Enhancement in Li-Ion battery cell state-of-charge estimation under uncertain model statistics

Menatalla Shehab El Din, Mamoun F. Abdel-Hafez, Ala A. Hussein

Research output: Contribution to journalArticlepeer-review

30 Citations (Scopus)

Abstract

Accurate battery state-of-charge (SOC) estimation in real time is desired in many applications. Among other methods, the extended Kalman filter (EKF) allows for high-accuracy real-time tracking of the SOC. However, an accurate SOC model is needed to guarantee convergence. Additionally, knowledge of the statistics of the process noise and the measurement noise is needed for high-accuracy SOC estimation. In this paper, two methods, namely, the multiple-model EKF (MM-EKF) and the autocovariance least squares technique, are proposed for estimating the SOC of lithium-ion (Li-ion) battery cells. The first method has the advantage of minimizing the EKF algorithm's dependence on the correct assumptions of the measurement's noise statistics, thus, minimizing the impact of model mismatch. The MM-EKF assumes a number of hypotheses for the unknown measurement noise covariance. An EKF is assigned for each assumed measurement noise covariance. The SOC estimate is then obtained by probabilistically summing up the estimates of the hypothesized EKFs. On the other hand, the second method assumes that the measurement noise is unknown and determines its value from the statistics of the EKF. Given an initial and possibly wrong assumption of the measurement noise covariance, the method accounts for possible correlation in the measurement innovations. The estimated measurement noise covariance is subsequently used to obtain an optimal SOC estimate. The proposed methods are evaluated and compared with the conventional EKF method on experimental test data obtained from a 3.6-V Li-ion battery cell.

Original languageEnglish
Article number7299686
Pages (from-to)4608-4618
Number of pages11
JournalIEEE Transactions on Vehicular Technology
Volume65
Issue number6
DOIs
Publication statusPublished - Jun 2016

Keywords

  • Adaptive filtering
  • autocovariance least squares (ALS)
  • extended Kalman filter (EKF)
  • multiple-model approach
  • state-of-charge (SOC)

ASJC Scopus subject areas

  • Automotive Engineering
  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Applied Mathematics

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