This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Powertrain Modeling and Model Predictive Longitudinal Dynamics Control for Hybrid Electric Vehicles
Technical Paper
2018-01-0996
ISSN: 0148-7191, e-ISSN: 2688-3627
This content contains downloadable datasets
Annotation ability available
Sector:
Language:
English
Abstract
This paper discusses modeling of a power-split hybrid electric vehicle and the design of a longitudinal dynamics controller for the University of Waterloo’s self-driving vehicle project. The powertrain of Waterloo’s vehicle platform, a Lincoln MKZ Hybrid, is controlled only by accelerator pedal actuation. The vehicle’s power management strategy cannot be altered, so a novel approach to grey-box modeling of the OEM powertrain control architecture and dynamics was developed. The model uses a system of multiple neural networks to mimic the response of the vehicle’s torque control module and estimate the distribution of torque between the powertrain’s internal combustion engine and electric motors. The vehicle’s power-split drivetrain and longitudinal dynamics were modeled in MapleSim, a modeling and simulation software, using a physics-based analytical approach. All model parameters were identified using Controller Area Network (CAN) data and measurements of wheel torque data that were gathered during vehicle road testing. Using the grey-box powertrain model as a framework, a look-ahead linear time-varying (LTV) model predictive controller (MPC) for reference velocity tracking is proposed. Using some simplifying assumptions about the powertrain dynamics, a control-oriented model was formulated. The performance of the MPC was tested using multiple model in the loop (MIL) reference velocity tracking scenarios, and benchmarked against a tuned proportional-integral (PI) controller. Using the novel control-oriented model of the OEM powertrain, the MPC was found to track the desired velocity trajectory and reject measurable disturbance inputs, such as road slope, better than the PI controller.
Recommended Content
Citation
Hosking, B. and McPhee, J., "Powertrain Modeling and Model Predictive Longitudinal Dynamics Control for Hybrid Electric Vehicles," SAE Technical Paper 2018-01-0996, 2018, https://doi.org/10.4271/2018-01-0996.Data Sets - Support Documents
| Title | Description | Download |
|---|---|---|
| Unnamed Dataset 1 | ||
| Unnamed Dataset 2 | ||
| Unnamed Dataset 3 | ||
| Unnamed Dataset 4 |
Also In
References
- Al-alawi , B. and Bradley , T. Review of Hybrid, Plug-in Hybrid, and Electric Vehicle Market modeling Studies Renewable and Sustainable Energy Reviews 21 190 203 2013 10.1016/j.rser.2012.12.048
- Syed , F. , Kuang , M. , Czubay , J. , and Ying , H. Derivation and Experimental Validation of a Power-Split Hybrid Electric Vehicle Model IEEE Transactions on Vehicular Technology 55 6 1731 1747 2006 10.1109/TVT.2006.878563
- Liu , J. , Peng , H. , and Filipi , Z. Modeling and Analysis of the Toyota Hybrid System International Conference on Advanced Intelligent Mechatronics Monteray, CA, USA Jul. 24-28 2008
- Hofman , T. , Steinbuch , M. , and Druten , R. Modeling for Simulation of Hybrid Drivetrain Components Vehicle Power and Propulsion Conference Windsor, UK Sep. 6-8 2006 10.1109/VPPC.2006.364269
- Baumann , B. , Rizzoni , G. , and Washington , G. Intelligent Control of Hybrid Vehicles Using Neural Networks and Fuzzy Logic SAE Technical Paper 981061 1998 10.4271/981061
- Wang , S. , Yu , D. , Gomm , J. , Page , G. , and Douglas , S. Adaptive Neural Network Model Based Predictive Control for Air-Fuel Ratio of SI Engines Engineering Applications of Artificial Intelligence 19 2 189 200 2006 10.1016/j.engappai.2005.08.005
- Pan , Y. and Wang , J. Model Predictive Control of Unknown Nonlinear Dynamical Systems Based on Recurrent Neural Networks IEEE Transactions on Industrial Electronics 59 8 3089 3101 2011 10.1109/TIE.2011.2169636
- Wang , S. , Yu , D. , Gomm , J. , Page , G. , and Douglas , S. Adaptive Neural Network Model Based Predictive Control of an Internal Combustion Engine with a New Optimization Algorithm Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 220 2 195 208 2006 10.1243/095440706X72754
- Sun , C. Velocity Predictors for Predictive Energy Management in Hybrid Electric Vehicles IEEE Transactions on Control Systems Technology 23 3 1197 1204 2014 10.1109/TCST.2014.2359176
- Marx , M. , Shen , X. , and Söffker , D. A Data-Driven Online Identification and Control Optimization Approach applied to a Hybrid Electric Powertrain System IFAC Proceedings Volumes 45 2 153 158 2012 10.3182/20120215-3-AT-3016.00027
- Borrelli , F. , Bemporad , A. , Fodor , M. , and Hrovat , D. An MPC/Hybrid System Approach to Traction Control IEEE Transactions on Control Systems Technology 14 3 541 552 2006 1109/TCST.2005.860527
- Corona , D. and De Schutter , B. Adaptive Cruise Control for a SMART Car: A Comparison Benchmark for MPC-PWA Control Methods IEEE Transactions on Control Systems Technology 16 2 365 372 2008 10.1109/TCST.2007.908212
- Van Gennip , M. , and McPhee , J. Parameter Identification for Combined Slip Tire Models Using Vehicle Measurement System SAE World Congress , Detroit, MI, USA Apr. 10-12 2018
- Hornik , K. , Stinchcombe , M. , and White , H. Multilayer Feedforward Networks Are Universal Approximators Neural Networks 2 5 359 366 1989 10.1016/0893-6080(89)90020-8
- Pacejka , H. and Bakker , E. The Magic Formula Tyre Model International Journal of Vehicle Mechanics and Mobility 21 sup001 1 18 1992 10.1080/00423119208969994