INTERNAL RESISTANCE BASED ASSESSMENT MODEL for the DEGRADATION of LI-ION BATTERY PACK

Iffandya Popy Wulandari, Min Chun Pan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

As one pioneer means for energy storage, Li-ion battery packs have a complex and critical issue about degradation monitoring and remaining useful life estimation. It induces challenges on condition characterization of Li-ion battery packs such as internal resistance (IR). The IR is an essential parameter of a Li-ion battery pack, relating to the energy efficiency, power performance, degradation, and physical life of the li-ion battery pack. This study aims to obtain reliable IR through applying an evaluation test that acquires data such as voltage, current, and temperature provided by the battery management system (BMS). Additionally, this paper proposes an approach to predict the degradation of Li-ion battery pack using support vector regression (SVR) with RBF kernel. The modeling approach using the relationship between internal resistance, different SOC levels 20%-100%, and cycle at the beginning of life 1 cycle until cycle 500. The data-driven method is used here to achieve battery life prediction.based on internal resistance behavior in every period using supervised machine learning, SVR. Our experiment result shows that the internal resistance was increasing non-linear, approximately 0.24%, and it happened if the cycle rise until 500 cycles. Besides, using SVR algorithm, the quality of the fitting was evaluated using coefficient determination R2, and the score is 0.96. In the proposed modeling process of the battery pack, the value of MSE is 0.000035.

Original languageEnglish
Title of host publicationEnergy
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791884560
DOIs
StatePublished - 2020
EventASME 2020 International Mechanical Engineering Congress and Exposition, IMECE 2020 - Virtual, Online
Duration: 16 Nov 202019 Nov 2020

Publication series

NameASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)
Volume8

Conference

ConferenceASME 2020 International Mechanical Engineering Congress and Exposition, IMECE 2020
CityVirtual, Online
Period16/11/2019/11/20

Keywords

  • Data- Driven Method
  • Internal Resistance
  • Li-ion battery pack
  • Support Vector Regression.

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