Data-driven understanding and forecasting of electrochemical systems
type: CreativeWork
description: Electrochemical energy systems such as batteries and supercapacitors have played an important role in society for two centuries. The rise of the consumer electronics industry has been underpinned by innovations in battery technology, and batteries are subject to more public interest now than ever before due to the uptake of electric vehicles and the demand for grid-scale batteries to stabilise an intermittent renewable energy supply. Most electrochemical energy systems are complex. A multitude of processes over different length- and time-scales drive their realised performance. One source of complexity arises from the enigmatic mechanism by which inter-particle interactions at the atomistic scale give rise to critical macroscopic behaviours. Examples of this can be found in liquid electrolytes, for which electrostatic screening lengths and ion conductivity are both observed to deviate significantly from predictions of classical theories at high concentrations. A second source of complexity is that each system is operated in a different way in practice, and the way a system is used strongly influences both short- and long- term performance, with each system following a unique degradation trajectory. This thesis will demonstrate that both of these challenges can be tackled through the combination of data, physical intuition and machine learning. Machine learning models can learn from orders of magnitude more data than humans can, and we will see that such models can be trained to make more accurate predictions about how electrochemical systems will perform under different operating conditions. In addition, machine learning can act as a “computational microscope”, offering new ways of understanding the molecular origin of macroscopic properties. I begin by addressing the enigmatic under-screening effect observed in concentrated electrolytes, and explore whether discrepancies between classical theory and experiment can be explained using the concept of ion pairing, by i…
abstract: Electrochemical energy systems such as batteries and supercapacitors have played an important role in society for two centuries. The rise of the consumer electronics industry has been underpinned by innovations in battery technology, and batteries are subject to more public interest now than ever before due to the uptake of electric vehicles and the demand for grid-scale batteries to stabilise an intermittent renewable energy supply. Most electrochemical energy systems are complex. A multitude of processes over different length- and time-scales drive their realised performance. One source of complexity arises from the enigmatic mechanism by which inter-particle interactions at the atomistic scale give rise to critical macroscopic behaviours. Examples of this can be found in liquid electrolytes, for which electrostatic screening lengths and ion conductivity are both observed to deviate significantly from predictions of classical theories at high concentrations. A second source of complexity is that each system is operated in a different way in practice, and the way a system is used strongly influences both short- and long- term performance, with each system following a unique degradation trajectory. This thesis will demonstrate that both of these challenges can be tackled through the combination of data, physical intuition and machine learning. Machine learning models can learn from orders of magnitude more data than humans can, and we will see that such models can be trained to make more accurate predictions about how electrochemical systems will perform under different operating conditions. In addition, machine learning can act as a “computational microscope”, offering new ways of understanding the molecular origin of macroscopic properties. I begin by addressing the enigmatic under-screening effect observed in concentrated electrolytes, and explore whether discrepancies between classical theory and experiment can be explained using the concept of ion pairing, by i…
author: Jones, Penelope
language: eng
keywords: battery, conductivity, electrochemistry, electrolyte, energy storage, machine learning
license: https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/c3f7ae00-df12-471c-93a3-79043fcbb562/download
license: https://creativecommons.org/licenses/by/4.0/
date published: 2023-01-22
date modified: 2026-07-22T22:24:17Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.96629