Structural and Multidisciplinary Optimization | 2019

PHEV powertrain co-design with vehicle performance considerations using MDSDO

 
 
 
 

Abstract


The complexity of plug-in hybrid-electric vehicles (PHEVs) motivates the simultaneous integration of component design and supervisory control strategy decisions. Methods from combined optimal design and control (co-design) are generally used to manage such integrated system design decisions. Although several studies have investigated the PHEV powertrain co-design problem, the impact of key vehicle performance criteria such as 0–60 mph acceleration time and all-electric-range (AER) has rarely been explicitly included in such system co-design problems. This is problematic as these vehicle performance criteria strongly affect component sizing and control strategy in a way that a non-performance-based co-design solution could become sub-optimal. Therefore, this study addresses this issue by formulating and solving a PHEV powertrain co-design problem that explicitly includes vehicle-level performance constraints. In particular, a three-phase, PHEV powertrain co-design problem is solved to simultaneously identify the optimal supervisory control strategies during the acceleration performance and standard duty cycle phases, along with the optimal component designs spanning all phases (acceleration performance, standard duty cycle, and AER performance) such that the vehicle powertrain cost is minimized. A relatively new, balanced co-design approach known as multidisciplinary dynamic system design optimization (MDSDO) is used to solve the problem. The optimal design and standard duty cycle supervisory control trajectories are compared to the solution of a non-performance-based co-design problem. The results indicate that the formal inclusion of vehicle performance criteria in a co-design problem significantly affects component design and supervisory control strategies.

Volume None
Pages 1-15
DOI 10.1007/S00158-019-02264-0
Language English
Journal Structural and Multidisciplinary Optimization

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