This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Comparative Analysis on Fuel Consumption Between Two Online Strategies for P2 Hybrid Electric Vehicles: Adaptive-RuleBased (A-RB) vs Adaptive-Equivalent Consumption Minimization Strategy (A-ECMS)
Technical Paper
2022-01-0740
ISSN: 0148-7191, e-ISSN: 2688-3627
Annotation ability available
Sector:
Language:
English
Abstract
Hybrid electric vehicles (HEVs) represent one of the main technological options for reducing vehicle CO2 emissions, helping car manufacturers (OEMs) to meet the stricter targets which are set by the European Green Deal for new passenger cars at 80 g CO2/km by 2025. The optimal power-split between the internal combustion engine (ICE) and the electric motor is a challenge since it depends on many unpredictable variables. In fact, HEV improvements in fuel economy and emissions strongly depend on the energy management strategy (EMS) on-board of the vehicle. Dynamic Programming approach (DP), direct methods and Pontryagin’s minimum principle (PMP) are some of the most used methodologies to optimize the HEV power-split. In this paper two online strategies are evaluated: an Adaptive-RuleBased (A-RB) and an Adaptive-Equivalent Consumption Minimization Strategy (A-ECMS). At first, a description of the P2 HEV model is made. Second, the two sub-optimal strategies are described in detail and then implemented on the HEV model to derive the fuel-optimal control strategy managing the power split between the thermal and electric engine to satisfy the driver's power request, including the engine on/off operating mode and the best gear selection. Finally, the two proposed strategies are tested on different driving cycles and then compared to other commercial strategies available in literature, such as the Equivalent Consumption Minimization Strategy (ECMS) and a RuleBased (RB) strategy. The results show that the A-ECMS is more conservative in terms of state of charge (SoC) compared to the A-RB. In fact, in the A-ECMS the SoC is always within the admissible range with considerable margin from the upper and lower limits for tested cycles, while in the A-RB a deep discharge of the battery is allowed. This behavior leads to a better fuel consumption of the A-RB compared to the A-ECMS, both in the WLTC and in the FTP-75 cycle.
Authors
- Giovanni Giardiello - Università di Napoli Federico II
- Francesco de Nola - Teoresi Group
- Michele Pipicelli - Università di Napoli Federico II
- Antonio Troiano - Teoresi Group
- Francesco Parascandolo - Teoresi Group
- Alfredo Gimelli - Università di Napoli Federico II
- Dario Santoro - Teoresi Group
- Bernardo Sessa - Teoresi Group
Topic
Citation
Giardiello, G., de Nola, F., Pipicelli, M., Troiano, A. et al., "Comparative Analysis on Fuel Consumption Between Two Online Strategies for P2 Hybrid Electric Vehicles: Adaptive-RuleBased (A-RB) vs Adaptive-Equivalent Consumption Minimization Strategy (A-ECMS)," SAE Technical Paper 2022-01-0740, 2022, https://doi.org/10.4271/2022-01-0740.Also In
References
- Huang , Y. , Ng , E.C.Y. , Zhou , J.L. , Surawski , N.C. et al. Eco-Driving Technology for Sustainable Road Transport: A Review Renewable and Sustainable Energy Reviews 93 October 2018 596 609 https://doi.org/10.1016/j.rser.2018.05.030
- Zahedi , S. , Batista-Foguet , Joan Manuel , van Wunnik , Lucas Exploring the Public's Willingness to Reduce Air Pollution and Greenhouse Gas Emissions from Private Road Transport in Catalonia Science of The Total Environment 646 1 January 2019 850 861 https://doi.org/10.1016/j.scitotenv.2018.07.361
- Singh , K.V. , Bansal , H.O. , and Singh , D. A Comprehensive Review on Hybrid Electric Vehicles: Architectures and Components Journal of Modern Transportation 27 2019 77 107
- Badin , F. , Scordia , J. , Trigui , R. et al. Hybrid Electric Vehicles Energy Consumption Decrease According to Drive Train Architecture, Energy Management and Vehicle Use IET Hybrid Veh Conf 2006 2006 213 223 https://doi.org/10.1049/cp:20060610
- https://eur-lex.europa.eu/legal-content/IT/TXT/PDF/?uri=CELEX:32019R0631&from=EN
- https://www.eea.europa.eu/data-and-maps/indicators/average-co2-emissions-from-motor-vehicles-1/assessment
- Serrao , L. , Onori , S. , and Rizzoni , G. A Comparative Analysis of Energy Management Strategies for Hybrid Electric Vehicles J. Dyn. Sys., Meas., Control 133 3 May 2011 031012 https://doi.org/10.1115/1.4003267
- Gonder , J. and Markel , T. Energy Management Strategies for Plug-in Hybrid Electric Vehicles SAE Technical Paper 2007-01-0290 2007 https://doi.org/10.4271/2007-01-0290
- Kumar , V. and Dadam , S. Intelligent Auxiliary Battery Control - A Connected Approach SAE Technical Paper 2021-01-1248 2021 https://doi.org/10.4271/2021-01-1248
- Jentz , R. , Lenzen , T. , and Meissner , H. Diagnostic Evaluation of Exhaust Gas Recirculation (EGR) System on Gasoline Electric Hybrid Vehicle SAE Technical Paper 2020-01-0902 2020 https://doi.org/10.4271/2020-01-0902
- Zhu , D. , Pritchard , E. , Dadam , S.R. , Kumar , V. et al. Optimization of Rule-Based Energy Management Strategies for Hybrid Vehicles Using Dynamic Programming Combustion Engines 184 1 2021 3 10 10.19206/CE-131967
- Lin , C.C. , Peng , H. , Grizzle , J.W. , and Kang , J.M. Power Management Strategy for a Parallel Hybrid Electric Truck IEEE Trans Contr Syst Technol 11 6 2013 839 849 10.1109/TCST.2003.815606
- Serrao , L. , Onori , S. , and Rizzoni , G. ECMS as a Realization of Pontryagin's Minimum Principle for HEV Control American Control Conference IEEE 2009 3964 3969 10.1109/ACC.2009.5160628
- Murphey , Y.L. et al. Intelligent Hybrid Vehicle Power Control - Part II: Online Intelligent Energy Management IEEE Trans Veh Technol 62 1 2013 69 79 10.1109/TVT.2012.2217362
- Gu , B. and Rizzoni , G. An Adaptive Algorithm for Hybrid Electric Vehicle Energy Management based on Driving Pattern Recognition ASME 2006 International Mechanical Engineering Congress and Exposition 2006 .
- Guzzella , L. and Sciarretta , A. Vehicle Propulsion System - Introduction to Modeling and Optimization Third Heidelberg Springer 2013
- Paganelli , G. Conception et commande d'une chaîne de traction pour véhicule hybride parallèle thermique et électrique Valenciennes 1999
- Musardo , C. , Rizzoni , G. , Guezennec , Y. , and Staccia , B. A-ECMS: An Adaptive Algorithm for Hybrid Electric Vehicle Energy Management European Journal of Control 2005 509 524
- Balazs , A. , Morra , E. , and Pischinger , S. Optimization of Electrified Powertrains for City Cars SAE International 1 2 2012 381 394
- Onori , S. , Serrao , L. , and Rizzoni , G. Hybrid Electric Vehicles - Energy Management Strategies Heidelberg Springer 2016
- Rizzoni , G. and Onori , S. Energy Management of Hybrid Electric Vehicles: 15 Years of Development at the Ohio State University Oil & Gas Science and Technology Vol. 70 2015 41 54
- https://www.anl.gov/es/downloadable-dynamometer-database