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Enhancing electric vehicle sustainability through battery life optimal charging

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  • Schoch, Jennifer
  • Gaerttner, Johannes
  • Schuller, Alexander
  • Setzer, Thomas

Abstract

In this article, we investigate the potential for battery life prolongation through optimized charging under consideration of individual mobility requirements. Based on a comprehensive battery aging model we introduce a continuous quadratic programming model to derive battery life optimal charging (OPT). The strategy indicates when and how much to charge to maximize the potential range throughout the battery life. We find that OPT has the potential to more than double the expected battery life compared to simple and often abundant recharging activities as observable today. The degree of battery life prolongation strongly depends on the operating temperature. Since optimal charging would require deterministic knowledge of future trips and corresponding charging levels we investigate a more convenient charging heuristic derived from “As-Late-As-Possible” (ALAP) charging. ALAP charging considers range buffers between 5% and 60% over the range required until the next re-charging opportunity. We analyze the trade-off between (long-term) battery life and (short-term) range flexibility. We find that for decreasing temperatures the trade-off between battery life and flexibility is solved with increasing range buffers. From our results battery degradation aware charging heuristics can be easily derived and applied in real-world settings.

Suggested Citation

  • Schoch, Jennifer & Gaerttner, Johannes & Schuller, Alexander & Setzer, Thomas, 2018. "Enhancing electric vehicle sustainability through battery life optimal charging," Transportation Research Part B: Methodological, Elsevier, vol. 112(C), pages 1-18.
    • Handle: RePEc:eee:transb:v:112:y:2018:i:c:p:1-18
    DOI: 10.1016/j.trb.2018.03.016
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    Cited by:

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    4. Guo, Fang & Zhang, Jingjing & Huang, Zhihong & Huang, Weilai, 2022. "Simultaneous charging station location-routing problem for electric vehicles: Effect of nonlinear partial charging and battery degradation," Energy, Elsevier, vol. 250(C).
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    6. Leonardo Dias & Brigitte Jaumard & Lackis Eleftheriadis, 2024. "Taking Advantage of Spare Battery Capacity in Cellular Networks to Provide Grid Frequency Regulation," Energies, MDPI, vol. 17(15), pages 1-28, July.
    7. Maxwell Woody & Michael T. Craig & Parth T. Vaishnav & Geoffrey M. Lewis & Gregory A. Keoleian, 2022. "Optimizing future cost and emissions of electric delivery vehicles," Journal of Industrial Ecology, Yale University, vol. 26(3), pages 1108-1122, June.
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    10. Vepsäläinen, Jari & Otto, Kevin & Lajunen, Antti & Tammi, Kari, 2019. "Computationally efficient model for energy demand prediction of electric city bus in varying operating conditions," Energy, Elsevier, vol. 169(C), pages 433-443.
    11. Adnane Houbbadi & Rochdi Trigui & Serge Pelissier & Eduardo Redondo-Iglesias & Tanguy Bouton, 2019. "Optimal Scheduling to Manage an Electric Bus Fleet Overnight Charging," Energies, MDPI, vol. 12(14), pages 1-17, July.
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    13. Nandan Gopinathan & Prabhakar Karthikeyan Shanmugam, 2022. "Energy Anxiety in Decentralized Electricity Markets: A Critical Review on EV Models," Energies, MDPI, vol. 15(14), pages 1-40, July.
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