Final Report Summary - COMBAT (Computational Modeling and Design of Lithium-Ion Batteries)
Lithium-ion batteries (LIBs) are among the most promising solutions for energy storage. Compared with other resources such as bio-fuel, solar cells, fuel cells or lead acid batteries, rechargeable batteries are more portable and allow for quick energy storage and release. The higher power and energy density make batteries suitable as the energy resource for most portable elect. devices including future vehicles. Among the rechargeable batteries, LIBs have the most potential because of their quick charging rate and high power and energy density. However, ageing of LIBs and the related capacity and power fade is a major concern. For the improvement and future development of batteries, computational modeling and design is an important complementary part to experimental testing which is expensive, time-consuming and sometimes unfeasible.
In this project, we developed and implemented a computational framework to support the development of future batteries and battery systems. Furthermore, we carried out simulations in order to identify the best candidate for electrode materials. The framework consists of the following key ingredients:
1. Models of batteries at different length scales.
2. Suitable methods to model material failure and fracture in batteries which is key to determine the life-time and durability of batteries.
3. Suitable multiscale methods to transfer information between different length scales.
4. Methods for uncertainty analysis and uncertainty quantification and their application to the computational design of batteries.
5. Validation through comparison to experimental data. Note the experimental data is also subject to uncertainties.
6. Optimization methods over different length scale and their application to the design of batteries.
In the course of this project, we published 59 manuscripts related to the different workpackages presented above. More than 20 scientific co-workers took part in this project and 3 PhD students already completed their thesis.
In this project, we developed and implemented a computational framework to support the development of future batteries and battery systems. Furthermore, we carried out simulations in order to identify the best candidate for electrode materials. The framework consists of the following key ingredients:
1. Models of batteries at different length scales.
2. Suitable methods to model material failure and fracture in batteries which is key to determine the life-time and durability of batteries.
3. Suitable multiscale methods to transfer information between different length scales.
4. Methods for uncertainty analysis and uncertainty quantification and their application to the computational design of batteries.
5. Validation through comparison to experimental data. Note the experimental data is also subject to uncertainties.
6. Optimization methods over different length scale and their application to the design of batteries.
In the course of this project, we published 59 manuscripts related to the different workpackages presented above. More than 20 scientific co-workers took part in this project and 3 PhD students already completed their thesis.