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Enhanced Machine Learning and Data Mining Methods for Analysing Large Hybrid Electric Vehicle Fleets based on Load Spectrum Data

  • Philipp Bergmeir

Table of contents

  1. Front Matter
    Pages I-XXXII
  2. Philipp Bergmeir
    Pages 1-6
  3. Philipp Bergmeir
    Pages 7-17
  4. Philipp Bergmeir
    Pages 147-151
  5. Philipp Bergmeir
    Pages 153-153
  6. Back Matter
    Pages 155-166

About this book

Introduction

Philipp Bergmeir works on the development and enhancement of data mining and machine learning methods with the aim of analysing automatically huge amounts of load spectrum data that are recorded for large hybrid electric vehicle fleets. In particular, he presents new approaches for uncovering and describing stress and usage patterns that are related to failures of selected components of the hybrid power-train.

Contents
  • Classifying Component Failures of a Vehicle Fleet
  • Visualising Different Kinds of Vehicle Stress and Usage
  • Identifying Usage and Stress Patterns in a Vehicle Fleet
Target Groups 
  • Students and scientists in the field of automotive engineering and data science
  • Engineers in the automotive industry
About the Author
Philipp Bergmeir did a PhD in the doctoral program “Promotionskolleg HYBRID” at the Institute for Internal Combustion Engines and Automotive Engineering, University of Stuttgart, in cooperation with the Esslingen University of Applied Sciences and a well-known vehicle manufacturer. Currently, he is working as a data scientist in the automotive industry.

Keywords

Logged On-board Data Balanced Random Forest t-Distributed Stochastic Neighbour Embedding Rule Learning Classification vehicle fleet Off-board data hybrid car battery stress patterns vehicle usage patterns

Authors and affiliations

  • Philipp Bergmeir
    • 1
  1. 1.Lehrstuhl für FahrzeugantriebeUniversität Stuttgart/IVK, Fakultät 7 StuttgartGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-658-20367-2
  • Copyright Information Springer Fachmedien Wiesbaden GmbH 2018
  • Publisher Name Springer Vieweg, Wiesbaden
  • eBook Packages Engineering
  • Print ISBN 978-3-658-20366-5
  • Online ISBN 978-3-658-20367-2
  • Buy this book on publisher's site