The Role of Digital Twins in Reducing Environmental Impact of Autonomous Driving
By Umang Dayal
October 18, 2024
Digital twins are becoming a crucial tool in the development and operation of autonomous driving. By enabling virtual testing and managing increasing system complexity, digital twins form the foundation for advanced data analysis, forecasting, and design optimization. Automotive companies have shown great interest in digital twins given their potential for cost savings and lifetime carbon footprint reduction.
Environmental issues in automotive production cannot be overemphasized because of the nature of the manufacturing processes involved. The traditional automotive supply chain is complex due to the high differentiation of the vehicle's component structure and the number of suppliers, who are not always direct investors.
Manufacturing processes also generate substantial waste and emissions, including high carbon footprints and volatile organic compound (VOC) emissions. However, the rising societal demand for energy efficiency and lower emissions can be addressed effectively with the adoption of digital twin technology.
Understanding Digital Twins
A digital twin is a detailed virtual replica of a physical asset, mimicking its characteristics and behaviors. It can be used to observe conditions that cannot ethically or practically be implemented in the real world or must simply be produced faster and cheaper in virtual representation.
Therefore, digital twins can be useful throughout the complete lifecycle of a product, from the original concept, through design, manufacturing, and on to end-of-life. By integrating feeds and feedback, the digital twin will change and evolve through modeling and data analytics. Over time, it will include the complete, double-sided traceability of each detail according to strict quality features, including design specifications, architecture, and performance.
Applications of Digital Twins in Autonomous Driving
Digital twins, data-driven models of the real world, provide a collaborative and reliable way to make manufacturing decisions. It is said that the digital twin market is set to grow at a CAGR of 61.3% between 2023 and 2028 to reach $110.0 billion. Here are a few applications of digital twins in autonomous driving.
Design and Engineering Phase
In the main design phase of stock production processes, the role of the DT of the first type is to create a digital model of the future production process. It contributes to the modeling and optimization of the production process from the point of view of its efficiency. This approach allows the early prediction of environmental impact and the early elimination of compromises in the decision-making process during the design and preparation of the production system. It also leads to suitable final outcomes for the constructed production plant and its environmental performance. The potential of carbon footprint reduction can also affect material selection, the formation of components, assembly processes, and the amount of energy consumption.
The enhanced CO2 footprint reduction is also linked with the main design phase in the factory's logistics system. The role of digital twin technology is logistic planning and execution and contributing to the collection of current, real-time internal logistics processing, and manufacturing data with high precision. With DT, a realistic environment modeling can be set up for the purpose of testing complex real-time control and logistics algorithms. The carbon footprint can be minimized by lowering the possibility of distribution and warehousing at every stage of the production process, from the stage of initial conversion of the material to the formal stage of the distribution of the final product to the customer.
Manufacturing Phase
The second phase where the digital twin can optimize autonomous vehicles is the manufacturing phase, where it is used not only for predictive quality testing but also for the monitoring of the production parameters of machinery and processing equipment. Indeed, it is essential to use real-time data to monitor and control the production step, improving the performance, product quality, and overall productivity of the plant. Concerning the latter, it is noted that by optimizing the performance of the equipment or the overall production, a reduction in energy consumption is possible, while preserving the same production capacity - with an evident positive impact on environmental performance. An accurate digital twin is therefore an excellent tool for monitoring and optimizing production machinery and for improving their reliability. According to GSMA, North America's total number of consumer and industrial IoT connections is forecast to grow to $5.4 billion by 2025.
Operational Phase
The application of a digital twin is not restricted to development or production, as it can deliver a lot of value in the long phase of the vehicle lifecycle. While models are extremely useful for simulating the behavior of a system, this is done via guesswork. A sensor can measure the actual behavior of the real system, reducing the need for guesswork to zero. The last stage of a vehicle's lifecycle involves its utilization. Data collected for vehicle utilization, combined with more data on its operation (location, load, driver behavior, road signs, status of subsystems), provide critical information that can be used in training for emergency simulations and safety validation.
Environmental Benefits of Digital Twins
The available empirical evidence that investigates the determinants of people’s transport carbon footprint helps to identify variables that may affect both car travel distance and car fuel efficiency.
Resource Optimization
The current approach in the automotive industry is to track the resources consumed daily for analyzing resource reduction potential. It is difficult to measure resource losses and their impact, as not all resource use is directly visible or is only measured at a high level. For more detailed information, sensors must be applied to track the consumption of single equipment.
In some cases, high-cost calculations are performed by the accounting office to calculate resource consumptions or losses. As a result, optimization of production lines or single equipment, or improved utilization, is often calculated at a gross level covering all the single items that consume resources. 29% of global manufacturing companies have either fully or partially implemented their digital twin strategies. A digital twin approach can be applied to simulating the consumption of the resources at a much deeper level with a cost-effective plan to calculate how possible changes could reduce resource loss.
Waste Reduction and Recycling
Waste disposal challenges are global issues that the world is trying to manage. Waste management systems are complex. They have to be designed and positioned in specific scenarios. For this reason, a hierarchy in management needs to be applied. The order of the hierarchy consists of prevention, reuse, recycling, recovery, and disposal. The higher up the hierarchy, the more preferable the solution.
Efforts are, therefore, needed in the prevention and re-use of the waste. Recycle, recover, and dispose of solutions are strategies to strongly orient. Digital twins are, therefore, expected to play a significant role in their development considering that the European automotive industry, in a circular economy framework, is strongly oriented towards the reuse and recycling of waste in an optimal way.
Energy Efficiency
Energy efficiency is becoming an issue due to increasing electricity prices. Through the use of real-time data, digital twins can optimize energy-efficient operation strategies and be applied in use cases such as:
Intelligent control of high energy-consuming systems and equipment such as press shops, paint, and welding systems.
Concept validation and design application in energy-efficient applications.
Optimization of cyclical energy consumption.
Monitoring and analysis of energy efficiency in real-time.
The digital twin provided by the proposed framework applies its data-driven modules to successfully forecast energy consumption from industrial systems and components.
Read more: Enhancing In-Cabin Monitoring Systems for Autonomous Vehicles with Data Annotation
Challenges and Future of Digital Twin Technologies
While the physical-model parts of DTs provide low generalization against real-world data, optimizing these models using real-world data also creates difficulties due to potential damage to the physical models. More robust hybrid architectures accompanied by a data-centric approach can be alternative ways to solve this problem as research directions in this context. Besides this, the infrastructure and software necessary for obtaining data from manufacturing equipment are progressively advanced. Thus, the implementation of DT may be problematic due to high costs in many firms.
Data Security and Privacy Challenges in Digital Twins
Data security and privacy are critical concerns in digital twin technology. Complex data environments and interconnected systems, such as those in Industry 4.0 and IoT, are vulnerable to potential threats. Companies must take control to implement robust security measures to mitigate these risks.
Read more: Top 8 Use Cases of Digital Twin in Autonomous Driving
Conclusion
The environmental impact of any production system remains to be of paramount importance. A successful and sustainable industry is one that considers its own environmental impact.
The future of autonomous driving relies on continuously innovating systems to meet the ever-changing demands of autonomy and road safety. Digital Twin technology is a powerful tool that can accelerate development and reduce the environmental impact of autonomous driving. These simulations facilitate the development of intelligent driving systems, resource optimization, energy efficiency, recycling, and more.
Digital Divide Data offers comprehensive digital twin solutions with our expertise in autonomous driving. Our team ensures that your autonomous models align with data security, reliability, and safety standards. You can talk to our experts and learn more about how our digital twin solutions can help your autonomous models reach their full potential.
