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Joint European Conference on Machine Learning and Knowledge Discovery in Databases

ECML PKDD 2012: Machine Learning and Knowledge Discovery in Databases pp 195–210Cite as

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Learning Policies for Battery Usage Optimization in Electric Vehicles

Learning Policies for Battery Usage Optimization in Electric Vehicles

  • Stefano Ermon21,
  • Yexiang Xue21,
  • Carla Gomes21 &
  • …
  • Bart Selman21 
  • Conference paper
  • 4864 Accesses

  • 4 Citations

Part of the Lecture Notes in Computer Science book series (LNAI,volume 7524)

Abstract

The high cost, limited capacity, and long recharge time of batteries pose a number of obstacles for the widespread adoption of electric vehicles. Multi-battery systems that combine a standard battery with supercapacitors are currently one of the most promising ways to increase battery lifespan and reduce operating costs. However, their performance crucially depends on how they are designed and operated.

In this paper, we formalize the problem of optimizing real-time energy management of multi-battery systems as a stochastic planning problem, and we propose a novel solution based on a combination of optimization, machine learning and data-mining techniques. We evaluate the performance of our intelligent energy management system on various large datasets of commuter trips crowdsourced in the United States. We show that our policy significantly outperforms the leading algorithms that were previously proposed as part of an open algorithmic challenge.

Keywords

  • Electric Vehicle
  • Model Predictive Control
  • Markov Decision Process
  • Capacity Level
  • Learn Policy

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Cornell University, USA, Ithaca, NY

    Stefano Ermon, Yexiang Xue, Carla Gomes & Bart Selman

Authors
  1. Stefano Ermon
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  2. Yexiang Xue
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  3. Carla Gomes
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  4. Bart Selman
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Editor information

Editors and Affiliations

  1. Intelligent Systems Laboratory, University of Bristol, Merchant Venturers Building, Woodland Road, BS8 1UB, Bristol, UK

    Peter A. Flach

  2. Intelligent Systems Laboratory, University of Bristol, Merchant Venturers Building, Woodland Road,, BS8 1UB, Bristol, UK

    Tijl De Bie & Nello Cristianini & 

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Cite this paper

Ermon, S., Xue, Y., Gomes, C., Selman, B. (2012). Learning Policies for Battery Usage Optimization in Electric Vehicles. In: Flach, P.A., De Bie, T., Cristianini, N. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2012. Lecture Notes in Computer Science(), vol 7524. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33486-3_13

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  • DOI: https://doi.org/10.1007/978-3-642-33486-3_13

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