Modeling and co-design optimization for heavy duty trucks

• Main Authors: Tran, Dai Duong, Hegazy, Omar, Van Mierlo, Joeri, Smijtink, Rafael, Hellgren, Jonas, Lindgarde, Olof, Pham, Thinh, Wilkins, Steven
• Format: Conference Object
• Language: English
• Published: 2018
• Subjects: Co-design Optimization; ECMS; Energy Management Strategy; Genetic Algorithm; Plug-in Hybrid Heavy Duty Truck; Orca
• Online Access: https://research.tue.nl/en/publications/ff5981b8-53ef-4cd7-8e00-8210a1823b77; http://www.scopus.com/inward/record.url?scp=85073106220&partnerID=8YFLogxK

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 ) without impairing the performance requirements. The 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.