Automotive Data Analytics, Methods and Design of Experiments (DoE): Proceedings of the International Calibration Conference
The book will expand on the topics discussed in the precursors entitled "DoE in Powertrain Development" with the related areas of "machine learning" and "big data". Now it its ninth outing, it will thus be a forum on which to critically engage with the future challenges...
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| Format: | Elektronisch E-Book |
| Sprache: | Deutsch |
| Veröffentlicht: |
Tübingen
expert verlag GmbH
2017
|
| Ausgabe: | [1. Auflage] |
| Schlagwörter: | |
| Links: | https://elibrary.narr.digital/book/99.125005/9783816983811 https://elibrary.narr.digital/book/99.125005/9783816983811 https://elibrary.narr.digital/book/99.125005/9783816983811 |
| Zusammenfassung: | The book will expand on the topics discussed in the precursors entitled "DoE in Powertrain Development" with the related areas of "machine learning" and "big data". Now it its ninth outing, it will thus be a forum on which to critically engage with the future challenges of the digital revolution. Real driving emissions (RDE), worldwide harmonized light-duty test procedures (WLTP) and the next round of CO2 guidelines all demand ongoing technical refinement of the drive train. The combination of changed environmental requirements, stricter limit values and new measurement techniques additionally require changes to existing processes and the development of new methods. To reduce costs, many OEMs are scaling down the size of their engine ranges. A small number of standard engines are then installed in numerous vehicle models with minor hardware modifications. The result is an increased focus on the use of derivatives and the systematic validation of an application. Contents: Machine learning and artificial intelligence for engine calibration - Big Data and Machine Learning Made Easy - Automated Calibration Using Simulation and Robust Design Optimization Improving Shift and Launch Quality of Automatic Transmissions - Development of a Simulation Platform for Validation and Optimisation of Real-World Emissions - Implementation of data-based models using dedicated machine learning hardware (AMU) and ist impact on function development and the calibration processes - The Global DoE Model Based Calibration and the Test Automation of the Gasoline Engine - Optimization of ECU Map Sampling Point Values and Positions with Model-Based Calibration - Dynamic Route-Based Design of Experiments (R-DoE) - System for Real-time Evaluation of Real Drive Emission (RDE) Data - System optimization for automated calibration of ECU functions - Dynamic MBC Methodology for Transient Engine Combustion Optimization - Implementing a real time exhaust gas temperature model for a Diesel engine with ASC@ECU - Dynamic Modelling for Gasoline Direct Injection Engines - Excitation Signal Design for Nonlinear Dynamic Systems - Application of a DoE based robust design process chain for system simulation of engine systems - Application of Emulator Models in Hybrid Vehicle Development - Fast response surrogates and sensitivity analysis based on physico-chemical engine simulation applied to modern compression ignition engines - The Connected Car and ist new possibilities in ECU calibration - Processing vehicle-related measurement data - On the selection of appropriate data from routine vehicle operation for system identification of a diesel engine gas system - Data Plausibility at the Engine Test Bench: How im-portant is the Human Factor in the Process? - Non-Convex Hulls for Engineering Applications - Modern Online DoE Methods for Calibration: Constraint Modeling, Continuous Boundary Estimation, |
| Umfang: | 1 Online-Ressource (369 Seiten) |
| ISBN: | 9783816983811 |
Internformat
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| 245 | 1 | 0 | |a Automotive Data Analytics, Methods and Design of Experiments (DoE) |b Proceedings of the International Calibration Conference |c Karsten Röpke, Clemens Gühmann |
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| 520 | |a The book will expand on the topics discussed in the precursors entitled "DoE in Powertrain Development" with the related areas of "machine learning" and "big data". Now it its ninth outing, it will thus be a forum on which to critically engage with the future challenges of the digital revolution. Real driving emissions (RDE), worldwide harmonized light-duty test procedures (WLTP) and the next round of CO2 guidelines all demand ongoing technical refinement of the drive train. The combination of changed environmental requirements, stricter limit values and new measurement techniques additionally require changes to existing processes and the development of new methods. To reduce costs, many OEMs are scaling down the size of their engine ranges. A small number of standard engines are then installed in numerous vehicle models with minor hardware modifications. The result is an increased focus on the use of derivatives and the systematic validation of an application. | ||
| 520 | |a Contents: Machine learning and artificial intelligence for engine calibration - Big Data and Machine Learning Made Easy - Automated Calibration Using Simulation and Robust Design Optimization Improving Shift and Launch Quality of Automatic Transmissions - Development of a Simulation Platform for Validation and Optimisation of Real-World Emissions - Implementation of data-based models using dedicated machine learning hardware (AMU) and ist impact on function development and the calibration processes - The Global DoE Model Based Calibration and the Test Automation of the Gasoline Engine - Optimization of ECU Map Sampling Point Values and Positions with Model-Based Calibration - Dynamic Route-Based Design of Experiments (R-DoE) - System for Real-time Evaluation of Real Drive Emission (RDE) Data - System optimization for automated calibration of ECU functions - Dynamic MBC Methodology for Transient Engine Combustion Optimization - Implementing a real time exhaust gas | ||
| 520 | |a temperature model for a Diesel engine with ASC@ECU - Dynamic Modelling for Gasoline Direct Injection Engines - Excitation Signal Design for Nonlinear Dynamic Systems - Application of a DoE based robust design process chain for system simulation of engine systems - Application of Emulator Models in Hybrid Vehicle Development - Fast response surrogates and sensitivity analysis based on physico-chemical engine simulation applied to modern compression ignition engines - The Connected Car and ist new possibilities in ECU calibration - Processing vehicle-related measurement data - On the selection of appropriate data from routine vehicle operation for system identification of a diesel engine gas system - Data Plausibility at the Engine Test Bench: How im-portant is the Human Factor in the Process? - Non-Convex Hulls for Engineering Applications - Modern Online DoE Methods for Calibration: Constraint Modeling, Continuous Boundary Estimation, | ||
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Datensatz im Suchindex
| _version_ | 1824483445679587328 |
|---|---|
| adam_text | |
| any_adam_object | |
| author | Gühmann, Clemens 1962- |
| author2 | Röpke, Karsten 1966- |
| author2_role | edt |
| author2_variant | k r kr |
| author_GND | (DE-588)11555923X (DE-588)172108837 |
| author_facet | Gühmann, Clemens 1962- Röpke, Karsten 1966- |
| author_role | aut |
| author_sort | Gühmann, Clemens 1962- |
| author_variant | c g cg |
| building | Verbundindex |
| bvnumber | BV047140221 |
| classification_rvk | ZO 4250 |
| collection | ZDB-71-NAR |
| ctrlnum | (ZDB-71-NAR)9783816983811 (OCoLC)1238061880 (DE-599)BVBBV047140221 |
| discipline | Verkehr / Transport |
| edition | [1. Auflage] |
| format | Electronic eBook |
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| id | DE-604.BV047140221 |
| illustrated | Not Illustrated |
| indexdate | 2025-02-19T11:03:22Z |
| institution | BVB |
| isbn | 9783816983811 |
| language | German |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032546158 |
| oclc_num | 1238061880 |
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| publisher | expert verlag GmbH |
| record_format | marc |
| spelling | Automotive Data Analytics, Methods and Design of Experiments (DoE) Proceedings of the International Calibration Conference Karsten Röpke, Clemens Gühmann [1. Auflage] Tübingen expert verlag GmbH 2017 1 Online-Ressource (369 Seiten) txt rdacontent c rdamedia cr rdacarrier The book will expand on the topics discussed in the precursors entitled "DoE in Powertrain Development" with the related areas of "machine learning" and "big data". Now it its ninth outing, it will thus be a forum on which to critically engage with the future challenges of the digital revolution. Real driving emissions (RDE), worldwide harmonized light-duty test procedures (WLTP) and the next round of CO2 guidelines all demand ongoing technical refinement of the drive train. The combination of changed environmental requirements, stricter limit values and new measurement techniques additionally require changes to existing processes and the development of new methods. To reduce costs, many OEMs are scaling down the size of their engine ranges. A small number of standard engines are then installed in numerous vehicle models with minor hardware modifications. The result is an increased focus on the use of derivatives and the systematic validation of an application. Contents: Machine learning and artificial intelligence for engine calibration - Big Data and Machine Learning Made Easy - Automated Calibration Using Simulation and Robust Design Optimization Improving Shift and Launch Quality of Automatic Transmissions - Development of a Simulation Platform for Validation and Optimisation of Real-World Emissions - Implementation of data-based models using dedicated machine learning hardware (AMU) and ist impact on function development and the calibration processes - The Global DoE Model Based Calibration and the Test Automation of the Gasoline Engine - Optimization of ECU Map Sampling Point Values and Positions with Model-Based Calibration - Dynamic Route-Based Design of Experiments (R-DoE) - System for Real-time Evaluation of Real Drive Emission (RDE) Data - System optimization for automated calibration of ECU functions - Dynamic MBC Methodology for Transient Engine Combustion Optimization - Implementing a real time exhaust gas temperature model for a Diesel engine with ASC@ECU - Dynamic Modelling for Gasoline Direct Injection Engines - Excitation Signal Design for Nonlinear Dynamic Systems - Application of a DoE based robust design process chain for system simulation of engine systems - Application of Emulator Models in Hybrid Vehicle Development - Fast response surrogates and sensitivity analysis based on physico-chemical engine simulation applied to modern compression ignition engines - The Connected Car and ist new possibilities in ECU calibration - Processing vehicle-related measurement data - On the selection of appropriate data from routine vehicle operation for system identification of a diesel engine gas system - Data Plausibility at the Engine Test Bench: How im-portant is the Human Factor in the Process? - Non-Convex Hulls for Engineering Applications - Modern Online DoE Methods for Calibration: Constraint Modeling, Continuous Boundary Estimation, Motorsteuerung (DE-588)7578671-0 gnd rswk-swf Big Data (DE-588)4802620-7 gnd rswk-swf Motorenbau (DE-588)4275781-2 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Kraftfahrzeugmotor (DE-588)4127798-3 gnd rswk-swf Automatisches Kalibrieren (DE-588)4343426-5 gnd rswk-swf Steuergerät (DE-588)103238512X gnd rswk-swf Verbrennungsmotor (DE-588)4062661-1 gnd rswk-swf Versuchsplanung (DE-588)4078859-3 gnd rswk-swf (DE-588)1071861417 Konferenzschrift 2017 Berlin gnd-content Motorenbau (DE-588)4275781-2 s Kraftfahrzeugmotor (DE-588)4127798-3 s Verbrennungsmotor (DE-588)4062661-1 s Motorsteuerung (DE-588)7578671-0 s Steuergerät (DE-588)103238512X s Automatisches Kalibrieren (DE-588)4343426-5 s Versuchsplanung (DE-588)4078859-3 s Maschinelles Lernen (DE-588)4193754-5 s Big Data (DE-588)4802620-7 s DE-604 Röpke, Karsten 1966- (DE-588)11555923X edt Gühmann, Clemens 1962- (DE-588)172108837 aut Erscheint auch als Druck-Ausgabe 9783816933816 https://elibrary.narr.digital/book/99.125005/9783816983811 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Gühmann, Clemens 1962- Automotive Data Analytics, Methods and Design of Experiments (DoE) Proceedings of the International Calibration Conference Motorsteuerung (DE-588)7578671-0 gnd Big Data (DE-588)4802620-7 gnd Motorenbau (DE-588)4275781-2 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Kraftfahrzeugmotor (DE-588)4127798-3 gnd Automatisches Kalibrieren (DE-588)4343426-5 gnd Steuergerät (DE-588)103238512X gnd Verbrennungsmotor (DE-588)4062661-1 gnd Versuchsplanung (DE-588)4078859-3 gnd |
| subject_GND | (DE-588)7578671-0 (DE-588)4802620-7 (DE-588)4275781-2 (DE-588)4193754-5 (DE-588)4127798-3 (DE-588)4343426-5 (DE-588)103238512X (DE-588)4062661-1 (DE-588)4078859-3 (DE-588)1071861417 |
| title | Automotive Data Analytics, Methods and Design of Experiments (DoE) Proceedings of the International Calibration Conference |
| title_auth | Automotive Data Analytics, Methods and Design of Experiments (DoE) Proceedings of the International Calibration Conference |
| title_exact_search | Automotive Data Analytics, Methods and Design of Experiments (DoE) Proceedings of the International Calibration Conference |
| title_full | Automotive Data Analytics, Methods and Design of Experiments (DoE) Proceedings of the International Calibration Conference Karsten Röpke, Clemens Gühmann |
| title_fullStr | Automotive Data Analytics, Methods and Design of Experiments (DoE) Proceedings of the International Calibration Conference Karsten Röpke, Clemens Gühmann |
| title_full_unstemmed | Automotive Data Analytics, Methods and Design of Experiments (DoE) Proceedings of the International Calibration Conference Karsten Röpke, Clemens Gühmann |
| title_short | Automotive Data Analytics, Methods and Design of Experiments (DoE) |
| title_sort | automotive data analytics methods and design of experiments doe proceedings of the international calibration conference |
| title_sub | Proceedings of the International Calibration Conference |
| topic | Motorsteuerung (DE-588)7578671-0 gnd Big Data (DE-588)4802620-7 gnd Motorenbau (DE-588)4275781-2 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Kraftfahrzeugmotor (DE-588)4127798-3 gnd Automatisches Kalibrieren (DE-588)4343426-5 gnd Steuergerät (DE-588)103238512X gnd Verbrennungsmotor (DE-588)4062661-1 gnd Versuchsplanung (DE-588)4078859-3 gnd |
| topic_facet | Motorsteuerung Big Data Motorenbau Maschinelles Lernen Kraftfahrzeugmotor Automatisches Kalibrieren Steuergerät Verbrennungsmotor Versuchsplanung Konferenzschrift 2017 Berlin |
| url | https://elibrary.narr.digital/book/99.125005/9783816983811 |
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