A Multitask Data-Driven Model for Battery Remaining Useful Life Prediction

  • Thien Pham Ho Chi Minh University of Technology
  • Loi Truong Ho Chi Minh University of Technology
  • Anh Bui Ho Chi Minh University of Technology and Education
  • Dang Minh Nguyen Ho Chi Minh University of Technology
  • Dat Nguyen Ho Chi Minh University of Technology
  • Akhil Garg Huazhong University of Science and Technology, Wuhan
  • Liang Gao Huazhong University of Science and Technology, Wuhan
  • Quan Thanh Tho Ho Chi Minh University of Technology
DOI: https://doi.org/10.21553/rev-jec.290

Abstract

Lithium-ion batteries (LIBs) have recently been used widely in moving devices. Understand status of the batteries can help to predict the failure and improve the effectiveness of using them. There are some lithium-ion information that define the battery health over time. These are state-of-charge (SOC), state-of-health (SOH), and remaining-useful-life (RUL). Normally, a LIB is working under charging and discharging cycles continuously. In this paper, we will focus on the data dependency of different time-slots in a cycle and in a sequence of cycles to retrieve RUL. We leverage multi-channel inputs such as temperature, voltage, current and the nature of peaks cross the cycles to improve our prediction. Comparing to existing methods, the experiments show that we can improve from 0.040 to 0.033 (reduce 17.5%) in RMSE loss, which is significant.
Published
2022-06-14
Issue
Vol. 12, No. 1-2 (Jan-Jun, 2022)
Section
Regular articles
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