Abstract
The efficient control of electrical vehicles may contribute to sustainable use of energy. In recent studies, a model has been analyzed and several algorithms based on branch and bound have been presented. In this work, we discuss a reformulated model on the control of an electric vehicle based on the minimization of the energy consumption during an imposed displacement. We will show that similar results can be obtained by applying standard software. Moreover, this paper shows that the specified control problem can be handled from a dynamic programming perspective.
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
Similar content being viewed by others
References
Bellman, R.: Dynamic Programming. Princeton University Press, Princeton (1957)
Cots, O.: Geometric and numerical methods for a state constrained minimum time control problem of an electric vehicle. ESAIM: COCV 23(4), 1715–1749 (2017)
Fiori, C., Ahn, K., Rakha, H.A.: Power-based electric vehicle energy consumption model: model development and validation. Appl. Energy 168, 257–268 (2016)
Genikomsakis, K.N., Mitrentsis, G.: A computationally efficient simulation model for estimating energy consumption of electric vehicles in the context of route planning applications. Transp. Res. Part D Transport Environ. 50, 98–118 (2017)
Hendrix, E.M.T., Olieman, N.J.: The smoothed Monte Carlo method in robustness optimization. Optim. Methods Softw. 23(5), 717–729 (2008)
Lei, F., Bai, Y., Zhu, W., Liu, J.: A novel approach for electric powertrain optimization considering vehicle power performance, energy consumption and ride comfort. Energy 167, 1040–1050 (2019)
Luin, B., Petelin, S., Al-Mansour, F.: Microsimulation of electric vehicle energy consumption. Energy 174, 24–32 (2019)
Merakeb, A., Messine, F.: Toward global minimum solutions for the problem of the energy consumption of an electrical vehicle. In: Cafiery, S., et al. (eds.) Proceedings of the Toulouse Global Optimization Workshop, pp. 85–88. ENSEEIHT (2010)
Merakeb, A., Messine, F., Aidéne, M.: A branch and bound algorithm for minimizing the energy consumption of an electrical vehicle. 4OR 12(3), 261–283 (2014)
Onori, S., Serrao, L., Rizzoni, G.: Hybrid Electric Vehicles: Energy Management Strategies. Springer, London (2016). https://doi.org/10.1007/978-1-4471-6781-5
Sabri, M., Danapalasingam, K., Rahmat, M.: A review on hybrid electric vehicles architecture and energy management strategies. Renew. Sustain. Energy Rev. 53, 1433–1442 (2016)
Sager, S., Claeys, M., Messine, F.: Efficient upper and lower bounds for global mixed-integer optimal control. J. Glob. Optim. 61(4), 721–743 (2015)
Sciarretta, A., De Nunzio, G., Ojeda, L.L.: Optimal ecodriving control: energy-efficient driving of road vehicles as an optimal control problem. IEEE Control Syst. Mag. 35(5), 71–90 (2015)
Sciarretta, A., Guzzella, L.: Control of hybrid electric vehicles. IEEE Control Syst. Mag. 27(2), 60–70 (2007)
Yuan, X., Zhang, C., Hong, G., Huang, X., Li, L.: Method for evaluating the real-world driving energy consumptions of electric vehicles. Energy 141, 1955–1968 (2017)
Acknowledgments
This paper has been supported by The Spanish Ministry (RTI2018-095993) in part financed by the European Regional Development Fund (ERDF) and by FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Hendrix, E.M.T., Rocha, A.M.A.C., García, I. (2019). On Trajectory Optimization of an Electric Vehicle. In: Misra, S., et al. Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science(), vol 11621. Springer, Cham. https://doi.org/10.1007/978-3-030-24302-9_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-24302-9_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-24301-2
Online ISBN: 978-3-030-24302-9
eBook Packages: Computer ScienceComputer Science (R0)