Predicting Lithium-ion Battery Resistance Degradation using a Log-Linear Model

Søren Byg Vilsen, Søren Knudsen Kær, Daniel-Ioan Stroe

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

3 Citations (Scopus)
28 Downloads (Pure)


The resistance is one of the parameters that describes the performance of Lithium-ion (Li-ion) batteries, as it offers information about the battery efficiency and its power capability. However, similar to other performance parameters of Li-ion batteries, the resistance is dependent on the operating
conditions and increases while the battery is aging. Traditionally, to capture these dependencies, Li-ion cells are aged at different conditions using synthetic mission profiles and periodically the aging tests are stopped in order to measure the resistance at standard conditions. Most of the times, even though accurate information about the resistance behavior is obtained, they do not reflect the behavior from real-life applications. Thus, in this work we propose a method for extracting, modelling, and predicting the resistance directly from the battery dynamic mission profile. While the extraction mainly relied on data manipulation and bookkeeping, the modelling and subsequent prediction of the resistance used a log-linear model.
Original languageEnglish
Title of host publicationProceedings of 2019 IEEE Energy Conversion Congress and Exposition (ECCE)
Number of pages8
PublisherIEEE Press
Publication dateSep 2019
Article number8912770
ISBN (Print)978-1-7281-0396-9
ISBN (Electronic)978-1-7281-0395-2
Publication statusPublished - Sep 2019
Event2019 IEEE Energy Conversion Congress and Exposition (ECCE) - Baltimore, United States
Duration: 29 Sep 20193 Oct 2019


Conference2019 IEEE Energy Conversion Congress and Exposition (ECCE)
Country/TerritoryUnited States
City Baltimore
SeriesIEEE Energy Conversion Congress and Exposition


  • Lithium-ion battery
  • Resistance estimation
  • Battery Degradation
  • Dynamic aging profile
  • Log-linear model


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