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Modelling of modular battery systems under cell capacity variation and degradation

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  • Rogers, Daniel J.
  • Aslett, Louis J.M.
  • Troffaes, Matthias C.M.

Abstract

We propose a simple statistical model of electrochemical cell degradation based on the general characteristics observed in previous large-scale experimental studies of cell degradation. This model is used to statistically explore the behaviour and lifetime performance of battery systems where the cells are organised into modules that are controlled semi-independently. Intuitively, such systems should offer improved reliability and energy availability compared to monolithic systems as the system ages and cells degrade and fail. To validate this intuition, this paper explores the capacity evolution of populations of systems composed of random populations of cells. This approach allows the probability that a given system design meets a given lifetime specification to be calculated. A cost model that includes the effect of uncertainty in degradation behaviour is introduced and used to explore the cost-benefit trade-offs arising from the interaction of degradation and module size. Case studies of an electric vehicle battery pack and a grid-connected energy storage system are used to demonstrate the use of the model to find lifetime cost-optimum designs. It is observed that breaking a battery energy storage system up into smaller modules can lead to large increases in accessible system capacity and may lead to a decision to use lower-quality, lower-cost cells in a cost-optimum system.

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  • Rogers, Daniel J. & Aslett, Louis J.M. & Troffaes, Matthias C.M., 2021. "Modelling of modular battery systems under cell capacity variation and degradation," Applied Energy, Elsevier, vol. 283(C).
    • Handle: RePEc:eee:appene:v:283:y:2021:i:c:s0306261920317372
    DOI: 10.1016/j.apenergy.2020.116360
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    1. Kristen A. Severson & Peter M. Attia & Norman Jin & Nicholas Perkins & Benben Jiang & Zi Yang & Michael H. Chen & Muratahan Aykol & Patrick K. Herring & Dimitrios Fraggedakis & Martin Z. Bazant & Step, 2019. "Data-driven prediction of battery cycle life before capacity degradation," Nature Energy, Nature, vol. 4(5), pages 383-391, May.
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    2. Tomás P. Corrêa & Thales A. C. Maia & Braz J. Cardoso Filho, 2022. "High-Performance Power Electronic Battery Pack Based on a Back-to-Back Converter," Energies, MDPI, vol. 16(1), pages 1-17, December.
    3. Reniers, Jorn M. & Howey, David A., 2023. "Digital twin of a MWh-scale grid battery system for efficiency and degradation analysis," Applied Energy, Elsevier, vol. 336(C).
    4. Chang, Long & Ma, Chen & Zhang, Chenghui & Duan, Bin & Cui, Naxin & Li, Changlong, 2023. "Correlations of lithium-ion battery parameter variations and connected configurations on pack statistics," Applied Energy, Elsevier, vol. 329(C).
    5. Pan, Yongjun & Zhang, Xiaoxi & Liu, Yue & Wang, Huacui & Cao, Yangzheng & Liu, Xin & Liu, Binghe, 2022. "Dynamic behavior prediction of modules in crushing via FEA-DNN technique for durable battery-pack system design," Applied Energy, Elsevier, vol. 322(C).
    6. Smolenski, Robert & Szczesniak, Pawel & Drozdz, Wojciech & Kasperski, Lukasz, 2022. "Advanced metering infrastructure and energy storage for location and mitigation of power quality disturbances in the utility grid with high penetration of renewables," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).

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