Watch On:
Summary
The research titled ‘Impedance-based forecasting of lithium-ion battery performance amid uneven usage,’ published in the scientific journal Nature Communications focused on lithium cobalt oxide cells widely used in rechargeable batteries. The algorithm developed by the Cambridge team forecasts the end of life in batteries and records the performance specifically under varying usage conditions. Researchers are also exploring how their algorithm-based framework could be used to develop optimal fast charging protocols to reduce EV charging times without causing degradation.
Show Notes
Lithium-ion batteries are largely used to power the majority of EVs on roads globally.
The algorithm developed by the Cambridge team forecasts the end of life in batteries and records the performance specifically under varying usage conditions.
The scalar State of Health (SOH) metric is used to quantify the extent of degradation within the battery cells.
The new study highlighted that SOH lacks clarity in recording the level of degradation in cells of lithium-ion batteries.
In July 2020, Imperial College London developed a new machine-learning algorithm that could improve the design and performance of lithium-ion batteries and fuel cells.
Source
https://mercomindia.com/machine-learning-algorithm-study-ev-battery-charging-performance/
