 What if you could recharge your electric car in under 10 minutes? About the time it takes to stop at gas station. The overall goal of this research is to develop methods that allow us to accelerate battery testing but without degrading it a large amount. When batteries are charged at a very fast rate, it puts greatest strain on the battery which causes a degradation on their overall life. The main bottleneck with advances in electric vehicle batteries is testing times. Starting from the laboratory to an actual deployment in an EV can take years. The method we've developed is a machine learning technique to reduce testing times by about 98%. So typically it takes about a couple of months to finish an experiment in this case and we were able to reduce that time down to four days. We've done this, I think, through a variety of different approaches. One is to accelerate the test itself and then the other is to accelerate the entire process of generating those tests and determining what tests to run. So instead of testing hundreds or possibly millions of combinations, we test just a few and use a machine learning algorithm to determine what the outcomes would be on all the others. The method is very general and it can be applied to many other cases that involve time-consuming experiments. So there are basically two components to the research. The researchers wrote a program that based only on a few cycles of charging would figure out what's the lifetime of a battery and the other component of the research was an algorithm which figures out which charging protocols to test and when. So this is an exciting academic and industrial collaboration between Stanford, MIT and the Toyota Research Institute. The Stanford groups were focused first on experiments, providing the data. At TRI they've helped with the design of the data collection pipeline and the cloud infrastructure. The group at MIT was mostly involved in making early predictions. The current work improves the efficiency of the optimization. The exact details depend on the optimization problem. But broadly speaking, we are reducing the number of experiments to the bare minimum needed to reach a conclusion. The specific goal of this research was to optimize fast charging. Can we charge a battery in 10 minutes and what's the best way to do that? The data that is being processed for these machine learning algorithms is being packaged into cycles. We take each time we charge and discharge the battery as a distinct unit and we both clean and standardize that data. There are substantial variations in performance from one battery to the next, which makes it difficult to apply traditional optimization methods. However, there are machine learning algorithms that are specifically designed to address problems of this nature. It also helped us identify novel charging protocols which we did not know existed that could help us charge a battery much faster. Using a data pipeline is the best practice for data at scale, especially as scientific experiments start generating more and more data. The volume increases. This becomes a much more important practice. I think one of the reasons this was not done before is that it really required a lot of interdisciplinary collaboration between and the expertise both in the machine learning world and what kind of algorithms we should use for this particular application. And a lot of expertise in terms of like battery technology. This provides a better way to do science and experiments in the future as science becomes much more data intensive. This provides the ability to utilize that data in an optimal manner. How come we use AI and machine learning optimal experimental design, the kind of technologies that we've just talked about, not only to optimize for something but actually help us come up with a better understanding of the physical processes. The next steps for this project are to apply this method to much more challenging problems, problems in manufacturing of batteries and problems in real world use cases like when do you charge your battery or how long do you keep it charged. We learned a lot through this process, both about the application of these algorithms and also that the algorithm found that delivering the highest current in the middle of the charge actually results in batteries with the longest lifetime.