Browse Publications Technical Papers 2002-01-0929

Automated Manual Transmissions - A European Survey and Proposed Quality Shift Metrics 2002-01-0929

In Europe there is considerable growth in the application of automated manual transmissions (AMT) predominantly for reasons of cost and CO2 efficiency. It is believed that restricted functionality and the poor shift quality of this “first wave” of manual-based transmissions may hinder this trend being repeated in markets dominated by conventional automatic transmissions (ATs) such as the USA.
This paper addresses two areas:
  • Results and analysis of measured and subjective data from a comprehensive, independent survey of six AMT vehicles available in Europe.
  • Techniques to predict subjective ratings of shift quality from physical variables.
Data is presented from a wider survey of current production “robotised” manual transmissions sold in the European market. This study included vehicles from B, C, and D/E classes and the light truck sector. The transmissions featured a single dry clutch, single layshaft and either electrical or electro-hydraulic actuation of the clutch and the shift rails. Universally, it was found that shift quality was poor in comparison to conventional ATs by at least 1.5 points on a 1-10 rating scale, as assessed by a panel of experienced transmission calibrators. The paper discusses the powertrain factors contributing to this difference and how the shift quality could be improved prior to the next generation of manual-based transmissions, namely twin clutch designs (AMT-2).
Initial research is presented for shift quality metrics to allow prediction of subjective ratings from objective measures. Novel techniques are discussed to improve the correlation between engineering parameters and subjective ratings:
  • Cost function technique relative to an “ideal shift”.
  • Metrics associated with estimated human frame forces leading to variables associated with pressure profiles and neuromuscular effort.
  • System identification techniques.
  • Neural Network to convert engineering parameters to subjective ratings.


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