Journal Article

FZJ-2022-04189

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Data-driven systematic parameter identification of an electrochemical model for lithium-ion batteries with artificial intelligence

Li, W. (Corresponding author) ; Demir, I. ; Cao, D. ; Jöst, D. ; Ringbeck, F. ; Junker, M. ; Sauer, D. U.FZJ*

2022
Elsevier Amsterdam

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Please use a persistent id in citations: doi:10.1016/j.ensm.2021.10.023  doi:10.34734/FZJ-2022-04189

Abstract: Electrochemical models are more and more widely applied in battery diagnostics, prognostics and fast charging control, considering their high fidelity, high extrapolability and physical interpretability. However, parameter identification of electrochemical models is challenging due to the complicated model structure and a large number of physical parameters with different identifiability. The scope of this work is the development of a data-driven parameter identification framework for electrochemical models for lithium-ion batteries in real-world operations with artificial intelligence, i.e., the cuckoo search algorithm. Only current and voltage data are used as input for the multi-objective global optimization of the parameters considering both voltage error between the model and the battery and the relative capacity error between two electrodes. The multi-step identification process based on sensitivity analysis increases the identification accuracy of the parameters with low sensitivity. Moreover, the novel identification process inspired by the training process in machine learning further overcomes the overfitting problem using limited battery data. The data-driven approach achieves a maximum root mean square error of 9 mV and 12.7 mV for the full cell voltage under constant current discharging and real-world driving cycles, respectively, which is only 17.9% and 42.9% of that of the experimental identification approach.

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Contributing Institute(s):

1. Helmholtz-Institut Münster Ionenleiter für Energiespeicher (IEK-12)
2. JARA-ENERGY (JARA-ENERGY)

Research Program(s):

1. 1223 - Batteries in Application (POF4-122) (POF4-122)
2. EVERLASTING - Electric Vehicle Enhanced Range, Lifetime And Safety Through INGenious battery management (713771) (713771)
3. BMBF 03XP0334 - Model2Life- Modellbasierte Systemauslegung für 2nd-Life-Nutzungsszenarien von mobilen Batteriesystemen (03XP0334) (03XP0334)

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Database coverage:
; ; ; Clarivate Analytics Master Journal List ; Current Contents - Engineering, Computing and Technology ; Current Contents - Physical, Chemical and Earth Sciences ; Essential Science Indicators ; IF >= 15 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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