Measuring Battery Overdischarge-to-Failure: Investigating Case Deformation Based on Surface Strain
While minimizing degradation and improving safety are crucial for lithium-ion batteries (LIBs), the link between degradation and safety is not well understood. Excessive operating conditions intensify degradation mechanisms or side reactions, introducing gases and heat into the battery's system, potentially causing failure. Severe degradation can lead to thermal runaway, directly impacting battery safety. Studies confirm that battery cases deform under extreme conditions, making it challenging to characterize battery degradation and failure during and after cycling. The project focuses on overdischarge tests and mechanical deformation of batteries.
Supervisors:
- Dr. Huizhi Wang, Department of Mechanical Engineering
- Xinlei Gao, Department of Mechanical Engineering
Design of a Fast Charging Controller for Electric Vehicle Batteries to Optimize Degradation Rate
Battery Electric Vehicles (BEVs) have become a key component of plans towards achieving net zero emissions in many countries. However, two main obstacles to maximizing their potential are refuelling times and battery degradation. Fast charging has been implemented globally to reduce charging times, but it raises concerns about the associated degradation rate. This thesis aims to design an instrument to test fast charging protocols that minimize degradation. The algorithm integrates a semi-empirical battery degradation model with battery control and thermal models to predict degradation across different charging patterns.
Supervisors:
- Dr. Jorge Varela Barreras, Department of Civil and Environmental Engineering
- Dr. Michael Schimpe, Audi AG
Recursively adaptive current derating system for battery energy storage systems: a numerical model
Lithium-ion batteries are a key storage technology for electrical energy. However, a key challenge faced when investing in and operating them is their limited lifetime, due to chemical degradation of the battery, both with time and with each cycle. Derating is the practice of deliberately limiting battery performance in certain conditions to limit aging and heuristic, ‘rule-of-thumb’ methods of derating are common today (such as limiting state-of-charge to 80%). This project builds upon a pre-existing degradation model to develop an adaptive derating algorithm and tests it numerically in a BESS simulation. Such an optimisation could improve battery lifespan while maximising performance.
Supervisors:
- Dr. Jorge Varela Barreras, Department of Civil and Environmental Engineering
- Dr. Michael Schimpe, Audi AG
Modelling and Experimental Characterisation of Automotive Battery Cells
Lithium-ion batteries stand as the dominant power source technology for EVs. Understanding the behaviour of lithium-ion battery cells is crucial for the optimisation of their performance, and the development of the electrical automotive industry. This project aims to design an experimental campaign for battery model characterisation and validation. This thesis focuses on developing methods for Li-ion battery testing and model parametrisation at cell level, which can be eventually used to investigate battery module and pack designs. The test plan was run with the help of the experienced EV research team of the Centro Tecnolóxico de Automoción de Galicia in Spain.
Supervisors:
- Dr. Jorge Varela Barreras, Department of Civil and Environmental Engineering
- Dr. Damian Gonzalez Figueroa, CTAG
Model-Based Analysis of Battery Degradation and Balancing System Algorithm
The electrification of transportation is crucial for reducing emissions and tackling climate change. Consequently, battery electric vehicles (BEVs) and lithium-ion batteries (LIBs) have seen significant advancements. Over time, battery performance degrades, affecting capacity and internal resistance, with heterogeneous degradation leading to cell-to-cell variations. Balancing systems are one method that addresses these disparities. This project aims to develop a simulation that considers cell-to-cell variations, with a focus on developing a balancing control algorithm to reduce cell-to-cell differences in degradation rates, prolonging battery lifespans.
Supervisors:
- Dr. Jorge Varela Barreras, Department of Civil and Environmental Engineering
- Dr. Michael Schimpe, Audi AG
The Evolution of Battery Efficiency Over Time
In this project, a study was conducted on how different cycling protocols affect the performance and longevity of two types of lithium-ion batteries: the LiFun 622 AG 432 mAh MLP and the LiFun LFP AG 860 mAh cells. Each battery was tested under two protocols: partial cycling at higher currents within a limited State of Charge (SOC) range, and deep cycling at lower currents across the full SOC range. Every 100 cycles, Reference Performance Tests (RPT) were performed, including pulse testing to assess power capabilities and extract efficiency and internal resistance values. Using this data, Coulombic Efficiency (CE), Round-Trip Efficiency (RTE), State of Health (SoH), and Incremental Capacity (IC) were analysed to gain insights into optimising battery performance and extending lifespan.”
Supervisors:
- Dr. Jacqueline Edge, Department of Mechanical Engineering
Modelling and experimental characterization of automotive battery modules
This project focuses on developing Li-ion battery modelling methods for electric vehicle (EV) applications. The scope includes electro-thermal modelling and characterization of EV battery cells, utilizing MATLAB Simulink with the Simscape Battery toolbox for simulation. We designed and conducted experimental campaigns for model characterization and validation in CTAG's EV lab in Spain, under supervision. This research excludes degradation studies due to time constraints and is intended to contribute to the understanding and optimization of battery designs in the rapidly evolving EV sector.
Supervisors:
- Dr. Jorge Varela Barreras, Department of Civil and Environmental Engineering
- Dr. Damian Gonzalez Figueroa, CTAG