High-performance, long-lasting batteries are the future. But, possible improvements should first take place, which is now possible, thanks to AI.
Artificial intelligence (AI) is changing the world in many ways. With its wide-ranging tools that allow us to rethink how we analyze data and integrate information, we’re able to obtain insights that help improve decision making. Now, with the application of AI in the battery sector, experts might find ways to improve battery tech performance.
Applying AI in the Study of Battery
The application of artificial intelligence in the battery sector can help address issues in its early development. It is also essential in identifying clues on how to enhance recharging rates without having to increase the lithium-ion batteries’ degradation. But, to further understand how artificial intelligence could influence research on fuel cells and how to improve its performance, it’s first necessary to be clear about AI.
What exactly is Artificial Intelligence?
There’s no specific definition of AI that is uniformly agreed upon, but in essence, it refers to intelligence displayed by machines. Often, it is characterized by learning and its ability to adapt. Some researchers claimed that the software systems make decisions that would normally require a level of expertise of humans, helping people deal with issues as they appear. As such, it operates in an adaptive, intelligent, and intentional way.
However, it is important to remember that AI isn’t the same as automation, as automation has already been widely used to speed up processes throughout the insights industry. Automation simply refers to a set of rules a machine follows to do something or perform a task without any human assistance. Learning is actually what distinguishes automation from artificial intelligence.
How can AI help boost the performance of lithium-ion batteries?
The use of an innovative machine learning algorithm will enable researchers to discover potential designs for the lithium-ion batteries and fuel cells’ microstructure before they run 3-D simulations. This will help the researchers identify ways on how to improve batteries and fuel cells’ performance.
The Dilemma
Fuel cells are using clean hydrogen fuel, which wind and solar energy can generate in order to produce electricity and heat. Lithium-ion batteries are a common type of energy storage often found in electronic devices like laptops, smartphones, and even electric vehicles.
Their performance is closely linked to their microstructure. The way the pores or holes within their electrodes are arranged and shaped can affect the amount of power generated by the fuel cells, and how fast batteries discharge and charge. The problem is that the micrometer-scale is very small, making it challenging to study their specific sizes and shapes at a high resolution, so it would be easier to identify a relationship with cell performance.
The Solution
This is where artificial intelligence comes in. The application of machine learning techniques will help researchers explore the pores effectively and then run three-dimension simulations. This way, it would be easier for them to calculate the performance of cells according to their microstructure.
The machine learning algorithms could learn to produce three-dimensional image data of the microstructure from the training data generated from the nano-scale imaging performed synchrotrons. Through clearer data images, they could identify more information to improve their study of cells.
The use of this technique helps researchers zoom in on cells and batteries to identify which properties influence performance. When running three-dimensional simulations to identify and predict the performance of cells, they require a large volume of data that’s sufficient to be statistically considered representative of the entire cell.
Currently, it is difficult to get large image data volumes of their microstructure at the resolution needed to make assumptions. But, researchers discovered it is possible to just train their code to produce larger sets of data with similar properties or purposely generate structures that models suggest would lead to batteries that perform better.
Newer findings will be of great help to researchers in designing and creating optimized electrode that could improve cell performance. It is definitely an exciting for the machine learning community and the energy storage industry as there are several ways to explore the interface of such disciplines.
How AI is also used to improve recharging rates
This is not the first time that artificial intelligence is used by the battery sector. In the past, the industry has been taking advantage of machine learning to predict the battery life of lithium-ion batteries. Now, they are also turning to AI to identify clues to boost recharging rates without further increasing the battery’s degradation.
Battery research used to take a long time. When testing new battery designs, the standard way to do it is to continuously charge and discharge the cells until they drain out and die. Since batteries normally have a long lifetime, the process can take months and even years. Not only is it time-consuming, but also expensive.
This is why some companies have tried using AI in an effort to cut the charging times of lithium-ion batteries down to 5 minutes. One firm was able to successfully recharge a battery for an electric scooter in less than ten minutes. But, the problem with charging it extremely fast is that the battery heats up and degrades.
Then, it was found that even if AI can help identify a way to charge electric cars as quickly as filling a gas tank, it will still take a while for the car industry to implement the technology. So, speeding up charging time without affecting battery life negatively is still a welcome development. With current studies being done in the industry and further discoveries are identified with the application of AI, it’s just a matter of time before the battery sector can find new means to improve the recharging capabilities of lithium-ion batteries.
Conclusion
There is a bright future ahead of the energy storage industry and the battery sector, thanks to machine learning. AI will definitely play a more important role in this development. This is why AI will undoubtedly become one of the most influential human innovations in history.