This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Towards Self-Learning Energy Management for Optimal PHEV Operation Around Zero Emission Zones
Technical Paper
2022-01-0734
ISSN: 0148-7191, e-ISSN: 2688-3627
Annotation ability available
Sector:
Language:
English
Abstract
Self-learning energy management is a promising concept, which optimizes real-world system performance by automated, on-line adaptation of control settings. In this work, the potential of self-learning capabilities related to optimization is studied for energy management in Plug-in Hybrid Electric Vehicles (PHEV). These vehicles are of great interest for the transport sector, since they combine high fuel efficiency with last mile full-electric driving. We focus on a specific use case: PHEV operation through future Zero Emission (ZE) zones of cities. As a first step towards self-learning control, we introduce a novel, adaptive supervisory controller that combines modular energy and emission management (MEEM) and deals with varying constraints and system uncertainty. This optimal control strategy is based on Pontryagin’s Minimum Principle and maximizes overall energy efficiency. The constraints are directly related to having sufficient battery energy for full electric driving and to meet real-world tailpipe NOx emissions. This control strategy is extended with a new adaptation mechanism for control parameters, including references, based on pre-view information. For a given mission, these control parameters are numerically optimized.
Simulations are done using a validated hybrid truck model with Euro-VI Diesel engine and urea-based SCR system. To demonstrate the self-learning capabilities, we study the effect of battery ageing and changing route (i.e. detour due to unexpected traffic jam). For the specified mission, the performance of the optimal MEEM is compared with a standard MEEM strategy without information on system or environment state. Cold start after the ZE zone is found to be challenging. From these results, it is concluded that vehicle operational costs can be reduced by 18% while meeting real-world emission limits if information is available on long-term battery state and route.
Recommended Content
Authors
Topic
Citation
Kupper, F., Mentink, P., Avramis, N., Meima, N. et al., "Towards Self-Learning Energy Management for Optimal PHEV Operation Around Zero Emission Zones," SAE Technical Paper 2022-01-0734, 2022, https://doi.org/10.4271/2022-01-0734.Also In
References
- EU 2 https://eur-lex.europa.eu/eli/reg/2019/1242/oj
- ICCT https://theicct.org/sites/default/files/publications/Combustion-engine-phase-out-briefing-may11.2020.pdf
- Wilkins , S. , Pham , T. , Tran , D.D. , Hegazy , O. , et al. Orca Project: Optimisation Framework For Next Generation Heavy Duty Hybrids 15th International Symposium on Heavy Vehicle Transportation Technology Rotterdam 2017
- 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 https://doi.org/10.3166/ejc.11.509-524
- Biswas , D. , Ghosh , S. , Sengupta , S. , Mukhopadhyay , S. A Predictive Supervisory Controller for an HEV Operating in a Zero Emission Zone 2019 IEEE Transportation Electrification Conference and Expo (ITEC) 10.1109/ITEC.2019.8790631
- Capancioni , A. , Brunelli , L. , Cavina , N. , and Perazzo , A. Development of Adaptive-ECMS and Predictive Functions for Plug-in HEVs to Handle Zero-Emission Zones Using Navigation Data SAE 15th International Conference on Engines & Vehicles 2021 https://doi.org/10.4271/2021-24-0105
- Soldo , J. , Škugor , B. , and Deur , J. Synthesis of Optimal Battery State-of-Charge Trajectory for Blended Regime of Plug-in Hybrid Electric Vehicles in the Presence of Low-Emission Zones and Varying Road Grades MPDI PHEVs: Latest Advances and Prospects 2019 https://doi.org/10.3390/en12224296
- Willems , F. , Spronkmans , S. and Kessels , J. Integrated Powertrain Control to Meet Low CO 2 Emissions for a Hybrid Distribution Truck with SCR-deNO x system Proc ASME 2011 Dynamic Systems & Control Conference 1 6 ASME
- Bouwman , K. , Pham , T. , Wilkins , T. , and Hofman , T. Predictive Energy Management Strategy Including Traffic Flow Data for Hybrid Electric Vehicles Toulouse, France IFAC World Congress 2017
- Nageshrao , S. , Jacob , J. , and Wilkins , S. Charging Cost Optimization for EV Buses using Neural Network based Energy Predictor Toulouse, France IFAC World Congress 2017
- Wilkins , S. , Zamudio López , O. , and Lloret Iglesias , L. Forecasting Battery SoC for Electric Buses with Dynamic Route Information and Neural Networks 32nd International Electric Vehicle Symposium Lyon, France 2019
- Cloudt , R. , Saenen , J. , Van den Eijnden , E. , and Rojer , C. Virtual Exhaust Line for Model-Based Diesel Aftertreatment Development SAE Technical Paper 2010-01-0888 2010 https://doi.org/10.4271/2010-01-0888
- Guo , Q. and Liu , B. Simulation and Physical Measurement of Seamless Passenger Airbag Door Deployment SAE Technical Paper 2012-01-0082 2012 https://doi.org/10.4271/2012-01-0082
- Kunkel , S. , Zimmer , T. , and Wachtmeister , G. Friction Analysis of Oil Control Rings during Running-In SAE Technical Paper 2011-01-2428 2012 https://doi.org/10.4271/2011-01-2428
- Morgan , R. , Scullion , P. , Nix , L. , Kan , C. et al. Injury Risk Investigation of the Small, Rear-Seat Occupant in Side Impact SAE Technical Paper 2012-01-0092 2012 https://doi.org/10.4271/2012-01-0092
- Kimura , Y. and Murakami , M. Analysis of Piston Friction - Effects of Cylinder Bore Temperature Distribution and Oil Temperature SAE Int. J. Fuels Lubr. 5 1 2012 1 6 10.4271/2011-01-1746
- City Logistics http://www.citylogistics.info/policies/zere-emission-zones-in-the-netherlands-2025-2027-and-later/
- Waag , W. , Fleischer , C. , Schäper , C. , Berger , J. , and Sauer , D.U. Self-Adapting-on-Board Diagnostic Algorithms for Lithium-Ion Batteries Advanced Battery Development for Automotive and Utility Applications and their Electric Power Grid Integration Aachen, Germany March 2011 14 Self-Learning State-of-Available-power Prediction for Lithium-Ion Batteries in Electrical Vehicles https://www.researchgate.net/publication/260019921_Self-learning_state-of-available-power_prediction_for_lithium-ion_batteries_in_electrical_vehicles
- Fleischer , C. , Waag , W. , Bai , Z. , and Sauer , D. Self-Learning State-of-Available-Power Prediction for Lithium-Ion Batteries in Electrical Vehicles Proc. of IEEE VPPC Conference 2012 370 375
- Ni , C. 2014
- Romijn , T. , Pham , T. , and Wilkins , S. Modular ECMS Framework for Hybrid Vehicles IFAC-Papers On Line 52 5 2019 128 133 https://doi.org/10.1016/j.ifacol.2019.09.021
- Cloudt , R. and Willems , F. Integrated Emission Management Strategy for Cost-Optimal Engine-Aftertreatment Operation SAE Int. J. Engines 4 1 2011 1784 1797 https://doi.org/10.4271/2011-01-1310
- Willems , F. , Mentink , P. , Kupper , F. , and Van den Eijnden , E. Integrated Emission Management for Cost Optimal EGR-SCR Balancing in Diesels IFAC Proc. Volumes 46 21 2013 711 716 10.3182/20130904-4-JP-2042.00089
- Mentink , P. , Van den Nieuwenhof , R. , Kupper , F. , Willems , F. et al. Robust Emission Management Strategy to Meet Real-World Emission Requirements for HD Diesel Engines SAE Int. J. Engines 8 3 2015 10.4271/2015-01-0998
- Kumar , V. Lithium-Ion Battery Pack System Design Attributes for Plug-In Hybrid Electric Vehicle IQPC Conference on Energy Management and Battery Technology for EV/PHEV Berlin 2016