Intangles, an artificial intelligence (AI)-powered predictive maintenance platform for the transportation industry, added a new feature to its product suite that informs fleets on the quality of diesel particulate filter (DPF) regeneration to optimize the fuel efficiency and the engine performance of commercial vehicles.
“We’re always looking for ways to leverage our physics-based AI to save fleets time and money,” says Craig Vanderheide, director of product management, Intangles. “This new feature is designed to take the guesswork out of forced DPF regens and keep soot levels at an optimal level. Fleets deserve to have better insights when it comes to DPF.”
Key takeaways:
- The Intangles solution monitors the performance of the DPF system in real time, and predicts future performance of the system based on environmental and vehicle specific conditions.
- The solution also informs the user of the quality of each DPF regeneration; provides insights on the impact of inefficient regeneration; guides users on how to optimize forced DPF regeneration; and optimizes fuel efficiency, engine performance, and extends the life of a vehicle.
- The DPF solution is built using Intangles’ proprietary hardware, adaptive algorithms and scalable cloud infrastructure to solve problems in new ways. Intangles leverages digital twin technology to create a virtual replica of a vehicle’s DPF in the cloud. Once a replica is created, Intangles can test how the virtual DPF will perform under varying conditions such as extreme cold weather, periods of prolonged idling, or stop/start driving situation. As a result, the Intangles solution not only predicts how a truck’s DPF will perform in differing conditions, but also grants insight into how best to optimize soot load and regeneration.
“Our fleets know firsthand how a clogged DPF can lead to a loss of power and fuel economy,” adds Vanderheide. “And I don’t know of any drivers who are thrilled to deal with DPF issues when they are focused on getting where they need to be. We knew this was an issue and we set out to solve it.”
