Digital Automotive Engineering
Racing the sunrise!
Southern California offers some of the most spectacular roads in the world. The magnificent Angeles Crest is my personal favorite. It rises to ten thousand feet at its peak, crisscrossing the rugged San Gabriel mountains. On a clear morning, you can even view the panoramic Antelope Valley super bloom in the distance. I've enjoyed breathtaking sunrises on these roads — usually while revving glorious, naturally aspirated engines to sensational RPM apexes.
Today’s sports cars have come a long way. They deliver immersive driving experiences through the converged application of race-car dynamics, components, and advanced software technologies.
For example, now double wishbones replace MacPherson struts to improve ride suspension and optimize wheel motion. Live sensors from the vehicle feed data into back-end systems, deriving predictive maintenance guidance and design improvements. In electric vehicles — EVs — mechanical/hydraulic anti-locking brakes pair with sensor-driven regenerative-braking technology to convert kinetic energy into electricity. The synergies are many. The combined results are bespoke.
This level of integration, and resulting customization, is gaining traction because of various software advances.
Digital Twin is one such. Although not a novel concept, massive improvements in Cloud, AI, IoT, and other complementary technologies have positioned Digital Twin as a capstone solution for high-end automotive tuning and operational refinements.
Many years ago, I used Java 3D, a scene-graph-based API that runs on OpenGL, and VRML (now called X3D) for my grad school thesis. I leveraged these technologies to render some sections of our CompSci building dynamically. Digital Twin is a leapfrog of the same concept. It also entails creating and analyzing digital replicas of physical objects.
The most significant distinction, however, is that the Digital Twins are live — always reflecting the current state of their physical counterparts:
Sensors and edge devices transmit data in real-time to the Digital Twin.
Neural/geospatial algorithms analyze the data using techniques like digital-twin graphing in the context of scenarios and simulations.
Information/insights are transmitted back to enhance mobility and vehicle reliability.
Formula 1 cars pioneered this technology a few years ago. Their sophisticated Drag Reduction (DRS) and Advanced Traction Management (ATMS) systems exemplify how Digital Twins have changed Formula 1 racing. DRS electronically lowers the flap of the diffuser, temporarily boosting the top speed when accelerating down a straight line. On curves, as the foot comes off the accelerator, the DRS automatically shuts off, reattaching airflow to the rear wing and increasing pure downforce. The airflow control also creates low pressure under the car, pulling it downwards — and the car rockets out of the curve with minimum loss of traction or speed.
These systems are now available on a few street-legal sports cars, thanks to trickle-down performance engineering. I've experienced it first hand in a closed circuit — pushing close to 1.8g(s) for the more inclined. It can be nerve-wracking and exhilarating at the same time. Another fantastic outcome of the application of Digital Twins is the elimination of the infamous Y250 vortex. This is only the beginning: The potential improvements are endless.
Creation and maintenance of a Digital Twin system requires a standards-based digital representation of the physical item. Singh & Wilcox at MIT first defined this representation as a Digital Thread. It is akin to a communication framework for sharing information among traditionally isolated departments. It can also be thought of as a digital product definition representing, at a broader level, a common taxonomy for all stakeholders. This standard definition needs continual updating through reciprocal data transmission, effectively forming a manufacturing 360.
Accurate digital twins and threads supporting mass-production vehicles demand intricate, arduous effort. PaaS offerings like AWS IoT Twinmaker and Azure Digital Twins present flexible options to develop authentic expressions of physical systems in digital spaces.
With modelers, data connectors, and workspaces that can house visual assets, these platforms facilitate digital twinning in a scalable and secure manner. Moreover, they offer frameworks to customize taxonomies, design unique integration architectures, and manage internal organizational changes.
These platforms provide transparent APIs, bolstered by a thriving ecosystem of expert consultants specializing in IoT, data integration, and cloud computing. Hint, hint!
When these are done right, OEMs can gain efficiencies by implementing Digital Twin/Threads. However, successful execution and value realization stems from the effective confluence of Operational Technology (OT) and Information Technology (IT). The crucial element? A seasoned, skilled IT development team making SME contributions to the OT requirements. Equally important, industry veterans emphatically discourage a boil-the-ocean approach. What counts is an MVP approach that incrementally demonstrates business value.
As the technology continues to roll out, vehicle owners and commuters can expect to enjoy more advanced autonomous capabilities and enhanced ride quality. Meanwhile, the gearheads among us will continue to seek those adrenaline-inducing shifts, heel-toeing our way across the tachometer's power band.
Either way, those SoCal mountain sunrises are worth the effort.