A Consortium of Companies and Universities cooperated to design and manufacture electric motors for brake actuation. The project, in an Industry 4.0 framework, aimed to combine the design of both product and process.The electric motors for brakes have been optimized to shrink as much as possible the geometric dimensions while keeping high output torque, with the constraints of reduced production cost and extremely high reliable performance. A smart production plant has been studied and optimized together with the electric motor. Multi-objective optimization has been used to design the brushless DC motor. Fifteen design variables were considered for the definition of stator and rotor geometry, pole pieces and permanent magnets. The performance indices are peak torque, efficiency, rotor mass and inertia. The design constraints refer to components stress levels and temperature thresholds. The physical mathematical models used for optimal design refer to electromagnetic field and related currents computation, to thermo-fluid dynamics simulation, to local stress and vibration assessment. A surrogate model based on Artificial Intelligence has been used to speed-up the simulations. An Artificial Neural Network model, trained with an iterative procedure, was employed. Pareto-optimal solutions resulting from the design process are presented. Significant improvements of the performance indices with respect to a reference solution have been found.
Optimal design of a DC brushless motor for automotive brake actuation
Antonino DI GERLANDO;Massimiliano GOBBI;Gianpiero MASTINU;
2020-01-01
Abstract
A Consortium of Companies and Universities cooperated to design and manufacture electric motors for brake actuation. The project, in an Industry 4.0 framework, aimed to combine the design of both product and process.The electric motors for brakes have been optimized to shrink as much as possible the geometric dimensions while keeping high output torque, with the constraints of reduced production cost and extremely high reliable performance. A smart production plant has been studied and optimized together with the electric motor. Multi-objective optimization has been used to design the brushless DC motor. Fifteen design variables were considered for the definition of stator and rotor geometry, pole pieces and permanent magnets. The performance indices are peak torque, efficiency, rotor mass and inertia. The design constraints refer to components stress levels and temperature thresholds. The physical mathematical models used for optimal design refer to electromagnetic field and related currents computation, to thermo-fluid dynamics simulation, to local stress and vibration assessment. A surrogate model based on Artificial Intelligence has been used to speed-up the simulations. An Artificial Neural Network model, trained with an iterative procedure, was employed. Pareto-optimal solutions resulting from the design process are presented. Significant improvements of the performance indices with respect to a reference solution have been found.File | Dimensione | Formato | |
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