Vehicle Driveability Assessment using Neural Networks for Development, Calibration and Quality Tests
Actual automotive themes in the beginning century are globalization and platform concepts. Platforms reduce manpower for basic power train development and enable a higher vehicle quality by sharing development cost to many models. New drive train generations with direct injected diesel and gasoline engines, variable valve train systems and hybrid drives require complex electronic control systems with many control parameters, which must be calibrated for each platform model to fulfill the targets for emissions, diagnostics and driveability. Calibration becomes a critical procedure in vehicle development. A negative effect of the platform is the reduced possibility to give a model or an OEM a brand specific driveability character, traditionally an important sales - promoting factor. The paper describes a tool for the objective real time assessment of vehicle driveability and vehicle character, using a new subjective - objective approach.