1,230 research outputs found
Identifiability of generalised Randles circuit models
The Randles circuit (including a parallel resistor and capacitor in series
with another resistor) and its generalised topology have widely been employed
in electrochemical energy storage systems such as batteries, fuel cells and
supercapacitors, also in biomedical engineering, for example, to model the
electrode-tissue interface in electroencephalography and baroreceptor dynamics.
This paper studies identifiability of generalised Randles circuit models, that
is, whether the model parameters can be estimated uniquely from the
input-output data. It is shown that generalised Randles circuit models are
structurally locally identifiable. The condition that makes the model structure
globally identifiable is then discussed. Finally, the estimation accuracy is
evaluated through extensive simulations
Parameter estimation for stochastic hybrid model applied to urban traffic flow estimation
This study proposes a novel data-based approach for estimating the parameters of a stochastic hybrid model describing the traffic flow in an urban traffic network with signalized intersections. The model represents the evolution of the traffic flow rate, measuring the number of vehicles passing a given location per time unit. This traffic flow rate is described using a mode-dependent first-order autoregressive (AR) stochastic process. The parameters of the AR process take different values depending on the mode of traffic operation – free flowing, congested or faulty – making this a hybrid stochastic process. Mode switching occurs according to a first-order Markov chain. This study proposes an expectation-maximization (EM) technique for estimating the transition matrix of this Markovian mode process and the parameters of the AR models for each mode. The technique is applied to actual traffic flow data from the city of Jakarta, Indonesia. The model thus obtained is validated by using the smoothed inference algorithms and an online particle filter. The authors also develop an EM parameter estimation that, in combination with a time-window shift technique, can be useful and practical for periodically updating the parameters of hybrid model leading to an adaptive traffic flow state estimator
Detection and Isolation of Small Faults in Lithium-Ion Batteries via the Asymptotic Local Approach
This contribution presents a diagnosis scheme for batteries to detect and
isolate internal faults in the form of small parameter changes. This scheme is
based on an electrochemical reduced-order model of the battery, which allows
the inclusion of physically meaningful faults that might affect the battery
performance. The sensitivity properties of the model are analyzed. The model is
then used to compute residuals based on an unscented Kalman filter. Primary
residuals and a limiting covariance matrix are obtained thanks to the local
approach, allowing for fault detection and isolation by chi-squared statistical
tests. Results show that faults resulting in limited 0.15% capacity and 0.004%
power fade can be effectively detected by the local approach. The algorithm is
also able to correctly isolate faults related with sensitive parameters,
whereas parameters with low sensitivity or linearly correlated are more
difficult to precise.Comment: 8 pages, 2 figures, 3 tables, conferenc
A step towards digital operations -- A novel grey-box approach for modelling the heat dynamics of Ultra-low temperature freezing chambers
Ultra-low temperature (ULT) freezers store perishable bio-contents and have
high energy consumption, which highlight a demand for reliable methods for
intelligent surveillance and smart energy management. This study introduces a
novel grey-box modelling approach based on stochastic differential equations to
describe the heat dynamics of the ULT freezing chambers. The proposed modelling
approach only requires temperature data measured by the embedded sensors and
uses data from the regular operation periods for model identification. The
model encompasses three states: chamber temperature, envelope temperature, and
local evaporator temperature. Special attention is given to the local
evaporator temperature state, which is modelled as a time-variant system, to
characterize the time delay and dynamic variations in cooling intensity. Two
ULT freezers with different operational patterns are modelled. The unknown
model parameters are estimated using the maximum likelihood method. The results
demonstrate that the models can accurately predict the chamber temperature
measured by the control probe (RMSE < 0.19 {\deg}C) and are promising to be
applied for forecasting future states. In addition, the model for local
evaporator temperature can effectively adapt to different operational patterns
and provide insight into the local cooling supply status. The proposed approach
greatly promotes the practical feasibility of grey-box modelling of the heat
dynamics for ULT freezers and can serve several potential digital applications.
A major limitation of the modelling approach is the low identifiability, which
can potentially be addressed by inferring model parameters based on relative
parameter changes
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