Online Multi Chemistry SoC Estimation Technique Using Data Driven Battery Model Parameter Estimation

Lysander De Sutter, Alexandros Nikolian, Jean-Marc Timmermans, Noshin Omar, Joeri Van Mierlo

Research output: Contribution to journalArticle

6 Citations (Scopus)
167 Downloads (Pure)


Kalman filters have shown to be a very accurate and robust method for State of Charge estimation. However, their performance depends heavily on the accuracy of the used battery model and its parameters. These battery model parameters have shown to vary with the State of Health, cell chemistry, temperature and load current. This paper studies a data driven battery model parameter estimation technique based on the recursive least squares method as an alternative to extensively characterizing every cell of interest with time-consuming test procedures. The performance of two commonly used electrical models is compared and extensively validated on three different cell chemistries (Nickel Cobalt Manganese, Lithium Iron Phosphate and Lithium Titanate Oxide), under load conditions of varying dynamic nature representative for electric vehicle (EV) applications, using a Dynamic Discharge Pulse Test (DDPT) and the Worldwide harmonized Light vehicles Test Procedure (WLTP). The developed model is able to identify and update battery model parameters online, for three different chemistries, potentially reducing offline characterization efforts and allowing monitoring of battery electrical behavior and state estimation over its entire lifetime.

Original languageEnglish
Article number16
Number of pages15
JournalWorld Electric Vehicle Journal
Issue number2
Publication statusPublished - 1 Aug 2018


  • Battery management system
  • Battery model
  • Electric vehicle
  • Modeling
  • State of charge


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