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Collective learning of lithium-ion aging model parameters for battery health-conscious demand response in datacenters

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  • Mamun, A.
  • Sivasubramaniam, A.
  • Fathy, H.K.

Abstract

This paper examines the degree to which a large-scale datacenter employing lithium-ion (Li-ion) batteries for demand response can learn the physics-based aging and degradation dynamics of the underlying batteries by measuring their input/output current/voltage data. Battery degradation dynamics are chemistry dependent and change significantly for newer chemistries. Moreover, characterizing these degradation dynamics requires time-consuming and expensive laboratory testing. Together, these facts motivate the following question: is it possible to use battery current and voltage measurements to learn battery degradation behavior in a datacenter where numerous distributed batteries are being used for demand response? If so, what are the benefits and challenges associated with such learning? The goal of this paper is to provide preliminary answers to these questions building on earlier work by authors on health-conscious stochastic battery control. Specifically, we show that when datacenters exploit its demand management flexibility at the rack-level to control different batteries in accordance with different management policies, the resulting data is sufficiently rich to the point where (1) the learning of degradation behavior is possible within the span of approximately 1 year, (2) with a reasonable number of cells, (3) even when the batteries are used simultaneously for degradation learning and demand response.

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  • Mamun, A. & Sivasubramaniam, A. & Fathy, H.K., 2018. "Collective learning of lithium-ion aging model parameters for battery health-conscious demand response in datacenters," Energy, Elsevier, vol. 154(C), pages 80-95.
    • Handle: RePEc:eee:energy:v:154:y:2018:i:c:p:80-95
    DOI: 10.1016/j.energy.2018.04.070
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    1. Tanim, Tanvir R. & Rahn, Christopher D. & Wang, Chao-Yang, 2015. "State of charge estimation of a lithium ion cell based on a temperature dependent and electrolyte enhanced single particle model," Energy, Elsevier, vol. 80(C), pages 731-739.
    2. Wang, Yujie & Liu, Chang & Pan, Rui & Chen, Zonghai, 2017. "Modeling and state-of-charge prediction of lithium-ion battery and ultracapacitor hybrids with a co-estimator," Energy, Elsevier, vol. 121(C), pages 739-750.
    3. Galeotti, Matteo & Cinà, Lucio & Giammanco, Corrado & Cordiner, Stefano & Di Carlo, Aldo, 2015. "Performance analysis and SOH (state of health) evaluation of lithium polymer batteries through electrochemical impedance spectroscopy," Energy, Elsevier, vol. 89(C), pages 678-686.
    4. Björn Nykvist & Måns Nilsson, 2015. "Rapidly falling costs of battery packs for electric vehicles," Nature Climate Change, Nature, vol. 5(4), pages 329-332, April.
    5. J.-M. Tarascon & M. Armand, 2001. "Issues and challenges facing rechargeable lithium batteries," Nature, Nature, vol. 414(6861), pages 359-367, November.
    6. Ou, Ting-Chia & Hong, Chih-Ming, 2014. "Dynamic operation and control of microgrid hybrid power systems," Energy, Elsevier, vol. 66(C), pages 314-323.
    7. Li, Junfu & Lai, Qingzhi & Wang, Lixin & Lyu, Chao & Wang, Han, 2016. "A method for SOC estimation based on simplified mechanistic model for LiFePO4 battery," Energy, Elsevier, vol. 114(C), pages 1266-1276.
    8. Ting-Chia Ou & Kai-Hung Lu & Chiou-Jye Huang, 2017. "Improvement of Transient Stability in a Hybrid Power Multi-System Using a Designed NIDC (Novel Intelligent Damping Controller)," Energies, MDPI, vol. 10(4), pages 1-16, April.
    9. Berecibar, Maitane & Garmendia, Maitane & Gandiaga, Iñigo & Crego, Jon & Villarreal, Igor, 2016. "State of health estimation algorithm of LiFePO4 battery packs based on differential voltage curves for battery management system application," Energy, Elsevier, vol. 103(C), pages 784-796.
    10. Zheng, Fangdan & Jiang, Jiuchun & Sun, Bingxiang & Zhang, Weige & Pecht, Michael, 2016. "Temperature dependent power capability estimation of lithium-ion batteries for hybrid electric vehicles," Energy, Elsevier, vol. 113(C), pages 64-75.
    11. Deng, Zhongwei & Yang, Lin & Cai, Yishan & Deng, Hao & Sun, Liu, 2016. "Online available capacity prediction and state of charge estimation based on advanced data-driven algorithms for lithium iron phosphate battery," Energy, Elsevier, vol. 112(C), pages 469-480.
    12. Uddin, Kotub & Gough, Rebecca & Radcliffe, Jonathan & Marco, James & Jennings, Paul, 2017. "Techno-economic analysis of the viability of residential photovoltaic systems using lithium-ion batteries for energy storage in the United Kingdom," Applied Energy, Elsevier, vol. 206(C), pages 12-21.
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    Cited by:

    1. Alaperä, Ilari & Honkapuro, Samuli & Paananen, Janne, 2018. "Data centers as a source of dynamic flexibility in smart girds," Applied Energy, Elsevier, vol. 229(C), pages 69-79.

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