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

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: $\mathbf{Nu=0.181\ Ta_m^{0.207}}$Comment: 2015 Tenth International Conference on Ecological Vehicles and Renewable Energies (EVER), 10 figures, 7 page

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$_4$ 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

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

Gaussian Process Regression for In-situ Capacity Estimation of Lithium-ion Batteries

Accurate on-board capacity estimation is of critical importance in lithium-ion battery applications. Battery charging/discharging often occurs under a constant current load, and hence voltage vs. time measurements under this condition may be accessible in practice. This paper presents a data-driven diagnostic technique, Gaussian Process regression for In-situ Capacity Estimation (GP-ICE), which estimates battery capacity using voltage measurements over short periods of galvanostatic operation. Unlike previous works, GP-ICE does not rely on interpreting the voltage-time data as Incremental Capacity (IC) or Differential Voltage (DV) curves. This overcomes the need to differentiate the voltage-time data (a process which amplifies measurement noise), and the requirement that the range of voltage measurements encompasses the peaks in the IC/DV curves. GP-ICE is applied to two datasets, consisting of 8 and 20 cells respectively. In each case, within certain voltage ranges, as little as 10 seconds of galvanostatic operation enables capacity estimates with approximately 2-3% RMSE.Comment: 12 pages, 10 figures, submitted to IEEE Transactions on Industrial Informatic