399 research outputs found
Air-Gap Convection in a Switched Reluctance Machine
Switched reluctance machines (SRMs) have recently become popular in the
automotive market as they are a good alternative to the permanent magnet
machines commonly employed for an electric powertrain. Lumped parameter thermal
networks are usually used for thermal analysis of motors due to their low
computational cost and relatively accurate results. A critical aspect to be
modelled is the rotor-stator air-gap heat transfer, and this is particularly
challenging in an SRM due to the salient pole geometry. This work presents
firstly a review of the literature including the most relevant correlations for
this geometry, and secondly, numerical CFD simulations of air-gap heat transfer
for a typical configuration. A new correlation has been derived:
Comment: 2015 Tenth International Conference on Ecological Vehicles and
Renewable Energies (EVER), 10 figures, 7 page
Thermal design of air-cooled axial flux permanent magnet machines
Accurate thermal analysis of axial flux permanent magnet (AFPM) machines is crucial in
predicting maximum power output, and a number of heat transfer paths exist making it
difficult to undertake a general analysis. Stator convective heat transfer is one of the most
important and least investigated heat transfer mechanisms and therefore is the focus of the
present work.
Experimental measurements were undertaken using a thin-film electrical heating method
based on a printed circuit board heater array, providing radially resolved steady state heat
transfer data from an experimental rotor-stator system designed as a geometric mockup of a
through-flow ventilated AFPM machine.
Using a flat rotor, local Nusselt numbers Nu(r) = hR/k were measured across 0.6<r/R< 1,
as a function of non-dimensional gap ratio 0.0106 < G < 0.0467 and rotational Reynolds
number 3.7e4 < Re [Theta]1e6 where G = g/R and Re [Theta] = [omega]R2/[Nu]. Averaged results Nu were
correlated with a power law and it was found that Nu [is approximately equal to] ARe0.7 [Theta] in the fully turbulent regime
(Re [Theta] > 3e5), with A being a function of G. In the laminar regime, stator Nu was found
to be similar to that of the free rotor. Transition at the stator occurred at Re [Theta] = 3e5 for
all G and is particularly marked at G < 0.02. Increased Nusselt numbers at the periphery
were always observed because of the ingress of ambient air along the stator due to the
rotor pumping effect. A slotted rotor was also tested, and was found to improve stator heat
transfer compared with a flat rotor.
The measurements were compared with computational fluid dynamics simulations. These
were found to give a conservative estimate of heat transfer, with inaccuracies near the edge
(r/R > 0.85) and in the transitional flow regime. Predicted stator heat transfer was found to
be relatively insensitive to the choice of turbulence model and the two-equation SST model
was used for most of the simulations
High Speed Peltier Calorimeter for the Calibration of High Bandwidth Power Measurement Equipment
Accurate power measurements of electronic components operating at high
frequencies are vital in determining where power losses occur in a system such
as a power converter. Such power measurements must be carried out with
equipment that can accurately measure real power at high frequency. We present
the design of a high speed calorimeter to address this requirement, capable of
reaching a steady state in less than 10 minutes. The system uses Peltier
thermoelectric coolers to remove heat generated in a load resistance, and was
calibrated against known real power measurements using an artificial neural
network. A dead zone controller was used to achieve stable power measurements.
The calibration was validated and shown to have an absolute accuracy of +/-8 mW
(95% confidence interval) for measurements of real power from 0.1 to 5 W
Sensorless Battery Internal Temperature Estimation using a Kalman Filter with Impedance Measurement
This study presents a method of estimating battery cell core and surface
temperature using a thermal model coupled with electrical impedance
measurement, rather than using direct surface temperature measurements. This is
advantageous over previous methods of estimating temperature from impedance,
which only estimate the average internal temperature. The performance of the
method is demonstrated experimentally on a 2.3 Ah lithium-ion iron phosphate
cell fitted with surface and core thermocouples for validation. An extended
Kalman filter, consisting of a reduced order thermal model coupled with
current, voltage and impedance measurements, is shown to accurately predict
core and surface temperatures for a current excitation profile based on a
vehicle drive cycle. A dual extended Kalman filter (DEKF) based on the same
thermal model and impedance measurement input is capable of estimating the
convection coefficient at the cell surface when the latter is unknown. The
performance of the DEKF using impedance as the measurement input is comparable
to an equivalent dual Kalman filter using a conventional surface temperature
sensor as measurement input.Comment: 10 pages, 9 figures, accepted for publication in IEEE Transactions on
Sustainable Energy, 201
On-board monitoring of 2-D spatially-resolved temperatures in cylindrical lithium-ion batteries: Part II. State estimation via impedance-based temperature sensing
Impedance-based temperature detection (ITD) is a promising approach for rapid
estimation of internal cell temperature based on the correlation between
temperature and electrochemical impedance. Previously, ITD was used as part of
an Extended Kalman Filter (EKF) state-estimator in conjunction with a thermal
model to enable estimation of the 1-D temperature distribution of a cylindrical
lithium-ion battery. Here, we extend this method to enable estimation of the
2-D temperature field of a battery with temperature gradients in both the
radial and axial directions.
An EKF using a parameterised 2-D spectral-Galerkin model with ITD measurement
input (the imaginary part of the impedance at 215 Hz) is shown to accurately
predict the core temperature and multiple surface temperatures of a 32113
LiFePO cell, using current excitation profiles based on an Artemis HEV
drive cycle. The method is validated experimentally on a cell fitted with a
heat sink and asymmetrically cooled via forced air convection.
A novel approach to impedance-temperature calibration is also presented,
which uses data from a single drive cycle, rather than measurements at multiple
uniform cell temperatures as in previous studies. This greatly reduces the time
required for calibration, since it overcomes the need for repeated cell thermal
equalization.Comment: 11 pages, 8 figures, submitted to the Journal of Power Source
Gaussian process regression for forecasting battery state of health
Accurately predicting the future capacity and remaining useful life of
batteries is necessary to ensure reliable system operation and to minimise
maintenance costs. The complex nature of battery degradation has meant that
mechanistic modelling of capacity fade has thus far remained intractable;
however, with the advent of cloud-connected devices, data from cells in various
applications is becoming increasingly available, and the feasibility of
data-driven methods for battery prognostics is increasing. Here we propose
Gaussian process (GP) regression for forecasting battery state of health, and
highlight various advantages of GPs over other data-driven and mechanistic
approaches. GPs are a type of Bayesian non-parametric method, and hence can
model complex systems whilst handling uncertainty in a principled manner. Prior
information can be exploited by GPs in a variety of ways: explicit mean
functions can be used if the functional form of the underlying degradation
model is available, and multiple-output GPs can effectively exploit
correlations between data from different cells. We demonstrate the predictive
capability of GPs for short-term and long-term (remaining useful life)
forecasting on a selection of capacity vs. cycle datasets from lithium-ion
cells.Comment: 13 pages, 7 figures, published in the Journal of Power Sources, 201
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
Circuit Synthesis of Electrochemical Supercapacitor Models
This paper is concerned with the synthesis of RC electrical circuits from
physics-based supercapacitor models describing conservation and diffusion
relationships. The proposed synthesis procedure uses model discretisation,
linearisation, balanced model order reduction and passive network synthesis to
form the circuits. Circuits with different topologies are synthesized from
several physical models. This work will give greater understanding to the
physical interpretation of electrical circuits and will enable the development
of more generalised circuits, since the synthesized impedance functions are
generated by considering the physics, not from experimental fitting which may
ignore certain dynamics
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