Performance Comparison of Long Short-Term Memory and a Temporal Convolutional Network for State of Health Estimation of a Lithium-Ion Battery using Its Charging Characteristics

Jikai Bi, Jae-Cheon Lee and Hao Liu
Additional contact information
Jikai Bi: Department of Mechanical Engineering, Keimyung University, Daegu 42601, Korea
Jae-Cheon Lee: Department of Mechanical Engineering, Keimyung University, Daegu 42601, Korea
Hao Liu: Department of Mechanical Engineering, Keimyung University, Daegu 42601, Korea

Energies, 2022, vol. 15, issue 7, 1-24

Abstract: The market for eco-friendly batteries is increasing owing to population growth, environmental pollution, and energy crises. The widespread application of lithium-ion batteries necessitates their state of health (SOH) estimation, which is a popular and difficult area of research. In general, the capacity of a battery is selected as a direct health factor to characterize the degradation state of the battery’s SOH. However, it is difficult to directly measure the actual capacity of a battery. Therefore, this study extracted three features from the current, voltage, and internal resistance of a lithium-ion battery during its charging–discharging process to estimate its SOH. A battery-accelerated aging test system was designed to obtain time series battery degradation data. A performance comparison of lithium-ion battery SOH fitting results was conducted for two different deep learning architectures, a long short-term memory (LSTM) network and temporal convolution network (TCN), which are time series deep learning networks based on a recurrent neural network (RNN) and convolutional neural network (CNN), respectively. The results showed that the proposed method has high prediction accuracy, while the performance of the TCN was 3% better than that of the LSTM regarding the average maximum relative error in SOH estimation of a lithium-ion battery.

Keywords: lithium-ion; state of health (SOH); remaining useful life (RUL); charging properties; long short-term memory (LSTM); temporal convolution network (TCN) (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/7/2448/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/7/2448/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:7:p:2448-:d:780335

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().