Digital Twin Technology in the trucking industry

First, the assets suitable for digital twinning tend to be high value, which makes the associated cost feasible; a new semi truck is typically priced anywhere from US$100K to US$200K, and annual operating costs can easily exceed the list price.

Second, they operate in environments with such a degree of complexity that effective analysis requires accounting for multiple inputs and outputs—that is, they are complex systems operating in complex environments. A truck’s performance is affected by driver decisions, mechanical condition and maintenance, fuel costs, traffic, weather and road conditions, and a host of other factors.

Third, a Digital Twin’s most important benefit is its ability to enable data-based decision-making, so it functions best in systems where decision-making is feasible and important. For example, the driver of a passenger vehicle is unlikely to derive much benefit from a Digital Twin of their car or need to use one for routine maintenance and on-road decision making. In contrast, commercial vehicles are operated with oversight from fleet managers and, to a greater extent than passenger cars, from operators. A Digital Twin in the context of trucking can inform vital decision making.

Finally, a Digital Twin relies on data. The more data and the higher their quality, the more robust its outputs will potentially be. Today’s trucks typically have as many as 400 on-board sensors, 70 electronic control units (ECUs) and 130 million lines of code. That is a lot of data, but autonomous vehicles, which have larger sensor arrays, are expected to produce even more in future—as much as 5 terabytes an hour when operating. The challenge, however, has long been that the data a truck generates are both too large and too technical for practical use by fleet managers, and only a small portion is collected by original equipment manufacturers (OEMs).

To fill the gap, third-party IT providers such as Intangles can install in the truck a hardware interface called a telematics device, which collects, parses and transmits sensor data to the cloud, where they can be filtered and enhanced. This transmission of relevant data enables real-time monitoring of vehicle health indicators, from coolant temperature and battery condition to air intake and exhaust flow. In the cloud, advanced algorithms are applied to the Digital Twin’s data to enhance remote diagnostics and create probability-based modeling of potential breakdowns, fuel overconsumption and other issues.

Moreover, the base of data from which the Digital Twin truck “learns” is always growing, as more Digital Twins are put on the virtual road. For example, Intangles, through its InGenious™ telematics device, monitors more than 450 sensor signals on each of the more than 175,000 vehicles on its platform. The company currently processes over 18 billion sensory data points per day, and its model is built on 60 terabytes of data—more than the industry-leading AI platform ChatGPT.

The benefits for fleet managers and operators are clear. With telematics, data analytics and sophisticated algorithms, each vehicle in a fleet can be replicated virtually in a Digital Twin. Its performance and maintenance can be monitored in real time, and potential breakdowns can be identified through predictive modeling. As a result, Digital Twins can dramatically enhance the usefulness of the mega-data a truck generates and give fleet managers and operators a new, powerful tool to reduce downtime, save on maintenance and repairs, and increase efficiency.