HOW AI TECHNOLOGY SUBVERTS THE DEVELOPMENT OF NEW BATTERIES
2020-04-22
Using AI technology has become an importantway to develop new batteries. Foreign media reported that researchers at Cambridge University and Newcastle University devised a new method to detectbatteries by sending electrical pulses to the batteries and measuring theresponse. The measurement results are processed by machine learning algorithmsto predict the health and service life of the battery.
The researchers claim that the technologycan predict the health of batteries with an accuracy that is 10 times higherthan current industry standards, thereby helping to develop safer and more reliable batteries for electric vehicles and consumer electronics.
It is difficult to predict the healthstatus and remaining service life of lithium-ion batteries is one of the main problems that limit the widespread adoption of electric vehicles. After running for a period of time, the lithium battery will decline, which affects the battery's use state and life.
Current methods of predicting batteryhealth are based on tracking current and voltage during battery charging anddischarging, which misses an important function to indicate battery status.Therefore, there is currently a need to have new methods for tracking the many processes that take place in the battery and to detect the actual operation ofthe battery, as well as new algorithms that can detect subtle signals during charging and discharging.
Dr. Alpha Lee of the Cavendish Laboratoryat Cambridge University said: "Safety and reliability are the most important design standards because the batteries we develop can pack a lot ofenergy in a small space. By improving the monitoring of charging and discharging Software, and using data-driven software to control the charging process, I believe we can greatly improve battery performance. "The researchersdevised a method to monitor the battery by sending electrical pulses to the battery and measuring its response. Then use machine learning models to discover specific features in the electrical response that are signs of battery aging.
The researchers conducted more than 20,000 experimental measurements to train the model, which is the largest data set inits class. The researchers also show that machine learning models can be interpreted as giving hints of degraded physical mechanisms. The model can tell which electrical signals are most relevant to aging, which in turn allows them to design specific experiments to explore the causes and ways of battery degradation.
Researchers are now using their machine learning platform to understand the degradation of different battery chemistry.They also developed the best battery charging protocol, powered by machine learning, to achieve fast charging and minimize degradation.
It is worth noting that in addition to Cambridge University, universities and enterprises including Stanford University, Toyota Motor, Panasonic and others are also using AI technology to develop new batteries and improve battery performance. For example, Stanford University and Toyota researchers have developed a new machine learning method that is said to accelerate the development of electric vehicle batteries.Specifically, the research team of MIT Stanford and Toyota Research Institutehas developed a method based on machine learning, which shortens the battery charging test time from nearly two years to 16 days, a reduction of nearly 15times, which helps Speed up the development of new batteries. Panasonic has also developed an AI high-tech material analysis method that can visualize the behavior of lithium-ion battery internal materials during battery operation under high-speed and high-resolution conditions. The visualization of this state will greatly affect the lithium battery. The capacity density, charge and discharge speed and life are improved.
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