Intelligent Learning Method for Capacity Estimation of Lithium-Ion Batteries Based on Partial Charging Curves

Can Ding, Qing Guo, Lulu Zhang () and Tao Wang
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Can Ding: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
Qing Guo: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
Lulu Zhang: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
Tao Wang: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China

Energies, 2024, vol. 17, issue 11, 1-13

Abstract: Lithium-ion batteries are widely used in electric vehicles, energy storage power stations, and many other applications. Accurate and reliable monitoring of battery health status and remaining capacity is the key to establish a lithium-ion cell management system. In this paper, based on a Bayesian optimization algorithm, a deep neural network is structured to evaluate the whole charging curve of the battery using partial charging curve data as input. A 0.74 Ah battery is used for experiments, and the effect of different input data lengths is also investigated to check the high flexibility of the approach. The consequences show that using only 20 points of partial charging data as input, the whole charging profile of a cell can be exactly predicted with a root-mean-square error (RMSE) of less than 19.16 mAh (2.59% of the nominal capacity of 0.74 Ah), and its mean absolute percentage error (MAPE) is less than 1.84%. In addition, critical information including battery state-of-charge (SOC) and state-of-health (SOH) can be extracted in this way to provide a basis for safe and long-lasting battery operation.

Keywords: lithium-ion battery; charge curve estimation; Bayesian optimization; state-of-health; deep 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
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