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Design of Experiments (DoE) in Powertrain Development

62,00 €*

Sofort verfügbar, Lieferzeit: 1-3 Tage

Produktnummer: 186a3a5236cd36464db163f535cce5d277
Produktinformationen "Design of Experiments (DoE) in Powertrain Development"
Developing complex powertrains without model-based processes is inconceivable today. But which methods can be used to address future challenges in powertrain development such as real driving emissions (RDE) and the diversity of derivatives? Contents: A strategy to employ criteria for online location selection when using Gaussian Processes - Efficient online test plan calculation with integrated boundary detection for modern model based engine calibration - Benchmark Problem for Near Boundary Operation Control for Automotive Engine - Lookup Table Optimisation for Engineering Applications - A Dynamometer Dynamic Calibration Method for the Diesel Air-Path - Application of DoE Method on Synthetic Gas Bench for SCR studies in Early Stages of the Model-Based System Development Process - Application of Model Based Calibration to Mass Production Diesel Engine Development for Indian Market - Application of global model based calibration methodology to optimize a 2.3 litre diesel engine with SCR on WLTC cycle - Advanced Gaussian Process Modeling Techniques - Fast Engine Modelling Using On-Line Calibration Data - Steady State Calibration for Catalyst Heat-up Optimization on Gasoline Direct Injection Engine - Efficient variant calibration by automation using rules and dependencies - Efficient Calibration Process for Series Programme with Multiple Engine, Vehicle and Market Variants - Computing Optimized Calibration Maps including Model Prediction and Map Smoothness - DoE and beyond: the evolution of the model based development approach how legal trends are changing methodology - Vehicle and OEM generic HIL / SIL Model in the transmission development - Data-based Models on the ECU - Pattern recognition for classifying degradation states of lithium ion batteries - Simulation-Error Based Identification of Dynamic Calibration Models

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