For all the time and money spent on battery development, progress is measured in decades.
Researchers at MIT, Stanford and Toyota Research Institute say they can accurately predict the cycle life of lithium-ion batteries using early cycle data and machine learning. New collaborative research published today in Nature Energy shows that combining experimental data and artificial intelligence revealed the key for accurately predicting the useful life of lithium-ion batteries before their capacities started to wane.
After the researchers trained their machine learning model with a few hundred million data points, the algorithm predicted 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% 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% 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 researchers have made the data, said to be the largest of its kind, publicly available.
“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 co-lead author Peter Attia, Stanford doctoral candidate in Materials Science and Engineering. “It’s an expensive bottleneck in battery research.”
The work was carried out at the Center for Data-Driven Design of Batteries, an academic-industrial collaboration that integrates theory, experiments and data science. The Stanford researchers, led by William Chueh, assistant professor in Materials Science & Engineering, conducted the battery experiments. MIT’s team, led by Richard Braatz, professor in Chemical Engineering, performed the machine learning work. Kristen Severson is co-lead author of the research. She completed her Ph.D. in chemical engineering at MIT last spring.
Study co-authors 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.
Generally, the capacity of a lithium-ion battery is stable for a while. Then it takes a sharp turn downward. The plummet point varies widely, as most 21st century consumers know. In this project, the batteries lasted anywhere from 150 to 2300 cycles. That variance was partly the result of testing different methods of fast charging, but also due to the normal differences that emerge in commercially produced devices that depend on molecular interfaces.
About Ken Zino
Ken Zino, editor and publisher of AutoInformed, is a versatile auto industry participant with global experience spanning decades in print and broadcast journalism, as well as social media. He has automobile testing, marketing, public relations and communications experience. He is past president of The International Motor Press Assn, the Detroit Press Club, founding member and first President of the Automotive Press Assn. He is a member of APA, IMPA and the Midwest Automotive Press Assn.
He also brings an historical perspective while citing their contemporary relevance of the work of legendary auto writers such as Ken Purdy, Jim Dunne or Jerry Flint, or writers such as Red Smith, Mark Twain, Thomas Jefferson – all to bring perspective to a chaotic automotive universe.
Above all, decades after he first drove a car, Zino still revels in the sound of the exhaust as the throttle is blipped during a downshift and the driver’s rush that occurs when the entry, apex and exit points of a turn are smoothly and swiftly crossed. It’s the beginning of a perfect lap.
AutoInformed has an editorial philosophy that loves transportation machines of all kinds while promoting critical thinking about the future use of cars and trucks.
Zino builds AutoInformed from his background in automotive journalism starting at Hearst Publishing in New York City on Motor and MotorTech Magazines and car testing where he reviewed hundreds of vehicles in his decade-long stint as the Detroit Bureau Chief of Road & Track magazine. Zino has also worked in Europe, and Asia – now the largest automotive market in the world with China at its center.
MIT, Stanford and Toyota Research Institute Use AI to Predict Useful Life of Batteries.They lasted from 150 to 2300 cycles.
For all the time and money spent on battery development, progress is measured in decades.
Researchers at MIT, Stanford and Toyota Research Institute say they can accurately predict the cycle life of lithium-ion batteries using early cycle data and machine learning. New collaborative research published today in Nature Energy shows that combining experimental data and artificial intelligence revealed the key for accurately predicting the useful life of lithium-ion batteries before their capacities started to wane.
After the researchers trained their machine learning model with a few hundred million data points, the algorithm predicted 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% 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% 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 researchers have made the data, said to be the largest of its kind, publicly available.
“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 co-lead author Peter Attia, Stanford doctoral candidate in Materials Science and Engineering. “It’s an expensive bottleneck in battery research.”
The work was carried out at the Center for Data-Driven Design of Batteries, an academic-industrial collaboration that integrates theory, experiments and data science. The Stanford researchers, led by William Chueh, assistant professor in Materials Science & Engineering, conducted the battery experiments. MIT’s team, led by Richard Braatz, professor in Chemical Engineering, performed the machine learning work. Kristen Severson is co-lead author of the research. She completed her Ph.D. in chemical engineering at MIT last spring.
Study co-authors 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.
Generally, the capacity of a lithium-ion battery is stable for a while. Then it takes a sharp turn downward. The plummet point varies widely, as most 21st century consumers know. In this project, the batteries lasted anywhere from 150 to 2300 cycles. That variance was partly the result of testing different methods of fast charging, but also due to the normal differences that emerge in commercially produced devices that depend on molecular interfaces.
About Ken Zino
Ken Zino, editor and publisher of AutoInformed, is a versatile auto industry participant with global experience spanning decades in print and broadcast journalism, as well as social media. He has automobile testing, marketing, public relations and communications experience. He is past president of The International Motor Press Assn, the Detroit Press Club, founding member and first President of the Automotive Press Assn. He is a member of APA, IMPA and the Midwest Automotive Press Assn. He also brings an historical perspective while citing their contemporary relevance of the work of legendary auto writers such as Ken Purdy, Jim Dunne or Jerry Flint, or writers such as Red Smith, Mark Twain, Thomas Jefferson – all to bring perspective to a chaotic automotive universe. Above all, decades after he first drove a car, Zino still revels in the sound of the exhaust as the throttle is blipped during a downshift and the driver’s rush that occurs when the entry, apex and exit points of a turn are smoothly and swiftly crossed. It’s the beginning of a perfect lap. AutoInformed has an editorial philosophy that loves transportation machines of all kinds while promoting critical thinking about the future use of cars and trucks. Zino builds AutoInformed from his background in automotive journalism starting at Hearst Publishing in New York City on Motor and MotorTech Magazines and car testing where he reviewed hundreds of vehicles in his decade-long stint as the Detroit Bureau Chief of Road & Track magazine. Zino has also worked in Europe, and Asia – now the largest automotive market in the world with China at its center.