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Modelling of Engine Cooling System with a New Modelling Approach Based on Dynamic Neural Network
Technical Paper
2021-01-0203
ISSN: 0148-7191, e-ISSN: 2688-3627
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SAE WCX Digital Summit
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English
Abstract
Thermal management has always played a significant role in reducing emissions and improving the fuel efficiency of the internal combustion engines (ICEs). With a momentous influence on the thermal behavior of the engines, the cooling system has a considerable impact on ICE performance. In this scenario, a method based on artificial neural network (ANN) of the cooling system was proposed in this work. Specific modeling methods were adopted for the various operating conditions and flow circuits of the cooling system. To describe these varied dynamic characteristics, four ANN sub-models were established to simulate the system at different temperature stages. As a closed-loop system, the temperature of the cooling system can be regarded as a result of all the experienced operating points. Therefore, integral parameters describing the trajectory of the system were selected as the input of the ANNs. The training data was segmented into multiple segmentations and parallel training was utilized. With this training method, each segmented data can be regarded as a brand-new learning content since a new trajectory is generated due to the initialization of the segmented data. In this way, more training content was generated and the model was able to represent the system with different initial temperature. Finally, in order to simulate the switching of the cooling and heating processes in the high-temperature stage, a net switching logic considering the engine operation conditions was proposed, based on which the model obtained the ability to predict the supercooling and superheating.
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Zhang, H., Weyhing, T., Fan, X., Blesinger, G. et al., "Modelling of Engine Cooling System with a New Modelling Approach Based on Dynamic Neural Network," SAE Technical Paper 2021-01-0203, 2021, https://doi.org/10.4271/2021-01-0203.Data Sets - Support Documents
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References
- Pavlovic , J. , Marotta , A. , and Ciuffo , B. CO2 Emissions and Energy Demands of Vehicles Tested under the NEDC and the New WLTP Type Approval Test Procedures Applied Energy 177 661 670 2016
- Zukünftige Kraftstoffe Energiewende des Transports als ein weltweites Klimaziel Springer-Verlag 2019
- Dickinson , A. Proposal for a Regulation of the European Parliament and of the Council Setting Emission Performance Standards for New Passenger Cars and for New Light Commercial Vehicles as Part of the Union's Integrated Approach to Reduce CO 2 Emissions From Light-duty Vehicles and Amending Regulation. (Recast) ('Brussels I bis' Regulation) European Parliament October, 2018
- Varella , R.A. , Gonçalves , G. , Duarte , G. et al. Cold-Running NO x Emissions Comparison between Conventional and Hybrid Powertrain Configurations Using Real World Driving Data SAE Technical Paper 2016-01-1010 2016 https://doi.org/10.4271/2016-01-1010
- Kim , J.H. , Kim , T. , Park , S.J. et al. Effect of Engine Oil Heater Using EGR on the Fuel Economy and NOx Emission of a Full Size Sedan During Cold Start SAE Int. J. Engines 9 2 719 728 2016 https://doi.org/10.4271/2016-01-0656
- Jander , B.S. , and Baar , R. Modeling Thermal Engine Behavior Using Artificial Neural Network SAE Technical Paper 2017-01-0534 2017 https://doi.org/10.4271/2017-01-0534
- Roy , S. , Banerjee , R. , and Bose , P.K. Performance and Exhaust Emissions Prediction of a CRDI Assisted Single Cylinder Diesel Engine Coupled with EGR Using Artificial Neural Network Applied Energy 119 330 340 2014
- Luján , J.M. , Climent , H. , García-Cuevas , L.M. et al. Volumetric Efficiency Modelling of Internal Combustion Engines Based on a Novel Adaptive Learning Algorithm of Artificial Neural Networks Applied Thermal Engineering 123 625 634 2017
- Cay , Y. Prediction of a Gasoline Engine Performance with Artificial Neural Network Fuel 111 324 331 2013
- Togun , N.K. , and Baysec , S. Prediction of Torque and Specific Fuel Consumption of a Gasoline Engine by Using Artificial Neural Networks Applied Energy 87 1 349 355 2010
- Rahimi-Gorji , M. , Ghajar , M. , Kakaee , A.H. et al. Modeling of the Air Conditions Effects on the Power and Fuel Consumption of the SI Engine Using Neural Networks and Regression Journal of the Brazilian Society of Mechanical Sciences and Engineering 39 2 375 384 2017
- Rezaei , J. , Shahbakhti , M. , Bahri , B. et al. Performance Prediction of HCCI Engines with Oxygenated Fuels Using Artificial Neural Networks Applied Energy 138 460 473 2015
- Syahputra , R. Application of Neuro-Fuzzy Method for Prediction of Vehicle Fuel Consumption Journal of Theoretical & Applied Information Technology 86 1 2016
- Siami-Irdemoosa , E. , and Dindarloo , S.R. Prediction of Fuel Consumption of Mining Dump Trucks: A Neural Networks Approach Applied Energy 151 77 84 2015
- Wickramanayake , S. , Bandara , H.M.N.D. Fuel Consumption Prediction of Fleet Vehicles Using Machine Learning: A Comparative Study 2016 Moratuwa Engineering Research Conference (MERCon) 2016 90 95
- Perrotta , F. , Parry , T. , Neves , L.C. Application of Machine Learning for Fuel Consumption Modelling of Trucks 2017 IEEE International Conference on Big Data (Big Data) IEEE 2017 3810 3815
- Wu , J.D. , and Liu , J.C. A Forecasting System for Car Fuel Consumption Using a Radial Basis Function Neural Network Expert Systems with Applications 39 2 1883 1888 2012
- Wu , J.D. , and Liu , J.C. Development of a Predictive System for Car Fuel Consumption Using an Artificial Neural Network Expert Systems with Applications 38 5 4967 4971 2011
- Syahputra , R. Application of Neuro-Fuzzy Method for Prediction of Vehicle Fuel Consumption Journal of Theoretical & Applied Information Technology 86 1 2016
- Ahn , K. , Rakha , H. , Trani , A. et al. Estimating Vehicle Fuel Consumption and Emissions Based on Instantaneous Speed and Acceleration Levels Journal of Transportation Engineering 128 2 182 190 2002