This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Data-Driven Framework for Fuel Efficiency Improvement in Extended Range Electric Vehicle Used in Package Delivery Applications
Technical Paper
2020-01-0589
ISSN: 0148-7191, e-ISSN: 2688-3627
This content contains downloadable datasets
Annotation ability available
Sector:
Language:
English
Abstract
Extended range electric vehicles (EREVs) are a potential solution for fossil fuel usage mitigation and on-road emissions reduction. The use of EREVs can be shown to yield significant fuel economy improvements when proper energy management strategies (EMSs) are employed. However, many in-use EREVs achieve only moderate fuel reduction compared to conventional vehicles due to the fact that their EMS is far from optimal. This paper focuses on in-use rule-based EMSs to improve the fuel efficiency of EREV last-mile delivery vehicles equipped with two-way Vehicle-to-Could (V2C) connectivity. The method uses previous vehicle data collected on actual delivery routes and machine learning methods to improve the fuel economy of future routes. The paper first introduces the main challenges of the project, such as inherent uncertainty in human driver behavior and in the roadway environment. Then, the framework of our practical physics-model guided data-driven approach is introduced. For vehicles with small amounts of prior data, a Bayesian method is used to adjust a control parameter in the EMS offline for each vehicle with introduced prior information derived from large numbers of trips from other vehicles in the fleet. For vehicles with many delivery trips, a reinforcement learning algorithm is used to optimize the parameter in real-time without requiring future information of the trip. Although our data-driven framework cannot achieve a globally optimal solution with respect to the fuel efficiency, it provides a systematic and immediate solution for in-use EREVs used for package delivery with a very low computation cost and no change of vehicle hardware. Also, this framework is ready to be extended for further fuel economy improvements if more information is available from advanced transportation infrastructures like Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) connectivity.
Topic
Citation
Wang, P. and Northrop, W., "Data-Driven Framework for Fuel Efficiency Improvement in Extended Range Electric Vehicle Used in Package Delivery Applications," SAE Technical Paper 2020-01-0589, 2020, https://doi.org/10.4271/2020-01-0589.Data Sets - Support Documents
| Title | Description | Download |
|---|---|---|
| Unnamed Dataset 1 | ||
| Unnamed Dataset 2 | ||
| Unnamed Dataset 3 | ||
| Unnamed Dataset 4 | ||
| Unnamed Dataset 5 |
Also In
References
- Wirasingha , S.G. and Emadi , A. Classification and Review of Control Strategies for Plug-In Hybrid Electric Vehicles IEEE Transactions on Vehicular Technology 60 1 111 122 2010
- Martinez , C.M. , Hu , X. , Cao , D. et al. Energy Management in Plug-In Hybrid Electric Vehicles: Recent Progress and a Connected Vehicles Perspective IEEE Transactions on Vehicular Technology 66 6 4534 4549 2016
- Martinez , C.M. , Heucke , M. , Wang , F.-Y. et al. Driving Style Recognition for Intelligent Vehicle Control and Advanced Driver Assistance: A Survey IEEE Transactions on Intelligent Transportation Systems 19 3 666 676 2017
- McNew , J.-M. Predicting Cruising Speed through Data-Driven Driver Modeling 2012 15th International IEEE Conference on Intelligent Transportation Systems 2012 1789 1796
- Bender , F.A. , Kaszynski , M. , and Sawodny , O. Drive Cycle Prediction and Energy Management Optimization for Hybrid Hydraulic Vehicles IEEE Transactions on Vehicular Technology 62 8 3581 3592 2013
- Platho , M. , Groß , H.-M. , and Eggert , J. Predicting Velocity Profiles of Road Users at Intersections Using Configurations 2013 IEEE Intelligent Vehicles Symposium (IV) 2013 945 951
- Di Cairano , S. , Bernardini , D. , Bemporad , A. et al. Stochastic MPC with Learning for Driver-Predictive Vehicle Control and Its Application to HEV Energy Management IEEE Transactions on Control Systems Technology 22 3 1018 1031 2013
- Lefèvre , S. , Sun , C. , Bajcsy , R. et al. Comparison of Parametric and Non-Parametric Approaches for Vehicle Speed Prediction 2014 American Control Conference 2014 3494 3499
- Wu , G. , Boriboonsomsin , K. , and Barth , M.J. Development and Evaluation of an Intelligent Energy-Management Strategy for Plug-In Hybrid Electric Vehicles IEEE Transactions on Intelligent Transportation Systems 15 3 1091 1100 2014
- Park , J. , Murphey , Y.L. , McGee , R. et al. Intelligent Trip Modeling for the Prediction of an Origin-Destination Traveling Speed Profile IEEE Transactions on Intelligent Transportation Systems 15 3 1039 1053 2014
- Sun , C. , Moura , S.J. , Hu , X. , Hedrick , J.K. et al. Dynamic Traffic Feedback Data Enabled Energy Management in Plug-In Hybrid Electric Vehicles IEEE Transactions on Control Systems Technology 23 3 1075 1086 2014
- Tianheng , F. , Yang , L. , Qing , G. , Yanqing , H. et al. A Supervisory Control Strategy for Plug-In Hybrid Electric Vehicles Based on Energy Demand Prediction and Route Preview IEEE Transactions on Vehicular Technology 64 5 1691 1700 2014
- Ziegmann , J. , Shi , J. , Schnörer , T. et al. Analysis of Individual Driver Velocity Prediction Using Data-Driven Driver Models with Environmental Features 2017 IEEE Intelligent Vehicles Symposium (IV) 2017 517 522
- Jiang , B. and Fei , Y. Vehicle Speed Prediction by Two-Level Data Driven Models in Vehicular Networks IEEE Transactions on Intelligent Transportation Systems 18 7 1793 1801 2016
- Zhang , S. , Luo , Y. , Wang , J. , Wang , X. et al. Predictive Energy Management Strategy for Fully Electric Vehicles Based on Preceding Vehicle Movement IEEE Transactions on Intelligent Transportation Systems 18 11 3049 3060 2017
- Zhang , F. , Xi , J. , and Langari , R. Real-Time Energy Management Strategy Based on Velocity Forecasts Using V2V and V2I Communications IEEE Transactions on Intelligent Transportation Systems 18 2 416 430 2016
- Xie , S. , Hu , X. , Xin , Z. , and Liang , L. Time-Efficient Stochastic Model Predictive Energy Management for a Plug-In Hybrid Electric Bus with an Adaptive Reference State-of-Charge Advisory IEEE Transactions on Vehicular Technology 67 7 5671 5682 2018
- Liu , C. and Murphey , Y.L. Power management for Plug-In Hybrid Electric Vehicles using Reinforcement Learning with Trip Information 2014 IEEE Transportation Electrification Conference and Expo (ITEC) 2014 1 6
- Lin , X. , Bogdan , P. , Chang , N. , and Pedram , M. Machine Learning-Based Energy Management in a Hybrid Electric Vehicle to Minimize Total Operating Cost 2015 IEEE/ACM International Conference on Computer-Aided Design (ICCAD) 2015 627 634
- Zhao , P. , Wang , Y. , Chang , N. , Zhu , Q. et al. A Deep Reinforcement Learning Framework for Optimizing Fuel Economy of Hybrid Electric Vehicles 2018 23rd Asia and South Pacific Design Automation Conference (ASP-DAC) 2018 196 202
- Qi , X. , Wu , G. , Boriboonsomsin , K. , and Barth , M.J. A Novel Blended Real-Time Energy Management Strategy for Plug-In Hybrid Electric Vehicle Commute Trips 2015 IEEE 18th International Conference on Intelligent Transportation Systems 2015 1002 1007
- Chen , Z. , Liang , L. , Hu , X. , Yan , B. et al. Temporal-Difference Learning-Based Stochastic Energy Management for Plug-in Hybrid Electric Buses IEEE Transactions on Intelligent Transportation Systems 20 6 2378 2388 2018
- Simmons , R. , Browning , B. , Zhang , Y. , and Sadekar , V. Learning to Predict Driver Route and Destination Intent 2006 IEEE Intelligent Transportation Systems Conference 2006 127 132
- Froehlich , J. and Krumm , J. Route Prediction from Trip Observations SAE Technical Paper 2008-01-0201 2008 https://doi.org/10.4271/2008-01-0201
- Larsson , V. , Mårdh , L.J. , Egardt , B. , and Karlsson , S. Commuter Route Optimized Energy Management of Hybrid Electric Vehicles IEEE Transactions on Intelligent Transportation Systems 15 3 1145 1154 2014
- Gelman , A. , Carlin , J.B. , Stern , H.S. , Dunson , D.B. et al. Bayesian Data Analysis Chapman and Hall/CRC 2013
- Alpaydin , E. Introduction to Machine Learning MIT Press 2009
- Murphy , K.P. Conjugate Bayesian Analysis of the Gaussian Distribution Def 1 2 16 2007
- Sutton , R.S. and Barto , A.G. Reinforcement Learning: An Introduction MIT Press 2018
- Wang , P. , Li , Y. , Shekhar , S. , and Northrop , W.F. A Deep Reinforcement Learning Framework for Energy Management of Extended Range Electric Delivery Vehicles 2019 IEEE Intelligent Vehicles Symposium (IV) 2019 1837 1842
- Wang , P. , Li , Y. , Shekhar , S. , and Northrop , W.F. Actor-Critic based Deep Reinforcement Learning Framework for Energy Management of Extended Range Electric Delivery Vehicles 2019 IEEE/ASME Advanced Intelligent Mechatronics (AIM) 2019 1379 1384
- Lillicrap , T.P. , Hunt , J.J. , Pritzel , A. , Heess , N. et al. Continuous Control with Deep Reinforcement Learning 2015
- Silver , D. , Lever , G. , Heess , N. , Degris , T. et al. 2014
- O’Keefe , M.P. , Simpson , A. , Kelly , K.J. , and Pedersen , D.S. Duty Cycle Characterization and Evaluation towards Heavy Hybrid Vehicle Applications SAE Technical Paper 2007-01-0302 2007 https://doi.org/10.4271/2007-01-0302