Coupling of Real-World Data and Fast Response Algorithms to Improve Simulation Correlations and Optimise Construction Machine Performance

Many of the engine technologies used in future construction machines will be targeted towards reduced fuel consumption and increased power density. Fuel consumption forms a major component of owning and operating costs, as do both reliability and durability.

Reduced fuel consumption (or CO2) is strongly related to air system performance, combustion and frictional loss in the engine. These factors will be main focus areas during this project as they are key enablers for engine downsizing with its commensurate fuel savings. ICT will form a fundamental part of the design of these future CO2-reduced powerplants. In addition to enabling the deployment of promising technologies across a wide engine and machine range, simulation tools will be harnessed to optimise performance.

This project, using both simulation tools and real world validation, will realise exploitable outputs in the form of:

• More accurate prediction of CO2 performance for machines on specific customer worksites;
• Improved operator feedback in virtual environments which are “driven” by the system simulation tool
• Rapid replication of CO2-reduction technologies across the world widest construction machine range
• Standardised meta-tagged construction machine performance data
• Improved turbocharger durability
• A robust, widely deployable data mining, error-estimation and optimisation framework
• New optimisation algorithms applied to parameter identification in model development.