Latest Update: December 2023

The automotive industry is at a crossroads, with the rise of battery electric vehicles (BEV) to challenge traditional internal combustion, the proliferation of new industry players, shortened design cycles with increased operational functionality, and a shift towards software-defined vehicles (SDV). The industry is having to rapidly acquire and develop robust digital capabilities, including digital twins, to navigate its turbulent future. Today, digital twinning is nascent in automotive, despite traditional simulation and modeling being well established. However, digital twin adoption is on the increase throughout vehicle lifecycles, including:

• Prototyping and design to shorten design cycles and improve cross-functional collaboration
• Manufacturing for efficiency improvements and to enhance vehicle lifecycle management, particularly with the shift towards SDV and sophisticated battery management solutions for BEVs.
• Multi-national sales and distribution to improve customer experiences, enable upsell opportunities, and address the needs of different countries and markets.
• Reliable and sustainable energy management particularly for EV batteries and power management systems, battery lifecycle management, and coordination with smart city and smart grid initiatives.
• Software updates and upgrades to maintain the security and integrity of vehicle software throughout its lifecycle.
• Targeted recalls that leverage digital twin intelligence to minimize impact.
• Sustainable vehicle retirement with optimal recycling and repurposing.

Takeaways

• Digital twin adoption is rising in automotive, with a 11 percent CAGR in related online content since 2017.
• Over 43 percent of online content for automotive digital twins relates to Manufacturing.
• Companies like Siemens, Volkswagen, Nvidia, Dassault, BMW, Hyundai, Mercedes Benz, Stellantis, AWS, Synopsys, Wipro, Toyota, are most active with automotive digital twins.
• Digital twins are integral to the automotive industry's transformation and a bellwether for progress and disruption. This study highlights the current state of digital twins in automotive, but change is inevitable.

Background

Digital twin activities in the automotive industry are widely reported in online content. This study surveys this online content using Tolaga's web crawling and natural language processing (NLP) tools, and third-party Large Language Models (LLM), such as Open.ai and Perplexity.ai. Targeted keywords form the basis of primary web searches, and NLP tools are used to extend the analysis with subsequent web crawling. The process was repeated several times to expand and refine the corpus used for the study. Over 400 survey results (391 describing market activities and 62 thought leadership documents) spanning 280 organizations were identified, processed and cataloged for further study using a combination of NLP and LLM techniques.

Although automotive digital twin use cases are vast, there is a relatively consistent definition throughout thought leadership, which is summarized from the thought leadership documents in the corpus as:

While there is a relatively consistent definition of a digital twin, there are some cases where companies exaggerate the capabilities of their wares for a more significant market impact. While efforts are made throughout the study to eliminate non-conforming use cases from the corpus, some are still present. Typically, these non-conforming use cases are evolving towards fully-fledged digital twins but have yet to attain full digital twin functionality.
The authors of Digital Twin Enabling Technologies, Challenges and Open Research address definitional ambiguities for digital twins by introducing the notion of Digital Models, Digital Shadows, and Digital Twins, which differ in the data exchange between physical objects and their digital replicas. In particular, a Digital Model, more commonly called a simulation, has a manual data exchange between digital and physical objects and does not allow for automatic data exchange. Digital Shadows have automatic data exchange between physical and digital objects but manual data exchange in the reverse direction between the digital and physical objects. Digital Twins have two-way automatic data exchange. It is common for Digital Shadows to be misrepresented in online content as being Digital Twins.

Over 43.2 percent of the corpus content in the study was related to vehicle manufacturing,11.4 percent to prototyping and design,11.1 percent to software defined vehicles,6.4 percent to autonomous and assisted driving,6.1 percent to smart city and grid,4.5 percent to logistics and supply chain,3.9 percent to cockpit,3.3 percent to battery technology, and 1.9 percent to dealership sales and distribution.

Use Case Distribution of Online Automotive Digital Twin Content

Number of Online Records: 391

Digital twinning will increasingly become industry players' differentiation as vehicles become software and data-centric. This depends on the fidelity of individual digital twins and the integration amongst twins to ultimately enable end-to-end solutions that seamlessly traverse vehicle lifecycles 'from concept to retirement.' Today, much of the digital twin activity is siloed into specialized functions and confined to proprietary environments. Some silos will prevail, and others will disappear as the industry transforms. Data threads to dynamically share information amongst the twins will become essential and depend on robust industry standards, particularly for players that support a wide range of vehicle models and trims.

The Leaders

The only certainty as the automotive industry transforms is uncertainty. This creates tremendous challenges for industry players in an environment where today's leaders risk being tomorrow's laggards. Current automotive digital twin adoption is a bellwether for the broader industry transformation but does not reflect the industry end-state.
Based on the online content survey, the most prevalent companies in the document corpus were Siemens (50 weighted document points*), Volkswagen (30), Nvidia (29), Dassault (27), BMW (15), Hyundai (15), Mercedes Benz (12), Stellantis (12), AWS (12), Synopsys (10), and Toyota (9). Although each company has its focus areas, they all rely on strategic partnerships and collaborative innovations.

* Accounts for the number of corpus documents and their web impressions.

Leader Heatmap Key

2	4	5	7	9	10	12	15	27	29	30	50

Siemens

Volkswagen

Nvidia

Dassault

BMW

Hyundai

Mercedes Benz

Stellantis

AWS

Synopsys

Wipro

Toyota

Click to Show Leader Partner Activity Click to Hide Leader Partner Activity

AWS	BMW	Dassault
Hyundai	Mercedes Benz	Nvidia
Siemens	Stellantis	Synopsys
Toyota	Volkswagen	Wipro

Automotive Digital Twin Network

The Automotive Digital Twin Network interrogates published online content to identify the companies active in the space. These companies are represented with their logos in the network. Companies that are coincident in the articles are connected. The thickness of these connections represents the frequency of the coincidence between companies.

Siemens Digital Industrial Software

Siemens Digital Industrial Software is playing a prominent role in automotive digital twin technology in its Xcelerator portfolio, which provides a broad range of industrial digital transformation capabilities, including digital twins. Siemens has strategic partnerships with major players, including ARM, AWS and IBM with Xcelerator and, in conjunction with its portfolio partners, has announced automotive digital twin activities with OEMs and Tier 1's and Tier 2's, including BMW, BYD, CITIC Dicastal, Helixx, Hymer, Hyundai, Kia, Kassbohrer, MG India, MobileDrive (JV between FIH Mobile and Stellantis), and Plastic Omnium. In addition, the Xcelerator portfolio includes Siemens's Pave360 solution, which brings digital twins to vehicle prototyping and design. Pave360 targets industry challenges with long product designs that rely on hardware prototyping, shifting value creation towards software, increased computing and data complexity, and dynamic systems integration demands.

Nvidia

Nvidia has a growing presence in the automotive industry with high-performance semiconductor, AI and computing platforms in vehicles and manufacturing and production environments. Its Omniverse platform enables real-time 3D graphics capabilities that are used for digital twins for automotive vehicles and manufacturing facilities by a variety of companies, including BMW, ipolog, SyncTwin, T-Systems, Lucid, Gatik, Nio, Polestar, Hyperbat, Magic Leap, Mercedes Benz, Navvis, Robosense, rFPro, Varjo, and Volvo.

Dassault Systemes

Dassault Systemes develops software for 3D product design, simulation, and manufacturing, and its 3DEXPERIENCE and Delmia digital twin platforms are used for various automotive use cases. In particular, its 3DEXPERIENCE and Delmia platforms are used by many automotive companies, including BorgWarner, Brose, DS Automobiles, Dongfeng Faurecia, Honda, Ligier, NGK, Renault, and Tesla. Dassault also has collaboration initiatives with various companies, including Inceptra, Nokia, Omron, Orange, and SolidWorks, for automotive digital twin solutions.

AWS

The cloud plays a vital role in the ongoing evolution of the automotive industry toward SDV, shorter development cycles, and dynamic ecosystems. The cloud enables players to federate long-tail services to disrupt traditional industry hierarchies efficiently and respond to changing industry demands. Since AWS is an established leader in cloud services, it is uniquely positioned to support digital twin solutions in the automotive industry. Companies that are leveraging AWS' cloud platform for automotive digital twin capabilities include Blackberry, Continental, Draxlmaier, Marelli, Siemens, and Tier IV.

Synopsys

Synopsys provides a broad set of virtual prototyping technologies to enable digital twins in the semiconductor, aerospace, defense, automotive, and other industries. In August 2023, Synopsys acquired PikeTec (an automotive software testing and verification company), to bolster its automotive digital twin capabilities. In addition, Synopsys has partnered with Tasking to with digital twin capabilities to improve vehicle ECU's, and Infineon to advance automotive AI processors, and Continental to accelerate Software Defined Vehicle (SDV) technology.

Wipro

Wipro is a technology services and consulting company that offers a digital twin platform and cloud services to the automotive industry. It's Cloud Car Ecosystem and digital twin technology to support vehicle software development, automotive manufacturing efficiency, automotive warehousing, and cockpit technology.

OEM Digital Twin Activity

The automotive industry traditionally has vertically integrated supply chains to contend with the complexity and specialization of a typical vehicle's 2000 or more components. Automotive OEMs traditionally interface with Tier 1 providers, such as Continental and Denso, who provide integrated solutions that use systems provided by Tier 2 and modules, sensors, and individual parts supplied by Tier 3 providers. However, this structure is changing with electric vehicles that require fewer components, software-defined vehicles (SDV), and automotive-cloud services that enable long-tail service federation. Digital twins play a critical role in allowing this structural change, with digital twin partnerships between OEMs and non-traditional players becoming more prevalent.

OEM Summary Heatmap Key

1	2	3	4	5	11	12	13	14	17	24	32

Click to Show Heatmap Detail Click to Hide Heatmap Detail