidisciplinary dynamic system design optimization (MDSDO). In particular, MDSDO is implemented to simultaneously identify the optimal component designs, powertrain supervisory control strategies, and AER performance of a mid-size PHEV during a predefined vehicle duty cycle and a 0–60 mph acceleration maneuver such that the vehicle operating cost is minimized. A family of optimal solutions is generated by performing a parametric study for three distinct values of AER. The results from this study indicate that the formal inclusion of the performance criteria in a co-design problem has a significant impact on both component design and hybrid-mode supervisory control strategies.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Steinfeldt, B.A.: The multidisciplinary design problem as a dynamical system (Ph.D. thesis). Georgia Institute of Technology (2013)
Sobieszczanski-Sobieski, J., Haftka, R.T.: Multidisciplinary aerospace design optimization: survey of recent developments. Struct. Optim. 14(1), 1–23 (1997)
Assanis, D., Delagrammatikas, G., Fellini, R., Filipi, Z., Liedtke, J., Michelena, N., Papalambros, P., Reyes, D., Rosenbaum, D., Sales, A., et al.: Optimization approach to hybrid electric propulsion system design. J. Struct. Mech. 27(4), 393–421 (1999)
Patil, R.M.: Combined design and control optimization: application to optimal PHEV design and control for multiple objectives (Ph.D. thesis), The University of Michigan (2012)
Shiau, C.-S.N., Kaushal, N., Hendrickson, C.T., Peterson, S.B., Whitacre, J.F., Michalek, J.J.: Optimal plug-in hybrid electric vehicle design and allocation for minimum life cycle cost, petroleum consumption, and greenhouse gas emissions. J. Mech. Des. 132(9), 091013 (2010)
Allison, J.T., Herber, D.R.: Special section on multidisciplinary design optimization: multidisciplinary design optimization of dynamic engineering systems. AIAA J. 52(4), 691–710 (2014)
Bollino, K., Lewis, L.R., Sekhavat, P., Ross, I.M.: Pseudospectral optimal control: a clear road for autonomous intelligent path planning. In: AIAA Infotech@ Aerospace 2007 Conference and Exhibit. Rohnert Park, California (2007). https://doi.org/10.2514/6.2007-2831
Rousseau, A., Pagerit, S., Gao, D.: Plug-in hybrid electric vehicle control strategy parameter optimization. J. Asian Electr. Veh. 6(2), 1125–1133 (2007)
Fathy, H.K., Reyer, J.A., Papalambros, P.Y., Ulsov, A.G.: On the coupling between the plant and controller optimization problems. Am. Contr. Conf. 3, 1864–1869 (2001)
Martins, J.R.R.A., Lambe, A.B.: Multidisciplinary design optimization: a survey of architectures. AIAA J. 51(9), 2049–2075 (2013)
Deshmukh, A.: Multidisciplinary design optimization of dynamic systems using surrogate modeling approach (Master’s thesis), University of Illinois at Urbana-Champaign (2013)
Rao, A.V.: A survey of numerical methods for optimal control. Adv. Astronaut. Sci. 135(1), 497–528 (2009)
Biegler, L.T.: Nonlinear Programming: Concepts, Algorithms, and Applications to Chemical Processes. SIAM, Philadelphia (2010)
Haftka, R.T.: Simultaneous analysis and design. AIAA J. 23(7), 1099–1103 (1985)
Betts, J.T.: Practical Methods for Optimal Control and Estimation Using Nonlinear Programming. SIAM, Philadelphia (2010)
Shiau, C.-S.N., Samaras, C., Hauffe, R., Michalek, J.J.: Impact of battery weight and charging patterns on the economic and environmental benefits of plug-in hybrid vehicles. Energ. Pol. 37(7), 2653–2663 (2009)
Patil, R., Adornato, B., Filipi, Z.: Impact of naturalistic driving patterns on PHEV performance and system design, SAE Technical Paper (2009). https://doi.org/10.4271/2009-01-2715
Wu, X., Cao, B., Li, X., Xu, J., Ren, X.: Component sizing optimization of plug-in hybrid electric vehicles. Appl. Energ. 88(3), 799–804 (2011)
Egardt, B., Murgovski, N., Pourabdollah, M., Mardh, L.J.: Electromobility studies based on convex optimization: design and control issues regarding vehicle electrification. IEEE Contr. Syst. 34(2), 32–49 (2014)
Houshmand, A.: Multidisciplinary dynamic system design optimization of hybrid electric vehicle powertrains (Master’s thesis), University of Cincinnati (2016)
Allison, J.T., Guo, T., Han, Z.: Co-design of an active suspension using simultaneous dynamic optimization. J. Mech. Des. 136(8), 081003 (2014)
Liu, J., Peng, H.: Modeling and control of a power-split hybrid vehicle. IEEE Trans. Contr. Syst. Technol. 16(9), 1242–1251 (2008)
Van Mierlo, J., Van den Bossche, P., Maggetto, G.: Models of energy sources for EV and HEV: fuel cells, batteries, ultracapacitors, flywheels and engine-generators. J. Power Sources 128(1), 76–89 (2004)
Markel, T., Simpson, A.: Plug-in hybrid electric vehicle energy storage system design. In: Proceedings of the Advanced Automotive Battery Conference, Baltimore, MD, USA, 1–9, 2006
Patterson, M.A., Rao, A.V.: GPOPS-II: a MATLAB software for solving multiple-phase optimal control problems using hp-adaptive Gaussian quadrature collocation methods and sparse nonlinear programming. ACM Trans. Math. Softw. 41(1), 1–37 (2014)
Matthews, R.D., Beckel, S.A., Shizhi, M., Peters, J.E.: A new technique for thermodynamic engine modeling. J. Energ. 7(6), 667–675 (1983)
Nam, E.K., Giannelli, R.: Fuel consumption modeling of conventional and advanced technology vehicles in the physical emission rate estimator (PERE). US Environmental Protection Agency, Technical report (2005)
Heywood, J.: Internal Combustion Engine Fundamentals. McGraw-Hill Education, Alapakkam (1988)
Soong, W.L.: Design and modeling of axially-laminated interior permanent magnet motor drives for field-weakening applications (Ph.D. thesis), University of Glasgow (1993)
Duan, Y.: Method for design and optimization of surface mount permanent magnet machines and induction machines (Ph.D. thesis), Georgia Institute of Technology (2010)
Burress, T.A., Coomer, C.L., Campbell, S.L., Seiber, L.E., Marlino, L.D., Staunton, R.H., Cunningham, J.P.: Evaluation of the 2007 Toyota Camry hybrid synergy drive system, Oak Ridge National Laboratory (ORNL), Oak Ridge, TN, Technical report (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Azad, S., Behtash, M., Houshmand, A., Alexander-Ramos, M. (2018). Comprehensive PHEV Powertrain Co-design Performance Studies Using MDSDO. In: Schumacher, A., Vietor, T., Fiebig, S., Bletzinger, KU., Maute, K. (eds) Advances in Structural and Multidisciplinary Optimization. WCSMO 2017. Springer, Cham. https://doi.org/10.1007/978-3-319-67988-4_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-67988-4_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67987-7
Online ISBN: 978-3-319-67988-4
eBook Packages: EngineeringEngineering (R0)