Route-Optimized Energy Management of Connected and Automated Multi-Mode Plug-In Hybrid Electric Vehicle Using Dynamic Programming
Abstract
This paper presents a methodology to optimize the blending of charge-depleting (CD) and charge-sustaining (CS) modes in a multi-mode plug-in hybrid electric vehicle (PHEV) that reduces overall energy consumption when the selected route cannot be completely driven in all-electric mode. The PHEV used in this investigation is the second-generation Chevrolet Volt and as many as four instrumented vehicles were utilized simultaneously on road to acquire validation data. The optimization method used is dynamic programming (DP) paired with a reduced-order powertrain model to enable onboard embedded controller compatibility and computational efficiency in optimally blending CD, CS modes over the entire drive route. The objective of the optimizer is to enable future Connected and Automated Vehicles (CAVs) to best utilize onboard energy for minimum overall energy consumption based on speed and elevation profile information from Intelligent Transportation Systems (ITS), Internet of Things (IoT), High-definition Mapping, and onboard sensing technologies. Emphasis is placed on runtime minimization to quickly react and plan an optimal mode scheme in highly dynamic road conditions with minimal computational resources. On-road performance of the optimizer paired with automated CD and CS mode selection is evaluated on a fleet of four instrumented Chevrolet Volts in a variety of driving scenarios.more »
- Authors:
-
- Michigan Technological Univ., Houghton, MI (United States)
- Publication Date:
- Research Org.:
- Michigan Technological Univ., Houghton, MI (United States)
- Sponsoring Org.:
- USDOE Advanced Research Projects Agency - Energy (ARPA-E)
- OSTI Identifier:
- 1572169
- Grant/Contract Number:
- AR0000788
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Society of Automotive Engineers Technical Paper Series
- Additional Journal Information:
- Journal Volume: 1; Journal ID: ISSN 0148-7191
- Publisher:
- SAE International
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 33 ADVANCED PROPULSION SYSTEMS
Citation Formats
Rama, Neeraj, Wang, Huanqing, Orlando, Joshua, Robinette, Darrell, and Chen, Bo. Route-Optimized Energy Management of Connected and Automated Multi-Mode Plug-In Hybrid Electric Vehicle Using Dynamic Programming. United States: N. p., 2019.
Web. doi:10.4271/2019-01-1209.
Rama, Neeraj, Wang, Huanqing, Orlando, Joshua, Robinette, Darrell, & Chen, Bo. Route-Optimized Energy Management of Connected and Automated Multi-Mode Plug-In Hybrid Electric Vehicle Using Dynamic Programming. United States. https://doi.org/10.4271/2019-01-1209
Rama, Neeraj, Wang, Huanqing, Orlando, Joshua, Robinette, Darrell, and Chen, Bo. Tue .
"Route-Optimized Energy Management of Connected and Automated Multi-Mode Plug-In Hybrid Electric Vehicle Using Dynamic Programming". United States. https://doi.org/10.4271/2019-01-1209. https://www.osti.gov/servlets/purl/1572169.
@article{osti_1572169,
title = {Route-Optimized Energy Management of Connected and Automated Multi-Mode Plug-In Hybrid Electric Vehicle Using Dynamic Programming},
author = {Rama, Neeraj and Wang, Huanqing and Orlando, Joshua and Robinette, Darrell and Chen, Bo},
abstractNote = {This paper presents a methodology to optimize the blending of charge-depleting (CD) and charge-sustaining (CS) modes in a multi-mode plug-in hybrid electric vehicle (PHEV) that reduces overall energy consumption when the selected route cannot be completely driven in all-electric mode. The PHEV used in this investigation is the second-generation Chevrolet Volt and as many as four instrumented vehicles were utilized simultaneously on road to acquire validation data. The optimization method used is dynamic programming (DP) paired with a reduced-order powertrain model to enable onboard embedded controller compatibility and computational efficiency in optimally blending CD, CS modes over the entire drive route. The objective of the optimizer is to enable future Connected and Automated Vehicles (CAVs) to best utilize onboard energy for minimum overall energy consumption based on speed and elevation profile information from Intelligent Transportation Systems (ITS), Internet of Things (IoT), High-definition Mapping, and onboard sensing technologies. Emphasis is placed on runtime minimization to quickly react and plan an optimal mode scheme in highly dynamic road conditions with minimal computational resources. On-road performance of the optimizer paired with automated CD and CS mode selection is evaluated on a fleet of four instrumented Chevrolet Volts in a variety of driving scenarios. Here, the results indicate variable energy savings depending on the drive route and initial battery SOC with potential ranging between 2 to 12% and less than 7 seconds initial optimization for a 24-mile drive cycle.},
doi = {10.4271/2019-01-1209},
journal = {Society of Automotive Engineers Technical Paper Series},
number = ,
volume = 1,
place = {United States},
year = {Tue Apr 02 00:00:00 EDT 2019},
month = {Tue Apr 02 00:00:00 EDT 2019}
}
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