(Professor Richard Braatz of MIT (left) and William Chueh, assistant professor of materials science and engineering at Stanford University (right) Photo source: Amos Enshen Lu)
If mobile phone battery manufacturers can determine which batteries can be used for at least two years, they can sell such batteries to mobile phone manufacturers and other types of batteries to equipment manufacturers that are not so demanding. According to foreign media reports, a new study conducted by Stanford University, the Massachusetts Institute of Technology and Toyota Research Institute found that manufacturers can do this, And this technology can not only be used to classify batteries, but also help new battery designs come to market faster. Scientists combined comprehensive experimental data and artificial intelligence to reveal the key to accurately predict the battery life before lithium-ion battery capacity begins to decline. The researchers used hundreds of millions of battery charge and discharge data points to train a machine learning model. The algorithm predicts how many charge and discharge cycles each battery can continue based on voltage drops and some other factors in the early cycle of the battery.
The predicted value is within 9% of the number of cycles the battery can actually last. In addition, the algorithm classifies the battery as a long-life battery or a short-life battery according to the first 5 charge-discharge cycles of the battery. Moreover, 95% of the predictions are correct. This machine learning method can accelerate the development of new battery designs and other applications, reducing production time and costs. Researchers have published the data set, which is the largest in its class.
A key point of the project is to find a better way to fully charge the battery within 10 minutes. This feature may accelerate the popularity of electric vehicles. In order to generate the training data set, the research team continued to charge and discharge the battery until the battery's service life was exhausted. The researchers defined the battery capacity loss as 20%. In the process of optimizing fast charging, the researchers wanted to find out whether it is necessary to drain the battery all the time, and whether the answer can be found in the early charge and discharge cycle of the battery?
Generally speaking, the capacity of a lithium-ion battery is stable for a period of time before it drops sharply. Most consumers in the 21st century know that there will be big differences in the sharp drop point. In this project, the battery used can last 150 to 2300 cycles. The difference in the number of cycles is partly due to the different fast charging methods tested, but the difference in battery manufacturing also caused the difference in the number of battery cycles.
There are many potential applications for new methods developed by researchers. For example, it is possible to shorten the verification time of new batteries. Considering the rapid development of battery materials, it is very important to shorten the verification time. In addition, with this type of classification technology, batteries with a shorter life for electric vehicles can be used to power street lamps or as a backup power source for data centers. In addition, companies that recycle batteries can also find batteries with sufficient capacity from second-hand electric vehicle battery packs for secondary use.
In addition, another potential application is to optimize battery manufacturing. The final step in battery manufacturing is "forming", which usually takes a few days to a few weeks. But the methods provided by the researchers can significantly shorten the process, thereby reducing production costs. At present, researchers are using its machine learning model to optimize the method of fully charging the battery within 10 minutes. They said that it can reduce the charging time by more than 10 times.
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