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Hey J.,Imperial College London | Howey D.A.,Imperial College London | Martinez-Botas R.,Imperial College London | Lamperth M.,Evo Electric Ltd.
Institution of Mechanical Engineers - VTMS 10, Vehicle Thermal Management Systems Conference and Exhibition | Year: 2011

This paper presents the development of a transient thermal model of the EVO Electric AFM 140 Axial Flux Permanent Magnet (AFPM) machine based on a hybrid finite difference and lumped parameter method. A maximum deviation between simulated and measured temperature of 2.4°C is recorded after using a Monte Carlo simulation to optimise model parameters representing a 53% reduction in temperature deviation. The simulated temperature deviations are lower than the measurement error on average and the thermal model is computationally simple to solve. It is thus suitable for transient temperature prediction and can be integrated with the system control loop for feed forward temperature prediction to achieve active thermal management of the system. Source

Agency: GTR | Branch: Innovate UK | Program: | Phase: Large Scale Demonstrator | Award Amount: 9.43M | Year: 2010

REEVolution is an accelerated development and integration programme of new technologies, from concept through to validated components and systems intended to produce robust technology demonstrator vehicles. The aim has been to deliver high performance Range Extended Electric Vehicles (REEV) and Plug-in Hybrids Electric Vehicles (PHEV). Delivering 70-75% CO2 reductions through implementation of advanced technologies into three very different best in class premium vehicle applications, building on and using the skills of all the collaborative partners. The project has successfully developed key UK technology suppliers with novel Ultra Low Carbon (ULC) technologies towards tier 1 capability by working with three major UK vehicle manufacturers. The goal has been to lay the foundations for a robust and globally competitive UK supply base by drawing on the product development processes of the vehicle manufacturers. The REEVolution consortium, led by Jaguar Land Rover and consists of three suppliers: Axeon Technologies Ltd, EVO Electric Ltd and Xtrac Limited; and three vehicle manufacturers: Jaguar Cars, Lotus Cars and Infiniti along with Lotus Engineering. This acceleration development was made possible by the UK Government through the mechanism of the Technology Strategy Board.

Hey J.,Imperial College London | Malloy A.,Evo Electric Ltd. | Martinez-Botas R.,Imperial College London | Lamperth M.,Evo Electric Ltd.
Proceedings of the 15th International Heat Transfer Conference, IHTC 2014 | Year: 2014

Energy conversion device suffer from thermal loading as a result of inefficiencies during their operation which may lead to device degradation and possible failure. It is of interest to monitor the internal temperature of the device to ensure its safe operation. Mathematical models of different complexities have been developed for the purpose of real time temperature monitoring. Temperature estimation accuracy is dependent on the thermal parameters such as the material conductivities and convective heat transfer coefficients. The complex construction of such devices means that the exact value of the thermal parameters is often not known. This paper presents the use of an inverse identification technique for the estimation of thermal parameters of an axial flux permanent magnet device designed for vehicular applications. The proposed method provides a practical approach to determine the thermal parameters indirectly from temperature measurements. A constraint least square method coupled to an analytical solution of the one step ahead predictor of temperature is used for parameter estimation. A parametric study is performed and it is shown that some of these parameters vary as a function of the operating point of the device. The estimated parameters are then used in an analytical thermal model for real time temperature monitoring during a drive cycle. A maximum time averaged error of 1.8°C or an equivalent error of about 3% of the measurement range is observed for the estimated winding temperature. Source

Malloy A.C.,Imperial College London | Martinez-Botas R.F.,Imperial College London | Jaensch M.,Evo Electric Ltd. | Lamperth M.,Evo Electric Ltd.
IET Conference Publications | Year: 2012

This paper presents an experimental method for measuring heat generation rate in the permanent magnets of rotating electrical machines. The results obtained from the experimental work are used to derive an empirical correlation which is subsequently used to predict the total thermal energy stored in a magnet after a speed varying torque load. The results of an uncertainty analysis are offered in order to show the usefulness of the technique. An axial flux permanent magnet machine has been used as a case study in this work, though the methodology could certainly be applied to other topologies. Source

Hey J.,Imperial College London | Howey D.A.,Imperial College London | Martinez-Botas R.,Imperial College London | Lamperth M.,Evo Electric Ltd.
World Academy of Science, Engineering and Technology | Year: 2010

This paper presents the development of a hybrid thermal model for the EVO Electric AFM 140 Axial Flux Permanent Magnet (AFPM) machine as used in hybrid and electric vehicles. The adopted approach is based on a hybrid lumped parameter and finite difference method. The proposed method divides each motor component into regular elements which are connected together in a thermal resistance network representing all the physical connections in all three dimensions. The element shape and size are chosen according to the component geometry to ensure consistency. The fluid domain is lumped into one region with averaged heat transfer parameters connecting it to the solid domain. Some model parameters are obtained from Computation Fluid Dynamic (CFD) simulation and empirical data. The hybrid thermal model is described by a set of coupled linear first order differential equations which is discretised and solved iteratively to obtain the temperature profile. The computation involved is low and thus the model is suitable for transient temperature predictions. The maximum error in temperature prediction is 3.4% and the mean error is consistently lower than the mean error due to uncertainty in measurements. The details of the model development, temperature predictions and suggestions for design improvements are presented in this paper. Source

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