Online Optimization based Predictive Energy Management Functionality of Plug-In Hybrid Powertrain using Trajectory Planning Methods
Published March 28, 2017 by SAE International in United States
Downloadable datasets for this paper availableAnnotation of this paper is available
Powertrain systems exploiting information from vehicle connectivity have widened the system boundary resulting in additional degrees-of-freedom for predictive trajectory planning. Heuristic methods based on component characteristics are currently widely used for Energy Management (EM) functionality of hybridized powertrains. Despite their better usability, increased calibration effort and sensitivity to synthetic calibration scenarios are drawbacks of such control methods. Availability of predictive data, better computing power and challenges posed by various scenarios in real driving, have led to interest in online-optimizing EM functionality. Equivalent Consumption Minimization Strategy (ECMS) approaches based on Indirect optimal control /Pontryagin Minimum principle have difficulty in handling inequality state constraints. Extensions of ECMS make use of modifications to the equivalence factor/co-state, based on prediction of driving conditions.
The proposed method uses limited time horizon prediction data to optimize engine on/off state and torque split among the energy converters using direct optimal control. Along with its ability to handle inequality constraints on the system states directly, the proposed method does not require an explicit model of additional dynamics. Further, the developed EM functionality adapts in real-time based on situation-aware prediction along with offering possibility to tune online the optimization process using heuristics on constraint-limits. These advantages along with this real-time capability and flexibility to handle change of control objectives as well as variation of control weighting reduces calibration effort. Results of the functionality shall be compared with predictive ECMS method. The functionalities developed along with their real-time capability will be demonstrated using the Combustion Engine Assist (CEA) concept.
CitationVadamalu, R. and Beidl, C., "Online Optimization based Predictive Energy Management Functionality of Plug-In Hybrid Powertrain using Trajectory Planning Methods," SAE Technical Paper 2017-01-1254, 2017, https://doi.org/10.4271/2017-01-1254.
Data Sets - Support Documents
|[Unnamed Dataset 1]|
|[Unnamed Dataset 2]|
- Roadmap to a Single European Transport Area — Towards a competitive and resource efficient transport system, WHITE PAPER, EUROPEAN COMMISSION, 2011.
- Wirth M., Bartsch L., Ploumen S. and Weber C., In a Hybridized Future with Alternative Fuels: Is the SI Engine the Winning Concept?, SIA POWERTRAIN, 2015.
- Nemry F., Leduc G., Muñoz A., Plug-in Hybrid and Battery-Electric Vehicles: State of the research and development and comparative analysis of energy and cost efficiency, Joint Research Centre, European Commission, 2009.
- Mock P., Kühlwein J., Tietge U., Franco V., Bandivadekar A., German J., The WLTP: How a new test procedure for cars will affect fuel consumption values in the EU, WORKING PAPER, INTERNATIONAL COUNCIL ON CLEAN TRANSPORTATION, 2014.
- Vreeswijk J. and Blokpoel R., How much can we realistically reduce with ITS?, 9th ITS European Congress, 2013.
- Stübing H., Bechler M., Heussner D., May T.,Radusch I. and Rechner H., Vogel P., sim TD: A Car-to-X System Architecture for Field Operational Tests, IEEE Communications Magazine, 2010.
- Vreeswijk J.D., Mahmod M.K.M., and van Arem B., , Energy Efficient Traffic Management and Control -the eCoMove Approach and Expected Benefits, IEEE Conference on Intelligent Transportation Systems, 2010.
- Guzzella L. and Sciarretta A., Vehicle propulsion systems - Introduction to modeling and optimization,Springer, 2013.
- Commission Implementing Decision 2013/529/EU of 25 October 2013 on the approval of the Bosch system for navigation-based preconditioning of the battery state of charge for hybrid vehicles in Official Journal of the European Union, Brussels, 2013.
- 2017 and Later Model Year Light-Duty Vehicle Greenhouse Gas Emissions and Corporate Average Fuel Economy Standards: EPA Response to Comments, United States Environmental Protection Agency, 2012.
- Wahl H.-G., Optimale Regelung eines prädiktiven Energiemanagements von Hybridfahrzeugen, Dissertation KIT, 2015.
- Sciarretta A. and Guzzella L., Control of hybrid vehicles, IEEE Control Systems Magazine, 2007.
- Korzenietz P., Kluin M. and Winner H., A Generic System Architecture for Energetic Optimization of Vehicles by Using Driver Assistance Systems, 16th ASME International Conference on Advanced Vehicle Technologies, 2014.
- Vadamalu R., Thiem M., and Beidl C., Methodology for Model-based Development, Validation and Calibration of Connected Electrified Powertrain Systems, 5th Conference on Future Automotive Technology, 2016.
- Beidl C., Buch D., Hohenberg G., Bacher C., Hammer J. and Kufferath A.,Effizienter E-Fahrzeugantrieb mit dem kompakten CEA-Konzept - Combustion Engine Assist, 7. MTZ-Fachtagung Der Antrieb von morgen, 2012.
- Vadamalu R., Beidl C., Predictive Energy Management of Hybrid Powertrains using Deterministic and Markov Chain Predictions, 13th International Symposium on Advanced Vehicle Control, 2016.
- Bellman R.E., Dynamic Programming, Princeton University, 1957.
- Betts, J. T. Survey of Numerical Methods for Trajectory Optimization, Journal of Guidance, Control, and Dynamics, 1998.
- Serrao L., Onori S. and Rizzoni G., ECMS as a realization of Pontryagin’s minimum principle for HEV control, American Control Conference, 2009.
- Kim N., Cha S. and Peng H., Optimal Control of Hybrid Electric Vehicles Based on Pontryagins Minimum Principle, IEEE Transactions on Control Systems Technology, 2011.
- Serrao L., Sciarretta A., , Open Issues in Supervisory Control of Hybrid Electric Vehicles: A Unified Approach Using Optimal Control Methods, Oil & Gas Science and Technology, IFP, 2013.
- Lacandia F., Tribioli L., Onori S. and Rizzoni G.,Adaptive Energy Management Strategy Calibration in PHEVs Based on a Sensitivity Study, 11th International Conference on Engines and Vehicles, 2013.
- Ambühl Daniel, Energy Management Strategues for Hybrid Electric Vehicles, Dissertation ETH Zürich, 2009.
- Nueesch T., Elbert P., Flankl M., Onder C. and Guzzella L., Convex Optimization for the Energy Management of Hybrid Electric Vehicles Considering Engine Start and Gearshift Costs, Energies, 2013.
- Vadamalu R. and Beidl C., Online optimization based energy management of hybrid electric vehicles using direct optimal control method, 16th Stuttgart International Symposium, 2016.
- Vadamalu R. and Beidl C., Online MPC based PHEV Energy Management using Conic Interior-point Methods, IEEE Intelligent Vehicles Symposium, 2016.
- Bier M., Buch D., Kluin M., Beidl C.,Development and optimization of Hybrid Powertrains at the X-In-The-Loop Engine Testbed, MTZ worldwide, 2012.
- Suri G., Onori S., A control-oriented cycle-life model for hybrid electric vehicle lithium-ion batteries, Energy, 2016.
- Sundstroem O. and Guzzella L., A Generic Dynamic Programming Matlab Function, IEEE International Conference on Control Applications, 2009.
- Onori S., Spagnol P., Marano V., Guezennec Y. and Rizzoni G., A new life estimation method for lithium-ion batteries in plug-in hybrid electric vehicles applications, International Journal of Power Electronics, 2012.
- Safari M., Morcrette M., Teyssot A. and Delacourt C., Life-Prediction Methods for Lithium-Ion Batteries Derived from a Fatigue Approach: II Introduction: Capacity-Loss Prediction Based on Damage Accumulation in Journal of The Electrochemical Society, 2010.
- Bender R., Wenzl H., Beck H.-P., Jiang M., Ohms D., Schaedlich G., Electrochemical and thermal modeling of lithium-ion cells for use in HEV or EV applications, World Electric Vehicle Journal, 2009.
- Murashko K., Thermal modelling of commercial Lithium-Ion batteries, Dissertation, Lappeenranta University of Technology, Finland, 2016.
- Beck R., Prädiktives Energiemanagement von Hybridfahrzeugen, Dissertation RWTH Aachen, 2010.