Electronics continues to be a part of our daily routines, from toys to laundry machines to electric cars.
FREMONT, CA: One matter that hinders the rapid growth of battery technology is that it takes a long time to test and monitor battery 'health' that impacts battery life. Therefore, better methods are required to predict battery life, but they are too hard to develop.
Therefore, a new plan was made to find how to accurately determine the useful lifespan of lithium-ion batteries used in devices from mobile phones to electric cars in advance, which could speed up the development of batteries and enhance manufacturing.
Machine learning builds models that accurately predict battery life using data collected from charging-discharge cycles computed in the early stages of the life of a battery.
Society needs to stop producing carbon emissions to tackle the imminent climate crisis. A double strategy has surfaced to attain this objective: the electricity sector uses renewable energy sources, and electric vehicles are supplanting those using conventional combustion engines. However, both changes come with barriers of their own.
One of the main obstacles to renewable energy is that the sources are often localized, creating a demand-supply imbalance. In addition, the problems with electrified transport guarantee that when conventional combustion engines are no longer used, sufficient electricity is generated to charge all vehicles and the integration of charging infrastructure with the electrical grid.
Therefore, the marketing of electric and hybrid cars has triggered an increasing demand for long-lived batteries for driving and grid buffering. Consequently, battery health assessment methods are turning more and more crucial.
A battery's runtime prediction relies on the start (State of Charge) SoC and other factors such as battery health and enforced road profile. The self-remedial regression model is proposed and implemented to overcome this difficulty. SoC estimation's main issue is determining a battery's initial SoC. Extensive experiments are needed to calculate the initial SoC and may vary with the battery life. Data-driven modeling through machine learning is a victorious route for lithium-ion battery prognostics and could help develop, manufacture, and optimize emerging battery technology.
