Nonlinear observers for predicting state-of-charge and state-of-health of lead-acid batteries for hybrid-electric vehicles

Bhangu, B and Bentley, P and Stone, D A and Bingham, Chris (2005) Nonlinear observers for predicting state-of-charge and state-of-health of lead-acid batteries for hybrid-electric vehicles. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 54 (3). pp. 783-794. ISSN 0018-9545

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Nonlinear observers for predicting state-of-charge and state-of-health of lead-acid batteries for hybrid-electric vehicles
Abstract—This paper describes the application of state-estimation techniques for the real-time prediction of the state-of-charge (SoC) and state-of-health (SoH) of lead-acid cells. Specifically, approaches based on the well-known Kalman Filter (KF) and Extended Kalman Filter (EKF), are presented, using a generic cell model, to provide correction for offset, drift, and long-term state divergence—an unfortunate feature of more traditional coulomb-counting techniques. The underlying dynamic behavior of each cell is modeled using two capacitors (bulk and surface) and three resistors (terminal, surface, and end), from which the SoC is determined from the voltage present on the bulk capacitor. Although the structure of the model has been previously reported for describing the characteristics of lithium-ion cells, here it is shown to also provide an alternative to commonly employed models of lead-acid cells when used in conjunction with a KF to estimate SoC and an EKF to predict state-of-health (SoH). Measurements using real-time road data are used to compare the performance of conventional integration-based methods for estimating SoC with those predicted from the presented state estimation schemes. Results show that the proposed methodologies are superior to more traditional techniques, with accuracy in determining the SoC within 2% being demonstrated. Moreover, by accounting for the nonlinearities present within the dynamic cell model, the application of an EKF is shown to provide verifiable indications of SoH of the cell pack.
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Full text URL: http://dx.doi.org/10.1109/TVT.2004.842461

Abstract

Abstract—This paper describes the application of state-estimation
techniques for the real-time prediction of the state-of-charge
(SoC) and state-of-health (SoH) of lead-acid cells. Specifically,
approaches based on the well-known Kalman Filter (KF) and
Extended Kalman Filter (EKF), are presented, using a generic
cell model, to provide correction for offset, drift, and long-term
state divergence—an unfortunate feature of more traditional
coulomb-counting techniques. The underlying dynamic behavior
of each cell is modeled using two capacitors (bulk and surface) and
three resistors (terminal, surface, and end), from which the SoC
is determined from the voltage present on the bulk capacitor. Although
the structure of the model has been previously reported for
describing the characteristics of lithium-ion cells, here it is shown
to also provide an alternative to commonly employed models of
lead-acid cells when used in conjunction with a KF to estimate
SoC and an EKF to predict state-of-health (SoH). Measurements
using real-time road data are used to compare the performance
of conventional integration-based methods for estimating SoC
with those predicted from the presented state estimation schemes.
Results show that the proposed methodologies are superior to
more traditional techniques, with accuracy in determining the
SoC within 2% being demonstrated. Moreover, by accounting
for the nonlinearities present within the dynamic cell model, the
application of an EKF is shown to provide verifiable indications of
SoH of the cell pack.

Item Type:Article
Additional Information:Abstract—This paper describes the application of state-estimation techniques for the real-time prediction of the state-of-charge (SoC) and state-of-health (SoH) of lead-acid cells. Specifically, approaches based on the well-known Kalman Filter (KF) and Extended Kalman Filter (EKF), are presented, using a generic cell model, to provide correction for offset, drift, and long-term state divergence—an unfortunate feature of more traditional coulomb-counting techniques. The underlying dynamic behavior of each cell is modeled using two capacitors (bulk and surface) and three resistors (terminal, surface, and end), from which the SoC is determined from the voltage present on the bulk capacitor. Although the structure of the model has been previously reported for describing the characteristics of lithium-ion cells, here it is shown to also provide an alternative to commonly employed models of lead-acid cells when used in conjunction with a KF to estimate SoC and an EKF to predict state-of-health (SoH). Measurements using real-time road data are used to compare the performance of conventional integration-based methods for estimating SoC with those predicted from the presented state estimation schemes. Results show that the proposed methodologies are superior to more traditional techniques, with accuracy in determining the SoC within 2% being demonstrated. Moreover, by accounting for the nonlinearities present within the dynamic cell model, the application of an EKF is shown to provide verifiable indications of SoH of the cell pack.
Keywords:state of charge, state of health, electric vehicles, batteries, kalman filter
Subjects:H Engineering > H600 Electronic and Electrical Engineering
Divisions:College of Science > School of Engineering
ID Code:2333
Deposited By:INVALID USER
Deposited On:22 Apr 2010 17:40
Last Modified:28 Aug 2014 09:24

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