Estimation of Differential Capacity in Lithium-Ion Batteries Using Machine Learning Approaches

Eirik Odinsen, Mahshid N. Amiri, Odne S. Burheim and Jacob J. Lamb ()
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Eirik Odinsen: Sustainable Energy Systems Research Group, Department of Energy and Process Engineering, Faculty of Engineering, NTNU, NO-7491 Trondheim, Norway
Mahshid N. Amiri: Sustainable Energy Systems Research Group, Department of Energy and Process Engineering, Faculty of Engineering, NTNU, NO-7491 Trondheim, Norway
Odne S. Burheim: Sustainable Energy Systems Research Group, Department of Energy and Process Engineering, Faculty of Engineering, NTNU, NO-7491 Trondheim, Norway
Jacob J. Lamb: Sustainable Energy Systems Research Group, Department of Energy and Process Engineering, Faculty of Engineering, NTNU, NO-7491 Trondheim, Norway

Energies, 2024, vol. 17, issue 19, 1-15

Abstract: Comprehending the electrochemical condition of a lithium-ion battery (LiB) is essential for guaranteeing its safe and effective operation. This insight is increasingly obtained through characterization tests such as a differential capacity analysis, a characterization test well suited for the electric transportation sector due to its dependency on the available voltage and current (E–I) data. However, a drawback of this technique is its time dependency, as it requires extensive time due to the need to conduct it at low charge rates, typically around C/20. This work seeks to forecast characterization data utilizing 1C cycle data at increased temperatures, thereby reducing the time required for testing. To achieve this, three neural network architectures were utilized as the following: a recurrent neural network (RNN), feed forward neural network (FNN), and long short-term memory neural network (LSTM). The LSTM demonstrated superior performance with evaluation scores of the mean squared error (MSE) of 0.49 and mean absolute error (MAE) of 4.38, compared to the FNN (MSE: 1.25, MAE: 7.37) and the RNN (MSE: 0.89, MAE: 6.05) in predicting differential capacity analysis, with all models completing their computations within a time range of 49 to 299 ms. The methodology utilized here offers a straightforward way of predicting LiB degradation modes without relying on polynomial fits or physics-based models. This work highlights the feasibility of forecasting differential capacity profiles using 1C data at various elevated temperatures. In conclusion, neural networks, particularly an LSTM, can effectively provide insights into electrochemical conditions based on 1C cycling data.

Keywords: lithium-ion batteries; machine learning; differential capacity analysis; neural networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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