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Predictive Energy Management Strategies for Hybrid Electric Vehicles: Fuel Economy Improvement and Battery Capacity Sensitivity Analysis
Technical Paper
2018-01-0998
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
This paper shows the influence of different battery charge management strategies on the fuel economy of a hybrid parallel axle-split vehicle in a real driving scenario, for a vehicle control system that has the additional possibility to split the torque between front and rear axles. The first section regards the validation of a self-developed Model in the Loop (MiL) environment of a P1-P4 plug-in hybrid electric car, using experimental data of a New European Driving Cycle test. In its original version, which is implemented on-board the vehicle, the energy management supervisor implements a heuristic, or rule-based, Energy Management Strategy (EMS). During this project, a different EMS has been developed, consisting of a sub-optimal control scheme called Equivalent Consumption Minimization Strategy (ECMS), explained in detail in the second section. After that, the focus is on the evaluation of the benefits coming from different battery charge management strategies, which can be charge-sustaining, charge-depleting/charge-sustaining or charge blended, since the vehicle is a PHEV. The fuel economy improvements, using each strategy, are compared and one of them is then combined with the knowledge of future driving conditions (the so-called electronic horizon), mainly speed and altitude profiles. Therefore, the proposed controller would be ready for on-board implementation. In the last section, a sensitivity analysis that relates the results obtained with the battery capacity is carried out, to evaluate the influence of this strategic parameter on the battery charge management strategy choice. The paper shows the fuel economy potential of a physics-based approach like ECMS for a plug-in HEV, and how it can directly benefit from the prediction of future driving conditions, especially if the battery capacity is limited.
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Cavina, N., Caramia, G., Patassa, S., and Caggiano, M., "Predictive Energy Management Strategies for Hybrid Electric Vehicles: Fuel Economy Improvement and Battery Capacity Sensitivity Analysis," SAE Technical Paper 2018-01-0998, 2018, https://doi.org/10.4271/2018-01-0998.Data Sets - Support Documents
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References
- Guzzella , L. and Sciarretta , A. Vehicle Propulsion Systems 3d Springer Verlag 2012
- Serrao , L. 2009
- Sciarretta , A. , and Guzzella , L. Control of Hybrid Electric Vehicles: Optimal Energy-Management Strategies Ieee Control Systems Magazine 60 70 2007 27
- Salmasi , F. R. Control Strategies for Hybrid Electric Vehicles: Evolution, Classification, Comparison, and Future Trends IEEE Transactions on Vehicular Technology 56 5 2393 2404 2007
- Koot , M. , Kessels , J. , Jager , B.D. , and Bosch , P.V.D. Fuel Reduction Potential of Energy Management for Vehicular Electric Power Systems International Journal of Alternative Propulsions 1 1 112 131 2006
- M. Koot , J. Kessels , B. DeJager , W. Heemels , P. VandenBosch , and M. Steinbuch Energy Management Strategies for Vehicular Electric Power Systems 54 2005
- Crolla , D. and Ren , Q. Controller Design for Hybrid Vehicles: State of the Art Review Vehicle Power, (since 2000) 1 6 2008
- Khayyam , H. , Kouzani , A. , Marano , V. , and Rizzoni , G. Intelligent Energy Management in Hybrid Electric Vehicles intechopen 2006
- Bianchi , D. and Rolando , L. 2010
- Liaw , B.Y. Fuzzy Logic Based Driving Pattern Recognition for Driving Cycle Analysis.Pdf Journal of Asian Electric Vehicles 3 2 551 556 2004
- Kirk , D.E. optimal control theory: an introduction Dover Publications 1970
- Bertsekas , D. Dynamic Programming and Optimal Control 3rd I Belmont, Massachussets Athena Scientific 2005
- Powell , W.B. Approximate dynamic programming 2nd WILEY 2011
- Sniedovich , M. Dynamic Programming: Foundations and Principles 2nd CRC Press 2011
- Kim , N. , Cha , S. , and Peng , H. Optimal Control of Hybrid Electric Vehicles Based on Pontryagin’s Minimum Principle IEEE Transactions on Control Systems Technology 19 5 1279 1287 2011
- Paganelli , G. , Delprat , S. , Guerra , T. M. , Rimaux , J. , and Santin , J.-J. Equivalent Consumption Minimization Strategy for Parallel Hybrid Powertrains Vehicular Technology Conference, 2002. VTC spring 2002. IEEE 55th 2002 4 2076 2081
- Cerofolini , A. 2014
- Trivic , I. 2012
- Manzie , C. , Dewangan , P. , Corde , G. , Grondin , O. , Sciarretta , A. State of Charge Management for Plug in Hybrid Electric Vehicles with Uncertain Distance to Recharge Control Conference (ASCC) 2013 9th Asian 1 6 2013
- Tribioli , L. , Onori , S. Analysis of Energy Management Strategies in Plug-In Hybrid Electric Vehicles: Application to the GM Chevrolet Volt American Control Conference USA 2013
- Shankar , R. , Marco , J. , Assadian , F. Design of an Optimized Charge-Blended Energy Management Strategy for a Plug-in Hybrid Vehicle Proceedings of the UKACC (United Kingdom Automatic Control Council) International Conference on Control, Cardiff UK 2012 1 6
- Kural , E. and Güvenç , B.A. Predictive-Equivalent Consumption Minimization Strategy for Energy Management of a Parallel Hybrid Vehicle for Optimal Recuperation Journal of Polytechnic, 2015 18 3 113 124 2015
- Onori , S. , Rizzoni , G. Energy Management of Hybrid Electric Vehicles: 15 Years of Development at the Ohio State University IFP Energies nouvelles 2014
- Serrao , L. , Onori , S. , Rizzoni , G. 2009 ECMS as a Realization of Pontryagin’s Minimum Principle for HEV Control Proceedings of the 2009 American Control Conference 2009