Remaining Useful Life Prediction of Lithium-Ion Battery Using ICC-CNN-LSTM Methodology

Catherine Rincón-Maya, Fernando Guevara-Carazas, Freddy Hernández-Barajas, Carmen Patino-Rodriguez and Olga Usuga-Manco ()
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Catherine Rincón-Maya: Departamento de Ingeniería Industrial, Universidad de Antioquia, Medellín 050010, Colombia
Fernando Guevara-Carazas: Departamento de Ingeniería Mecánica, Universidad Nacional de Colombia, Sede Medellín, Medellín 050034, Colombia
Freddy Hernández-Barajas: Escuela de Estadística, Universidad Nacional de Colombia, Sede Medellín, Medellín 050034, Colombia
Carmen Patino-Rodriguez: Departamento de Ingeniería Industrial, Universidad de Antioquia, Medellín 050010, Colombia
Olga Usuga-Manco: Departamento de Ingeniería Industrial, Universidad de Antioquia, Medellín 050010, Colombia

Energies, 2023, vol. 16, issue 20, 1-20

Abstract: In recent years, lithium-ion batteries have gained significant attention due to their crucial role in various applications, such as electric vehicles and renewable energy storage. Accurate prediction of the remaining useful life (RUL) of these batteries is essential for optimizing their performance and ensuring reliable operation. In this paper, we propose a novel methodology for RUL prediction using an individual control chart (ICC) to identify and remove degraded data, a convolutional neural network (CNN) to smooth the noise of sensor data and long short-term memory (LSTM) networks to effectively capture both spatial and temporal dependencies within battery data, enabling accurate RUL estimation. We evaluate our proposed model using a comprehensive dataset, and experimental results demonstrate its superior performance compared to existing methods. Our findings highlight the potential of ICC-CNN-LSTM for RUL prediction in lithium-ion batteries and provide valuable insights for future research.

Keywords: RUL prediction; ICC; CNN; LSTM networks; R package; deep learning (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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