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Battery prices and capacity sensitivity: Electric drive vehicles


  • Juul, Nina


The increase in fluctuating power production requires an increase in flexibility in the system as well. Flexibility can be found in generation technologies with fast response times or in storage options. In the transport sector, the proportion of electric drive vehicles is expected to increase over the next decade or two. These vehicles can provide some of the flexibility needed in the power system, in terms of both flexible demand and electricity storage. However, what are the batteries worth to the power system? And does the value depend on battery capacity? This article presents an analysis of the integrated power and transport system, focusing on the sensitivity of the power system configuration according to battery capacity and price of the electric drive vehicle. The value of different battery capacities is estimated, given that the batteries are used for both driving and storage. Likewise, the prices at which the electric drive vehicles become of interest to the power system are found. Smart charge, including the opportunity to discharge (vehicle-to-grid) is used in all scenarios. Analyses show that the marginal benefits decrease the larger the battery. For very high battery prices, large batteries imply that diesel vehicles are preferable to electric drive vehicles.

Suggested Citation

  • Juul, Nina, 2012. "Battery prices and capacity sensitivity: Electric drive vehicles," Energy, Elsevier, vol. 47(1), pages 403-410.
  • Handle: RePEc:eee:energy:v:47:y:2012:i:1:p:403-410
    DOI: 10.1016/

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    References listed on IDEAS

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    Cited by:

    1. Yang, Shengjie & Yao, Jiangang & Kang, Tong & Zhu, Xiangqian, 2014. "Dynamic operation model of the battery swapping station for EV (electric vehicle) in electricity market," Energy, Elsevier, vol. 65(C), pages 544-549.
    2. Biegel, Benjamin & Hansen, Lars Henrik & Stoustrup, Jakob & Andersen, Palle & Harbo, Silas, 2014. "Value of flexible consumption in the electricity markets," Energy, Elsevier, vol. 66(C), pages 354-362.
    3. Hu, Xiaosong & Li, Shengbo Eben & Jia, Zhenzhong & Egardt, Bo, 2014. "Enhanced sample entropy-based health management of Li-ion battery for electrified vehicles," Energy, Elsevier, vol. 64(C), pages 953-960.
    4. Zhou, Boya & Wu, Ye & Zhou, Bin & Wang, Renjie & Ke, Wenwei & Zhang, Shaojun & Hao, Jiming, 2016. "Real-world performance of battery electric buses and their life-cycle benefits with respect to energy consumption and carbon dioxide emissions," Energy, Elsevier, vol. 96(C), pages 603-613.
    5. Zhang, Shuo & Xiong, Rui & Zhang, Chengning & Sun, Fengchun, 2016. "An optimal structure selection and parameter design approach for a dual-motor-driven system used in an electric bus," Energy, Elsevier, vol. 96(C), pages 437-448.
    6. Fernández, I.J. & Calvillo, C.F. & Sánchez-Miralles, A. & Boal, J., 2013. "Capacity fade and aging models for electric batteries and optimal charging strategy for electric vehicles," Energy, Elsevier, vol. 60(C), pages 35-43.
    7. Arslan, Okan & Karasan, Oya Ekin, 2013. "Cost and emission impacts of virtual power plant formation in plug-in hybrid electric vehicle penetrated networks," Energy, Elsevier, vol. 60(C), pages 116-124.
    8. Hu, Jibin & Wu, Wei & Yuan, Shihua & Jing, Chongbo, 2013. "Fuel combustion under asymmetric piston motion: Tested results," Energy, Elsevier, vol. 55(C), pages 209-215.
    9. Mahmoudzadeh Andwari, Amin & Pesiridis, Apostolos & Rajoo, Srithar & Martinez-Botas, Ricardo & Esfahanian, Vahid, 2017. "A review of Battery Electric Vehicle technology and readiness levels," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 414-430.


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