Li-ion thermal control for high power applications
Lead Research Organisation:
University of Oxford
Department Name: Engineering Science
Abstract
'This project falls within the EPSRC Energy Storage research area'
Electric vehicles (EVs) progressively climb up the market share ladder, but their energy storage systems remain a topic of substantial research. Battery lifetime ranges between 5-10 years and estimating battery state of health provides users with necessary information about remaining capacity, which translates to range the vehicle can cover, and its performance. One of the key factors influencing battery performance is thermal control. However, predicting battery temperature and providing an optimal cooling to the battery pack becomes difficult with fast charge and fast discharge.
Current battery models fail to capture temperature evolution accurately above 4-5 C rate (which corresponds to 15-12 minutes), while vehicle users appreciate decreased charging time. Improving battery thermal prediction followed by cooling aims to enable faster charge/discharge while maintaining the battery within its safety limits.
The main objective of this project is to improve cell's spatial temperature prediction accuracy at 5 C rate and above (which corresponds to 12 minutes charge/discharge time). One of the aims is to investigate diffusion coefficient dependency with local concentration and temperature at a range of charge/discharge currents. The findings are to be directly implemented in the model. Another point of focus is an inclusion of solid electrolyte interphase (SEI) equation in the model and studying its importance at higher C-rates. Currently SEI is neglected in standard thermal models, but as high charge/discharge generate more heat, SEI grows consuming active material within the electrode. Initially modelling takes place in COMSOL environment due to inbuilt material libraries and the ease of use of the Multiphysics/electrochemical coupling. However, a further objective is to move developed in COMSOL model to MATLAB, python or Julia to improve its computation speed. The final objective is a publication describing improved thermal model for high C-rate applications and discussing parameters dependence.
The methodology involves setting up a thermal camera to measure surface temperature of the cell. The cell is subjected to a range of charge and discharge currents. Collected data is then used for characterisation and model parameter tuning.
Additional investigation includes subjecting battery to three different cooling scenarios during fast charge/discharge: free convection, surface liquid cooling and tab cooling (novel system designed by Qdot company). These methods extract heat from the battery in less or more uniform manner which may promote/reduce the degradation. Changing the boundary conditions challenges the model as new temperature gradients are expected to appear depending upon cooling system. A thorough analysis from these scenarios will help to improve the model but also addresses an industrial problem of cooling technologies used in the EVs.
Companies and collaborators involved:
Entropy degradation study with Lancaster University
Fast Charging modelling with Imperial College London
Battery cooling methods during fast charge applications study with Qdot start-up company
Electric vehicles (EVs) progressively climb up the market share ladder, but their energy storage systems remain a topic of substantial research. Battery lifetime ranges between 5-10 years and estimating battery state of health provides users with necessary information about remaining capacity, which translates to range the vehicle can cover, and its performance. One of the key factors influencing battery performance is thermal control. However, predicting battery temperature and providing an optimal cooling to the battery pack becomes difficult with fast charge and fast discharge.
Current battery models fail to capture temperature evolution accurately above 4-5 C rate (which corresponds to 15-12 minutes), while vehicle users appreciate decreased charging time. Improving battery thermal prediction followed by cooling aims to enable faster charge/discharge while maintaining the battery within its safety limits.
The main objective of this project is to improve cell's spatial temperature prediction accuracy at 5 C rate and above (which corresponds to 12 minutes charge/discharge time). One of the aims is to investigate diffusion coefficient dependency with local concentration and temperature at a range of charge/discharge currents. The findings are to be directly implemented in the model. Another point of focus is an inclusion of solid electrolyte interphase (SEI) equation in the model and studying its importance at higher C-rates. Currently SEI is neglected in standard thermal models, but as high charge/discharge generate more heat, SEI grows consuming active material within the electrode. Initially modelling takes place in COMSOL environment due to inbuilt material libraries and the ease of use of the Multiphysics/electrochemical coupling. However, a further objective is to move developed in COMSOL model to MATLAB, python or Julia to improve its computation speed. The final objective is a publication describing improved thermal model for high C-rate applications and discussing parameters dependence.
The methodology involves setting up a thermal camera to measure surface temperature of the cell. The cell is subjected to a range of charge and discharge currents. Collected data is then used for characterisation and model parameter tuning.
Additional investigation includes subjecting battery to three different cooling scenarios during fast charge/discharge: free convection, surface liquid cooling and tab cooling (novel system designed by Qdot company). These methods extract heat from the battery in less or more uniform manner which may promote/reduce the degradation. Changing the boundary conditions challenges the model as new temperature gradients are expected to appear depending upon cooling system. A thorough analysis from these scenarios will help to improve the model but also addresses an industrial problem of cooling technologies used in the EVs.
Companies and collaborators involved:
Entropy degradation study with Lancaster University
Fast Charging modelling with Imperial College London
Battery cooling methods during fast charge applications study with Qdot start-up company
