Modeling and Co-design Optimization for Heavy Duty Trucks

Dai-Duong Tran, Omar Hegazy, Joeri Van Mierlo, Rafael Klüppel Smijtink, Jonas Hellgren, Olof Lindgarde, Thinh Pham, Steven Wilkins

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

5 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationThe 31st International Electric Vehicles Symposium and Exhibition
Number of pages8
Publication statusPublished - 1 Oct 2018
EventEVS31 - Kobe, Japan
Duration: 30 Sep 20184 Oct 2018



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