How Porsche developed a digital twin to win the race for the virtual car concept
NASA used a precursor to these technologies to bring the Apollo 13 astronauts back to Earth. Lockheed Martin claims these types of solutions are one of six game-changing technologies in the defense industry. The opinion-forming Gartner includes them in its list of ten strategic technologies that can streamline corporate decision-making processes. When Porsche and Volkswagen Group reach for them, it’s a signal for the automotive industry to become interested in digital twin technology for good.
Although the concept of a digital twin had been developing in the space industry since the 1970s, it was not until the 1990s that it was first mentioned in the literature (the book entitled Mirror World, by David Gelernter). In industry, the technology was recognized even later, 30 years after the Apollo mission, when the authority in the field of PLM - Michael Grieves - disseminated it.
Today, the technology, which was officially named Digital Twin by NASA just over 10 years ago, is placed on the pinnacle of key solutions at the convergence of the virtual and real world. It works well wherever there is a high number of failures, work of coupled systems, and where the production process is long and burdened with numerous risks.
The automotive industry is one of these sectors, as demonstrated by the virtual car concept developed by Porsche for the new Taycan. What is a digital twin and what benefits does it bring?
- test prototypes for their functionality, durability and user expectations;
- predict defects and analyze possible design errors;
- save time and financial means;
- reduce design and production risks;
- improve monitoring capabilities of vehicle fleets;
- and best of all - it enables continuous product improvement, as it often collects data from not only one, but thousands of objects. This makes it learn faster and predict defects more precisely, as it is based on knowledge gathered from a vast number of sources.
In the case of cars, these could be sensors from dozens of systems spanning the entire vehicle lifecycle: from research and development to the manufacturing plant and OTA updates , to connected services .
„Chassis twin” - a virtual car concept developed by Porsche for TaycanThe „chassis twin” project has been in the process of development at Porsche for the past three years and then it was continued by the CARIAD company (the Volkswagen Group's vehicle software provider). The air suspension of the new Porsche Taycan was chosen as the main object.
Why the chassis and not the entire car? Because it is this part of the vehicle that is subjected to the most strain, especially on racetracks.
Porsche engineers used intelligent neural algorithms to centrally analyze the data, and in-car sensor data was collected not only from Porsche cars but also from Volkswagen Group vehicles. This increased the data pool by over 20 times. The "chassis twin" concept enables chassis loads to be detected, even if they are not noticeable inside the cabin, and notify the driver before faults appear, even when no suspicious sound or vibration has yet been noted by the driver or mechanic.
The data collected by the vehicles is sent via Porsche Connect to a central system in the cloud , where an algorithm calculates the relevant durability and vehicle operation thresholds for the whole fleet of vehicles to create a baseline. When these are possibly exceeded, the driver of a specific vehicle receives a notification via Porsche Communication Management (PCM), that the chassis may require inspection. Looped into the computational work, the algorithm recommends not only the type of service needed but also the scope of work to be carried out in the service center.
By removing a faulty or overloaded component early on, the driver will not only be able to avert potential malfunctions, but also keep the vehicle in better overall condition.
In the future, based on a vehicle's digital usage history, Porsche or a partner insurance broker can offer extended warranties and better services to the driver . The data can be classified, analyzed, and used not only for repairs to a specific vehicle but to predict life-cycle events for the entire product. This allows Porsche to create new services and features, and to test different scenarios for the development of a particular vehicle line. Thus, it saves the time and resources required to bring ill-conceived solutions into reality.
The other possible use case is that drivers themselves can use the data collected by the digital twin to negotiate with the prospective buyer. The buyer can view the vehicle's overall condition and chassis service history.
As for drivers' concerns about their own privacy, the manufacturer assures that it collects data anonymously and the system does not store any information that could identify the driver directly. The future of the concepts digitization of the automotive industry is gaining pace year after year. The digital twin concept may prove to be one of the key technologies that will push software-defined vehicles to new tracks and help companies create safer cars, provide new services and increase vehicle lifespan.
So far, half of the Taycan users have signed up for the Porsche pilot program, which collects data from the chassis of their sporty electric cars. In 2022, the program is to launch at a test level, and only sensor data directly from the mechatronic components will be evaluated. In the future, the concept is to reach its full potential, making it possible, among other things, to calculate the wear and tear of specific components without the need for physical measuring devices.
How will the "chassis twin" model developed by Porsche work out ? The future will tell. What is certain is that a return to the past is only possible in the movies. In 2022, Volkswagen is commencing an era in which a virtual equivalent will soon await the driver, in addition to their actual real vehicle.
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What's next for the digital twin
Digital twins, or virtual copies of material objects, are being used in various types of simulations and the automotive industry is tapping into the potential offered by this technology. Representatives of this market can comprehensively monitor equipment and systems and prevent numerous failures. But what does the future hold for Digital Twin solutions, and who will play the leading role in their development in the years ahead?
The concept of Digital Twin today
To get started, let's have a few words of reminder. A virtual model called a digital twin is based on data from an actual physical object, equipped with special sensors. The collected information allows to the creation of a simulation of the object’s behavior in the real world, while testing takes place in virtual space.
The concept of Digital Twins is developing by leaps and bounds, with its origins dating back to 2003. For many years, more components have been added to this technology . Currently, we distinguish the following:
- digital (virtual) aspect,
- physical object,
- the connection between the two,
- data,
- services.
The last two were added to the classification by experts only in recent years. This was triggered by developments such as machine learning, Big Data , IoT, and cybersecurity technologies.
Capabilities of digital twins in automotive
Digital twins are excelling in many fields when it comes to working on high-tech cars, especially those connected to the network. Below are selected areas of influence.
Designing the vehicle
3D modeling is a way of designing that has been around for many years in the widespread automotive manufacturing industry. But this one is not standing still, and the growing popularity of digital twins is proof of that. Digital replicas extend the concept of physical 3D modeling to virtual representations of software, interactive systems, and usage simulations. As such, they take the conceptual process to a higher level of sophistication.
Production stage
Design is not everything. In fact, the technology mentioned above also works well at the production stage . First and foremost, DT's solutions facilitate control over advanced manufacturing techniques. Since virtual twins improve real-time monitoring and management of facilities, they support the construction of increasingly complex products.
Besides, the safety of the work itself during the production of cars and parts adds to the issue. By simulating manufacturing processes , digital twins contribute to the creation of appropriate employment conditions.
Advanced event prediction
Virtual copies have the ability to simulate the physical state of a vehicle and thus predict the future. Predictive maintenance in this case is based on such reliable data as temperature, route, engine condition, or driver behavior. This can be used to ensure optimal vehicle performance.
Aspects of cyber security
DT predicted for automotive software can help simulate the risk of data theft or other cybersecurity threats. The digital twin of the whole Datacenter can be created to simulate different attack vectors. Continuous software monitoring is also helpful in the early detection of vulnerabilities to hacking attacks (and more)
Development of security-improving systems
Virtual replicas of vehicles and the real world also enable the prediction of specific driving situations and potential vehicle responses. This is valuable knowledge that can be used, for example, to further develop ADAS systems such as electronic stability control and autonomous driving. This is all aimed at ensuring safer, faster, and more economical driving.
How will the digital twin trend evolve in the coming years?
One of the leading trend analysis companies from the automotive world has developed its own prediction of the development of specific sub-trends within the scope of the digital twin. In this regard, the experts analyzed such areas of development as:
- Predictive Maintenance.
- Powertrain Control (e.g. vehicle speed and other software parameters).
- Cybersecurity.
- Vehicle Manufacturing.
- Development and Testing.
The analysis shows that all of the above issues will move into the mainstream in the third decade of the 21st century. On the other hand, some of them will develop at a slower pace in the years to come, while others will develop at a slightly higher rate.
Subtrend Powertrain Control will have a lot to say. As early as around 2025, we will see that basic control parameters will be defined and tested primarily in the digital twin.
To a lesser extent, but still, Development and Testing solutions will also be implemented. DTs will be created to simulate systems in such a way as to accelerate development processes. The same will be true in the area of Predictive Maintenance. Vehicle condition information will soon be sent in bulk to the cloud or database. There, a virtual copy will be used to predict how certain changes will affect maintenance needs.
Key players in DT development in automotive
The market is already witnessing the emergence of brands that will push (with varying intensity) DT technology in the broader automotive sector (cars, software, parts). Specifically standing out in this regard are:
- Tesla,
- BOSCH,
- SIEMENS,
- Porsche,
- Volkswagen,
- Continental.
Both OEMs and Suppliers will shift their focus to the Development and Testing area. The proportions are somewhat different in the case of Vehicle Manufacturing, as this slice of the pie tends to go to OEMs for the time being. However, it is possible that parts manufacturers will also get their share before long. On the other hand, without any doubt, the area of Cybersecurity already belongs to OEMs , and the percentage of such companies that use DT to improve cybersecurity is prevalent.
The digital twin and the future of automotive brands
The digital twin is a solution that helps address mature challenges specific to the entire modern automotive industry. It supports digitization processes and data-driven decision-making. Manufacturers can apply this technology at all stages of the production process, thus eliminating potential abnormalities.
In the upcoming years, we can expect DT-type applications to become more common, especially among OEMs.
So what are brands supposed to do if they want to secure a significant position in a market where the DM trend is becoming highly relevant? First, it's a good idea if they collaborate with those driving change. Second, it' s worth adopting a specific strategy, as not every sub-trend needs to be addressed in every scenario. This is brilliantly illustrated in the SBD chart below. The authors of this chart recommend certain behaviors, breaking them down into specific categories and relating them to specific market participants.
Based on this overview, it's good to see that the leaders don't have too much choice, and over the next 12 months, they should be releasing solutions that fall into every sub-trend. The issue of cyber security is becoming essential as well . The digital twins have great potential in developing it, so basically all stakeholders should focus on this area.
The next step for digital twin – virtual world
Digital Twin is a widely spread concept of creating a virtual representation of object state. The object may be small, like a raindrop, or huge as a factory. The goal is to simplify the operations on the object by creating a set of plain interfaces and limiting the amount of stored information. With a simple interface, the object can be easily manipulated and observed, while the state of its physical reflection is adjusted accordingly.
In the automotive and aerospace industries , this is a common approach to use virtual objects representation to design, develop, test, manufacture, and operate both parts of a vehicle, like an engine, drivetrain, chassis/fuselage, or a full vehicle – a whole car, motorcycle, truck or aircraft. Virtual representations are easier to experiment with, especially on a bigger scale, and to operate - especially in situations when connectivity between a vehicle and the cloud is not stable ability to query the state anyway is vital to provide a smooth user experience.
It’s not always critical to replicate the object with all details. For some use cases, like airflow modeling for calculating drag force, mainly exterior parts are important. For computer vision AI simulation, on the other hand, user checking if the doors and windows are locked only requires a boolean true/false state. And to simulate the combustion process in the engine, even the vehicle type is not important.
Today, artificial intelligence takes a significant role in a lot of car systems, to name a few: driver assistance, fatigue check, predictive maintenance, emergency braking, and collision avoidance, speed limit recognition, and prediction. Most of those systems do not live in a void - to operate correctly they require information about the surrounding world gathered through V2X connections, cameras, radars, lidars, GPS position, thermometers, or ABS/ESP sensors.
Let’s take Adaptive Cruise Control (ACC). The vehicle is kept in lane using computer vision and a front-facing camera. The distance to surrounding vehicles and obstacles is calculated using both a camera and a radar/lidar. Position on the map is gathered using GPS, and the speed limit is jointly calculated using the navigation system, road sign recognition, and distance to the vehicle ahead. This is an example of a complex system, which is hard to test - all parts of it have to be simulated separately, for example, by injecting a fake GPS path. Visualizing this kind of test system is complicated, and it’s hard to use data gathered from the car to reproduce the failure scenarios.
Here the Virtual World comes to help. The virtual world is an extension of the vehicle shadow concept where the multiple types of digital twins coexist in the same environment knowing their presence and interfaces. The system is composed of digital representation of physical assets whenever possible – including elements recognized via computer vision. Vehicles, road infrastructure, positioning systems, or even pedestrians are part of the virtual world. All vehicles are part of the same environment meaning they can share the data regarding the position of other traffic participants.
- Such a system provides multiple benefits: Improved accuracy of assistance systems, as the recognized infrastructure and traffic participants can come from other vehicles, and their position can be estimated even when they are still outside the range of sensors.
- Easier, more robust communication between infrastructure, vehicles, pedestrians, and cloud APIs as everything remains in the same digital system.
- Possibility to fully reproduce conditions of system failure as the state history of not just vehicle, but all of its surrounding remains in cloud and can be used to recreate and visualize the area.
- Ability to enhance existing systems leveraging data from the greater area - for example, immediately notifying about an obstacle on the road in 500 meters and suggestion to reduce speed.
- The extensive information set can be used to build new AI/ML applications, like real-time weather information (rain sensor) can be built to close sunroofs of vehicles parked in the area.
- The same system can be used to better simulate its behavior, even using data from real vehicles.
- Common interfaces allow for quicker implementation.
Obviously, there are also challenges - the amount of data to be stored is huge, so it should be heavily optimized, and storage has to be highly scalable. There is also an impact of the connection between the car and the cloud . Overall, the advantages overweight the disadvantages, and the Virtual World will be a common pattern in the next years with the growing implementation of software-defined vehicles and machine learning applications requiring more and more data to improve its operations.
Connected car: Challenges and opportunities for the automotive industry
The development of connected car technology accelerated digital disruption in the automotive industry. Verified Market Research valued the connected car market at USD 72.68 billion in 2019 and projected its value to reach USD 215.23 billion by 2027. Along with the rapid growth of this market’s worth, we observe the constant development of new customer-centric services that goes far beyond driving experience.
While the development of connected car technology created a demand for connectivity solutions and drive-assistance systems, companies willing to build their position in this market have to face some significant challenges. This article is the first one of the mini-series that guides you through the main obstacles with building software for connected cars. We start with the basics of a connected vehicle, then dive into the details of prototyping and providing production-ready solutions. Finally, we analyze and predict the future of verticals associated with automotive-rental car enterprises, insurers, and mobility providers.
This series provides you with hands-on knowledge based on our experience in developing production-grade and cutting-edge software for the leading automotive and car rental enterprises. We share our insights and pointers to overcome recurring issues that happen to every software development team working with these technologies.
What is a Connected Car?
A Connected Car is a vehicle that can communicate bidirectionally with other systems outside the car , such as infrastructure, other vehicles, or home/office. Connected cars belong to the expanding environment of devices that comprise the Internet of Things landscape. As well as all devices that are connected to the internet, some functions of a vehicle can be managed remotely.
Along with that, IoT devices are valuable resources of data and information that enable further development of associated services. And while most car owners would describe it as the mobile application paired with a car that allows users to check the fuel level, open/close doors, control air conditioning, and, in some cases, start the ignition, this technology goes much further.
V2C - Vehicle to Cloud
Let’s focus on some real-case scenarios to showcase the capabilities of connected car technology. If a car is connected, it may also have a sat-nav system with a traffic monitoring feature that can alert a driver if there is a traffic jam in front of them and suggest an alternative route. Or maybe there is a storm at the upcoming route and navigation can warn the driver. How does it work?
That is mostly possible thanks to what we call V2C - Vehicle to Cloud communication. Utilizing the fact that a car is connected, and it is sending and gathering data, a driver may also try to find it, in case it was stolen. Telematics data is also helpful to understand the reasons behind an accident on the road - we can analyze what happened before the accident and what may have led to the event. The data can be also used for predictive maintenance, even if the rules managing the dates are changing dynamically.
While this seems just like a nice-to-have feature for the drivers, it allows car manufacturers to provide an extensive set of subscription-based features and functionalities for the end-users. The availability of services may depend on the current car state - location, temperature, and technical availability. As an example: during the winter, if the car is equipped with heated seats and the temperature drops under 0 Celsius, but the subscription for this feature expires, the infotainment can propose to buy the new one - which is more tempting when the user is at this time cold.
V2I - Vehicle to Infrastructure
A vehicle equipped with connected car technology is not limited to communicating only with the cloud. Such a car is capable of exchanging data and information with road infrastructure, and this functionality is called V2I - Vehicle to Infrastructure communication. A car processes information from infrastructure components - road signs, lane markings, traffic lights to support the driving experience by suggesting decision makings. In the next steps, V2I can provide drivers with information about traffic jams and free parking spots.
Currently, in Stuttgart, Germany, the city’s infrastructure provides the data live traffic lights data for vehicle manufacturers, so drivers can see not just what light is on, but how long they have to wait for the red light to switch to green again. This part of connected car technology can rapidly develop with the utilization of wireless communication and the digitalization of road infrastructure.
V2V - Vehicle to Vehicle
Another highly valuable type of communication provided by connected car technology is V2V - Vehicle to Vehicle. By developing an environment in which numerous cars are able to wirelessly exchange data, the automotive industry offers a new experience - every vehicle can use the information provided by a car belonging to the network, which leads to more effective communication covering traffic, car parking, alternative routes, issues on the road, or even some worth-seeing spots.
It may also significantly increase safety on the road, when one car notifies another that drives a few hundred meters behind him that it just had a hard breaking or that the road surface is slippery, using the information from ABS, ESP, or TC systems. That has not just an informational value but is also used for Adaptive Cruise Control or Travel Assist systems and reduces the speed of vehicles automatically increasing the safety of the travelers. V2V communication makes use of network and scale effects - the more users have connected to the network, the more helpful and complete information the network provides.
The list of use cases for connected car technology is only limited by our imagination but is excelling rapidly as many teams are joining the movement aiming to transform the way we travel and communicate. The Connected Car revolution leads to many changes and impacts both user experience and business models of the associated industries.
How connected car technology impacts business models of the automotive industry
Connected cars bring innovative solutions to the whole environment comprising the automotive landscape. Original Equipment Manufacturers (OEMs) have gained new revenue streams. Now vehicles allow their users to access stores and purchase numerous features and associated services that enhance customer experience, such as infotainment systems. By delivering aftermarket services directly to a car, the automotive industry monetizes new channels. Furthermore, these systems enable automakers to deliver advertisements, which become an increasing source of revenue.
The development of new technology in automotive creates a similar change as we observed in the mobile phone market. When smartphones equipped with operating systems had become a new normal, significantly increased the number of new apps that now allow their users to manage numerous services and tasks using the device.
But it is just an introduction to numerous business opportunities provided by connected cars. Since data has become a new competitive advantage that fuels the digital economy, collecting and distributing data about user behavior and vehicle performance is seen as highly profitable, especially when taking into account the potential interest of insurers providers.
Assembled data while used properly gives OEMs powerful insights into customer behavior that should lead to the rapid growth of new technologies and products improving customer experiences, such as predictive maintenance or fleet management.
The architecture behind connected car technology
Automotive companies utilize data from vehicle sensors and allow 3rd party providers to access their systems through dedicated API layers. Let’s dive into such architecture.
High-Level Architecture
System components
Digital Twin in automotive
A digital twin is a virtual replica and software representation of a product, system, or process. This concept is being adopted and developed in the automotive industry, as carmakers utilize its powerful capabilities to increase customer satisfaction, improve the way they develop vehicles and their systems, and innovate. A digital twin empowers automotive companies to collect various information from numerous sensors, as this tool allows to capture operational and behavioral data generated by a vehicle. Equipped with these insights, the leading automotive enterprises work on enhancing performance and customize user experience, but meanwhile, they have to tackle significant challenges.
First of all, getting data from vehicles is problematic. Hardware built-in vehicles have particular limits, which leads to reduced capabilities in providing software. Unlike software, once shipped hardware cannot be easily adjusted to the changing conditions and works for several years at least. Furthermore, while willing to deliver a customer-centric experience, automakers still have to protect their users from numerous threats. To protect vehicles from denial of service attacks, vehicles can throttle the number of requests. Overall, it’s a good idea but can have a terrible impact when multiple applications are trying to get data from vehicles, e.g., in the rental domain. This complex problem can be simply solved by Digital Twin. It can expose data to all applications without them needing to connect to the vehicle by simply gathering all real-time vehicle data in the cloud.
Implementation of this pattern is possible by using NoSQL databases like MongoDB or Cassandra and reliable communication layers, examples are described below. Digital Twin may be implemented in two possible ways, uni- or bidirectional.
Unidirectional Digital Twin
Unidirectional Digital Twin is saving only values received from the vehicle, in case of conflict it resolves the situation based on event timestamp. However, it doesn’t mean that the event causing the conflict is discarded and lost, usually every event is sent to the data platform. The data platform is a useful concept for data analysis and became handy when implementing complex use cases like analyzing driver habits.
Bidirectional Digital Twin
The Bidirectional Digital Twin design is based on the concept of the current and desired state. The vehicle is reporting the current state to the platform, and on the other hand, the platform is trying to change the state in the vehicle to the desired value. In this situation, in case of conflict, not only the timestamp matters as some operations from the cloud may not be applied to the vehicle in every state, eg., the engine can’t be disabled when the vehicle is moving.
However, meeting the goal of developing a Digital Twin may be tricky though as it all depends on the OEM and provided API. Sometimes it doesn’t expose enough properties or doesn’t provide real-time updates. In such cases, it may be even impossible to implement this pattern.
API
At first, designing a Connected Car API isn’t different from designing an API for any other backend system. It should start with an in-depth analysis of a domain, in this case, automotive. Then user stories should be written down, and with that, the development team should be able to find common parts and requirements to be able to determine the most suitable communication protocol. There are a lot of possible solutions to choose from. There are several reliable and high-traffic oriented message brokers like Kafka or hosted solutions AWS Kinesis. However, the simplest solution based on HTTP can also handle the most complex cases when used with Server-Sent Events or WebSockets. When designing API for mobile applications, we should also consider implementing push notifications for a better user experience.
When designing API in the IoT ecosystem, you can’t rely too much on your connection with edge devices. There are a lot of connectivity challenges, for example, a weak cellular range. You can’t guarantee when your command to a car will be delivered, and if a car will respond in milliseconds or even at all. One of the best patterns here is to provide the asynchronous API. It doesn’t matter on which layer you’re building your software if it’s a connector between vehicle and cloud or a system communicating with the vehicle’s API provider. Asynchronous API allows you to limit your resource consumption and avoid timeouts that leave systems in an unknown state. It’s a good practice to include a connector, the logic which handles all connection flaws. Well designed and developed connectors should be responsible for retries, throttling, batching, and caching of request and response.
OEM’s are now implementing a unified API concept that enables its customers to communicate with their cars through the cloud at the same quality level as when they use direct connections (for example using Wi-Fi). This means that the developer sees no difference in communicating with the car directly or using the cloud. What‘s also worth noting: the unified API works well with the Digital Twin concept, which leads to cuts in communication with the vehicle as third-party apps are able to connect with the services in the cloud instead of communicating directly with an in-car software component.
What’s next for connected car technology
Once the challenges become tackled, connected vehicles provide automakers and adjacent industries with a chance to establish beneficial co-operations, build new revenue streams, or even create completely new business models. The possibilities delivered thanks to over-the-air communication (OTA) allowing to send fixes, updates, and upgrades to already sold cars, provide new monetization channels, and sustain customer relationships.
As previously mentioned, the global connected car market is projected to reach USD 215.23 billion by 2027. To acquire shares in this market, automotive companies are determined to adjust their processes and operations. Among key factors that impact the development of connected car technology, we can point out a few crucial. The average lifecycle of a car is about 10 years. Today, automakers make decisions regarding connected cars that will go into production two to four years from now. For the cellular connectivity strategy to remain relevant over 12 to 15 years, significant challenges and assumptions need to be collaboratively addressed by OEMs, telematics control unit suppliers, and service providers.
Automakers must manage software in the field reliably, cost-efficiently, and, most importantly, securely – not just patch fixes, but also continually upgrade and enhance the functionality. The availability of OTA updates reduces the burden on dealerships and certified repair centers but requires better and more extensive testing, as the breakage of critical features is not an option.
Cellular solutions need to be agile to be compatible with emerging network technologies over the vehicle lifetime, e.g., 5G to be the industry standard in the next few years. The chosen solution must deliver reliable, seamless, uninterrupted coverage in all countries and markets where the vehicles are sold and driven.
Solution developers must offer scalable, cost-effective ways to develop upgradeable software that can be universally deployed across technologies, hardware, and chipsets. A huge focus must be put on testing the changes automatically on both the cloud platform side and the vehicle side.
As Connected Vehicles proliferate, the auto industry will need to adapt and transform itself into the growing technological dependency. OEMs and Tier-1 manufacturers must partner with technology specialists to thrive in an era of software-defined vehicles. As connectivity requires skills and capabilities outside of the OEMs’ domain, automakers will necessarily have to be software developers. An open platform environment will go a long way to encourage external developers to design apps for vehicle connectivity platforms.
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