Brunell, Michael (2020) Modelling and predictive performance of lithium titanate. EngD thesis, University of Warwick.

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Abstract

Altairnano, a manufacturer of lithium titanate (LTO) battery cells, sponsored the work contained within this innovation report to explore the unique characteristics of its chemistry. Altairnano also sought to improve its business processes and capabilities via various projects conducted during this research.

A large portion of the work, that continued throughout the four years of the doctorate, involved exploring the capabilities of Altairnano’s LTO cells. Towards this goal, a study was put together regarding the hypothesis that their commercial cells could be stored at 0 V, a voltage level that causes damage to standard lithium ion batteries. This capability could enable a unique selling point and competitive edge, while also increasing safety level of its cells during transport, storage, and system manufacturing. Through this study the cells, which were stored at 0 V, had their performance after storage periods characterized and contrasted against Altairnano’s standard storage method. Following upon this doctorate and this research, the capability for low voltage storage has been evaluated by Altairnano’s customers to address various application and storage needs. This includes a unique application where due to environmental conditions charging cannot be guaranteed and extreme low voltages are possible. With the customer’s previous batteries, when it reached the low voltage state the battery would need to be replaced at significant cost and difficulty. Whereas due to Altairnano’s cell chemistry demonstrated capabilities, per this research, to withstand low voltage conditions without damage they are now able to bring a portable charger to raise the voltage back up and continue operation, significantly reducing cost for transportation and replacement.

A separate project worked to achieve the goal of improving Altairnano’s business capabilities. Towards this goal a framework tool was built around its multiple independent battery performance, aging, and thermal models. This tool would provide a platform for Altairnano’s future work on modelling. In addition, the platform simplified and streamlined the interaction between the models creating an expanded accessibility, both internal and external to the company, to its modelling knowledge and capabilities. This work and the tools developed have been implemented into Altairnano’s day-to-day operation, significantly reducing the turnaround time for modelling a customer’s application. This work has also been released to a customer directly to improve the quality of application needs discussed with Altairnano. This was achieved through the tool by allowing the customer to pre-qualify the estimated performance of a proposed system to meet their application requirements. This pre-qualification was not previously possible and helps to reduce the workload on Altairnano’s limited resources.

Further building upon that platform and modelling work, a new model was built for Altairnano that would utilize a novel method for generating a parameterization signal. The new method would incorporate real application data collected from a grid operator for frequency regulation, a commercial industry that Altairnano’s products participate in. The parameters from this signal would be incorporated into an equivalent circuit model, expanding Altairnano’s modelling capabilities beyond their original model while also increasing modelling accuracy. This work has not been implemented into Altairnano’s workflow due to testing resource constraints within the company, but it is planned for when resources become available to evaluate suitability in use with Altairnano’s newest generation chemistry.

Item Type:	Thesis or Dissertation (EngD)
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering T Technology > TN Mining engineering. Metallurgy
Library of Congress Subject Headings (LCSH):	Lithium titanate, Lithium cells, Storage batteries
Official Date:	27 November 2020
Dates:	Date Event 27 November 2020 UNSPECIFIED
Institution:	University of Warwick
Theses Department:	Warwick Manufacturing Group
Thesis Type:	EngD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	Loveridge, Melanie
Sponsors:	Altair Nanotechnologies
Format of File:	pdf
Extent:	xi, 128 leaves : illustrations (some colour)
Language:	eng

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