How Ford engineers cut costs and prototypes with CAE
Prior to the availability of advanced EE simulation tools, we depended on spreadsheet analysis to do simple calculations to help us study the interaction between components—for example, a control module and a switch. This approach was limited to studying specific parameters for components—such as ensuring the switch received sufficient current to make a good contact. While this form of analysis was limited, we learned a lot about the importance of data accuracy and of fully capturing and understanding the data parameters, so that we could enable accurate results, and create models that mirrored the real world.
Mirroring the Real World
Creating an analysis that is thorough, accurate and mirrors the real world is a top priority. Since the tools we use now are far more sophisticated than our early spreadsheet models, we spend a lot of resources ensuring that the models we use are accurate, whether they are sourced in-house or from suppliers. The alternative to CAE analysis—the use of physical prototypes—is becoming prohibitively expensive.
In addition to the cost of building the vehicle itself, program teams must pay to have access to the vehicle for each day of physical testing. The extensive tests that we run using CAE analysis tools would take many days of testing using physical prototypes and can’t even begin to approach the number of “what-if” scenarios that CAE analysis can cover.
Improving Robustness
As well as helping to reduce development costs, CAE analysis makes a significant contribution to the robustness of our designs. We run hundreds or thousands of analyses with variations of component tolerances and look at the effects of temperature changes and aging. We can then use data from the analysis tools to isolate the component tolerances that matter, and tighten ones of interest, or make other changes that improve the robustness of the overall subsystem.
Sometimes, the quality issues we uncover have implications for our suppliers. A circuit design may pass the verification tests at the component level, but may need improvement when used in the subsystem. We work closely with engineers and suppliers, providing feedback which results in more robust designs.
Developing CAE Plans
A typical vehicle CAE plan may have over 500 EE analyses. We use Monte Carlo analysis extensively to study variations in parameters across hundreds of scenarios. We also use our CAE environment to look at DC and transient analysis, as well as sensitivity. Pareto analysis (Figure 1) helps us with our detective work; we typically use it to understand what the significant contributors are to signal variation over many Monte Carlo runs.
Figure 1: Pareto analysis results. For full resolution click here.
An example of one of the types of analysis that we focus on establishes the robustness of shared signals (Figure 2) among different modules in the vehicle. One component may be the source of a signal that is monitored by several other components. The analysis of shared signals is critical to ensure that designs from several different component suppliers are able to perform all their functions with complete accuracy and robustness under all expected operating conditions. CAE analysis allows us to perform hundreds of different analyses to get statistically valid results. This is cost and timing prohibitive when trying to use a physical prototype in the lab.
Figure 2: Design example using shared signals. For full resolution click here.
Modeling Electro-Mechanical Systems
Electromechanical systems have been and will be increasing their footprint in vehicle system and subsystem designs. To better understand the mechanical effects within electro-mechanical systems, we need to be able to analyze physical components alongside the electrical ones. For example, we’ll model the mechanical properties of a motor.
On a more complex level, we have developed an electro-mechanical model for power window subsystem. The mechanical teams (who may have little experience with the electrical domain) can use the model to help them choose what size motor they will need, or the different torque/power losses in the system they will use, based on the characteristic interaction of their mechanical and electrical components.
Prototyping Software
The cost of developing software for automotive applications is growing enormously, because of the growth of software content in cars. Software verification is an issue that affects quality and the performance of safety-critical systems. Now that we have a methodology in place to reduce our use of physical prototypes for the electrical and electro-mechanical subsystems, our next area of focus is the software domain.
If we can have our developers use virtual models to do functional testing, we can start to reduce our dependency on the use of breadboards even further, as well as benefiting from faster system verification, better debug and improved quality in the software domain. Once we have a virtual software solution, we will be closer to being able to perform complete system validation using a combination of virtual prototyping and CAE analysis. As we evolve, and the industry evolves, we look forward to virtual prototyping, with links to Saber, evolving to bring that missing piece—the software piece—into play.
Solution summary
Our EECAE analysis environment integrates well into our design environment. It enables us to:
• Analyze electrical and mechanical components together,
• Use advanced statistical analysis to mirror real-life situations,
• Choose from thousands of pre-defined library components,
• Easily connect components from different domains so that we can analyze systems at different levels of abstraction, and
• Use the multi-domain simulator interface to look at mechanical, thermal and electrical issues to see how changes in one domain affects the others.
Project Profile
CAE analysis environment: Synopsys Saber
Algorithm and software modeling: MathWorks Simulink®
Harness design integration: Saber Frameway
Note: Ford’s EECAE team supports all vehicle program teams with electrical computer-aided engineering analysis and design verification. It also provides vehicle program teams with support services such as current-based limit testing and with EE design alternative investigation.
About the Authors
Asaad Makki earned a Ph.D. degree in Electrical and Computer Engineering from Wayne State University, in 1993. He is currently a member of the engineering management team at Ford Motor Company, where he is leading the global electrical CAE group.
Dave Beard began his EE career as a Technician in the U.S. Navy, working on satellite communication and cryptographic equipment. After receiving his BSEE from Lawrence Technological University, he performed robustness circuit analysis on small jet engine fuel systems with Williams International. Dave moved to Ford, and has held positions involved with manufacturing quality, value engineering, systems engineering, design and release, and, for the past 12 years. Dave’s current position is EECAE Senior Engineer, based in Dearborn, Mich.
Article by courtesy of EETimes Automotive DesignLine
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