Construction vehicles may become more efficient and environmentally-friendly in the future, thanks to scientists who are developing intelligent power systems for improved engine operation.
Researchers at the University of Warwick in the UK are working to optimise the fuel economy of the next generation of off-highway vehicles.
This could lead to significant fuel savings and fewer carbon emissions for the industry.
Researchers are analysing the fleet of a company that manufactures equipment for construction, agriculture, waste handling and demolition to better understand the opportunities for emissions reduction and intelligent control.
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, researchers said.
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.
Researchers are analysing the suitability for micro/mild hybridisation (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 whilst idle for much of their life – such as telescopic handlers, 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.
Scientists are researching a pioneering technology which 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.
An intelligence-based decision tool has been constructed based on the big data mining and knowledge from experts, to enable companies to target specific machines among their fleets for hybridisation.