TECHNICAL PAPER

Multidisciplinary Optimization of an Electric Motor for anAutomobile and Extension to a Model-Based Systems
Engineering Approach
Designing a modern electric motor for an electrified automobile requires striking the perfectbalance between cost, weight, and performance. From a modeling and simulation standpoint,
predicting the overall performance of the motor requires multiple multi-disciplinary analyses,
including electromagnetic, thermal, and stress analyses.
The traditional Computer-Aided Engineering (CAE) process, which consists of setting up a model,
running the simulation, post-processing data, reviewing results, extracting the outputs of one
simulation that are inputs into another, and iterating until an acceptable design is achieved issimply not possible when the validation of the design requires this many models and types ofsolvers.

MULTI-DISCIPLINARY OPTIMIZATION – GENERAL OVERVIEWMDO Process Execu�on Time Line
Baseline DesignS�ﬀness,
Strength

ThermalEM
Demagne�za�on

Targets / DV Bounds

Meta Model
Genera�onOpt. Problem(Example)

Objective:
Maximize TorqueDOE

Constraints:
Efficiency, Stress,
Temperature, etc.

Acous�cs

Study Prepara�on
EM Performance
Power, TorquePreprocessing

EM Eﬃciency

Valida�on

XN number of samples

Study Result: • IImprovedDesign Balance

Execute DOE

Postprocessing•-Be�er Objec�ve Results

Figure 1. Diagram of the overall Design & Optimization Process

Multidisciplinary Optimization of an Electric Motor for an Automobile //1/ Brief Introduction
As a brief introduction, ModelCenter® is a framework for model
based engineering and allows users to:•••••

The general design and optimization process includes the following
steps:• Generate a baseline design using e-motor design tool• Define the Design of Experiments (DoE) to explore the designspace• Run the DoE (including electromagnetics, thermal &mechanical analyses)• From the results of the design space, create reduced ordermodels that will replace the physics-based in themulti-disciplinary analyses so the workflow can be executed
much faster, thus making optimization feasible• Set-up the optimization problem• Run the optimization problem to come up with the optimum
designAutomate any modeling and simulation tool
Integrate these tools together to create repeatable simulation
workflows
Set simulation parameters
Automatically execute workflows
Streamline the development of complex systems by connecting
systems architecture and requirements to modeling andsimulation tools

ModelCenter®’s powerful integration capabilities provide engineers with the ability to quickly build automated multidisciplinary
workflows. In this example ModelCenter was used to solve the multidisciplinary problem of the design of an electric motor for an
automobile and brought together electromagnetic, thermal, and structural performance criteria in a single workflow so that all theperformance aspects and constraints can be simultaneously considered to design an efficient motor. The individual disciplines areevaluated using Altair solvers – Flux Motor (initial baseline motor design), Flux (EMAG), and OptiStruct (thermal & stress analyses).
ModelCenter® provides an easy way to connect the different analysis tools into a sophisticated workflow. There is a graphicalrepresentation of the workflow which makes clear the order of execution of the components and the data dependencies between
them. When components do not depend on each other, it is possible to run them in parallel to reduce execution time. An optimizerexecutes the workflow to search through the design space and find the best answer.
The design optimization problem is defined as follows:

MULTIPHYSIC OPTIMIZATION LOOP WITH MODELCENTER®
Op�miza�on•••Base point : speed, angle
Max speed : current, angle
Max speed 100kW : current,angle

Max stress around magnets

Objec�ve:••

Max Power
Min Torque Ripples

Constraints••••

Demagne�za�on
StressTemperature
180 Nm for Base Torque•••••base speed : torque ripple, eﬃciency,mean torqueMax speed: losses, torque ripples,mean torque
Check demagne�za�on
Max speed 100kW : losses
Temperature a�er 2 hours at max
speed (100 kW)

Figure 2. Multiphysic Optimization Loop with ModelCenter®

Multidisciplinary Optimization of an Electric Motor for an Automobile //2

The figure below shows the screen capture of the associated ModelCenter workflow:

The ModelCenter® GUI allows the user to extract key information as the workflow is being executed, which provides a real time
dashboard while the design iteration loops are running. In this particular instance, we selected a table with some of the key design
variables and outputs, as well as graphic images of geometry along with stress & thermal results. This allows the analyst to get realtime feedback on the analysis results and to monitor progress.
After the workflow has been created, we can explore the design space to gain insight into our design by generating and running adesign of experiments.

/ Design of Experiments

Figure 3. Twenty-Two Design Variables for the Magnets

Multidisciplinary Optimization of an Electric Motor for an Automobile //3Minimum
Torque Ripple

Minimum
winding temperature

Minimum
Demagne�za�on

Minimum
StressIni�al DesignMaximum
PowerMaximum
TorqueFigure 4. Design Exploration on DoE Results on Magnets

Based on the results of the DoE, we are now able to identify which design variables have the most impact on our design andperformance targets, and what are the usable ranges for these variables. This will help us define the optimization problem properly
so we can run an optimization loop to find best the compromise based on the current objectives.

/ Optimization
The optimization problem was set-up as follows;
Objective: Maximize efficiency over duty cycle
Constraints:•Base torque greater than 180Nm•
Base speed greater than 50,000 rpm• Automate any modeling and simulation tool
• Integrate these tools together to create repeatable simulation workflows

Figure 5. Results for Design Optimization

The optimum design was identified less than one
hours later;

Table 1. Efficiency Results for Optimum Design

In this particular case, we were able to increase the efficiency of our motor by 3% while meeting all of our constraints in less than one
hour.

Multidisciplinary Optimization of an Electric Motor for an Automobile //4/ Model-Based Systems Engineering
To manage the rapidly increasing complexity of products, many engineering organizations across multiple industries are embracing
model based systems engineering (MBSE) to be able to make informed decisions very early in the design process, understand how
decisions made for one system will impact other systems and the overall product, and manage trade-offs between performance,cost and requirements.
When MBSE models that capture architecture, behavior and requirements of a product are available, ModelCenter acts as the bridge
between these MBSE models and the simulation models that are used to evaluate a particular design and check whether therequirements are met. This integration allows engineers to benefit from engineering simulation models earlier in the design cycle, atthe architecture definition stage. It provides systems engineers with the ability to run trade studies and gain insight into their design
process by identifying very early on which of the requirements might be constraining the design and which architecture or design
variables are the most important to manage carefully. It also breaks down the silos between engineering disciplines by bringing
them into the same MBSE model and ensuring that all the engineering models reflect the same instance of the design.

Figure 6. SysML Model of E-Motor Block Diagram Describing E-Motor Components & Their RelationshipThe block diagram captures information about the e-motor design and in particular the magnet geometry and its properties, which
are the inputs into the e-motor simulation workflow we reviewed earlier. In the first direction of our bi-directional connection
between SysML models and simulation models, ModelCenter MBSE will leverage the authoritative source of truth that is this SysML
model and update the simulation workflows to reflect the architecture and design parameters defined in the SysML model.

Multidisciplinary Optimization of an Electric Motor for an Automobile //5Figure 7. E-Motor Requirements Diagram

In the second direction of our bi-directional connection between SysML models and simulation models, ModelCenter MBSE will bringback simulation results into the SysML model so they can be compared to these requirements – this comparison is shown in the tablebelow:

Figure 8. ModelCenter® MBSE showing Requirements from MBSE Model

Multidisciplinary Optimization of an Electric Motor for an Automobile //6

Phoenix Integration, Inc.,
An Ansys Company
Phoenix Integration Blacksburg
1715 Pratt Drive
Suite 2000
Blacksburg, VA 24060
U.S.A.
540.961.7215
modelcenter-sales@ansys.com

ModelCenter® is the environment for Model Based Engineering.
ModelCenter® is a vendor-neutral software framework for creating andautomating multi-tool workflows, optimizing product designs, andenabling Model Based Systems Engineering (MBSE). It is used by leadingorganizations worldwide to reduce development costs, improveengineering efficiency, stimulate innovation, and design more competitive
products. Successful applications can be found in multiple industries,including aerospace, automotive, defense, electronics, energy, heavy
industry, and shipbuilding.

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Multidisciplinary Optimization of an Electric Motor for an Automobile //7