University of Warwick (UK) engineers are optimizing the fuel economy of next-generation offhighway vehicles by introducing new intelligent power systems for improved engine operation. This could lead to significant fuel savings and fewer carbon emissions for the industry. The team is analyzing a current fleet of construction vehicles to better understand the opportunities for emissions reduction and intelligent control.

A JCB Fastrac 8250 tractor. (Photo: Creative Commons/Les Chatfield)

Today's construction industry is more environmentally conscious than ever, and the amount of CO2 emissions released by vehicles is a significant factor in deciding which ones to use during an assignment. As a result, it is now imperative that all construction fleets reduce their emissions so greener, more efficient vehicles will be more in demand in an increasingly competitive market.

Being studied is the suitability for micro/mild hybridization (MMH), a feasible solution that represents a simple, low-cost implementation to create high fuel efficiency with less energy use and fewer emissions. Many off-highway vehicles are left running at full power while idle for much of their life — such as heavy excavators and wheeled loaders — potentially wasting fuel, with a direct impact on local air quality.

The intelligent use of MMH could provide the opportunity to shut down the engine, or shift it to lower power, during these idle periods. This would have a measurable impact upon reducing fuel consumption, CO2 output, NOx formation, and particulate emissions.

The team has developed technology that predicts when machinery requires the shift between low power and high power, thus allowing users to run the machine with the lowest fuel consumption without sacrificing their working performance.

The advanced methodology for big data capturing, compression, and mining from telematics of the construction equipment fleets was designed for managing and analyzing the performance of various machine types. An intelligence-based decision tool has been developed by the team, based on big data mining and knowledge from experts, to enable companies to target specific machines among their fleets for hybridization.

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