Enhanced Machine Learning and Data Mining Methods for Analysing Large Hybrid Electric Vehicle Fleets based on Load Spectrum Data
Springer Fachmedien Wiesbaden GmbH (Verlag)
978-3-658-20366-5 (ISBN)
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.
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.
Classifying Component Failures of a Vehicle Fleet.- Visualising Different Kinds of Vehicle Stress and Usage.- Identifying Usage and Stress Patterns in a Vehicle Fleet.
| Erscheinungsdatum | 23.12.2017 |
|---|---|
| Reihe/Serie | Wissenschaftliche Reihe Fahrzeugtechnik Universität Stuttgart |
| Zusatzinfo | XXXII, 166 p. 34 illus., 11 illus. in color. |
| Verlagsort | Wiesbaden |
| Sprache | englisch |
| Maße | 148 x 210 mm |
| Gewicht | 265 g |
| Themenwelt | Technik ► Fahrzeugbau / Schiffbau |
| Technik ► Maschinenbau | |
| Schlagworte | Balanced Random Forest • classification • hybrid car battery • Logged On-board Data • Off-board data • rule learning • stress patterns • t-Distributed Stochastic Neighbour Embedding • vehicle eet • vehicle eet • vehicle fleet • vehicle usage patterns |
| ISBN-10 | 3-658-20366-8 / 3658203668 |
| ISBN-13 | 978-3-658-20366-5 / 9783658203665 |
| Zustand | Neuware |
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