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Applying Data Reduction Methods to Battery Degradation Prediction Models for Electric Vehicles

In: Electric Vehicles in Shared Fleets Mobility Management, Business Models, and Decision Support Systems

Author

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  • Philipp Staudt
  • Jennifer Schoch
  • Thomas Setzer

Abstract

Battery electric vehicles (EVs) are increasingly used in mobility services such as carsharing. A severe problem with EVs is battery degradation, leading to a reduction of the already very limited range of an EV. Analytic models are required to determine the impact of service usage to provide guidance on how to drive and charge and also to support service tasks such as predictive maintenance. However, while the increasing number of sensor data in automotive applications allows for more fine-grained model parametrization and better predictive outcomes, in practical settings, the amount of storage and transmission bandwidth is limited by technical and economic considerations. By means of a simulation-based analysis, dynamic user behavior is simulated in this chapter based on real-world driving profiles, parametrized by different driver characteristics and ambient conditions. We find that by using a shrunken subset of variables, the required storage can be reduced considerably at low costs in terms of only slightly decreased predictive accuracy. This will especially help electric vehicle fleet operators to monitor their car park and to ensure maximum availability and reduced downtime for their customers.

Suggested Citation

  • Philipp Staudt & Jennifer Schoch & Thomas Setzer, 2022. "Applying Data Reduction Methods to Battery Degradation Prediction Models for Electric Vehicles," World Scientific Book Chapters, in: Kenan Degirmenci & Thomas M Cerbe & Wolfgang E Pfau (ed.), Electric Vehicles in Shared Fleets Mobility Management, Business Models, and Decision Support Systems, chapter 11, pages 233-254, World Scientific Publishing Co. Pte. Ltd..
    • Handle: RePEc:wsi:wschap:9781800611429_0011
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    More about this item

    Keywords

    Electric Vehicles; Shared Fleets; Carsharing; Mobility; Mobility Management; Business Models; Decision Support Systems; Information Systems;
    All these keywords.

    JEL classification:

    • L9 - Industrial Organization - - Industry Studies: Transportation and Utilities
    • M1 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D

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