Microsoft devises a system that learns from your habits to increase battery life
battery life is one of the biggest problems facing laptops and mobiles in our time. These devices are capable of doing more things than ever before, but because of that, users expect to be able to use them for longer and more intensive, something that unable to meet current power management technologies.
At Microsoft Research they think they have found an idea that would help solve this.It&39;s about instead of waiting for a new battery technology to come along and try to use more intelligently the technologies that are available nowadays."
Microsoft's idea is to dynamically use different types of batteries optimized for different tasksThis would be accomplished by mixing different types of batteries, and switching between their use dynamically, through software, depending on the task at hand realizing the device in question (be it a phone, smart watch, laptop, etc), instead of always using hardware-managed lithium-ion batteries, which is what happens today.
That way, the operating system would detect if the user is typing in a Word document or doing a more powerful task, like editing video, and accordingly would activate a type of battery specially optimized for that task.
This technology would automatically optimize the use of batteries according to user habitsThis technology would also use machine learning to improve battery management according to user habits (when you use the device, when it charges it and what tasks it performs at all times).
For example, if someone always charges their laptop at 2:45 PM, and then unplugs it to project a long PowerPoint presentation, this system would be able to identify that pattern and automatically use afast charging mode when connecting the equipment at that time.
It is important to mention that this technology is still in the experimental and prototype development phase, but even so, it could perfectly be implemented in final products for consumers in the coming years.
Via | Next at Microsoft