How to build a better battery (Part 2) – by Jeremy Meyers, Ph.D., Technology Development Leader, Black Diamond Structures

trics, but most of those test results only give you a passing or a failing grade.

An acceptance test can be like taking the SAT to get into the college of your choice. All the admissions office is likely to look at is what the score was. However, if you’re developing a product, the way that you passed or failed is often revealed in the data. That insight can be invaluable if you can sort through it.

A mentor of mine from early in my career used to read every single test report from every single stack we tested. He impressed upon me the importance of reviewing and digging into performance test data. “Those reports are like gold,” he told me. He was so familiar with the way that stacks performed that he could ascribe personalities to them. He used to describe the way that a particular stack design “loved” water or had “healthy lungs.”

Each battery developer or OEM has their own set of (slightly different) test protocols. After a few validation trials, then, you’ll have a pretty good indication of how your battery will behave and start to get a sense of its “personality.” You just need to know how to look for the behavior. You probably won’t recognize the patterns until you’ve seen a lot of data.

By examining the performance trends, you can start to see evidence of the mechanisms at work. Mastery of the data that you have to get to satisfy customers will also make life better for you. In the end, learning from their tests will reduce the number of experiments that you have to perform for your own R&D.

Which brings us to the next point:

Battery testing is fine, but you’ll need to design some experiments.
“I have not failed. I have just found 10,000 things that do not work.”–Thomas Edison

After you get a sense of where your battery performs and where it doesn’t, you’re going to want to make it better. To do that, you’re going to need to change something about it. You could tweak your recipe or your design, make a full-size battery, and subject it to the testing protocols that your customers demand. That’s a remarkably inefficient way to do it, though.
Even if you haven’t yet figured out what you need to change in your battery and are compelled to follow an “Edisonian” approach, you’d like that to be as efficient as possible. Finding Edison’s “10,000 things that do not work” will take a lot less time if it takes a few hours to try each thing instead of a few days or weeks. An experimental design that accentuates the effect you’re trying to optimize will shorten that cycle time.

A proper experimental design might result in a cell that doesn’t resemble the battery under development. That’s ok if it helps you to isolate the phenomenon of interest. You also need to know enough, though, to make sure that you haven’t introduced phenomena that don’t exist in your full-scale battery. This is a good time to go back and re-visit the Pourbaix diagram (see previous blog artcle)

In a battery, the figure of merit on which you’ll be judged is a complex product of many different factors in your cell design. In the experiments you design to develop your next battery, you’d like to devise a figure of merit that is dependent on as few factors as possible. This will help in the interpretation of results and point you in the right direction for next steps to take.

If you have the capability to do some modeling, it’ll probably be worth it.

Unless you have a reductively simple system, multiple coupled processes occur at the same time in your battery. Charge transfer, dissolution, migration, diffusion, and other processes combine to dictate battery performance. Most of the time, you won’t be able to study each process independently.

Electrochemical systems are highly coupled because of the long-distance nature of coulombic attraction and electroneutrality. If you have a working electrode, you’re going to have to have a counter electrode. That counter electrode had better not be limiting. If you have a porous electrode, you’ll need to know its utilization. You’re going to want to know how your reaction rate varies down the channel. You’ll want to know how the fundamental processes interact in the microstructure of interest. That’s where modeling can be invaluable.

It’s unlikely that you’re going to want or need a transient model of your entire battery. You’ll often find that spreadsheet models and perhaps some basic PDE solvers will assist greatly in the analysis and quantification of your results.

Figure out how you’re going to manufacture it. The sooner you do it, the better.

This doesn’t matter for academic researchers who simply want to show a proof of concept. If you’re trying to develop a product, though, you need to know how much your system is going to cost.

You don’t want to lock yourself into a high-cost component that dictates the price of your system. It’s perhaps acceptable to use a component made with an expensive process for demonstration or prototyping purposes. Such expense is only acceptable if you know that you can lower its cost with appropriate capital investments in tooling or automation.

If you’re using a component that is too expensive and you don’t know how you’re going to make it in a way that meets your cost requirements to go to market, you should consider it a red flag. See if changes to the other components of your design could enable a broader set of specifications on your expensive component.

I hope that you’ve found this tutorial helpful. The Black Diamond Structures team has cooperated in the development of dozens of different batteries that vary in chemistry, design, and application, ensuring that our products are compatible with standard manufacturing processes, make for higher quality pastes, and deliver great value with no disruption to existing battery manufacturing processes.

If you’re interested in working with us to improve your battery system, please contact us at