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Benchmarking Computational Time of Dynamic Programming for Autonomous Vehicle Powertrain Control
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 14, 2020 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Dynamic programming (DP) has been used for optimal control of hybrid powertrain and vehicle speed optimization particularly in design phase for over a couple of decades. With the advent of autonomous and connected vehicle technologies, automotive industry is getting closer to implementing predictive optimal control strategies in real time applications. The biggest challenge in implementation of optimal controls is the limitation on hardware which includes processor speed, IO speed, and random access memory. Due to the use of autonomous features, modern vehicles are equipped with better onboard computational resources. In this paper we present a comparison between multiple hardware options for dynamic programming. The optimal control problem considered, is the optimization of travel time and fuel economy by tuning the torque split ratio and vehicle speed while maintaining charge sustaining operation. The system has two states - battery state of charge and vehicle speed, and two inputs namely, total torque and torque split ratio. First, we develop a Matlab® based program to solve the optimal control problem. The Matlab® code is optimized for performance and memory on PC. Secondly, we deploy the code in C++ based application using hand written code. The code is prepared to be able to use with parallel processing. Finally, we compare four different hardware options for computational efficiency. The hardware options considered are a single core on a PC (7th gen intel Xeon processor), a cluster, a GPU (NVDIA DRIVE™ PX2) and an Android smartphone. The GPU chosen is specifically designed for automotive applications, and an android phone is chosen since it is the most realistic situation. The results of this paper suggest that the DP can be used in real time applications if the problem can be simplified to 1 state 2 input case.
CitationPerez, W., Ruhela, A., and Tulpule, P., "Benchmarking Computational Time of Dynamic Programming for Autonomous Vehicle Powertrain Control," SAE Technical Paper 2020-01-0968, 2020, https://doi.org/10.4271/2020-01-0968.
Data Sets - Support Documents
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- Lin , L. , Gong , S. , Li , T. , and Peeta , S. Deep Learning-Based Human-Driven Vehicle Trajectory Prediction and Its Application for Platoon Control of Connected and Autonomous Vehicles The Autonomous Vehicles Symposium 2018 2018
- Li , T. Modeling Uncertainty in Vehicle Trajectory Prediction in a Mixed Connected and Autonomous Vehicle Environment Using Deep Learning and Kernel Density Estimation The Fourth Annual Symposium on Transportation Informatics 2018
- Huang , Y. , Wang , H. , Khajepour , A. , He , H. et al. Model Predictive Control Power Management Strategies for HEVs: A Review Journal of Power Sources 341 91 106 2017
- Johannesson , L. , Asbogard , M. , and Egardt , B. Assessing the Potential of Predictive Control for Hybrid Vehicle Powertrains Using Stochastic Dynamic Programming IEEE Transactions on Intelligent Transportation Systems 8 1 71 83 2007
- Oruganti , P.S. , Jung , D. , Arasu , M. , Ahmed , Q. et al. Optimal Energy Management in a Range Extender PHEV Using a Cascaded Dynamic Programming Approach ASME 2018 Dynamic Systems and Control Conference 2018 V002T27A003 V002T27A003
- Sundstrom , O. and Guzzella , L. A Generic Dynamic Programming Matlab Function 2009 IEEE Control Applications (CCA) & Intelligent Control (ISIC) 2009 1625 1630
- Olin , P. , Aggoune , K. , Tang , L. , Confer , K. et al. Reducing Fuel Consumption by Using Information from Connected and Automated Vehicle Modules to Optimize Propulsion System Control SAE Technical Paper 2019-01-1213 2019 https://doi.org/10.4271/2019-01-1213
- Johannesson , L. and Egardt , B. Approximate Dynamic Programming Applied to Parallel Hybrid Powertrains IFAC Proceedings 41 2 3374 3379 2008
- Brahma , A. , Guezennec , Y. , and Rizzoni , G. Optimal Energy Management in Series Hybrid Electric Vehicles Proceedings of the 2000 American Control Conference, ACC 2000 1 6 60 64
- Paganelli , G. , Delprat , S. , Guerra , T.-M. , Rimaux , J. et al. Equivalent Consumption Minimization Strategy for Parallel Hybrid Powertrains IEEE 55th Vehicular Technology Conference 2002 4 2076 2081
- Bovee , K.M. 2015
- Bianchi , D. , Rolando , L. , Serrao , L. , Onori , S. et al. A Rule-Based Strategy for a Series/Parallel Hybrid Electric Vehicle: An Approach Based on Dynamic Programming ASME 2010 Dynamic Systems and Control Conference 2010 507 514
- Bertsekas , D.P. Dynamic Programming and Optimal Control Volume 1 Fourth Athena Scientific 2018