This content is not included in your SAE MOBILUS subscription, or you are not logged in.
Computationally Efficient Reduced-Order Powertrain Model of a Multi-Mode Plug-In Hybrid Electric Vehicle for Connected and Automated Vehicles
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 02, 2019 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
This paper presents the development of a reduced-order powertrain model for energy and SOC estimation of a multi-mode plug-in hybrid electric vehicle using only vehicle speed profile and route elevation as inputs. Such a model is intended to overcome the computational inefficiencies of higher fidelity powertrain and vehicle models in short and long horizon energy optimization efforts such as Coordinated Adaptive Cruise Control (CACC), Eco Approach and Departure (EcoAND), Eco Routing, and PHEV mode blending. The reduced-order powertrain model enables Connected and Automated Vehicles (CAVs) to utilize the onboard sensor and connected data to quickly react and plan their maneuvers to highly dynamic road conditions with minimal computational resources. Although overall estimation accuracy is less than neural network and high-fidelity models, emphasis on runtime minimization with reasonable estimation accuracy enables energy optimization of CAVs without a need for computationally expensive server-based models. Performance of the model is evaluated on a fleet of second-generation Chevrolet Volts in a variety of driving scenarios and drive cycle durations. On-road testing indicates that the model can estimate actual vehicle behavior and energy consumption with a median estimation accuracy of over 90% and a runtime less than 0.3 seconds. This makes the model highly advantageous for real-time energy optimization in CAVs.
CitationRama, N. and Robinette, D., "Computationally Efficient Reduced-Order Powertrain Model of a Multi-Mode Plug-In Hybrid Electric Vehicle for Connected and Automated Vehicles," SAE Technical Paper 2019-01-1210, 2019, https://doi.org/10.4271/2019-01-1210.
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]|
- Department of Energy, “The Transforming Mobility Ecosystem: Enabling an Energy-Efficient Future; United States Department of Energy, Energy Efficiency & Renewable Energy,” https://energy.gov/sites/prod/files/2017/01/f34/The%20Transforming%20Mobility%20EcosystemEnabling%20an%20Energy%20Efficient%20Future_0117_1.pdf, accessed Sep. 2018.
- Guanetti, J., Kim, Y., and Borrelli, F., “Control of Connected and Automated Vehicles: State of the Art and Future Challenges,” Annual Reviews in Control 45:8-40, 2018, doi:10.1016/j.arcontrol.2018.04.011.
- Chen, X., Li, L., and Shi, Q., Stochastic Evolutions of Dynamic Traffic Flow (Springer, 2015), 1-2, doi:10.1007/978-3-662-44572-3_1.
- Wipke, K.B., Cuddy, M.R., and Burch, S.D., “ADVISOR 2.1: a User-Friendly Advanced Powertrain Simulation Using a Combined Backward/Forward Approach,” IEEE Transactions on Vehicular Technology 48(6):1751-1761, 1999, doi:10.1109/25.806767.
- Brooker, A., Gonder, J., Wang, L., Wood, E. et al., “FASTSim: A Model to Estimate Vehicle Efficiency, Cost and Performance,” SAE Technical Paper 2015-01-0973, 2015, doi:10.4271/2015-01-0973.
- Fiori, C., Ahn, K., and Rakha, H.A., “Microscopic Series Plug-In Hybrid Electric Vehicle Energy Consumption Model: Model Development and Validation,” Transportation Research Part D: Transport and Environment 63:175-185, 2018, doi:10.1016/j.trd.2018.04.022.
- Fiori, C., Ahn, K., and Rakha, H.A., “Power-Based Electric Vehicle Energy Consumption Model: Model Development and Validation,” Applied Energy 168:257-268, 2016, doi:10.1016/j.apenergy.2016.01.097.
- Rakha, H., Ahn, K., Moran, K., Saerens, B. et al., “Virginia Tech Comprehensive Power-Based Fuel Consumption Model: Model Development and Testing,” Transportation Research Part D: Transport and Environment 16(7):492-503, 2011, doi:10.1016/j.trd.2011.05.008.
- Gonder, J., Wood, E., and Rajagopalan, S., “Connectivity-Enhanced Route Selection and Adaptive Control for the Chevrolet Volt,” Journal of Traffic and Transportation Engineering 4:49-60, 2016, doi:10.17265/2328-2142/2016.01.006.
- Advanced Research Projects Agency - Energy, “Michigan Technological University (MTU) - Hybrid Electric Vehicle Platooning Control,” https://arpa-e.energy.gov/?q=slick-sheet-project/hybrid-electric-vehicle-platooning-control, accessed Sep. 2018.
- SAE International Surface Vehicle Information Report, “Hybrid Electric Vehicle (HEV) and Electric Vehicle (EV) Terminology,” SAE Standard J1715, Rev. Oct. 2014.
- Conlon, B., Blohm, T., Harpster, M., Holmes, A. et al., “The Next Generation “Voltec” Extended Range EV Propulsion System,” SAE Int. J. Alt. Power. 4(2):248-259, 2015, doi:10.4271/2015-01-1152.
- Jurkovic, S., Rahman, K., Patel, N., and Savagian, P., “Next Generation Voltec Electric Machines; Design and Optimization for Performance and Rare-Earth Mitigation,” SAE Int. J. Alt. Power. 4(2):336-342, 2015, doi:10.4271/2015-01-1208.
- Jocsak, J., White, D., Armand, C., and Davis, R., “Development of the Combustion System for General Motors' High-Efficiency Range Extender Ecotec Small Gas Engine,” SAE Int. J. Engines 8(4):1587-1601, 2015, doi:10.4271/2015-01-1272.
- Duhon, A., Sevel, K., Tarnowsky, S., and Savagian, P., “Chevrolet Volt Electric Utilization,” SAE Int. J. Alt. Power. 4(2):269-276, 2015, doi:10.4271/2015-01-1164.
- Quarteroni, A. and Rozza, G., Reduced Order Methods for Modeling and Computational Reduction (Springer, 2014), doi:10.1007/978-3-319-02090-7.
- Rama, N., Wang, H., Orlando, J., Robinette, D. et al., “Route-optimized Energy Management of Connected and Automated Multi-mode Plug-in Hybrid Electric Vehicle using Dynamic Programming,” in presented at 2019 SAE World Congress, USA, April 9-11, 2019.
- Jeong, J., Choi, S., Kim, N., Lee, H. et al., “Model Validation of the Chevrolet Volt 2016,” SAE Technical Paper 2018-01-0420, 2018, doi:10.4271/2018-01-0420.