25,708 research outputs found
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
Observer techniques for estimating the state-of-charge and state-of-health of VRLABs for hybrid electric vehicles
The paper describes the application of observer-based state-estimation techniques for the real-time prediction of state-of-charge (SoC) and state-of-health (SoH) of lead-acid cells. Specifically, an approach based on the well-known Kalman filter, is employed, to estimate SoC, and the subsequent use of the EKF to accommodate model non-linearities to predict battery SoH. The underlying dynamic behaviour of each cell is based on a generic Randles' equivalent circuit comprising of two-capacitors (bulk and surface) and three resistors, (terminal, transfer and self-discharging). The presented techniques are shown to correct for offset, drift and long-term state divergence-an unfortunate feature of employing stand-alone models and more traditional coulomb-counting techniques. 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 with SoC being estimated to be within 1% of measured. 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
Phase Separation Dynamics in Isotropic Ion-Intercalation Particles
Lithium-ion batteries exhibit complex nonlinear dynamics, resulting from
diffusion and phase transformations coupled to ion intercalation reactions.
Using the recently developed Cahn-Hilliard reaction (CHR) theory, we
investigate a simple mathematical model of ion intercalation in a spherical
solid nanoparticle, which predicts transitions from solid-solution radial
diffusion to two-phase shrinking-core dynamics. This general approach extends
previous Li-ion battery models, which either neglect phase separation or
postulate a spherical shrinking-core phase boundary, by predicting phase
separation only under appropriate circumstances. The effect of the applied
current is captured by generalized Butler-Volmer kinetics, formulated in terms
of diffusional chemical potentials, and the model consistently links the
evolving concentration profile to the battery voltage. We examine sources of
charge/discharge asymmetry, such as asymmetric charge transfer and surface
"wetting" by ions within the solid, which can lead to three distinct phase
regions. In order to solve the fourth-order nonlinear CHR
initial-boundary-value problem, a control-volume discretization is developed in
spherical coordinates. The basic physics are illustrated by simulating many
representative cases, including a simple model of the popular cathode material,
lithium iron phosphate (neglecting crystal anisotropy and coherency strain).
Analytical approximations are also derived for the voltage plateau as a
function of the applied current
Lithium-ion battery thermal-electrochemical model-based state estimation using orthogonal collocation and a modified extended Kalman filter
This paper investigates the state estimation of a high-fidelity spatially
resolved thermal- electrochemical lithium-ion battery model commonly referred
to as the pseudo two-dimensional model. The partial-differential algebraic
equations (PDAEs) constituting the model are spatially discretised using
Chebyshev orthogonal collocation enabling fast and accurate simulations up to
high C-rates. This implementation of the pseudo-2D model is then used in
combination with an extended Kalman filter algorithm for differential-algebraic
equations to estimate the states of the model. The state estimation algorithm
is able to rapidly recover the model states from current, voltage and
temperature measurements. Results show that the error on the state estimate
falls below 1 % in less than 200 s despite a 30 % error on battery initial
state-of-charge and additive measurement noise with 10 mV and 0.5 K standard
deviations.Comment: Submitted to the Journal of Power Source
Detection of Lying Electrical Vehicles in Charging Coordination Application Using Deep Learning
The simultaneous charging of many electric vehicles (EVs) stresses the
distribution system and may cause grid instability in severe cases. The best
way to avoid this problem is by charging coordination. The idea is that the EVs
should report data (such as state-of-charge (SoC) of the battery) to run a
mechanism to prioritize the charging requests and select the EVs that should
charge during this time slot and defer other requests to future time slots.
However, EVs may lie and send false data to receive high charging priority
illegally. In this paper, we first study this attack to evaluate the gains of
the lying EVs and how their behavior impacts the honest EVs and the performance
of charging coordination mechanism. Our evaluations indicate that lying EVs
have a greater chance to get charged comparing to honest EVs and they degrade
the performance of the charging coordination mechanism. Then, an anomaly based
detector that is using deep neural networks (DNN) is devised to identify the
lying EVs. To do that, we first create an honest dataset for charging
coordination application using real driving traces and information revealed by
EV manufacturers, and then we also propose a number of attacks to create
malicious data. We trained and evaluated two models, which are the multi-layer
perceptron (MLP) and the gated recurrent unit (GRU) using this dataset and the
GRU detector gives better results. Our evaluations indicate that our detector
can detect lying EVs with high accuracy and low false positive rate
Determination of Oxygen Nonstoichiometry and Diffusivity in Mixed Conducting Oxides by Oxygen Coulometric Titration. II Oxygen Nonstoichiometry and Defect Model for La0.8Sr0.2CoO3-delta
The oxygen nonstoichiometry of La0.8Sr0.2CoO3-delta has been determined as a function of oxygen partial pressure and temperature using a high-temperature coulometric titration cell. For each measured value of the oxygen chemical potential, the oxygen nonstoichiometry is found to be nearly independent of temperature. The equilibrium partial energy and entropy associated with oxygen incorporation have been determined as a function of oxygen nonstoichiometry and temperature. The results are interpreted in terms of a model in which it is assumed that conduction electrons, created during vacancy formation, gradually fill electron states in a wide electron band. A new relation between vacancy concentration, temperature, and oxygen partial pressure has been formulated which does not have the familiar appearance of a mass action type of equation
Study of space battery accelerated testing techniques. Phase 2 report - Ideal approaches towards accelerated tests and analysis of data
Ideal approaches to accelerated life tests and data analysis applied to space batterie
Identifiability and parameter estimation of the single particle lithium-ion battery model
This paper investigates the identifiability and estimation of the parameters
of the single particle model (SPM) for lithium-ion battery simulation.
Identifiability is addressed both in principle and in practice. The approach
begins by grouping parameters and partially non-dimensionalising the SPM to
determine the maximum expected degrees of freedom in the problem. We discover
that, excluding open circuit voltage, there are only six independent
parameters. We then examine the structural identifiability by considering
whether the transfer function of the linearised SPM is unique. It is found that
the model is unique provided that the electrode open circuit voltage functions
have a known non-zero gradient, the parameters are ordered, and the electrode
kinetics are lumped into a single charge transfer resistance parameter. We then
demonstrate the practical estimation of model parameters from measured
frequency-domain experimental electrochemical impedance spectroscopy (EIS)
data, and show additionally that the parametrised model provides good
predictive capabilities in the time domain, exhibiting a maximum voltage error
of 20 mV between model and experiment over a 10 minute dynamic discharge.Comment: 16 pages, 9 figures, pre-print submitted to the IEEE Transactions on
Control Systems Technolog
- …