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Machine-Learning Approach to Behavioral Identification of Hybrid Propulsion System and Component
Technical Paper
2022-01-0229
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Accurate determination of driveshaft torque is desired for robust control, calibration, and diagnosis of propulsion system behaviors. The real-time knowledge of driveshaft torque is also valuable for vehicle motion controls. However, online identification of driveshaft torque is difficult during transient drive conditions because of its coupling with vehicle mass, road grade, and drive resistance as well as the presence of numerous noise factors. A physical torque sensor such as a strain-gauge or magneto-elastic type is considered impractical for volume production vehicles because of packaging requirements, unit cost, and manufacturing investment. This paper describes a novel online method, referred to as Virtual Torque Sensor (VTS), for estimating driveshaft torque based on Machine-Learning (ML) approach. VTS maps a signal from Inertial Measurement Unit (IMU) and vehicle speed to driveshaft torque. The unique advantage is that VTS does not explicitly rely on the first principles unlike other estimation methods. A robust mapping framework implicitly accounts for road grade, while compensating the effects of vehicle mass and drive resistance. Mapping coefficients are automatically and adaptively learned during selective drive conditions and continuously updated by means of Kalman filtering. VTS is implemented in a test vehicle with a P2 hybrid electric propulsion system for the assessment of robustness and sensitivity to drive conditions. The accurate estimate of driveshaft torque from VTS is utilized to determine the characteristics of a wet clutch which is employed for cranking an internal combustion engine during EV-HEV mode transition. VTS demonstrates a ML-based data-driven solution to the accurate determination of driveshaft torque and wet clutch behaviors. VTS framework can be readily extended to broader applications, including battery electric vehicle, with additional capabilities such as wheel torque estimation during braking and steering.
Authors
- Yijing Zhang - Ford Motor Company
- Fengyi Chen - Ford Motor Company
- Weitian Chen - Ford Motor Company
- Akshay Bichkar - Ford Motor Company
- Conor Sullivan - Ford Motor Company
- Ankit Saini - Ford Motor Company
- Thirumal Nagadi - Ford Motor Company
- Michael Leads - Ford Motor Company
- Bradley Riedle - Ford Motor Company
- Yuji Fujii - Ford Motor Company
Citation
Zhang, Y., Chen, F., Chen, W., Bichkar, A. et al., "Machine-Learning Approach to Behavioral Identification of Hybrid Propulsion System and Component," SAE Technical Paper 2022-01-0229, 2022, https://doi.org/10.4271/2022-01-0229.Also In
References
- Gardner , G. 10R80 MHT Transmission SAE Innovations in Mobility Symposium Novi, MI Oct. 2019
- Xu , X. , Liang , Y. , Jordan , M. , Tenberge , P. et al. Optimized Control of Engine Start Assisted by the Disconnect Clutch in a P2 Hybrid Automatic Transmission Journal of Mechanical Systems and Signal Processing 124 2019 313 329
- Eriksson , L. and Nielsen , L. Modeling and Control of Engines and Drivelines John Wiley & Sons 2014
- Heywood , J. Internal Combustion Engine Fundamentals McGraw-Hill 1988
- Fujii , Y. , Kapas , N. , and Tseng , J. Clutch Wet Encyclopedia of Automotive Engineering New York Wiley & Sons 2014
- Fujii , Y. , Tobler , W. , Pietron , G. , Cao , M. et al. Review of Wet Friction Component Models for Automatic Transmission Shift Analysis SAE Technical Paper 2003-01-1665 2003 https://doi.org/10.4271/2003-01-1665.
- Deur , J. , Petric , J. , Asgari , J. , and Hrovat , D. Modeling of Wet Clutch Engagement Including a Thorough Experimental Validation SAE Technical Paper 2005-01-0877 2005 https://doi.org/10.4271/2005-01-0877.
- Zhu , X. , Meng , F. , Zhang , H. , and Cui , Y. Robust Driveshaft Torque Observer Design for Stepped Ratio Transmission in Electric Vehicles Neurocomputing 164 2015 262 271
- Pettersson , M. and Nielsen , L. Gear Shifting by Engine Control IEEE Transaction on Control Systems Technology 8 3 2000 495 507
- Baumann , J. , Torkzadeh , D. , Ramstein , A. , Kiencke , U. et al. Model-Based Predictive Anti-Jerk Control Control Engineering Practice 14 2006 259 266
- Ghosh , J. , Foulard , S. , and Fietzek , R. Vehicle Mass Estimation from CAN Data and Drivetrain Torque Observer SAE Technical Paper 2017-01-1590 2017 https://doi.org/10.4271/2017-01-1590.
- Girbes , V. , Hernandez , D. , Armesto , L. , Dols , J. et al. Drive Force and Longitudinal Dynamics Estimation in Heavy-Duty Vehicles Sensors 19 2019 3515 10.3390/s19163515
- Jensen , K. , Santos , I. , Clemmensen , L. , Theodorsen , S. et al. Mass Estimation of Ground Vehicles Based on Longitudinal Dynamics Using IMU and CAN-Bus Data Mechanical System and Signal Processing 162 2022 107982
- Kidambi , N. , Harne , R. , Fujii , Y. , Pietron , G. et al. Methods in Vehicle Mass and Road Grade Estimation SAE Int. J. of Passeng. Cars - Mech. Syst 7 3 2014 https://doi.org/10.4271/2014-01-0111.
- Kidambi , N. , Pietron , G. , Boesch , M. , Fujii , Y. et al. Accuracy and Robustness of Parallel Vehicle Mass and Road Grade Estimation SAE Int. J. Veh. Dyn., Stab., and NVH 1 2 2017 https://doi.org/10.4271/2017-01-1586.
- Gao , B. , Chen , H. , Ma , Y. , and Sanada , K. Design of Nonlinear Shaft Torque Observer for Trucks with Automated Manual Transmission Mechatronics 21 2011 1034 1042
- Pietron , G. , Fujii , Y. , Kucharski , J. , Yanakiev , D. et al. Development of Magneto-Elastic Torque Sensor for Automatic Transmission Applications SAE Int. J. of Passeng. Cars - Mech. Syst 6 2 2013 529 534 https://doi.org/10.4271/2013-01-0301.
- Zhang , Y. , Chen , F. , Riedle , B. , Chen , W. , Fujii , Y. 2021
- Chen , F. , Zhang , Y. , Riedle , B. , Chen , W. , Fujii , Y. Vehicle Powertrain Control System 2022
- Haria , H. , Pietron , G. , Meyer , J. , Fujii , Y. et al. In-Vehicle Characterization of Wet Clutch Engagement Behaviors in Automatic Transmission Systems SAE Int. J. Passeng. Cars - Mech. Syst 11 5 2018 369 375
- Haria , H. , Fujii , Y. , Pietron , M.G. , Sun , A. et al. Advanced Bench Test Methodology for Generating Wet Clutch Engagement Transfer Function for Enhanced Shift Simulations SAE Technical Paper 2019-01-2340 (JSAE 20199181) 2019 https://doi.org/10.4271/2019-01-2340.
- Shui , H. , Zhang , Y. , Yang , H. , Upadhyay , D. et al. Machine Learning Approach for Constructing Wet Clutch Torque Transfer Function SAE Technical Paper 2021-01-0712 2021 https://doi.org/10.4271/2021-01-0712
- Shui , H. , Zhang , Y. , Yi , E. , Bichkar , A. et al. Optimization of Gaussian Process Regression Model for Characterization of In-Vehicle Wet Clutch Behavior SAE Technical Paper 2022-01-0222 2022 https://doi.org/10.4271/2022-01-0222
- Gillespie , T. Fundamentals of Vehicle Dynamics Society of Automotive Engineers 1992
- Bai , S. , Maguire , J. , and Peng , H. Dynamic Analysis and Control System Design SAE International 2013 978-0-7680-7604-2
- Korst , H. and White , R. Coastdown Tests: Determining Road Loads Versus Drag Component Evaluation SAE Technical Paper 901767 1990 https://doi.org/10.4271/901767
- Chapin , C. Road Load Measurement and Dynamometer Simulation Using Coastdown Techniques SAE Technical Paper 810828 1981 https://doi.org/10.4271/810828.
- Altinisik , A. Aerodynamic Coastdown Analysis of a Passenger Car for Various Configurations Int. J Automot. Technol. 18 2017 245 254