Capacity State-of-Health Estimation of Electric Vehicle Batteries Using Machine Learning and Impedance Measurements

Alberto Barragan-Moreno*, Erik Schaltz, Alejandro Gismero, Daniel-Ioan Stroe

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

6 Citations (Scopus)
48 Downloads (Pure)


With the increasing adoption of electric vehicles (EVs) by the general public, a lot of research is being conducted in Li-ion battery-related topics, where state-of-health (SoH) estimation has a prominent role. Accurate knowledge of this parameter is essential for efficient and safe EV operation. In this work, machine-learning techniques are applied to estimate this parameter in EV applications and in diverse scenarios. After thoroughly analysing cell ageing in different storage conditions, a novel approach based on impedance data is developed for SoH estimation. A fully-connected feed-forward neural network (FC-FNN) is employed to estimate the battery’s maximum available capacity from a small set of impedance measurements. The method was tested for estimation in long-term scenarios and for diverse degradation procedures with data from real EV batteries. High accuracy was obtained in all situations, with a mean absolute error as low as 0.9%. Thus, the proposed algorithm constitutes a powerful and viable solution for fast and accurate SoH estimation in real-world battery management systems.
Original languageEnglish
Article number1414
Issue number9
Number of pages11
Publication statusPublished - 2022


  • electric vehicle
  • lithium-ion battery
  • state-of-health
  • machine learning
  • neural networks
  • capacity degradation
  • battery management system
  • battery impedance
  • battery ageing
  • Li-ion battery
  • SoH


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