A method for the optimal design of complex systems is developed by effectively combining multi-objective optimization and analytical target cascading techniques. The complex systems with high dimensionality are partitioned into manageable subsystems that can be optimized using dedicated algorithms. The multiple objective functions in each subsystem are treated simultaneously, and the interactions between subsystems are managed using linking variables and shared variables. The analytical target cascading algorithm ensures the convergence of the optimal solution that meets the system level targets while complying with the subsystem level constraints. A design optimization of electric vehicles with in-wheel motors is formulated as a two-level hierarchical scheme where the top level has a model representing the electric vehicle and the bottom level contains models of battery and suspension. The vehicle model includes an electric motor model and a power electronics model. Pareto-optimal solutions are derived holistically. The effectiveness of the proposed method for optimizing the complex systems is compared against the conventional all-in-one optimization approach.
Multidisciplinary Design of Electric Vehicles Based on Hierarchical Multi-Objective Optimization
Contributed by the Design Automation Committee of ASME for publication in the Journal of Mechanical Design. Manuscript received October 9, 2017; final manuscript received May 17, 2019; published online July 19, 2019. Assoc. Editor: Mian Li.
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Ramakrishnan, K., Mastinu, G., and Gobbi, M. (July 19, 2019). "Multidisciplinary Design of Electric Vehicles Based on Hierarchical Multi-Objective Optimization." ASME. J. Mech. Des. September 2019; 141(9): 091404. https://doi.org/10.1115/1.4043840
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