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Lightning Systems Using AI to Monitor Fuel Economy, Emissions

Lightning Systems are using artificial intelligence to predict the fuel economy and tailpipe emissions of fleet vehicles which are designed to help fleets monitor vehicle maintenance and track routes in an effort to decrease fuel usage and emissions.

by Staff
February 1, 2018
Lightning Systems Using AI to Monitor Fuel Economy, Emissions

Photo courtesy of Lightning Hybrids.

2 min to read


Photo courtesy of Lightning Hybrids.

Lightning Systems is using artificial intelligence to predict the fuel economy and tailpipe emissions of fleet vehicles, which looks to help fleets monitor vehicle maintenance and track routes in an effort to decrease fuel usage and emissions.

Lightning Systems is using artificial neural networks, which it is deploying to make vehicle-fleet management predictions with high accuracy, the company said in a release. Artificial neural networks are computing systems made up of a number of highly interconnected processing elements, that process information and predict outcomes.

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“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,” said Tyler Yadon, director of analytics for Lightning Systems.

Recently, Lightning Systems announced its LightningElectric zero-emissions package for the heavy-duty Ford Transit, according to the company. The company also offers a hydraulic hybrid energy recovery system called LightningHybrid, which is a hydraulic hybrid system is retrofitted onto trucks, buses and other large transit and delivery vehicles, It provides conventional vehicles with upgraded energy-management and powertrain-control systems. LightningAnalytics is embedded in both of these products.

“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,” said Yadon.

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