AI open data predicts usable life of batteries

AI open data predicts usable life of batteries

Technology News |
By Nick Flaherty

The researchers at the Toyota Research Institute (TRI) with Massachusetts Institute of Technology (MIT) and Stanford University for the Centre for Data-Driven Design of Batteries found that combining comprehensive experimental data and artificial intelligence revealed the key for accurately predicting the useful life of lithium-ion batteries.

The team trained a machine learning model with a few hundred million data points, predicting how many more cycles each battery would last based on voltage declines and a few other factors among the early cycles. The predictions were within 9 percent of the actual cycle life. Separately, the algorithm categorized batteries as either long or short life expectancy based on just the first five charge/discharge cycles. Here, the predictions were correct 95 percent of the time.

This machine learning method could accelerate the research and development of new battery designs, and reduce the time and cost of production, among other applications. “The standard way to test new battery designs is to charge and discharge the cells until they die. Since batteries have a long lifetime, this process can take many months and even years,” said Peter Attia at Stanford’s Materials Science and Engineering. “It’s an expensive bottleneck in battery research.”

One of the critical tasks in data-driven, multi-institute research projects is ensuring that the large streams of data produced at experimental facilities are managed and transferred between different research groups efficiently. Muratahan Aykol and Patrick Herring brought TRI’s experience with big data to the project and their own expertise on battery development to enable effective management and seamless flow of battery data, which was essential for this collaboration to create accurate machine-learning models for the early-prediction of battery failure.

One focus in the project was to find a better way to charge batteries in ten minutes, a feature that could accelerate the mass adoption of electric vehicles. To generate the training data set, the team charged and discharged the batteries until each one reached the end of its useful life, which they defined as capacity loss of 20 percent. En route to optimizing fast charging, the researchers wanted to find out whether if it was necessary to run their batteries into the ground.

The new method has many potential applications says Attia. For example, it can shorten the time for validating batteries with new chemistries, which is especially important given rapid advances in materials. Also, manufacturers can use the sorting technique to grade batteries with longer lifetimes to be sold at higher prices for more demanding uses, like electric vehicles. Recyclers can use the method to find cells in used EV battery packs that have enough life in them for secondary uses.

Yet another use is optimizing battery manufacturing. “The last step in manufacturing batteries is called ‘formation’ which can take days to weeks,” he said. “Using our approach could shorten that significantly and lower the production cost.”

The researchers are using this early prediction model to optimize charging procedures that could enable batteries to be charged in ten minutes. By using this model, the optimization time can be cut by more than a factor of ten, significantly accelerating research and development.

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