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

By B. S. Bhangu, P. Bentley, D. Stone, C. Bingham. IEEE Transactions on Vehicular Technology, 2005

509 citations recorded by the source corpus.

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.

Subfields: Automotive Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/bfbed02e4e4ee0d382c58c3d33a15355358119ee

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Citations in the corpus

A survey of mathematics-based equivalent-circuit and electrochemical battery models for hybrid and electric vehicle simulation (2014, unknown): “[15] use a circuit model with an EKF to determine SOH by measuring the change in bulk capacitance.”
An integrated approach to battery health monitoring using bayesian regression and state estimation (2007, methodology): “State estimation techniques, like the Extended Kalman Filter (EKF), have been applied for real-time prediction of SOC and SOH of automotive batteries [2].”
Estimation of State of Charge, Unknown Nonlinearities, and State of Health of a Lithium-Ion Battery Based on a Comprehensive Unobservable Model (2014, background): “In [5], the 60% decrease of battery maximum power compared to its initial value is reported to be an indication of the battery’s EoF.”
State-of-Charge Estimation for Lithium-Ion Batteries Using Neural Networks and EKF (2010, methodology): “As a result, the battery model is reduced to an RC model, where the governing differential equation between the voltage and current is of the first order [16], [30], [31].”
Comparison of prognostic algorithms for estimating remaining useful life of batteries (2009, methodology): “State estimation techniques, like the extended Kalman filter (EKF), have been applied for real-time prediction of SOC and SOH of automotive batteries (Bhangu et al., 2005).”
Remaining useful life prediction of lithium-ion battery with unscented particle filter technique (2013, methodology): “[16] investigated the real-time state-estimation techniques for lead-acid battery, and employed a Kalman filter to estimate SOC and an extended Kalman filter to predict SOH.”
A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation (2015, methodology): “In [18], EKF is used with KF in a dual filtering framework, where KF is used for SOC estimation and EKF for SOH estimation of a lead acid battery.”
Prognostics for state of health estimation of lithium-ion batteries based on combination Gaussian process functional regression (2013, methodology): “An extended Kalman filter (EKF) has been applied for real-time prediction of SOC and SOH of automotive batteries [3].”
A Model Predictive Control System for a Hybrid Battery-Ultracapacitor Power Source (2014, methodology): “We use an RC battery model and parameters of an 8 Ah sealed valve-regulated lead-acid battery cell by Hawker given and experimentally verified in [30].”
Online Adaptive Parameter Identification and State-of-Charge Coestimation for Lithium-Polymer Battery Cells (2014, background): “F. Baronti is with the Dipartimento di Ingegneria dell’Informazione, University of Pisa, 56100 Pisa, Italy (e-mail: f.baronti@iet.unipi.it).”
State of Charge and State of Health Estimation for Lithium Batteries Using Recurrent Neural Networks (2017, methodology): “This method is limited by the fact that it is prone to long term drifts that could arise from inefficient current sensor calibration [12], [13].”