Physics-Based Thermal Degradation Modelling of Lithium-Ion Batteries
Lead Research Organisation:
Newcastle University
Department Name: Sch of Engineering
Abstract
Lithium ion Battery (LiB) modelling has come a long way since its early days yet more work needs to be done. The regular operating principles of a fresh LiB are quite well understood, however, the same cannot be said about the modelling of aged cells. Some models have been developed in recent years to predict the degradation of LiB's, many of which empirical and hence have a very limited applicability range.
It is not well understood how the LiB will behave in a 2nd life application as the battery goes through many complex processes over its lifetime which will fundamentally change its operating principles. For example, if the battery is charged with a 1C rate when it is new, the temperature difference over its surface might only be a few degrees, later, once the battery was used for some time, this temperature gradient will change due to the formation and break down of certain materials in the battery. This change in expected operating temperature might lead to formation of new compounds which may not have formed in the normal temperature range or uneven degradation which can cause a variety of other problems.
My research will focus on modelling not just the standard operating behaviour of a LiB but also how the battery degrades. Another pressing issue for people who want to work with 2nd life LiB's is knowing what state it is at. In order to understand the state of a LiB many tests must be carried out to measure such lumped parameters as state of charge (SoC), internal impedance, available capacity and so on. The problem here is that the parameters are lumped and these measurements cannot yield distributed parameters which are crucial to the understanding of degradation as mentioned above. A promising alternative to this is thermal imaging. It may be possible to correlate the state of the battery by running a current through the battery and measuring the corresponding temperature profiles.
This research can aid in better understanding of how lithium-ion batteries degrade, yield simpler ways to determine if a battery is usable in 2nd life applications and for how long as well as assist in developing more rapid charging methods for Electric vehicles (EV's) since currently, fast charging can causes significantly accelerated degradation of EV batteries.
Considering all of the above, the main objectives for my project are:
* Study lithium-ion batteries and understand the fundamentals
* Develop Physics based thermal degradation model of a lithium-ion battery cell
* Investigate 2nd life behaviour of a lithium-ion battery
* Investigate fast charging and thermal degradation impact
* Investigate how different geometry batteries degrade, e.g. pouch/cylindrical cells
It is not well understood how the LiB will behave in a 2nd life application as the battery goes through many complex processes over its lifetime which will fundamentally change its operating principles. For example, if the battery is charged with a 1C rate when it is new, the temperature difference over its surface might only be a few degrees, later, once the battery was used for some time, this temperature gradient will change due to the formation and break down of certain materials in the battery. This change in expected operating temperature might lead to formation of new compounds which may not have formed in the normal temperature range or uneven degradation which can cause a variety of other problems.
My research will focus on modelling not just the standard operating behaviour of a LiB but also how the battery degrades. Another pressing issue for people who want to work with 2nd life LiB's is knowing what state it is at. In order to understand the state of a LiB many tests must be carried out to measure such lumped parameters as state of charge (SoC), internal impedance, available capacity and so on. The problem here is that the parameters are lumped and these measurements cannot yield distributed parameters which are crucial to the understanding of degradation as mentioned above. A promising alternative to this is thermal imaging. It may be possible to correlate the state of the battery by running a current through the battery and measuring the corresponding temperature profiles.
This research can aid in better understanding of how lithium-ion batteries degrade, yield simpler ways to determine if a battery is usable in 2nd life applications and for how long as well as assist in developing more rapid charging methods for Electric vehicles (EV's) since currently, fast charging can causes significantly accelerated degradation of EV batteries.
Considering all of the above, the main objectives for my project are:
* Study lithium-ion batteries and understand the fundamentals
* Develop Physics based thermal degradation model of a lithium-ion battery cell
* Investigate 2nd life behaviour of a lithium-ion battery
* Investigate fast charging and thermal degradation impact
* Investigate how different geometry batteries degrade, e.g. pouch/cylindrical cells
Organisations
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/S514901/1 | 01/07/2018 | 31/03/2025 | |||
| 2490791 | Studentship | EP/S514901/1 | 07/01/2019 | 26/10/2023 | Ignas Andriunas |
| Description | The effect the solid-electrolyte interphase (SEI) layer on battery temperature has been investigated using mathematical modelling. The SEI forms on both the cathode and the anode but the SEI formed on the anode tends to be substantially thicker, therefore, only the SEI on the anode was investigated in the present work. The solid-electrolyte interphase forms as soon as the graphite contacts the electrolyte and grows over the lifespan of a battery. The SEI acts as a resistive layer, which also generates heat. During the work so far, the effect of the SEI layer on the battery temperature has been investigated at several discharge rates and with different cooling conditions. The results show that the increase in temperature caused by the SEI increases with discharge rate. However, as the temperature is increasing with increasing discharge rate, the time until discharge decreases, because a thicker SEI layer decreases the battery capacity. This creates a balancing effect between the SEI layer increasing the battery temperature while also decreasing the battery capacity. The decrease in capacity makes it so, despite the rate of temperature rise increases at the start of discharge due to the SEI creating more heat, the time until discharge decreases, hence, the temperature at the end of discharge stays the same, no matter what the SEI layer thickness, while the middle region of the discharge time is where the SEI layer has the most significant impact on the temperature rise of the battery. Results show that temperature increase caused by the SEI tends to be more significant in the 20-80% depth-of-discharge (DOD) region. This DOD range was found to shift more towards the low DOD region when the battery cooling was stronger. The magnitude of the heat generated by the SEI was also compared to the other heat generation sources within the battery. The underlying heat generation sources show that the SEI heat was significant when the thickness of the SEI layer was over 300 nm. |
| Exploitation Route | Experimental work: The work so far has been mainly done through mathematical modelling and more should be done to investigate these modelling results in real life applications, which is the next step of our research. This can be done by experimentalists in the academic area or in industry. Different chemistries: Different chemistries should be investigated as they will generate different amounts of heat through other heat generation sources which are not SEI. Therefore, the SEI heat might be proportionately higher or lower, compared to the main heat generation sources. This will effect how significant the effect of SEI heat will be on the total temperature rise. This would more likely be done by researchers, other than industry. Higher definition/distribution of SEI in the battery: In the present work, the SEI is modelled as a uniformly distributed, non-porous layer over the anode. Both of these are assumptions to make the modelling simpler. However, in reality, the SEI is a non-uniformly distributed, potentially part porous, part non-porous layer. These are both interesting avenues of higher fidelity SEI modelling to be investigated in the future, more likely to be undertaken by researchers, other than industry. |
| Sectors | Aerospace, Defence and Marine,Electronics,Energy,Manufacturing, including Industrial Biotechology,Transport |
| URL | https://www.sciencedirect.com/science/article/pii/S0378775322001483?dgcid=author |
| Description | Poster presented at the Faraday Conference 2020 |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Schools |
| Results and Impact | The poster was presented to hundreds of researchers on the conference Slack channel. |
| Year(s) Of Engagement Activity | 2020 |
| URL | https://www.faraday2020.org.uk/fi-posters/ |
| Description | Poster presented at the Oxford Battery Modelling Symposium 2020 |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Schools |
| Results and Impact | The poster was presented to several dozen researchers on the symposium Slack channel. |
| Year(s) Of Engagement Activity | 2020 |