Abstract
This chapter illustrates, with the help of two case studies, both involving design, implementation and experimental validation, the design process for energy management strategies. The first case is for a micro hybrid vehicle. Here, another method to numerically solve the optimization problem is introduced, namely Quadratic Programming, to handle the multiple decision variables used in the problem set-up for this case. The optimal solution for the powersplit is embedded in a Model Predictive Control frame work. The numerical solution allows a comparison between an optimal numerical strategy and a real-time strategy, as implemented in the vehicle. The implementation uses computing facilities that go beyond the standard ones in one of the vehicle’s computational units. The second case study is for a heavy-duty freight vehicle, namely a delivery truck. The implementation in this case uses a standard computational unit of the vehicle, which is possible by using a map-based approach. The experimental results show that the implementation of the optimal strategy is feasible and that this strategy achieves a better fuel economy than the built-in rule-based strategy.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Allen RW, Rosenthal TJ, Hogue JR (1996) Modeling and simulation of driver/vehicle interaction. In: SAE internat. congress & exposition, Detroit, MI, USA. SAE paper 960177
Åsbogård M, Edström F, Bringhed J, Larsson M, Hellgren J (2004) Evaluating potential of vehicle auxiliary system coordination using optimal control. In: Proc internat symp advanced veh control, Arnhem, The Netherlands
Back M, Simons M, Kirschaum F, Krebs V (2002) Predictive control of drivetrains. In: Proc 15th IFAC World Congress, Barcelona, Spain
Van den Bosch PPJ, Lootsma FA (1987) Scheduling of power generation via large-scale nonlinear optimization. J Optim Theory Appl 55:313–326
Buller S, Thele M, Karden E, de Doncker RW (2003) Impedance-based non-linear dynamic battery modeling for automotive applications. J Power Sources 113(2):422–430
Camacho EF, Bordons C (2004) Model predictive control, 2nd edn. Springer, London
De Jager B (2003) Predictive storage control for a class of power conversion systems. In: Proc European control conf, Cambridge, UK, pp 1–6
De Jager B (2004) The horizon in predictive energy storage control. In: Proc American control conf, Boston, MA, USA, pp 186–187
Kessels J, Koot M, Hendrix W, Ellenbroek R, Heemels M, Pesgens M, Steinbuch M, Van den Bosch P (2004) Vehicle modeling for energy management strategies. In: Proc internat symp advanced veh control, Arnhem, The Netherlands
Kessels JTBA, Koot M, De Jager B, Van den Bosch PPJ (2005) Energy management for vehicle power net with flexible electric load demand. In: Proc IEEE conf control appl. IEEE, Toronto, pp 1504–1509
Kessels JTBA, Koot M, De Jager B, Van den Bosch PPJ, Aneke NEPI, Kok DB (2007) Energy management for the electrical powernet in vehicles with a conventional drivetrain. IEEE Trans Control Syst Technol 15(3):494–505
Van Keulen T, De Jager B, Kessels JTBA, Steinbuch M (2012) Design, implementation, and evaluation of optimal power split control in hybrid vehicles. Control Eng Pract 20:547–558
Koot M, Kessels JTBA, De Jager B, Heemels WPMH, Van den Bosch PPJ, Steinbuch M (2005) Energy management strategies for vehicular electric power systems. IEEE Trans Veh Technol 54(3):771–782
Koot M, Kessels J, De Jager B, Van den Bosch P (2006) Fuel reduction potential of energy management for vehicular electric power systems. Int J Alternative Prop 1(1):112–131
Tate ED, Boyd SP (2000) Finding ultimate limits of performance for hybrid electric vehicles. In: Hybrid electric vehicles (SP-1560), Costa Mesa, CA, SAE paper 2000-01-3099
West MJ, Bingham CM, Schofield N (2003) Predictive control for energy management in all/more electric vehicles with multiple energy storage units. In: Proc IEEE internat electric machines and drives conf, vol 1. IEEE, New York, pp 222–228
Wipke KB, Cuddy MR, Burch SD (1999) ADVISOR 2.1: a user-friendly advanced powertrain simulation using a combined backward/forward approach. IEEE Trans Veh Technol 48(6):1751–1761
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag London
About this chapter
Cite this chapter
de Jager, B., van Keulen, T., Kessels, J. (2013). Experimental Case Studies. In: Optimal Control of Hybrid Vehicles. Advances in Industrial Control. Springer, London. https://doi.org/10.1007/978-1-4471-5076-3_7
Download citation
DOI: https://doi.org/10.1007/978-1-4471-5076-3_7
Publisher Name: Springer, London
Print ISBN: 978-1-4471-5075-6
Online ISBN: 978-1-4471-5076-3
eBook Packages: EngineeringEngineering (R0)