Multi-temperature state-dependent equivalent circuit discharge model for lithium-sulfur batteries

JOURNAL OF POWER SOURCES

卷:328

Article

Propp, Karsten¹  Marinescu, Monica²  Auger, Daniel J.¹  O'Neill, Laura³  Fotouhi, Abbas¹  Somasundaram, Karthik^3,4  Offer, Gregory J.²  Minton, Geraint³  Longo, Stefano¹  Wild, Mark³  Knap, Vaclav^1,5 

[1] Cranfield Univ, Sch Aerosp Transport & Mfg, Coll Rd, Cranfield MK43 0AL, Beds, England

[2] Imperial Coll London, Dept Mech Engn, London SW7 2AZ, England

[3] OXIS Energy LTD, Culham Sci Ctr E1, Abingdon OX14 3DB, Oxon, England

[4] Natl Univ Singapore, Dept Biomol & Chem Engn, Singapore, Singapore

[5] Aalborg Univ, Dept Energy Technol, Aalborg, Denmark

关键词: Lithium-sulfur battery;    Parameter estimation;    System identification;    Battery model;   

DOI  :  10.1016/j.jpowsour.2016.07.090

来源: Elsevier

【 摘 要 】

Lithium-sulfur (Li-S) batteries are described extensively in the literature, but existing computational models aimed at scientific understanding are too complex for use in applications such as battery management. Computationally simple models are vital for exploitation. This paper proposes a non-linear state-of-charge dependent Li-S equivalent circuit network (ECN) model for a Li-S cell under discharge. Li-S batteries are fundamentally different to Li-ion batteries, and require chemistry-specific models. A new Li-S model is obtained using a 'behavioural' interpretation of the ECN model; as Li-S exhibits a 'steep' open-circuit voltage (OCV) profile at high states-of-charge, identification methods are designed to take into account OCV changes during current pulses. The prediction-error minimization technique is used. The model is parameterized from laboratory experiments using a mixed-size current pulse profile at four temperatures from 10 degrees C to 50 degrees C, giving linearized ECN parameters for a range of states-of-charge, currents and temperatures. These are used to create a nonlinear polynomial-based battery model suitable for use in a battery management system. When the model is used to predict the behaviour of a validation data set representing an automotive NEDC driving cycle, the terminal voltage predictions are judged accurate with a root mean square error of 32 mV. (C) 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license.

【 授权许可】

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