This content is not included in your SAE MOBILUS subscription, or you are not logged in.
A Fully-Analytical Fuel Consumption Estimation for the Optimal Design of Light- and Heavy-Duty Series Hybrid Electric Powertrains
ISSN: 0148-7191, e-ISSN: 2688-3627
Published March 28, 2017 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Fuel consumption is an essential factor that requires to be minimized in the design of a vehicle powertrain. Simple energy models can be of great help - by clarifying the role of powertrain dimensioning parameters and reducing the computation time of complex routines aiming at optimizing these parameters. In this paper, a Fully Analytical fuel Consumption Estimation (FACE) is developed based on a novel GRaphical-Analysis-Based fuel Energy Consumption Optimization (GRAB-ECO), both of which predict the fuel consumption of light- and heavy-duty series hybrid-electric powertrains that is minimized by an optimal control technique. When a drive cycle and dimensioning parameters (e.g. vehicle road load, as well as rated power, torque, volume of engine, motor/generators, and battery) are considered as inputs, FACE predicts the minimal fuel consumption in closed form, whereas GRAB-ECO minimizes fuel consumption via a graphical analysis of vehicle optimal operating modes. Parametric models of the main powertrain components are implemented in FACE and GRAB-ECO. Coefficients of these parametric models are expressed as a function of typical dimensioning parameters through analyzing characteristics of several engines, motors, and batteries belonging to similar technologies. Similarly, the common design constraints (i.e. acceleration and gradeability metrics) are scaled with the dimensioning parameters as well. Both virtual-light- and real-heavy-duty series hybrid electric powertrains are applied to assess the performance of FACE and GRAB-ECO by comparing with a benchmark based on Pontryagin’s Minimum Principle. Optimization of powertrain dimensioning parameters are performed on both series hybrid powertrains to demonstrate the effectiveness of FACE and GRAB-ECO in the powertrain optimal design application.
CitationZhao, J. and Sciarretta, A., "A Fully-Analytical Fuel Consumption Estimation for the Optimal Design of Light- and Heavy-Duty Series Hybrid Electric Powertrains," SAE Technical Paper 2017-01-0522, 2017, https://doi.org/10.4271/2017-01-0522.
Data Sets - Support Documents
|[Unnamed Dataset 1]|
|[Unnamed Dataset 2]|
|[Unnamed Dataset 3]|
|[Unnamed Dataset 4]|
|[Unnamed Dataset 5]|
|[Unnamed Dataset 6]|
|[Unnamed Dataset 7]|
|[Unnamed Dataset 8]|
|[Unnamed Dataset 9]|
|[Unnamed Dataset 10]|
- Birol Fati. World Energy Outlook 2015.International Energy Agency, Paris, 2015.
- Fontaras Georgios, Grigoratos Theodoros, Savvidis Dimitrios, Anagnostopoulos Konstantinos, Luz Raphael, Rexeis Martin, and Hausberger Stefa. An experimental evaluation of the methodology proposed for the monitoring and certification of CO2 emissions from heavy-duty vehicles in europe. Energy, 102:354–364, 2016.
- Fontaras, G., Rexeis, M., Dilara, P., Hausberger, S. , "The Development of a Simulation Tool for Monitoring Heavy-Duty Vehicle CO2 Emissions and Fuel Consumption in Europe," SAE Technical Paper 2013-24-0150, 2013, doi:10.4271/2013-24-0150.
- Franco Vicente, Delgado Oscar, and Muncrief Rache. Heavy-duty vehicle fuel-efficiency simulation: a comparison of US and EU tools. 2015.
- Sinoquet Delphine, Rousseau Gregory, and Milhau Yoha. Design optimization and optimal control for hybrid vehicles. Optimization and Engineering, 12(1-2):199–213, 2011.
- Chasse, A., Pognant-Gros, P., and Sciarretta, A. "Online Implementation of an Optimal Supervisory Control for a Parallel Hybrid Powertrain," SAE Int. J. Engines 2(1):1630–1638, 2009, doi:10.4271/2009-01-1868.
- Elbert Philip. Noncausal and causal optimization strategies for hybrid electric vehicles. PhD thesis, Eidgenössische Technische Hochschule (ETH) Zürich, Nr. 21522, 2014.
- Hofman Theo and Steinbuch Maarte. Topology optimization of hybrid power trains. In Optimization and Optimal Control in Automotive Systems, pages181–198. Springer, 2014.
- Sciarretta Antonio, Dabadie Jean-Charles, and Font Gregor. Automatic model-based generation of optimal management strategies for hybrid powertrains. 2015.
- Zhao Jianning and Sciarretta Antoni. Design and control co-optimization for hybrid powertrains: Development of dedicated optimal energy management strategy. IFAC-PapersOnLine, 49(11):277–284, 2016.
- ADEME. MELODYS: MEdium duty & LOw emission for DYStribution, 2009-2011. URL http://www.ademe.fr/melodys-materiels-livraison-marchandises-respectueux-lenvironnement. Accessed: 2016-10-10.
- Guzzella Lino and Sciarretta Antoni. Vehicle Propulsion Systems: Introduction to Modeling and Optimization. SpringerLink: Bücher. Springer Berlin Heidelberg, third edition, 2012. ISBN 9783642359132.
- Nam Edward K and Giannelli Rober. Fuel consumption modeling of conventional and advanced technology vehicles in the physical emission rate estimator (PERE). Technical report, US Environmental Protection Agency, 2005.
- Xaltenergy Battery, 2016. URL http://www.xaltenergy.com/index.php/solutions/technology/cells.html. Accessed: 2016-10-10.
- Petit Martin, Marc Nicolas, Badin Francois, Mingant Remy, and Sauvant-Moynot Valeri. A tool for vehicle electrical storage system sizing and modelling for system simulation. In 2014 IEEE Vehicle Power and Propulsion Conference (VPPC), pages 1–5. IEEE, 2014.
- Berr Fabrice Le, Abdelli Abdenour, Postariu D-M, and Benlamine . Design and optimization of future hybrid and electric propulsion systems: An advanced tool integrated in a complete workflow to study electric devices. Oil & Gas Science and Technology-Revue d’IFP Energies nouvelles, 67(4):547–562, 2012.
- Agreement concerning the adoption of uniform technical prescriptions for wheeled vehicles, equipment and parts which can be fitted and/or be used on wheeled vehicles and the conditions for reciprocal recognition of approvals granted on the basis of these prescriptions. UN Regulation No. 101, April 12, 2013. URL http://www.unece.org/trans/main/wp29/wp29regs101–120.html.
- 40 CFR Protection of Environment. Appendix I to Part 86 - Dynamometer Schedules. Code of Federal Regulations, July 1, 2010. URL http://http://www.ecfr.gov/cgi-bin/text-idx?tpl=/ecfrbrowse/Title40/40cfr86_main_02.tpl.
- 40 CFR 600 Fuel Economy of Motor Vehicles. Appendix I to Part 600 - Highway Fuel Economy Driving Schedule. Code of Federal Regulations, July 1, 2003. URL http://http://www.ecfr.gov/cgi-bin/text-idx?tpl=/ecfrbrowse/Title40/40cfr86_main_02.tpl.
- Baudrand Olivier, Blancher Linda, Dussolliet-Berthod Thierry, Härtel Enrico, Mingrant Rémy, Revel Renaud, Pognant-Gros Philippe, and Tafazzoli Baba. Melodys medium duty low emission distribution rapport intermédiaire 1. Technical report, IFPEN, 2010.
- Grunditz Emm. Design and Assessment of Battery Electric Vehicle Powertrain, with Respect to Performance, Energy Consumption and Electric Motor Thermal Capability. PhD thesis, Chalmers University of Technology, 2016.
- Zhou Meilan, Zhao Liping, Zhang Yu, Gao Zhaoming, and Pei Rongji. Pure electric vehicle power-train parameters matching based on vehicle performance. International Journal of Control and Automation, 8(9):53–62, 2015
- Crolla David and Mashadi Behroo. Vehicle powertrain systems: integration and optimization. John Wiley & Sons, 2011.
- SAE International Surface Vehicle Standard, "Clutch Application Powertrain Startability Rating Requirements for Truck and Bus Applications," SAE Standard J2469, Reaf Aug. 2004.
- Surcel, M., Michaelsen, J., Carme, R., and Brown, M. "Performance Evaluation of Heavy-Duty Vehicles Equipped with Automatic Transmissions and Powertrain Adaptive Systems in Forestry Transportation," SAE Technical Paper 2007-01-4212, 2007, doi:10.4271/2007-01-4212.