Modern cars have come a long way from their first three-wheeled model. Today’s consumer expects increasingly more advanced features to match their busy, on-the-go lifestyles: media and entertainment options, connectivity services, and smart convenience features, to name a few. Semiconductors play a critical role in powering these intelligent functions. Even basic cars rely on more than 100 microchips to power features such as climate control or automated air conditioning systems, and luxury cars that have more advanced technologies often require more than 150 chips to operate. Moreover, trends such as autonomous vehicles (AVs), or self-driving cars, will soon become the norm; the global market for AVs alone is expected to grow to 62.4 million units by 2030.
However, expanding consumer demands mean larger complexity, innovation, and security requirements for automotive manufacturers and designers. To meet these evolving needs for today’s and tomorrow’s cars, designers need to adopt or deploy powerful technologies early in the chip design process to operate complex systems without compromising security or efficiency. One of these underlying technologies is that of digital twins — a concept that enables many benefits, including faster time-to-market, closer integration of hardware and software, enhanced quality of products, and reduced operating costs.
Read on to learn more about the basics of digital twins, how they work, why this concept is critical for the EDA and automotive industry, Synopsys’ mark in the field, and future market trends within the automotive industry.
The Merriam-Webster dictionary defines twins as “one of two persons or things closely related to or resembling each other.” In technology, the concept of digital twins is the same. A relatively new term, digital twins are the digital counterparts of physical systems and processes. Essentially, a digital twin is a virtual model or representation of an object or system under development that helps designers or creators fine-tune their decision-making before finalizing the product or system.
NASA first coined and put the term into practice in the 1960s to create physical systems on Earth that could match those in space. Today, digital twins are used across multiple industries and engineering disciplines — from healthcare and construction to the retail and automotive fields — to optimize a system or a product’s design and performance.
When speaking of digital twins, it’s important to note it can be used both at a hardware and software level. In the automotive industry, a digital twin can be created for an entire car, its software, electrical system, systems-on-chip (SoCs), or nearly any other component. Regardless of one’s objective or focus area, digital twins are generally created in a similar manner: the product or system being studied is equipped with sensors that generate data critical to its performance, which is then relayed to a processing system and applied to its virtual copy, or digital twin.
The insights gathered from digital twins allow designers to study performance issues, test-run new features, and optimize different product elements throughout the design and manufacturing processes.
Modern vehicles contain over 100 million lines of code, with software being one of the key enablers of most of the car’s features or functions. These increased amounts of software content mean that validating system performance becomes significantly more challenging. Automotive designers not only need to improve software quality and integrity, but also virtual software testing and safety to ensure proper functioning.
There are different use cases for digital twins in this scenario. “Software-first” digital twins address the impact of software on verification, test, and validation activities, uniquely simulating each step during development to identify and prevent any potential failure. Digital twins also enhance software collaboration across the supply chain, serving as an executable specification and enablement in which one company’s system can be used as another’s subsystem. This also allows for seamless integration between a company’s digital twin and their customer’s digital twin, enabling improved interaction among different domains.
Digital twins are also valuable for “vehicle electrical/electronic (E/E)-system” validation, paving the way for a shift-left approach in automotive design that allows early hardware and software integration as well as frontload testing. This can help reduce resources spent on under- or over-design and save potential recall costs down the line. Digital twins can also provide scalability from an individual component through a full system company product. For example, in the electronic supply chain, this may start from an individual hardware IP to an SoC, then to an electronic subsystem, and finally to an entire system.
Overall, the concept brings a wide array of benefits to the automotive industry, including earlier development and testing, increased productivity and performance, enhanced collaboration within and across companies, and faster delivery of safer systems at a lower cost.
With highly autonomous and advanced driver assistance systems (ADAS) being one of the automotive industry’s main priorities in the short and mid-term, digital twin use cases will likely continue to evolve to support these. Some of these applications may include:
These are only a few examples of the many roles digital twins will play within the automotive industry in the coming years. Digital twins’ ability to deliver significant benefits to automakers, designers, and OEMs make it a key enabler of many of the industry’s advancements. While Synopsys already provides a broad portfolio of solution sets that support digital twins and has many collaborations with ecosystem partners, we’re thrilled to continue growing and innovating in this space to support the automotive industry and its emerging applications.
Synopsys provides a broad set of virtual prototyping technologies to enable digital twins in automotive electronic systems development, including:
These technologies and associated models can also be combined to establish a virtual vehicle development platform.
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