Improved OCV Model of a Li-Ion NMC Battery for Online SOC Estimation Using the Extended Kalman Filter

Ines Baccouche, Sabeur Jemmali, Bilal Manai, Noshin Omar, Najoua Essoukri Ben Amara

Research output: Contribution to journalArticle

46 Citations (Scopus)

Abstract

Accurate modeling of the nonlinear relationship between the open circuit voltage (OCV) and the state of charge (SOC) is required for adaptive SOC estimation during the lithium-ion (Li-ion) battery operation. Online SOC estimation should meet several constraints, such as the computational cost, the number of parameters, as well as the accuracy of the model. In this paper, these challenges
are considered by proposing an improved simplified and accurate OCV model of a nickel manganese cobalt (NMC) Li-ion battery, based on an empirical analytical characterization approach. In fact, composed of double exponential and simple quadratic functions containing only five parameters, the proposed model accurately follows the experimental curve with a minor fitting error of 1 mV.
The model is also valid at a wide temperature range and takes into account the voltage hysteresis of the OCV. Using this model in SOC estimation by the extended Kalman filter (EKF) contributes to minimizing the execution time and to reducing the SOC estimation error to only 3% compared to other existing models where the estimation error is about 5%. Experiments are also performed to
prove that the proposed OCV model incorporated in the EKF estimator exhibits good reliability and precision under various loading profiles and temperatures.
Original languageEnglish
Article number764
Pages (from-to)1-22
Number of pages22
JournalEnergies
Volume10
Issue number6
DOIs
Publication statusPublished - Jun 2017

Keywords

  • Battery characterization
  • Battery modeling
  • Extended Kalman filter
  • Li-ion batteries
  • Open circuit voltage
  • State of charge estimation

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