Transfer learning based generalized framework for state of health estimation of Li-ion cells.

Estimating the state of health (SOH) of batteries powering electronic devices in real-time while in use is a necessity. The applicability of most of the existing methods is limited to the datasets that are used to train the models. In this work, we propose a generic method for SOH estimation with much wider applicability.

An Incremental Voltage Difference Based Technique for Online State of Health Estimation of Li-ion Batteries.

Accurate state of health (SOH) estimation of rechargeable batteries is important for the safe and reliable operation of electric vehicles (EVs), smart phones, and other battery operated systems. We propose a novel method for accurate SOH estimation which does not necessarily need full charging data. Using only partial charging data during normal usage, 10 derived voltage values ([Formula: see text]) are collected.

Internal short circuit detection in Li-ion batteries using supervised machine learning.

With the proliferation of Li-ion batteries in smart phones, safety is the main concern and an on-line detection of battery faults is much wanting. Internal short circuit is a very critical issue that is often ascribed to be a cause of many accidents involving Li-ion batteries. A novel method that can detect the Internal short circuit in real time based on an advanced machine leaning approach, is proposed.