DEEPAK PATIL is CTO at iASYS Technology Solutions
The automotive industry has undergone a massive change as a result of disruptive trends and technologies – it has seen breakthroughs in the areas of vehicle safety; petrol and diesel-run vehicles have evolved into battery-run vehicles, and similarly, sharing transport has witnessed a substantial increase. Consequently, there has been a surge in challenges to meet changing regulatory and user demands along with meeting environmental and fuel efficiency standards.
Vehicles are visibly becoming more technologically advanced to adapt to these changing dynamics. The mechanical systems have evolved to include the synergistic integration of electronic systems. Evidently, system complexities have increased as more systems are added; there are also sub-systems of these systems. Product complexities in the automotive industry are necessitating the need for a defined architecture of systems. And to accommodate these changes and to address the challenges head on, the automotive industry has begun to adopt a modelling and simulation approach.
AN IDEAL SOLUTION
Model-based systems engineering (MBSE) presents itself as an ideal solution – it has become a powerful tool for validation, verification and testing processes. A systems-engineering methodology, MBSE tackles the challenges of developing complex systems by means of using linked models to represent and analyse systems throughout the development life cycle. Automotive engineers have been at the forefront of developing and adopting advanced techniques of MBSE.
MBSE is the use of models that document the context and architecture of a system under development. It is a system that can establish scalable structures, help analyse and test complex mechanisms resulting in minimising costs and cutting down the time-to-market. Changing the face of traditional document-based information exchange, MBSE relies on developing models of a system, which help co-ordinate system design activities among engineers and stakeholders.
Many organisations are known to have said that MBSE provides a significant return on investment. Problems and errors are noticed beforehand and eliminated to prevent cost overruns that might not have cropped up with traditional document-centric approaches. It is worth noting that it allows for traceability between market, technological and regulatory requirements as well as vehicle design and function.
EXPANDING SCOPE OF DATA
Naturally, for MBSE to run, data is needed. However, as systems are becoming more complex, the amount of “Big Data” is ever expanding. At such times, conventional approaches are instinctively unable to provide the highest fidelity of data essential to make the best decisions.
Hence, for MBSE to materialise its objective, the data needs to be democratised and standardised because more often than not, the data is in silos rendering free flow of data as important. Data in validation life cycles consists of on-road data, in-house testing data and simulation data. Simulation data can be strengthened and made more reliable by digitised and standardised data. And to reduce the number of poor prototypes made, structured data is essential. Iterations would be significantly reduced if data is rich in quality and is semantic. The raw data needs to be transformed into validation knowhow. Such a data would help realise MBSE targets faster as it would pave the way for more accurate decision-making.
Product validation management (PVM) makes a substantial contribution towards making data digitisation and standardisation possible, (1). PVM is instrumental in collecting data and making it quality data. MBSE can then hook onto this data and attain its goals in a faster way by saving maximum product development time. In other words, PVM is one of the data providers to MBSE. Clearly, many companies in India are working with global OEMs in setting up the infrastructure for MBSE.
The data pool created is not just for MBSE but also for the autonomous areas like artificial intelligence or machine learning. As the intricacies in automotive data and the systems are getting more complex, companies are focussing on making products faster without compromising on product safety (one of the most important impacts) and also meet the regulatory requirements. For instance, autonomous vehicles will have connected car systems through IoT. A significant amount of data needs to be recorded and taken care of. The more complex the issues are, the more decision-making has to happen. And to expedite this decision-making, organisations are working towards ensuring there are no gatekeepers to data and that such data is available in a digitised form.
MBSE stands out as an attractive solution, when the system interdependencies have become as convoluted as they are in the automotive industry. For businesses to save time for overall product validation, a structured data platform is imperative. The focus is on ensuring traceable and structured data are made available at the fingertips. Companies are ensuring the foundation for MBSE is not only implemented, but also evolved in an efficient manner. After all, such a competitive market calls for competitive, long-term indicators. MBSE has not only enabled data traceability, but has also proven its potential to be a powerful tool for automotive systems – from conception to optimisation.