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As artificial intelligence (AI) continues its rapid advance in all areas of life, the battery industry and scientists are embracing its power for faster testing and processing at mind-boggling speeds previously unimagined. Andrew Draper reports.

Richard Ahlfeld, founder of German start-up Monolith, said by embracing AI and machine learning principles, engineers are more easily able to navigate the intricate challenges of understanding and validating intractable physics of complex products more efficiently. This leads to streamlined development, optimised designs, and faster time to market, he said.

Monolith provides battery and other engineering testing solutions to OEMs and others, using AI. He said: Using charging voltage and temperature curves from early cycles that are yet to exhibit symptoms of battery failure, we apply data-driven models to both predict and classify the sample data by health condition based on the observational, empirical, physical, and statistical understanding of the multiscale systems.

The company offers a product called Next Test Recommender (NTR). It gives the example of an engineer trying to configure a fan to provide optimal cooling for all driving conditions. They had a test plan for this highly complex application that included a series of 129 tests.

When this test plan was inserted into NTR, it returned a ranked list of what tests should be run first. Of 129 tests, as shown, NTR recommended the last test number 129 should actually be among the first five to run and that 60 tests would be sufficient to characterise the full performance of the fan. That would give a 53% reduction in testing.

Ahlfeld said the best well-integrated machine learning models achieve a verified classification accuracy of 96.3% and an average misclassification test error of 7.7%. Our findings highlight the need for cloud-based artificial intelligence technology tailored to robustly and accurately predict battery failure in real-world applications, he said.

Its time for engineers and researchers to embrace machine learning as a vital tool in their pursuit of safer, longer-lasting, and more efficient batteries. As battery technology continues to evolve, the synergy between human expertise and machine intelligence will drive us toward a future where energy storage solutions are becoming both powerful and sustainable. Ahlfeld stresses the importance of clear plans for how to integrate machine learning solutions into existing testing processes.

The US Department of Energys Pacific Northwest National Laboratory announced in December the setting up of its Center for AI @PNNL to coordinate its research into projects focused on science, security, and energy resilience.

PNNL said while it has been working on AI for decades, the technology has only begun to surge in the past year with the ready availability of generative AI. This allows anyone to create persuasive content with small amounts of data. Sometimes it is errant, offering false facts and links that do not work, and photos that are made up. Scraped from the internet is the term used.

At the same time, AI is a vital tool for serious researchers as well as a subject all its own for scientists to create, explore and validate new ideas, it said. AI also presents an exciting opportunity for PNNL scientists to advance a critical area of science and chart the path forward. Court Corley, the laboratorys chief scientist for AI and director of the new centre, said: The field is moving at light speed, and we need to move quickly to keep PNNL at the frontier.

A priority of the centre is developing ways to keep AI secure and trustworthy, it said. It works with a network of academic and commercial partners: North Carolina State University, University of Washington, Washington State University, Microsoft, Micron, University of Texas at El Paso, Georgia Institute of Technology, Western Washington University, and other national laboratories and organisations. It also partners with the DOEs Office of Science Advanced Scientific Computing Research programme.

Many of the applications of AI research at PNNL are focused on energy resilience and security. Laboratory scientists use AI to improve the operation of the US electrical grid, keeping power flowing to homes and businesses. Others are using machine learning to explore new combinations of compounds that could power the next generation of lithium batteries.

PNNL and Microsoft scientists said a combination of advanced AI with advanced cloud computing is turbocharging the pace of discovery to speeds unimaginable just a few years ago.

They said PNNL scientists are testing a new battery material, a solid electrolyte, that was found in a matter of weeks, not years. They have been using advanced AI and high-performance computing (HPC), a type of cloud-based computing that combines large numbers of computers to solve complex scientific and mathematical tasks.

The new battery material was made using Microsofts Azure Quantum Elements cloud service to winnow 32 million potential inorganic materials to 18 promising candidates that could be used in battery development. That took just 80 hours.

Once made, artisan work stepped in. One of the first steps was to take solid precursors of the materials and to grind them by hand with a mortar and pestle, said PNNL materials scientist Shannon Lee. She uses a hydraulic press to compact the material into a small pellet. That goes into a vacuum tube and is heated to 450650C. It is then transferred to a box to keep it away from oxygen or water, and then ground into a powder for analysis.

Vijay Murugesan, material sciences group lead at PNNL, said a previous research project took several years to solve a problem and design a new material for use in a redox flow battery.

Nathan Baker, product leader for Microsoft, said: At every step of the simulation where I had to run a quantum chemistry calculation, instead Im calling the machine learning model. So I still get the insight and the detailed observations that come from running the simulation, but the simulation can be up to half a million times faster.

The bottleneck in the process is gaining access to the supercomputers, which are sometimes shared. Having AI tools in the cloud help relieve this, as the cloud is always available. The project has now generated a battery material which has been synthesised and turned in to a prototype battery for laboratory testing.

Murugesan said the story is not about this particular battery material, but rather the speed at which a new material was identified. It uses both lithium and sodium, as well as some other elements. The value of an alternative material to lithium is obvious: it does not rely on mining in a single or handful of countries and geopolitical issues, and it can be manufactured by volume.

A panel discussion last November among the scientific advisory board of physics-based UK battery management software company Breathe Battery Technologies, threw up a lot of questions. Its debate covered advancements in battery control and the rise of data. CTO Dr Yan Zhao said the burst of data and machine learning was super exciting but it was not yet clear what the goal was from leveraging data.

Professor of Electrical and Computer Engineering at University of Colorado, Gregory Plett, told the panel there is already synergy between physics and database-driven modelling. I think there are already synergies between the two. I mean, the models that we can create now that are on a pathway towards predicting (battery) lifetime.

Were not all the way there yet, but theyre already beginning to be useful and we can already run them so fast that we can generate data faster than we can actually analyse. So, we already need machine learning methods in order to just analyse all the virtual data that were generating which can augment the experimental data we gather in the lab.

A model that describes a battery life needs to be updated to account for aging and degradation. Getting it right, to ensure accuracy of things like remaining charge in an electric vehicle, is complex.

Plett said: It is a very complicated thing to do well, and I agree that its one that we need to solve because if we use a single model to describe the battery through its entire life, it may work very well at the beginning of life, but as the battery ages, that model will not continue to describe its behaviours very well.

And we may end up with a scenarioof someone being broken down on the side of the road because the model failed. So somehow, we have to have a model that describes the battery cell well at its present state of life.

He said there are a couple of methods to do that. One is if we can somehow use the data that were continuously measuring to adjust the parameters of the model that could provide a very satisfactory answer. Another is if we have a bank of battery models that are representative or descriptive of a battery at different stages of life and we simply select which model to use that is another alternative. And at this point, I dont know what the right answer is between those.

A research team at Stanford University led by professors Stefano Ermon and William Chueh announced in 2020 they had developed a machine learning-based method that slashed battery testing times by 98% when applied to battery charge speed.

Researchers at the Basque governments research body CIC energiGUNE have studied how AI can help battery recycling. They said the technological recycling routes developed have to be profitable, industrialisable and sustainable.

The research centre, which specialises in electrochemical and thermal energy storage, said in terms of waste sorting, the opportunities include:

As for recovery of materials, they identified:

They said studies on classification capacity have shown how AI is able to determine the location and type of waste to be treated through image recognition techniques. This makes it possible to determine which method or route is best for managing and handling that waste according to its toxicity etc.

AI solutions offer the ability to monitor and automate processes such as waste sorting and grouping or the recycling activity itself, which is an advantage in terms of efficiency, cost and safety, the authors said.

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Artificial intelligence turbocharges the pace of discovery and processes - bestmag