Battery Health Diagnosis via Neural Surrogate Model: From Lab to Field

Hojin Cheon, Jihun Jeon, Byungil Jung and Hongseok Kim ()
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Hojin Cheon: Department of Electronic Engineering, Sogang University, Seoul 04107, Republic of Korea
Jihun Jeon: Department of Electronic Engineering, Sogang University, Seoul 04107, Republic of Korea
Byungil Jung: Doosan Enerbility, Seongnam 13557, Republic of Korea
Hongseok Kim: Department of Electronic Engineering, Sogang University, Seoul 04107, Republic of Korea

Energies, 2025, vol. 18, issue 9, 1-15

Abstract: Batteries degrade over time. Such degradation leads to performance loss, but more importantly, safety issues arise. To evaluate the battery degradation, traditional diagnostic techniques rely on model-based or data-driven approaches; however, those methods often require controlled conditions or specific tests, which may not be applicable in real fields. In this regard, we propose a deep learning-based method addressing these limitations by accurately modeling batteries using real-world operational data from photovoltaic (PV)-integrated battery energy storage system (BESSs), where charging currents vary dynamically and SOC is capped at 70% by regulation. The proposed method is based on a neural surrogate model for batteries, employing a sequence-to-sequence architecture, which directly captures the dynamic behavior of batteries from operational data, eliminating the need for specialized characterization tests or feature extraction. The proposed model synthesizes the terminal voltage with a mean absolute error of 6.4 mV for lithium–iron–phosphate (LFP) cells and 49 mV for nickel–cobalt–manganese (NCM) battery modules, respectively, which is only 0.4% and 0.29% of the voltage swing. As a health indicator, we also propose the concept of voltage deviation (VD), defined as the deviation between the synthesized and actual terminal voltages. We demonstrate that VD can be evaluated not only in laboratory data but also in field data.

Keywords: Li-ion battery; deep learning; neural networks; battery management systems; energy storage systems (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: 2025
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