Automotive engine power performance tuning under numerical and nominal data

Wong, Pak Kin; Tam, Lap Mou; Li, Ke

doi:10.1016/j.conengprac.2011.11.006

Cited by 25 publications

(6 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, many performance indexes are somewhat in conflict with each other and the direct comparison is difficult [39]. Hence, an overall fitness is used as the evaluation method, which is similar to the fitness function proposed by [40]. The basic idea is to sum up various normalized performance indexes with different user-defined weights.…”

Section: Results and Analysismentioning

confidence: 99%

A Noise-Insensitive Semi-Active Air Suspension for Heavy-Duty Vehicles with an Integrated Fuzzy-Wheelbase Preview Control

Xie

Wong

Zhao

et al. 2013

Mathematical Problems in Engineering

Self Cite

View full text Add to dashboard Cite

Semi-active air suspension is increasingly used on heavy-duty vehicles due to its capabilities of consuming less power and low cost and providing better ride quality. In this study, a new low cost but effective approach, fuzzy-wheelbase preview controller with wavelet denoising filter (FPW), is developed for semi-active air suspension system. A semi-active suspension system with a rolling lobe air spring is firstly modeled and a novel front axle vertical acceleration-based road prediction model is constructed. By adopting a sensor on the front axle, the road prediction model can predict more reliable road information for the rear wheel. After filtering useless signal noise, the proposed FPW can generate a noise-insensitive control damping force. Simulation results show that the ride quality, the road holding, the handling capability, the road friendliness, and the comprehensive performance of the semi-active air suspension with FPW outperform those with the traditional active suspension with PID-wheelbase preview controller (APP). It can also be seen that, with the addition of the wavelet filter, the impact of sensor noise on the suspension performance can be minimized.

show abstract

Section: Results and Analysismentioning

confidence: 99%

A Noise-Insensitive Semi-Active Air Suspension for Heavy-Duty Vehicles with an Integrated Fuzzy-Wheelbase Preview Control

Xie

Wong

Zhao

et al. 2013

Mathematical Problems in Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…The results of (14) and (15) imply that (1) if φ i is included in the model (i.e. α i < ∞) yet q 2 i ≤ s i , then φ i can be deleted (i.e., set α i to ∞);…”

Section: Journal Of Control Science and Engineeringmentioning

confidence: 99%

“…(3) The amount of training data is usually large. Normally at least 200∼400 sets of training data is re-quired to build an accurate ANN engine model [15]. However, the collection of diesel engine emission and performance data is usually time consuming and costly, so the data set is usually lower than 50, resulting in that ANN may not be a good solution for diesel engine modelling.…”

Section: Introductionmentioning

confidence: 99%

Modelling and Prediction of Particulate Matter,NOx, and Performance of a Diesel Vehicle Engine under Rare Data Using Relevance Vector Machine

Wong

Cheung

2012

Journal of Control Science and Engineering

Self Cite

View full text Add to dashboard Cite

Traditionally, the performance maps and emissions of a diesel engine are obtained empirically through many testes on the dynamometers because no exact mathematical engine model exists. In the current literature, many artificial-neural-network- (ANN-) based approaches have been developed for diesel engine modelling. However, the drawbacks of ANN would make itself difficult to be put into some practices including multiple local minima, user burden on selection of optimal network structure, large training data size, and overfitting risk. To overcome the drawbacks, this paper proposes to apply one emerging technique, relevance vector machine (RVM), to model the diesel engine, and to predict the emissions and engine performance. With RVM, only a few experimental data sets can train the model due to the property of global optimal solution. In this study, the engine speed, load, and coolant temperature are used as the input parameters, while the brake thermal efficiency, brake-specific fuel consumption, concentrations of nitrogen oxides, and particulate matter are used as the output parameters. Experimental results show the model accuracy is fairly good even the training data is scarce. Moreover, the model accuracy is compared with that using typical ANN. Evaluation results also show that RVM is superior to typical ANN approach.

show abstract

“…Numerous tests have to be conducted before an optimal ECU setup can be obtained. Obviously, the ECU calibration process is costly in fuel and time, and every engine model must undergo similar calibration process in the engine development cycle [1,2]. Even for an experienced automotive engineer, the tune up and calibration of an ECU for an engine can take more than one year and can cost millions of dollars [3].…”

Section: Introductionmentioning

confidence: 99%

Sparse Bayesian extreme learning machine and its application to biofuel engine performance prediction

Wong¹,

Vong

Wong³

et al. 2015

Neurocomputing

Self Cite

View full text Add to dashboard Cite

Automotive engine power performance tuning under numerical and nominal data

Cited by 25 publications

References 16 publications

A Noise-Insensitive Semi-Active Air Suspension for Heavy-Duty Vehicles with an Integrated Fuzzy-Wheelbase Preview Control

A Noise-Insensitive Semi-Active Air Suspension for Heavy-Duty Vehicles with an Integrated Fuzzy-Wheelbase Preview Control

Modelling and Prediction of Particulate Matter,NOx, and Performance of a Diesel Vehicle Engine under Rare Data Using Relevance Vector Machine

Sparse Bayesian extreme learning machine and its application to biofuel engine performance prediction

Contact Info

Product

Resources

About