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Impedance Based Temperature Estimation of Lithium Ion Cells Using Artificial Neural Networks

Electrical Energy Storage Systems, Institute for Photovoltaics, University of Stuttgart, Pfaffenwaldring 47, 70569 Stuttgart, Germany
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Academic Editor: Catia Arbizzani
Batteries 2021, 7(4), 85; https://doi.org/10.3390/batteries7040085
Received: 31 July 2021 / Revised: 18 November 2021 / Accepted: 8 December 2021 / Published: 12 December 2021
(This article belongs to the Special Issue Battery Systems and Energy Storage beyond 2020)
Tracking the cell temperature is critical for battery safety and cell durability. It is not feasible to equip every cell with a temperature sensor in large battery systems such as those in electric vehicles. Apart from this, temperature sensors are usually mounted on the cell surface and do not detect the core temperature, which can mean detecting an offset due to the temperature gradient. Many sensorless methods require great computational effort for solving partial differential equations or require error-prone parameterization. This paper presents a sensorless temperature estimation method for lithium ion cells using data from electrochemical impedance spectroscopy in combination with artificial neural networks (ANNs). By training an ANN with data of 28 cells and estimating the cell temperatures of eight more cells of the same cell type, the neural network (a simple feed forward ANN with only one hidden layer) was able to achieve an estimation accuracy of ΔT= 1 K (10 C <T< 60 C) with low computational effort. The temperature estimations were investigated for different cell types at various states of charge (SoCs) with different superimposed direct currents. Our method is easy to use and can be completely automated, since there is no significant offset in monitoring temperature. In addition, the prospect of using the above mentioned approach to estimate additional battery states such as SoC and state of health (SoH) is discussed. View Full-Text
Keywords: lithium-ion batteries; temperature estimation; sensorless temperature measurement; artificial intelligence; artificial neural network lithium-ion batteries; temperature estimation; sensorless temperature measurement; artificial intelligence; artificial neural network
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MDPI and ACS Style

Ströbel, M.; Pross-Brakhage, J.; Kopp, M.; Birke, K.P. Impedance Based Temperature Estimation of Lithium Ion Cells Using Artificial Neural Networks. Batteries 2021, 7, 85. https://doi.org/10.3390/batteries7040085

AMA Style

Ströbel M, Pross-Brakhage J, Kopp M, Birke KP. Impedance Based Temperature Estimation of Lithium Ion Cells Using Artificial Neural Networks. Batteries. 2021; 7(4):85. https://doi.org/10.3390/batteries7040085

Chicago/Turabian Style

Ströbel, Marco, Julia Pross-Brakhage, Mike Kopp, and Kai P. Birke. 2021. "Impedance Based Temperature Estimation of Lithium Ion Cells Using Artificial Neural Networks" Batteries 7, no. 4: 85. https://doi.org/10.3390/batteries7040085

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