Battery Degradation

Using a suite of advanced modelling and characterisation techniques, the project aims to understand the mechanisms of degradation of lithium-ion batteries containing high Ni-content NMC, cobalt-free cathodes and a range of anode chemistries from graphite, graphite/SiOx composites and anode-free.

This project is examining how environmental and internal battery stresses (such as high temperatures, charging and discharging rates) degrade electric vehicle (EV) batteries over time. Results will include the optimisation of battery materials and cells to extend battery life (and hence EV range) and reduce battery costs.

Despite the recent reduction in cost of lithium-ion batteries driven by mass manufacture, the widespread adoption of battery electrical vehicles is still hindered by cost and durability, with the lifetimes of the batteries falling below the consumer expectation for long-term applications such as transport.

Additionally, fast charging of battery electric vehicles is crucial to help assuage range anxiety and provide the operational convenience required for mass adoption of the technology. Fast charging, however, can rapidly accelerate degradation and even trigger degradation mechanisms that are not present in ‘normal’ operating conditions. A key goal for the automotive industry is to understand more fully the causes and mechanisms of degradation to enable improved control and prediction of the state-of-health of battery systems.

The goal of the project is to create accurate models for use by the automotive industry to extend lifetime and performance.

Case Study: Accurate Battery Forecasting

Forecasting the state of health and remaining useful life of Li-ion batteries is an unsolved challenge that limits technologies such as consumer electronics and electric vehicles.

The Degradation team has built an accurate battery forecasting system by combining electrochemical impedance spectroscopy (EIS)-a real-time, non-invasive, and information-rich measurement that is previously underused in battery diagnosis -with Gaussian process machine learning.

Over 20,000 EIS spectra of commercial Li-ion batteries were collected at different states of health, states of charge, and temperatures -the largest dataset to our knowledge of its kind.

The Gaussian process model takes the entire spectrum as input, without further feature engineering, and automatically determines which spectral features predict degradation.

This model accurately predicts the remaining useful life, even without complete knowledge of past operating conditions of the battery.

The results demonstrate the value of EIS signals in battery management systems. This has multiple potential benefits, such as improving the economics of grid-based storage, assessing the length of second life batteries, and measuring the wear and tear on electric vehicles.

Timeline with milestones / deliverables (March 2025)

  • Identify the key stress-induced degradation processes and kinetics that occur in cells.
  • Link the electrical signatures of degradation with specific chemical and materials processes so that they can be identified in an operating battery pack.
  • Examine and understand the physicochemical mechanisms of degradation in high-nickel and colbalt-free positive electrode materials.
  • Examine and understand the physicochemical mechanisms of degradation of graphite and anode free electrode materials. Emphasis is being placed on the interaction, or ‘cross-talk’, effects of positive electrode materials on causing or accelerating these pathways at the electrode-electrolyte interface.

Project innovations

This project will provide a more complete understanding of the signatures of degradation, lead to increased lifetime and better prediction of failure, and accelerate the development of new battery chemistries through the holistic and coordinated efforts of the research. An ability to fully understand the causes of limited lifetime of lithium-ion batteries will place the UK at the forefront of the next generation of battery electric vehicle technology.

Comparison of simulation and experimental imaging results, both conducted at a delithiation rate of C/3. The predicted degree of delithiation (1-θ) on the basal plane of the particle at various times during the charge.

Comparison of simulation and experimental imaging results, both conducted at a delithiation rate of C/3. The predicted degree of delithiation (1-θ) on the basal plane of the particle at various times during the charge.

In one example of Faraday Institution research moving to the next stage of commercialisation, the SABRE project, selected as one of the Faraday Battery Challenge Round 4 projects in what was a highly competitive bidding process, leverages the knowledge, capabilities and know-how developed by UCL’s Electrochemical Innovation Lab and refined as part of the Faraday Institution extending battery life project.

Project funding
£22.7m
1 March 2018 – 31 March 2025

Principal Investigators
Professor Dame Clare Grey
University of Cambridge

Professor Louis Piper
University of Warwick

Project Leaders
Dr Rhodri Jervis
University College London
Dr Gabriela Horwitz
University of Cambridge Project Manager 
Dr Alex Kersting
University of Cambridge University Partners
University of Cambridge (Lead)
Imperial College London
Newcastle University
University College London
University of Birmingham
University of Oxford
University of Sheffield
University of Southampton
University of Warwick Research Organisations, Facilities and Institutes
National Physical Laboratory (NPL)
+ 8 Industrial Partners

 

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