Grant: Collaborative Research and Development Grants
University of Waterloo
$120,850.00
Optimizing Hybrid Electric Powertrains for Connected and Automated Vehicles (CAV)
EF 2021-22
Résumé
General Motors of Canada Company (GM) and University of Waterloo (UW) will research two important aspects of vehicle design with respect to connected and automated vehicles (CAV): (i) the effects of connected and automated vehicles on real-world driving habits that in turn influences powertrain design, and (ii) investigation of CAV's impact on hybrid electric vehicle energy management strategies in regards to effects on the emissions and fuel/energy consumption. To support these research topics, UW will convert an existing, production GM vehicle into a hybrid electric vehicle with integrated CAV and powertrain functionalities. This will act as a fully customizable vehicle test bed providing students with unique hands-on-learning and exposure to industry grade tools and processes. Students will also have a unique opportunity to be involved in the race to develop the next generation of intelligent transportation systems and vehicle automation. The first research topic relates to the influence CAV can have on drive cycles and the powertrain selection. Traditionally OEMs use government provided drive cycles to design their vehicles. By collaborating with GM, the effects that CAV will have on real-world driving habits will be captured and examined. By understanding the CAV factors that can influence vehicle design, GM will be able to better design their vehicle's to meet consumer needs. The second research topic will aid GM in understanding how CAV can have an impact on hybrid vehicle emissions and fuel consumption. An area of interest will be the study of how the development of vehicle global mapping, localization, prediction and path planning system will influence powertrain energy management to minimize energy consumption. Additionally, this research will measure the resources needed to migrate the software and hardware of the developed system from a laboratory setting to a consumer ready vehicle. With this information, GM will be able to quantify the costs of migrating the concepts to on-road vehicles. The benefit to Canadians are vehicles designed to reduce emissions and fuel consumption, which are not only tailored to our unique landscape and driving patterns, but also to the intelligent transportation infrastructure of the future.
Programme
Collaborative Research and Development Grants
Bénéficiaire
Emplacement
Ontario, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
537104-2018
Name nom
Fraser, Roydon
Department département
Mechanical Engineering and Mechatronics
Organization id
33
Institution établissement
University of Waterloo
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2021
Competition year année de concours
2018
Award amount
120850
Program id
CRDPJ
Program name en
Collaborative Research and Development Grants
Program name fr
Subventions de recherche et développement coopérative
Group en
Research partnerships programs
Group fr
Programmes de partenariat
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
306
Area of application group en
Energy resources
Area of application group fr
Ressources d'énergie
Area of application en
Energy efficiency
Area of application fr
Efficacité énergétique
Research subject code
2108
Research subject group en
Mechanical engineering
Research subject group fr
Génie mécanique
Research subject en
Aerospace, aeronautical and automotive engineering
Research subject fr
Génie aérospatial, aéronautique et automobile
Installment
inst:4
Partie
1
Nb partie
5
Application title
Optimizing Hybrid Electric Powertrains for Connected and Automated Vehicles (CAV)
Keyword
NSERC
Application summary
General Motors of Canada Company (GM) and University of Waterloo (UW) will research two important aspects of vehicle design with respect to connected and automated vehicles (CAV): (i) the effects of connected and automated vehicles on real-world driving habits that in turn influences powertrain design, and (ii) investigation of CAV's impact on hybrid electric vehicle energy management strategies in regards to effects on the emissions and fuel/energy consumption. To support these research topics, UW will convert an existing, production GM vehicle into a hybrid electric vehicle with integrated CAV and powertrain functionalities. This will act as a fully customizable vehicle test bed providing students with unique hands-on-learning and exposure to industry grade tools and processes. Students will also have a unique opportunity to be involved in the race to develop the next generation of intelligent transportation systems and vehicle automation. The first research topic relates to the influence CAV can have on drive cycles and the powertrain selection. Traditionally OEMs use government provided drive cycles to design their vehicles. By collaborating with GM, the effects that CAV will have on real-world driving habits will be captured and examined. By understanding the CAV factors that can influence vehicle design, GM will be able to better design their vehicle's to meet consumer needs. The second research topic will aid GM in understanding how CAV can have an impact on hybrid vehicle emissions and fuel consumption. An area of interest will be the study of how the development of vehicle global mapping, localization, prediction and path planning system will influence powertrain energy management to minimize energy consumption. Additionally, this research will measure the resources needed to migrate the software and hardware of the developed system from a laboratory setting to a consumer ready vehicle. With this information, GM will be able to quantify the costs of migrating the concepts to on-road vehicles. The benefit to Canadians are vehicles designed to reduce emissions and fuel consumption, which are not only tailored to our unique landscape and driving patterns, but also to the intelligent transportation infrastructure of the future.