Fuel Consumption Optimization in a MD Diesel Engine using Analytical Tools

Paper #:
  • 2015-36-0321

  • 2015-09-22
Soares, S., Capana, G., and Almeida, F., "Fuel Consumption Optimization in a MD Diesel Engine using Analytical Tools," SAE Technical Paper 2015-36-0321, 2015, https://doi.org/10.4271/2015-36-0321.
According to [1], nowadays the industry has been receiving an increasingly demand for efficient thermal machines. Particularly in the automotive field, the market is requiring strong reductions in emissions together with fuel consumption. It is important to be aware that the reduction both in emission and fuel consumption represents a challenging task, since it is one of the most complex trade-off of a diesel engine from the engineering standpoint.The present paper describes a comprehensive methodology to reduce the fuel consumption without jeopardizing engine emissions, particularly NOx, and analyze the after treatment durability aspects including urea crystallization hazard and catalyst efficiency. The engineering procedure proposed here comprises the analysis of customer representative route, analysis of customer steady-state chassis dynamometer test and the required emission cycles (European steady-state and transient cycles, ESC and ETC). This procedure provided the first estimation of working points in the engine operation that was used for a full-factorial Design of Experiments (DoE) over pre-selected combustion parameters in order to accommodate the NOx and fuel consumption trade-off. Additionally, the vehicle performance was investigated using simulation of typical accelerations modes and grade ability to further pursue additional improvements.The evaluation of the results was done based on integrated emissions test cycles levels, fuel consumption at the customer route via simulation and chassis dynamometer tests. Undesirable occurrences like crystallization and efficiency drop in the after treatment system (ATS) were also verified.Significant improvements during assessments in engine dynamometer were found to be nearly 3.5% on average. The total improvement in the customer vehicle route was estimated nearly in 2.2 % using vehicle simulation.
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