This content is not included in your SAE MOBILUS subscription, or you are not logged in.
Real Time Energy Management of Electrically Turbocharged Engines Based on Model Learning
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 02, 2019 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Engine downsizing is a promising trend to decarbonise vehicles but it also poses a challenge on vehicle driveability. Electric turbochargers can solve the dilemma between engine downsizing and vehicle driveability. Using the electric turbocharger, the transient response at low engine speeds can be recovered by air boosting assistance. Meanwhile, the introduction of electric machine makes the engine control more complicated. One emerging issue is to harness the augmented engine air system in a systematical way. Therefore, the boosting requirement can be achieved fast without violating exhaust emission standards. Another raised issue is to design an real time energy management strategy. This is of critical to minimise the required battery capacity. Moreover, using the on-board battery in a high efficient way is essential to avoid over-frequent switching of the electric machine. This requests the electric machine to work as a generator to recharge the battery. The capability of generating power strongly depends on the engine operating point. One big challenge is that the calibration of generating power capability is time-consuming in experiments. This paper proposes a neuro-fuzzy approach to model the engine. Based on the virtual engine model, the capability of generating power at arbitrary engine operating point can be obtained fast and accurately, which is applicable to implement in real time.
CitationZhao, D., Gu, W., and Mason, B., "Real Time Energy Management of Electrically Turbocharged Engines Based on Model Learning," SAE Technical Paper 2019-01-1056, 2019, https://doi.org/10.4271/2019-01-1056.
Data Sets - Support Documents
|Unnamed Dataset 1|
- Patil , C. , Varade , S. , and Wadkar , S. A Review of Engine Downsizing and Its Effects International Journal of Current Engineering and Technology 319 324 2017
- Terdich , N. 2015
- Galloni , E. , Fontana , G. , and Palmaccio , R. Effects of Exhaust Gas Recycle in a Downsized Gasoline Engine Applied Energy 105 99 107 2013
- Gerada , D. , Huang , X. , Zhang , C. , Zhang , H. , Zhang , X. , and Gerada , C. Electrical Machines for Automotive Electrically Assisted Turbocharging 2018
- Xue , X. and Rutledge , J. Potentials of Electrical Assist and Variable Geometry Turbocharging System for Heavy-Duty Diesel Engine Downsizing SAE 2017-01-1035 2017 10.4271/2017-01-1035
- Glenn , B. et al. Control Design of Electrically Assisted Boosting Systems for Diesel Powertrain Applications IEEE Transactions on Control Systems Technology 18 4 769 778 2010
- Ibaraki , S. et al. Development of the Hybrid Turbo, an Electrically Assisted Turbocharger Mitsubishi Heavy Industries Technical Review 43 3 1 5 2006
- Ekberg , K. et al. Improving Fuel Economy and Acceleration by Electric Turbocharger Control for Heavy Duty Long Haulage IFAC Papers Online 50 1 1105211057 2017
- Zhao , D. , Winward , E. , Yang , Z. , Stobart , R. et al. An Integrated Framework on Characterization, Control and Testing of an Electrical Turbocharger Assist IEEE Transactions on Industrial Electronics 65 6 4897 4908 2018
- Banfer O. , Hartmann B. , Nelles O. POLYMOT Versus HILOMOT - A Comparison of Two Different Training Algorithms for Local Model Networks 16th IFAC Symposium on System Identification 2012 1569 1574
- Zhao , D. , Liu , C. , Stobart , R. , Deng , J. et al. An Explicit Model Predictive Control Framework for Turbocharged Diesel Engines IEEE Transactions on Industrial Electronics 61 7 3540 3552 2014
- Teslic , L. , Hartmann , B. , Nelles , O. , and Skrjanc , I. Nonlinear System Identification by GustafsonKessel Fuzzy Clustering and Supervised Local Model Network Learning for the Drug Absorption Spectra Process IEEE Transactions on Neural Networks 22 12 1941 1951 2011