Item:OSW4a6ab634411a4916a50ff318e41dfc0e: Difference between revisions

Okay. So here's an outline of the talk. I'll give some context, motivation, and just general background on our group. And then really the three main sections I want to dive into a bit more detail. The first one is on modelling, which I'll hopefully give you a bit of context for why I think it's important, and how it can have an impact. And then towards the end of that, I'll talk about how we parametrise models, which is a really important challenge, I think. Second, major topics on degradation and lifetime. Again, I'll try to place that in context and then talk a bit about some of the work we've done. And then the final one is on control. So once we've got all this information, we've done some modelling, what do we do? What decisions do we make? What actions do we take? And again, I'll try and show some examples of how we can use this information to make batteries perform better.

== Context and Motivation ==

Okay. I'm sure this audience knows the context pretty well, so I'll just go through this quickly, but we know that lithium-ion demand is increasing rapidly. Cost has been dropping, astronomical amounts by 97% from the mid-90s to just a few years ago, and it's continuing to decrease. And this has really caused an acceleration in the rollout of batteries, not just for consumer electronics as it was in the early days, but these days, obviously for electric vehicles, grid storage and so on.

And this graph is quite interesting. These are just some projections, in this case from Bloomberg, but you can see that a lot of that future demand is really still being driven by passenger EVs. So bear that in mind. And then obviously one of the things that this then leads to is a lot of concern and research and development around where do the materials come from and how do we make the batteries last as long as possible and recycle at the end of life? I'm not going to talk about these topics today, but I just wanted to say that I think they are really, really important topics and they're going to become more and more important as time goes on.

So the bit where we fit in is maybe to say, "Okay, how can we extend the life as long as possible and even know what it is going to be for a particular usage application?"

So what is systems engineering? I guess it's quite a broad topic within engineering, but from a batteries point of view, I suppose what we're trying to say is that we want to look after batteries better. Batteries are a bit like people. They don't want to be too hot. They're mechanically fragile and unfortunately they degrade over time.

And so you could think of these kind of three areas. For example, it's key places where the stuff you put around the battery, the thermal management system, the mechanical containment and the usage electronics and sort of measurements and control systems can make a real difference between a system performing really well and not performing quite so well. So these are the kinds of topics that I'll be talking about.

Just to give you a little bit of background on the situation that we have here in Oxford University. We're really lucky to have great heritage. This was the place where John Goodenough and team back in 1980 actually developed the lithium-ion cathodes material. There's a plaque on a wall down the road that commemorates this.

In my group, we are interested in a variety of things, but mainly we're interested in modelling, we're interested in diagnosing health and performance from data. We do quite a bit of testing mainly of lifetime, and then we are interested in how we control batteries to make them perform better. And this is just a photo from our spinout company that Simon mentioned in the introduction, Brill Power, which builds highly modularized battery systems with a lot of active management of balancing and so on.

== Industry Collaboration and Achievements ==

And we've been lucky to have several successes in the last few years. I can't list them all, but I couldn't help listing a few of them, if you'll indulge me for a minute.

 

 

 

== Transitioning to Modelling ==

 

 

 

 

 

 

== Applications of Modelling ==

 

 

 

 

 

 

 

 

 

We can predict lifetime, which is important for warranties and so on. We can understand integration issues such as losses and voltage ranges, and so on and so forth. And there's many other things that are not mentioned in this slide, but I just wanted to give you a feel for some of the areas where modelling makes an impact.

 

 

One of the things that I think it's important for me to say, and, you know, I hope many of us appreciate this, but there isn't a single model which solves all of your problems. I think sometimes in the literature in this space, I see people write things like, "The Newman model, the P2D model, is more accurate than a circuit model," or whatever. That may be so, but actually, I think I'd flip it around and say there are different models for different purposes, and it depends what you want to do.

 

So this picture is a very complicated sort of picture, but I'll try and talk you through it. On the left-hand side, we have a bunch of example models, which are basically electrical models of batteries. The very simple one is just a voltage source and a resistor. Then we can step up, adding different components, and so on. And then if we jump across to the central region, we're talking here more about electrochemical models based typically on porous electrode theory.

 

But again, there is no such thing as the one electrochemical model. In fact, we could think of a whole family of different approaches, where in this case, if we go from right to left, we're increasing the macro-scale complexity. So, for example, if we have a large pouch cell, we might be interested in this left-hand column, where there'd be temperature variations from one corner to the other. Whereas if we're just modelling a coin cell, maybe the right-hand column would be sufficient.

 

 

 

 

 

 

One of the things I like to keep in the back of my mind, it's easy to get sucked down the rabbit hole of modelling. But actually, we should remember that most electrochemical devices, on a very basic level, we can think of them as a voltage source and a resistor. The voltage source, if you like, is representing the thermodynamics, and the resistors are representing all the losses. And this framework, although very simplistic, it actually maps quite well to different devices.

 

 

 

 

 

 

 

Now, if we care about things like heating and temperature distribution and degradation, then of course, we need to worry about that. But let's not lose the wood for the trees, as we say. The open-circuit voltage is critical.

 

 

So, at this point, I want to just take a quick aside to say I think there are still lots of interesting issues in modelling to be worked on and important issues. I've just mentioned how OCV is critical. One of the things that really messes up OCV, if you like, from a systems point of view, is voltage hysteresis. So this is the first important issue.

 

Voltage hysteresis is key. It's more evident in some materials. Here's an example of measurements from a lithium iron phosphate graphite cell. This is OCV. And you can see, first of all, that there's a significant voltage gap between charge and discharge. These are slow charge-discharge measurements. But then also interesting things happen.

 

 

 

 

 

 

So that's important issue number one. Important issue number two is that I mentioned earlier that people, in my opinion, are maybe too obsessed with the P2D model as the end of the road. Maybe all of the P2D model emphasis on local transport across the cell is a red herring in some situations. And I want to suggest to you that one situation where it might be more important to look at something else is when you have a large-format pouch cell.

 

 

 

 

 

What does this kind of exercise tell us? Well, it's quite interesting. I think it tells us a number of different things. The first thing we learn is that in this particular case, a large-scale measurement like the surface temperature, under these experimental conditions using a thermal camera, actually, the shape of that measurement relates strongly to our assumptions about diffusion in the solid.

 

So we can play around from a modelling point of view with the diffusion time. And in this case, you can see a huge range of different magnitudes of diffusion time. And you can create different horizontal curvatures in the temperature that you can see with a camera, depending on that assumption. And so we use that to then do inverse modelling from a number of these different pulse experiments and come up with quite plausible values, just from fitting for things like exchange current density, ionic connectivity, and so on.

 

 

 

 

 

The next topic I want to talk about, just to bring us into the end of this section on modelling, thanks for sticking with me, is this need to parameterise models in general. And one of the things I think is an active area of research in many groups around the world, including ours, is building tools to help parameterise models. This can be done in a couple of different ways. We can either rip batteries apart and measure stuff, or we can take data from full cells and try and fit it with models. And in practice, we'll probably do a combination of both those things.

 

But in our group, we've been working more on the second one. So that's the top-down approach, where we do measurements on full cells and then fit models to them, like I showed in the previous slide. Just in case you're interested, we are developing a tool called PyBOP, which is Python Battery Optimisation and Parameterisation. You can go to the GitHub and learn about it. It's all open source.

 

 

 

 

To show you some of the things that you could do, here are a couple of examples. One is parameterising model parameters. In this case, it's kind of a toy example, but we're just fitting a discharge curve. So we add some noise to the discharge curve, and then we want to estimate two parameters: the negative particle diffusivity and contact resistance using the single particle model. You can see here, we can plot a cost function by brute force, showing that there's a kind of sweet spot where you get the best fit between the model and the data. This allows you to visualise that and then try out lots of different approaches to finding the cost function. You can even try different cost functions if you want, which is what's shown on the right.

 

 

 

 

Interestingly, we also get insights into why it doesn't work when it doesn't work. So, for example, up here, maybe there are transport limitations, and over here, maybe the electrodes are unbalanced with respect to each other. So I think this is a really exciting new area to look at.

 

 

 

 

 

 

 

Lifetime is important. I think we all appreciate this, but let's just ground it in some reality. If you're an electric car owner, you want to know how long your car is going to last. How much will it be worth in five years' time? Should you sell it before the warranty runs out, for example? If you're investing in a large battery project, you want to know what the return on investment is. If you're doing maintenance on off-grid systems, you need to know how many spares to order. So lifetime modelling is important for these kinds of reasons.

 

Unfortunately, degradation of batteries is a complex topic. We know that it depends a lot on how you use the batteries. It's hard to generalise, but to give us a flavour, we know, for example, that higher depths of discharge tend to age batteries more. These cells which are ageing more in this work are higher depths of discharge. Time at high state of charge also makes a big difference. In this case, this is a model, but the SEI thickness growth rate is much higher if we hold the cell at a higher voltage range, for example.

 

 

 

 

The other part of the problem is lifetime modelling—predicting what is the lifetime going to do in the future, given a certain set of assumptions about usage. From that, we can start making decisions. Let's talk a little bit about both of these.

 

 

First of all, let's talk about lifetime modelling. There are a number of ways of doing this, with physical models and electrochemical models, the kind of DFN stuff that I talked about a few slides ago. The basic idea here is that we would take something like a simplified electrochemical model. This is just an example showing a single slice through a single layer in an 18-65 cell. Then we would hypothesise a mechanism for degradation. It can be whatever you like—a popular one, for example, is solvent diffusion and then SEI growth. Then you can work out what the equations for that are, implement them in a tool like PyBaMM, and build a kind of virtual battery cycler to see how this is going to degrade under different situations.

 

Challenges with that are where do we get the parameters from? Do we actually understand what's going on? And there are many more. In later life, also, other things—interactions with different mechanisms—become a really tricky problem.

 

 

 

A big challenge with this is that we don't really have rich enough datasets. To generalise, you need not just large datasets so you can deal with cell-to-cell variability, but rich enough datasets, because so many things can change—temperatures, SOC ranges, chemistries, and so forth. If you take a model, train it on some data (such as this blue curve), and then predict it on a different test condition, maybe a different application, you're probably not going to get a good result. So we need this kind of richness of data. We need more data. If you're working on experiments, please release the data so that we can all see it. Maybe field data can help, and we've done some work on this.

 

 

 

 

 

 

 

 

This graph shows the accuracy of that prediction under different assumptions. In the best-case scenario, it's about 85%, so it's not perfect. 50% here is flipping a coin, but it's about 20% higher than the benchmark, which doesn't do this kind of calibration step I talked about earlier. The cool thing about this is, first, we can give some useful information to an end user to say, "Look, the probability of failure on these 23 batteries is high." Then they can get the right stock to order. Secondly, we also get an ageing model out of this process, so we can look at what the classifier thinks is important. For example, in this data, obviously the resistance, which we fed in as an input, is important, but also the mean temperature and mean voltage are key factors impacting the life.

 

 

 

 

 

Imagine a situation where a battery manufacturer tracks data all the way through from components to systems that they build—pack diagnostics, BMS (Battery Management Systems), and so on—all the way through applications to end of life and recycling. They could then use later-life performance to influence earlier decisions. This would close the loop, creating a feedback system that spans the entire lifecycle of the battery.

 

There are, of course, challenges with this vision. This is happening, potentially 10, 15, or 25 years later than when the cell was made, and by that stage, the technology may have moved on. But we can imagine smaller feedback loops within this process, and that's what I want to focus on now.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

None of this work would have been possible without the contributions of my research group, collaborators, and funders. A special thank you to Simon for his ongoing collaboration and support, as well as to all of you for taking the time to attend this seminar. I hope you found it informative and thought-provoking.

 

 

I’d be delighted to hear your questions and engage in further discussions. Batteries are a fascinating and critical area of research, and there’s still so much to learn. Let’s work together to advance the field and create more sustainable, efficient energy storage systems.

 

Thank you.