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Title: Understanding and modelling the thermal behaviour of incumbent and future lithium ion batteries
Author: Hua, Xiao
ISNI:       0000 0004 9357 0968
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2020
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The thesis begins with a literature review on the thermal behaviours for an incumbent and a future lithium ion battery, which are Lithium iron phosphate (LFP) prismatic batteries and Lithium sulfur (Li-S) pouch batteries, respectively. Research gaps were identified for both types of batteries, requiring the development of novel experimental techniques and/or modelling approaches for each type. Lithium sulfur batteries are an important next generation high energy density battery technology. However, the phenomenon known as the polysulfide shuttle was identified as one of the most important challenges needing to be overcome. It causes accelerated degradation, reduced Coulombic efficiency and increased heat generation, particularly towards the end of charge. Research was conducted on how to track, quantify and therefore prevent the shuttle effect, in order to improve the safety and increase cycle life of Li-S batteries in real applications. This required the real-time detection of the onset of shuttle during charge. The diagnostic technique Differential Thermal Voltammetry (DTV) was used to track the shuttle effect during charging for the first time, and quantitative interpretations of the experimental DTV curves were performed by thermally-coupling a zero-dimensional Li-S model. The DTV technique, together with the model, is a promising tool for real-time detection of shuttle in applications, to inform control algorithms for deciding the end of charging, thus preventing excessive degradation and charge inefficiency. Lithium iron phosphate prismatic batteries are widely used in both sustainable transportation and stationary energy storage. However, system level thermal management for large format prismatic cells is rarely considered in the literature. Equivalent circuit models (ECM) were shortlisted, due to their ease of implementation and low complexity. The accuracy of an ECM is critical to the functionality and usefulness of the battery management system (BMS). However, their accuracy is limited by how easy they are parameterised, and therefore different experimental techniques and model parameter identification methods (PIM) have been widely studied. Yet, how to account for significant changes in time constants between operation under load and during relaxation has not been resolved. In this work a novel PIM and a modified ECM is presented that increases accuracy by 77.4% during drive cycle validation and 87.6% during constant current load validation for a large format LFP prismatic cell. The modified ECM uses switching RC network values for each phase, which is significant for this cell and particularly at low state-of-charge for all lithium ion batteries. Different characterisation tests and the corresponding experimental data have been trained together across a complete State-of-Charge (SoC) and temperature range, which enables a smooth transition between identified parameters. Ultimately, the model created using parameters captured by the proposed PIM shows an improved model accuracy in comparison with conventional PIM techniques. Large format prismatic cell’s thermal management is challenging due to the large internal heat generation rate, longer distance for internal battery core away from the heat exchange cooling interface and therefore larger thermal gradient across the cell. The standardised surface Cell Cooling Coefficient (CCC) can be used to quantify the degree of difficulty of a target cell to be thermally managed. Here, in this thesis, the novel metric surface CCC is introduced and implemented onto a large format LFP prismatic cell, with aluminium alloy prismatic casing. Further, based on developed PIM, a parameterised and discretised 3-dimentional Electro-Thermal Equivalent Circuit Model is developed. The developed model is validated using the experimental data through embedding corresponding boundary conditions, including drive cycle noisy load and constant current CCC square wave load, electrically and thermally at the same time. The study offers a quantitative guide of the trade-off between cell energy density and surface CCC, and also a casing selection analysis is conducted. The CCC metric together with proposed model enable the cell manufacturer and Original Equipment Makers (OEMs) to customise the cell design based on the casing material, single cell energy density, cell thickness and CCC/capability to be thermally managed. In the future cell design process, this study offers a cost-effective, time-efficient, convenient and quantitative way, in order to achieve a better and safe battery design (high capacity, power and longer lifetime) for wider application needs. Finally, it is concluded that, for both incumbent and future lithium ion batteries, understanding the thermal behaviour is the key for a safer, lighter, longer lifetime, longer range application. By using engineering customised experimental techniques together with empirical and/or physical simulations, enhanced understanding with quantitative battery optimisation and thermal management are achieved in this thesis. The findings in thesis are beneficial for wide range of communities including research community, industry OEMs, application engineers, battery management system developers, control engineers and electric vehicle end users.
Supervisor: Offer, Gregory ; Martinez-Botas, Ricardo Sponsor: Envision AESC China Ltd
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral