Can AI Accurately Predict Battery Health for EVs and Consumer Electronics?
Artificial intelligence (AI) and machine learning could be used to predict the health of a battery with 10 time’s higher accuracy than current techniques and Universities of Cambridge and Newcastle researchers in the UK, claim that their new machine learning model could also help the development of safer and more reliable batteries for electric vehicles and consumer electronics like smartphones and wearable devices.
How to Monitor Battery Degradation with AI? It occurs via a complex network of subtle chemical processes that collectively shorten performance and lifespan. And being able to monitor this is just as tricky as it is important, perhaps current methods of assessing a battery’s condition rely on the measurement of current and voltage during charge cycles.
How to use AI and Machine Learning Methods? employs a machine learning method to monitor batteries by sending electrical pulses into them and measuring the responses received from them and Artificial Intelligence-based non-invasive and retrofittable system.
Artificial Intelligence algorithm then assesses the different features of the responses to discover specific features that are the signs of a battery degrading and Over 20,000 experimental measurements to train their ML model, providing the underlying AI with a huge data set to compare new information.
Investigating How and Why Batteries Degrade? It can recognize processes taking place in a battery and compare them to those seen in previous data sets and produces increasingly more reliable results and new information and can be used to make significant improvements in battery technology.
How to Control Battery Charging Process? In ML model is currently being used to monitor processes in different battery systems and researchers behind it want to look at how degradation happens and how it can be stopped or slowed down.
