Standard

Modeling and Co-design Optimization for Heavy Duty Trucks. / Tran, Dai-Duong; Hegazy, Omar; Van Mierlo, Joeri; Klüppel Smijtink, Rafael; Hellgren, Jonas; Lindgarde, Olof; Pham, Thinh; Wilkins , Steven.

The 31st International Electric Vehicles Symposium and Exhibition. EVS31, 2018.

Research output: Chapter in Book/Report/Conference proceedingConference paper

Harvard

Tran, D-D, Hegazy, O, Van Mierlo, J, Klüppel Smijtink, R, Hellgren, J, Lindgarde, O, Pham, T & Wilkins , S 2018, Modeling and Co-design Optimization for Heavy Duty Trucks. in The 31st International Electric Vehicles Symposium and Exhibition. EVS31, EVS31, Kobe, Japan, 30/09/18.

APA

Tran, D-D., Hegazy, O., Van Mierlo, J., Klüppel Smijtink, R., Hellgren, J., Lindgarde, O., ... Wilkins , S. (2018). Modeling and Co-design Optimization for Heavy Duty Trucks. In The 31st International Electric Vehicles Symposium and Exhibition EVS31.

Vancouver

Tran D-D, Hegazy O, Van Mierlo J, Klüppel Smijtink R, Hellgren J, Lindgarde O et al. Modeling and Co-design Optimization for Heavy Duty Trucks. In The 31st International Electric Vehicles Symposium and Exhibition. EVS31. 2018

Author

Tran, Dai-Duong ; Hegazy, Omar ; Van Mierlo, Joeri ; Klüppel Smijtink, Rafael ; Hellgren, Jonas ; Lindgarde, Olof ; Pham, Thinh ; Wilkins , Steven. / Modeling and Co-design Optimization for Heavy Duty Trucks. The 31st International Electric Vehicles Symposium and Exhibition. EVS31, 2018.

BibTeX

@inproceedings{671bd7fc9b714dca86689675bf2b2ebd,
title = "Modeling and Co-design Optimization for Heavy Duty Trucks",
abstract = "This paper presents a co-design optimization framework for the heavy-duty trucks as a part of the ORCA European project. The proposed co-design framework composes of an optimal control strategy using the Equivalent Consumption Minimization Strategy (ECMS), which is nested into a component sizing optimization loop employing Genetic Algorithms (GA). Considering a particular transport assignment, the optimization objective is to find optimal sizing of key components such as Internal Combustion Engine (ICE), Electric Motor (EM) and battery system to minimize a Total Cost of Ownership for hybrid heavy-duty powertrain (denoted as pTCO) without impairing the performance requirements. The pTCO includes the investment cost of main powertrain components and operational cost over the lifetime of vehicle. In the co-design framework, maximum power (kW) of the ICE (kW), EM (kW) and battery capacity (kWh) are selected as design variables of optimization problem. Optimal solution of the developed GA-based co-design framework is verified via a comparison with that of Brute Force (BF) search method.",
keywords = "Plug-in Hybrid Heavy Duty Truck, Co-design Optimization, Genetic Algorithm, ECMS, Energy Management Strategy.",
author = "Dai-Duong Tran and Omar Hegazy and {Van Mierlo}, Joeri and {Kl{\"u}ppel Smijtink}, Rafael and Jonas Hellgren and Olof Lindgarde and Thinh Pham and Steven Wilkins",
year = "2018",
month = "10",
day = "1",
language = "English",
booktitle = "The 31st International Electric Vehicles Symposium and Exhibition",
publisher = "EVS31",

}

RIS

TY - GEN

T1 - Modeling and Co-design Optimization for Heavy Duty Trucks

AU - Tran, Dai-Duong

AU - Hegazy, Omar

AU - Van Mierlo, Joeri

AU - Klüppel Smijtink, Rafael

AU - Hellgren, Jonas

AU - Lindgarde, Olof

AU - Pham, Thinh

AU - Wilkins , Steven

PY - 2018/10/1

Y1 - 2018/10/1

N2 - This paper presents a co-design optimization framework for the heavy-duty trucks as a part of the ORCA European project. The proposed co-design framework composes of an optimal control strategy using the Equivalent Consumption Minimization Strategy (ECMS), which is nested into a component sizing optimization loop employing Genetic Algorithms (GA). Considering a particular transport assignment, the optimization objective is to find optimal sizing of key components such as Internal Combustion Engine (ICE), Electric Motor (EM) and battery system to minimize a Total Cost of Ownership for hybrid heavy-duty powertrain (denoted as pTCO) without impairing the performance requirements. The pTCO includes the investment cost of main powertrain components and operational cost over the lifetime of vehicle. In the co-design framework, maximum power (kW) of the ICE (kW), EM (kW) and battery capacity (kWh) are selected as design variables of optimization problem. Optimal solution of the developed GA-based co-design framework is verified via a comparison with that of Brute Force (BF) search method.

AB - This paper presents a co-design optimization framework for the heavy-duty trucks as a part of the ORCA European project. The proposed co-design framework composes of an optimal control strategy using the Equivalent Consumption Minimization Strategy (ECMS), which is nested into a component sizing optimization loop employing Genetic Algorithms (GA). Considering a particular transport assignment, the optimization objective is to find optimal sizing of key components such as Internal Combustion Engine (ICE), Electric Motor (EM) and battery system to minimize a Total Cost of Ownership for hybrid heavy-duty powertrain (denoted as pTCO) without impairing the performance requirements. The pTCO includes the investment cost of main powertrain components and operational cost over the lifetime of vehicle. In the co-design framework, maximum power (kW) of the ICE (kW), EM (kW) and battery capacity (kWh) are selected as design variables of optimization problem. Optimal solution of the developed GA-based co-design framework is verified via a comparison with that of Brute Force (BF) search method.

KW - Plug-in Hybrid Heavy Duty Truck, Co-design Optimization, Genetic Algorithm, ECMS, Energy Management Strategy.

M3 - Conference paper

BT - The 31st International Electric Vehicles Symposium and Exhibition

PB - EVS31

ER -

ID: 39898848