What determines how much power a battery can deliver or how much capacity it can provide? Connor Miles is studying how design choices inside cylindrical lithium-ion batteries work together to influence performance. In this Q&A, he discusses the parameters he is examining and how understanding their interactions could help engineers design batteries for different applications.
Q: How do the design choices inside a cylindrical lithium-ion battery affect its performance, and which parameters are you focusing on in your research?
The simple answer is all of them. A good example is the tab design of a battery. In a cylindrical lithium-ion battery, there are things called tabs, which transport current from the active material to the terminal. There are different kinds of tab designs you can have.
For instance, you can have one tab on each electrode, which is called a single-tab design. You can also have a bunch of tabs along the entire length of the electrode. Another example of a design parameter is particle size distribution. There are a lot of parameters that go into designing a battery to achieve a desired specification.
If you want a high power output, understanding how the tab design affects that and how the particle size distribution for each electrode material affects that are key things. I'm focusing on all of them, figuring out how they work together to produce the output that they do.
Conventionally, this consists of tearing the cell down to see it firsthand, but there are non-destructive techniques available to us that we can use as well. For example, the XCT scanner that we use from Lumafield can scan the cell and render a 3D reconstruction cell which can be used to identify single-tab vs. multi-tab or tab-less designs, the tab thickness and length, and so on. Later, alongside other data gained from the XCT scan (e.g. electrode thickness), we can decide which cells will be most informative upon teardown. After teardown, we can learn other parameters like the particle size distribution of each electrode material via SEM, for example.
Q: What are you hoping to discover about how these parameters work together to determine a battery's power output?
That's the million-dollar question. I want to focus on why these parameters lead to the performance that they do. Once we know why, we can figure out what to do.
For example, if you look in the literature, you can find a lot of studies on optimizing a cathode's particle size distribution. But I want to understand how to optimize that while considering its dependence on other parameters, such as the tab design. Understanding how to design a battery to optimize those interactions, and how the parameters depend on one another, is really the end goal.
Figuring out why is the first step. Once you know why, you can get to what to do. My goal is to figure out why these parameters work together the way they do and then what to do once we understand that.
Q: How could your findings help engineers design better batteries for real-world applications?
This goes along with what I just said: knowing why, then knowing what to do. As engineers, we're often focused on what we need to do to get a desired power output, for example.
But cells aren't always optimized for power. For some cells, you want to optimize the amount of capacity you can get for a low-power, long-term application. That's when it becomes important to understand why these parameters affect performance, because then you can make decisions about how to balance power and capacity to meet the needs of a particular application.
Understanding why is extremely important, and then we can move on to what to do. I think that will ultimately help engineers. This takes years to fully develop, but once we understand those interactions, we can make decisions about how to balance the parameters to achieve the capacity and power we need.