Analytic Model of Powertrain Drive Cycle Efficiency, with Application to the US New Vehicle Fleet

Event
SAE 2016 World Congress and Exhibition
Authors Abstract
Content
An analytic model of powertrain efficiency on a drive cycle was developed and evaluated using hundreds of cars and trucks from the US EPA ‘Test Car Lists’. The efficiency properties of naturally aspirated and downsized turbocharged engines were compared for vehicles with automatic transmissions on the US cycles.
The resulting powertrain cycle efficiency model is proportional to the powertrain marginal energy conversion efficiency K, which is also its upper limit. It decreases as the powertrain matching parameters, the displacement-to-mass ratio (D/M) and the gearing ratio (n/V), increase.
The inputs are the powertrain fuel consumption, the vehicle road load, and the cycle work requirement. They could be modeled simply with only minor approximations through the use of absolute inputs and outputs, and systematic use of scaling.
On the Highway test, conventional automatic transmission vehicles of moderate performance achieve between 25% and 30% powertrain efficiency. On the City test, the efficiency is reduced to 15% to 20% because of the higher fraction of open torque converter launches and power-off operation.
Compared to naturally aspirated engines, turbocharged and downsized engines are a trade-off between reduced losses and reduced efficiency. When the losses are comparatively high, as in the City test or in high D/M vehicles, the benefit is substantial. But when the losses are comparatively low, as in the Highway test or in low D/M vehicles, the benefit is small.
Meta TagsDetails
DOI
https://doi.org/10.4271/2016-01-0902
Pages
21
Citation
Phlips, P., "Analytic Model of Powertrain Drive Cycle Efficiency, with Application to the US New Vehicle Fleet," SAE Int. J. Fuels Lubr. 9(1):269-289, 2016, https://doi.org/10.4271/2016-01-0902.
Additional Details
Publisher
Published
Apr 5, 2016
Product Code
2016-01-0902
Content Type
Journal Article
Language
English