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Title: Practical magnetic tomography for lead batteries
Author: Harrison, Harry
ISNI:       0000 0004 7428 1570
Awarding Body: University of Sheffield
Current Institution: University of Sheffield
Date of Award: 2017
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A variety of economic factors currently motivate the development of electrochemical energy storage. The effective use of renewable energy requires short term storage, for which electrochemical cells may be used. Electrified transport is also driving development; stored energy limits the range of electric vehicles. In hybrid vehicles, improved dynamic charge acceptance will help to optimise powertrain efficiency. A non-invasive measurement of current distribution within a cell is a useful aid to understanding its operation and optimising its design. Here, the coupling between the cell current and the resulting magnetic field is exploited by taking measurements of magnetic flux density outside the cell and inferring the current distribution within. This technique may be termed magnetic tomography or magnetotomography. In this thesis, a practical system is implemented in order to observe the current distribution within a single lead acid cell. An existing method of constraining and solving the inverse problem is adapted for use in conjunction with 3D finite element software, to make it suitable for modelling the complex geometry of a commercial electrode. Some tolerance of unknown material conductance is built into the solver method. An array of sensors is used to obtain a set of magnetic field measurements simultaneously, allowing temporally- and spatially- resolved current distribution images. Solutions from the magnetic tomography system are verified against data from an array of ferrous cores, submerged in the electrolyte. Measurements are taken while the cell is operated at a current of approximately 0.625 C. The current distribution is found to be very uniform throughout most of the testing, although fatigue of the cell plates does lead to a non- uniform distribution. The magnetic tomography system is tested on both uniform and non- uniform distributions. Mean absolute errors of approximately 5 – 7 % are achieved. The effect of model errors on solution accuracy is investigated.
Supervisor: Green, James E. ; Stone, David A. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available