Connectivity-enhanced route selection and adaptive control for the Chevrolet Volt

Gonder, Jeffrey; Wood, Eric; Rajagopalan, Sai

doi:10.17265/2328-2142/2016.01.006

Title: Connectivity-enhanced route selection and adaptive control for the Chevrolet Volt

Full Record
Other Related Research

Abstract

The National Renewable Energy Laboratory and General Motors evaluated connectivity-enabled efficiency enhancements for the Chevrolet Volt. A high-level model was developed to predict vehicle fuel and electricity consumption based on driving characteristics and vehicle state inputs. These techniques were leveraged to optimize energy efficiency via green routing and intelligent control mode scheduling, which were evaluated using prospective driving routes between tens of thousands of real-world origin/destination pairs. The overall energy savings potential of green routing and intelligent mode scheduling was estimated at 5% and 3%, respectively. Furthermore, these represent substantial opportunities considering that they only require software adjustments to implement.

Authors:

Gonder, Jeffrey ^[1]; Wood, Eric ^[1]; Rajagopalan, Sai ^[2]

National Renewable Energy Lab. (NREL), Golden, CO (United States)
General Motors R&D, Warren, MI (United States)

Publication Date:: Fri Jan 01 00:00:00 EST 2016

Research Org.:: National Renewable Energy Lab. (NREL), Golden, CO (United States)

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V)

OSTI Identifier:: 1242679

Report Number(s):: NREL/JA-5400-65425
Journal ID: ISSN 2328-2142

Grant/Contract Number:: AC36-08GO28308

Resource Type:: Accepted Manuscript

Journal Name:: Journal of Traffic and Transportation Engineering

Additional Journal Information:: Journal Volume: 4; Journal Issue: 1; Journal ID: ISSN 2328-2142

Publisher:: David Publishing Company

Country of Publication:: United States

Language:: English

Subject:: 30 DIRECT ENERGY CONVERSION; intelligent transportation systems; green routing; vehicle drive cycles/profiles; route-based/adaptive powertrain control; vehicle telematics/navigation; vehicle energy use prediction

Citation Formats


                    Gonder, Jeffrey, Wood, Eric, and Rajagopalan, Sai. Connectivity-enhanced route selection and adaptive control for the Chevrolet Volt.  United States: N. p., 2016. 
Web.  doi:10.17265/2328-2142/2016.01.006.

Copy to clipboard


                    Gonder, Jeffrey, Wood, Eric, & Rajagopalan, Sai. Connectivity-enhanced route selection and adaptive control for the Chevrolet Volt.  United States.  https://doi.org/10.17265/2328-2142/2016.01.006

Copy to clipboard


                    Gonder, Jeffrey, Wood, Eric, and Rajagopalan, Sai. Fri .  
"Connectivity-enhanced route selection and adaptive control for the Chevrolet Volt".  United States.  https://doi.org/10.17265/2328-2142/2016.01.006.  https://www.osti.gov/servlets/purl/1242679.

Copy to clipboard


                    
@article{osti_1242679,

  title        = {Connectivity-enhanced route selection and adaptive control for the Chevrolet Volt},

  author       = {Gonder, Jeffrey and Wood, Eric and Rajagopalan, Sai},

  abstractNote = {The National Renewable Energy Laboratory and General Motors evaluated connectivity-enabled efficiency enhancements for the Chevrolet Volt. A high-level model was developed to predict vehicle fuel and electricity consumption based on driving characteristics and vehicle state inputs. These techniques were leveraged to optimize energy efficiency via green routing and intelligent control mode scheduling, which were evaluated using prospective driving routes between tens of thousands of real-world origin/destination pairs. The overall energy savings potential of green routing and intelligent mode scheduling was estimated at 5% and 3%, respectively. Furthermore, these represent substantial opportunities considering that they only require software adjustments to implement.},

  doi          = {10.17265/2328-2142/2016.01.006},

  journal      = {Journal of Traffic and Transportation Engineering},

  number       = 1,

  volume       = 4,

  place        = {United States},

  year         = {Fri Jan 01 00:00:00 EST 2016},

  month        = {Fri Jan 01 00:00:00 EST 2016}

}

Copy to clipboard

Journal Article:

Free Publicly Available Full Text

Accepted Manuscript (DOE)

Publisher's Version of Record

https://doi.org/10.17265/2328-2142/2016.01.006

Other availability

Search WorldCat to find libraries that may hold this journal

Save / Share:

Export Metadata

Save to My Library

Similar Records in DOE PAGES and OSTI.GOV collections:

Connectivity-Enhanced Route Selection and Adaptive Control for the Chevrolet Volt: Preprint

Conference Gonder, J. ; Wood, E. ; Rajagopalan, S.

The National Renewable Energy Laboratory and General Motors evaluated connectivity-enabled efficiency enhancements for the Chevrolet Volt. A high-level model was developed to predict vehicle fuel and electricity consumption based on driving characteristics and vehicle state inputs. These techniques were leveraged to optimize energy efficiency via green routing and intelligent control mode scheduling, which were evaluated using prospective driving routes between tens of thousands of real-world origin/destination pairs. The overall energy savings potential of green routing and intelligent mode scheduling was estimated at 5% and 3% respectively. These represent substantial opportunities considering that they only require software adjustments to implement.
Full Text Available
Route-Optimized Energy Management of Connected and Automated Multi-Mode Plug-In Hybrid Electric Vehicle Using Dynamic Programming

Journal Article Rama, Neeraj ; Wang, Huanqing ; Orlando, Joshua ; ... - Society of Automotive Engineers Technical Paper Series

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 drivemore »« less
https://doi.org/10.4271/2019-01-1209

Full Text Available
Actual Versus Estimated Utility Factor of a Large Set of Privately Owned Chevrolet Volts

Conference Smart, John ; Bradley, Thomas ; Schey, Stephen

In order to determine the overall fuel economy of a plug-in hybrid electric vehicle (PHEV), the amount of operation in charge depleting (CD) versus charge sustaining modes must be determined. Mode of operation is predominantly dependent on customer usage of the vehicle and is therefore highly variable. The utility factor (UF) concept was developed to quantify the distance a group of vehicles has traveled or may travel in CD mode. SAE J2841 presents a UF calculation method based on data collected from travel surveys of conventional vehicles. UF estimates have been used in a variety of areas, including the calculationmore »« less
Full Text Available
How much are Chevrolet Volts in The EV Project driven in EV Mode?

Technical Report Smart, John

This report summarizes key conclusions from analysis of data collected from Chevrolet Volts participating in The EV Project. Topics include how many miles are driven in EV mode, how far vehicles are driven between charging events, and how much energy is charged from the electric grid per charging event.
https://doi.org/10.2172/1114572

Full Text Available
How many electric miles do Nissan Leafs and Chevrolet Volts in The EV Project travel?

Technical Report Smart, John

This paper presents travel statistics and metrics describing the driving behavior of Nissan Leaf and Chevrolet Volt drivers in the EV Project. It specifically quantifies the distance each group of vehicles drives each month. This paper will be published to INL's external website and will be accessible by the general public.
https://doi.org/10.2172/1164857

Full Text Available

Similar Records