New AI method accurately predicts battery usable life

April 02, 2019 //By Nick Flaherty
AI accurately predicts battery life
Toyota has teamed up with MIT and Stanford University to predict the usable life of battery cells using early cell cycle data and machine learning. The resulting data has been made publicly available. 

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 nine 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


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