Lightning Systems using AI to predict fleet vehicle fuel economy, emissions

Friday February 2, 2018 0 comments Tags: Loveland, Lightning Systems, Tyler Yadon, Will Briggs, Brian Johnston, LightningAnalytics, artificial intelligence

LOVELAND -- Engineers at Lightning Systems are using artificial neural networks, a form of artificial intelligence, to accurately predict the fuel economy and tailpipe emissions of fleet vehicles.

Artificial neural networks are computing systems made up of a number of highly interconnected processing elements, that process information and predict outcomes, the company said.lightning-systems-logo_1

A global developer of efficiency and emissions improvement solutions for fleets, Lightning Systems said it is deploying this form of artificial intelligence to make vehicle-fleet management predictions with high accuracy.

"Using artificial neural networks, we are able to accurately predict fuel consumption and emissions of commercial and government fleets," said Tyler Yadon, Lightning Systems' director of analytics.

"Our computer modeling demonstrates the accuracy of predictive analytics to help fleets manage fuel consumption, decrease their fuel usage, and reduce emissions.

“The tools we are developing can take incredibly complex real-world problems and turn them into extremely accurate predictions about your fleet."

Yadon oversees Lightning Systems LightningAnalytics intelligent fleet management product. LightningAnalytics is a predictive analytics system that helps fleets monitor vehicle maintenance and track routes, thereby decreasing fuel usage and emissions.

The system predicts impending maintenance repairs that are influenced by fuel usage, drive cycles and routes, and driver behavior. This is achieved through high-frequency recording of many real-time parameters from the vehicle.

Lightning Systems recently announced its LightningElectric zero-emissions package for the heavy-duty Ford Transit. The company also offers a hydraulic hybrid energy recovery system called LightningHybrid.

The hydraulic hybrid system is retrofitted onto trucks, buses and other large transit and delivery vehicles, providing conventional vehicles with upgraded energy-management and powertrain-control systems.

LightningAnalytics is embedded in both products.

"Artificial neural networks are not only less computationally costly than existing simulation standards, but they are easier and faster to re-train and apply to new vehicles and drive cycles offering the potential for high accuracy estimates with reduced infrastructure requirements," said Brian Johnston, Lightning Systems’ director of emissions regulation and strategy and one of the co-authors of a paper to be published by Colorado State University.

"Our research demonstrates significant benefit for designing improved vehicle-control strategies, such as eco-driving and optimal energy management,” said Will Briggs, Lightning Systems’ lead test engineer and co-author of the paper.

“It also has the potential to reduce the need for physical vehicle testing, because this type of computer modeling accurately captures emissions results from slight drive-cycle variations and improves the understanding of real-world emissions and fuel impacts, enabling high-fidelity learning control in physical vehicles.

"Our analysis predicted fuel consumption with a margin for error as low as 0.1 percent, and predicted CO2, CO and NOx emissions with 97 to 100 percent accuracy."

"Customer drive cycles are not always reproducible for fuel consumption and emissions research," said Yadon,

"Our results indicate that artificial neural network models can be used for a variety of research applications due to their economic and computational benefits, such as improving vehicle-control strategies to reduce fuel consumption and emissions in modern vehicles.

Lightning Systems said it used data from the operation of hydraulic hybrid fleet vehicles in the United Kingdom and the U.S. to create the artificial neural network predictions on fuel consumption and emissions.

The company also used test-track and dynamometer testing data.

Research was conducted by Colorado State University engineering graduate students and the findings will be presented at the SAE World Congress in Detroit in April.