2017
DOI: 10.1016/j.applthermaleng.2017.05.171
|View full text |Cite
|
Sign up to set email alerts
|

Optimization and calibration strategy using design of experiment for a diesel engine

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
5

Citation Types

0
20
0

Year Published

2018
2018
2023
2023

Publication Types

Select...
9
1

Relationship

0
10

Authors

Journals

citations
Cited by 36 publications
(20 citation statements)
references
References 19 publications
0
20
0
Order By: Relevance
“…Since the effect chain for calibrating powertrain systems consists of few components with welldefined interfaces, parameter optimizations can be performed using only the simulation models of the respective component (e.g. the engine, transmission) [13], [17], [18]. On the contrary, the effect chain for automated driving functions is more complex (cf.…”
Section: Introductionmentioning
confidence: 99%
“…Since the effect chain for calibrating powertrain systems consists of few components with welldefined interfaces, parameter optimizations can be performed using only the simulation models of the respective component (e.g. the engine, transmission) [13], [17], [18]. On the contrary, the effect chain for automated driving functions is more complex (cf.…”
Section: Introductionmentioning
confidence: 99%
“…Yoneya (2002) optimised exhaust emissions and BSFC of diesel engine with EGR system using Taguchi method. Park et al (2017) calibrated the input parameters of diesel engine to improve fuel efficiency and exhaust emissions using design of experiment and response surface methodology. Saravanan et al (2017) optimised DI diesel engine parameters fuelled with isobutanol/diesel blends using response surface methodology.…”
Section: Introductionmentioning
confidence: 99%
“…In [9], the authors proposed a method to adjust the fuel injection parameters to improve the efficiency of the engine using a generic algorithm. In [10], intake and fuel control parameters were optimized using regression models. Because different engine loads have different optimal setting for parameters, knowing engine load information during operation helps to adjust parameters.…”
Section: Introductionmentioning
confidence: 99%