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The 4 Components of Future Engineering

Development departments in the automotive ecosystem require fundamentally new approaches to mastering complexity. 

July 24 2020Dr. Olaf Horstmann

Dreaming of the car of the future

If we look into the future of engineering, development, and construction of vehicles, we don't necessarily have to go for Doc Emmett Brown with his flux capacitor or the legendary Q with his Bond gimmicks (whether a car with a rocket launcher would ever be road legal is questionable anyway). It is enough to try to set a real example: Charles Franklin Kettering. 

Automotive development in the past and today

Car on a dune, the headlights shine far into the night sky. Someone sits on the bonnet

There's a famous quote of his: "I am very interested in the future because I will spend the rest of my life in it." Kettering was head of development at General Motors for 27 years. He made the electric starter suitable for series production and he is also responsible for electric vehicle lighting. If we are very generous, this makes him one of the forefathers of the E/E platforms. In addition, his credo was that innovations are created by thinking outside the box; today this is likely to be called interdisciplinary cooperation. 

The approach in automotive development today tends to be of breaking things down into small pieces in order to then construct individual vehicle components in specialized silos – especially for suppliers – which the OEM then assembles into the finished car, acting as a system integrator. This has been a functional and efficient approach over the last few decades – the art of automotive construction was to hold the many individual threads together to create an innovative car that was ready for market launch on schedule and could easily be adapted to diverse customer requirements. 

Increasing complexity in development

However, if we take stock of the current situation it is clear that things can't continue like this. Already today, production and development networks based on the division of labor, the abundance of models and variants, or the customer's personalization options create a great deal of complexity in automotive development, which is becoming a challenge for engineering in the face of ever shorter development cycles and limited budgets. Likewise, the influences of the CASE era (connected, autonomous, shared, electrified) and the increase of software dominance in the car are adding to this challenge. New technologies such as artificial intelligence must also be mastered during development – in addition to a much more intensive interaction between all system components. And as the cherry on the cake, there are also extended verification requirements, such as those stemming from increasing environmental requirements – see WLTP tests – or as a result of highly automated vehicles taking over an increasing number of safety-critical driving functions; the keyword is ISO 26262 when it comes to safeguarding these functions.

It is doubtful that the established silo approaches in design and development can cope with these developments. Automobile manufacturers have long since recognized this. They are currently working on introducing new methods, such as systems engineering and new forms of cooperation in vehicle development. But a new methodology only lays the foundation. In total, there are four components with which the engineering of the future must be implemented. 

Whitepaper: Future Engineering

The New Way of Car Design – Trends & Technologies in Product Development

In total there are four components with which an engineering of the future must be implemented:

  1. Systems Engineering
  2. Semantic Web for data
  3. Digital Twin
  4. Auto Live

1. Systems engineering

The first component is systems engineering. Systems engineering is a methodology established in some industries which is used in aircraft construction or space travel, for example, but also in other areas, such as large infrastructure projects. It was specifically developed to master complexity. In contrast to thinking in silos, it favors an interdisciplinary approach. In systems engineering the focus is very much on deriving an overall system architecture from customer requirements or the "mission" of a system. Only on the basis of this comprehensive architecture are individual work packages put together, for which in turn further refined or increasingly technical requirements are defined in a structured and comprehensible approach. Ultimately, this also demonstrates and ensures that each component of a system really contributes to the fulfillment of the mission as expected at any time. 

Ultimately, systems engineering means a change in the culture of engineering, which means that even the most complex systems can still function as desired and, incidentally, helps to make massive savings in development costs and reduce undesirable budget overruns, as already demonstrated in an analysis by Werner Gruhl at NASA in the 1990s. 

And finally, this segment has one more piece of good news for engineers: text-heavy and not always clearly interpretable specifications could soon be a thing of the past thanks to systems engineering. Systems engineering has also recently produced new techniques, such as model based systems engineering (MBSE), which make it possible to represent systems in the form of clearly structured, unambiguous, and parametrized models that even have the intelligence to simulate system behavior and thus to point out and help solve problems at an early stage.

2. Semantic Web for data

Digital transverse bars with binary codes on them

The second component of future engineering marks a turning point away from the focus on applications to a focus on data in engineering IT. The fact is, many specialized IT tools will continue to be needed along the development process in the future. This means that, in this area at least, the future outlook does not fundamentally differ from the current situation, even if the general trend is moving away from large, monolithic IT systems toward smaller tools – known as microservices – which provide the best possible support for a limited range of tasks. 

Up to now, product lifecycle management (PLM) in the world of engineering has had the task of creating a bracket for this variety of IT tools. However, most "one-size-fits-all" approaches, in which PLM systems were to be set up for all trades across the board, failed due to the high degree of specialization of the departmental processes and data models. Historically, the available PLM systems were typically too heavily focused on the needs of mechanical development, which led to ALM systems (application lifecycle management) for software areas, for example, becoming established alongside other specialized solutions for electrical and electronic systems, simulation data, etc. For a comprehensive view of the overall system, the more or less loose coupling or synchronization of such systems via various interfaces became necessary, with varying degrees of success and again with their own complexity.

The new approach to creating structure and transparency beyond the legion of applications is a return to data. The approach of the Semantic Web, which goes back to Tim Berners Lee, the inventor of the Internet, was originally to make the Internet more usable for machines by including the meaning of a term via special description languages. For example, a semantic search engine can directly deduce that "Paris" is either a city in France or a Trojan prince, and provide contextual information based on this conclusion (e.g. "French law applies in Paris" or "Paris kidnapped Helen"). In recent years, the Semantic Web approach has therefore become well established in science in order to build and evaluate knowledge networks, for example in disease diagnostics or biology.

Applied to the world of engineering, a Semantic Web which understands all "languages" of the various design and development disciplines can be used to create a uniform data pool, based on the existing application silos and the information contained in them. It is the Babel fish of engineering, so to speak. Anyone involved in the development process can draw from this data pool according to their requirements, and furthermore: with future generations of semantic platforms, the machine readability that now characterizes information and its interrelationships will allow the creation of algorithms and symbolic artificial intelligence that can assist engineers in maintaining the consistency of all information, in controlling projects, or in extracting necessary documentation from the knowledge network.

3. Digital twin

The third component is based on this uniform, semantic data pool: with the help of systems engineering, a network of information is created that virtually represents an initial image of the vehicle from an engineering perspective. You can guess where this is going: the digital twin. The digital twin becomes a companion for developers. It allows them to simulate and assess the effects of changes and development steps on the overall car system. It matures as the development process matures and increasingly eliminates the need to make physical prototypes. This saves a lot of time and costs. Before large sums of money are spent building a prototype, the digital twin is tested virtually. 

But the digital twin does not die when the development is completed. When production starts, a digital twin is derived from the "engineering twin" for each individual vehicle, which is enriched with data from production, quality, and documentation, for example. During its (hopefully) long life on the road, the vehicle will then continuously collect data itself, e.g. from sensors, during maintenance operations, or on the use of related service generators. Thus the digital twin is constantly evolving, and the information can be used everywhere via the semantic knowledge network.

4. Auto live

And this brings us to the fourth component: the digital twin is not a one-way street. With the operating data fed back, engineers gain insights into how the products or components they have developed behave in real operation. This data can be used for the further development of successor models, in model maintenance measures, as well as in ad-hoc quality assurance measures. Designers will not be interested in individual data, but rather in statistical statements, e.g. regarding the reliability of a component. IoT platforms will therefore systematically collect the data of the operational twins of the individual vehicles and statistically evaluate them with data analysis tools. Such evaluations and the context in which they were made will then be available to engineers via semantic knowledge networking. These value-added services of the digital twin allow for goal-oriented development in terms of "design to cost", "design to reliability”, and "design to usage"; they avoid over- or under-engineering and support continuous improvement.

We know that the digital twin also offers added value for the driver and passengers – for example, by creating the conditions for over-the-air updates, on-demand entertainment, and sharing vehicles. But this is ultimately a matter for the future of engineering. And in engineering, the most important measure of success for a car continues to be optimal function in road traffic. 

A summary of this post can be found in our whitepaper "Future Engineering: The New Way of Car Design". Download it, and let's dream of the flux capacitor together.

About the author
Black and white portrait of Dr. Olaf Horstmann

Dr. Olaf Horstmann

Senior Business Development Manager in Sales Automotive & Manufacturing , T-Systems International

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