scholarly journals Design and Multi-Objective Optimization of a 12-Slot/10-Pole Integrated OBC Using Magnetic Equivalent Circuit Approach

Machines ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 329
Author(s):  
Mohamed Y. Metwly ◽  
Ahmed Hemeida ◽  
Ayman S. Abdel-Khalik ◽  
Mostafa S. Hamad ◽  
Shehab Ahmed
Keyword(s):  
Equivalent Circuit ◽  
Optimization Approach ◽  
Permanent Magnet Machines ◽  
Initial Surface ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Magnetic Equivalent Circuit ◽  
Battery Chargers ◽  
Charging Mode ◽  
Optimal Machine

Permanent magnet machines (PMs) equipped with fractional slot concentrated windings (FSCWs) have been preferably proposed for electric vehicle (EV) applications. Moreover, integrated on-board battery chargers (OBCs), which employ the powertrain elements in the charging process, promote the zero-emission future envisaged for transportation through the transition to EVs. Based on the available literature, the employed machine, as well as the adopted winding configuration, highly affects the performance of the integrated OBC. However, the optimal design of the FSCW-based PM machine in the charging mode of operation has not been conceived thus far. In this paper, the design and multi-objective optimization of an asymmetrical 12-slot/10-pole integrated OBC based on the efficient magnetic equivalent circuit (MEC) approach are presented, shedding light on machine performance during charging mode. An ‘initial’ surface-mounted PM (SPM) machine is first designed based on the magnetic equivalent circuit (MEC) model. Afterwards, a multi-objective genetic algorithm is utilized to define the optimal machine parameters. Finally, the optimal machine is compared to the ‘initial’ design using finite element (FE) simulations in order to validate the proposed optimization approach and to highlight the performance superiority of the optimal machine over its initial counterpart.

scholarly journals Multi-Objective Multidisciplinary Design Optimization of a Robotic Fish System

2021 ◽  
Vol 9 (5) ◽  
pp. 478
Author(s):  
Hao Chen ◽  
Weikun Li ◽  
Weicheng Cui ◽  
Ping Yang ◽  
Linke Chen
Keyword(s):  
Design Optimization ◽  
Optimization Algorithm ◽  
Multidisciplinary Design Optimization ◽  
Robotic Fish ◽  
Multidisciplinary Design ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Cfd Method ◽  
The Individual

Biomimetic robotic fish systems have attracted huge attention due to the advantages of flexibility and adaptability. They are typically complex systems that involve many disciplines. The design of robotic fish is a multi-objective multidisciplinary design optimization problem. However, the research on the design optimization of robotic fish is rare. In this paper, by combining an efficient multidisciplinary design optimization approach and a novel multi-objective optimization algorithm, a multi-objective multidisciplinary design optimization (MMDO) strategy named IDF-DMOEOA is proposed for the conceptual design of a three-joint robotic fish system. In the proposed IDF-DMOEOA strategy, the individual discipline feasible (IDF) approach is adopted. A novel multi-objective optimization algorithm, disruption-based multi-objective equilibrium optimization algorithm (DMOEOA), is utilized as the optimizer. The proposed MMDO strategy is first applied to the design optimization of the robotic fish system, and the robotic fish system is decomposed into four disciplines: hydrodynamics, propulsion, weight and equilibrium, and energy. The computational fluid dynamics (CFD) method is employed to predict the robotic fish’s hydrodynamics characteristics, and the backpropagation neural network is adopted as the surrogate model to reduce the CFD method’s computational expense. The optimization results indicate that the optimized robotic fish shows better performance than the initial design, proving the proposed IDF-DMOEOA strategy’s effectiveness.

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