Universal Adaptive Stabilization-Based Trend Filtering for Impending Battery Voltage Collapse Detection

Abstract

Battery operated devices are common in everyday life. Deciding when a warning for impending battery voltage collapse should be triggered is not trivial. This letter develops an impending battery terminal voltage collapse detection system using a universal adaptive stabilizer (UAS) and a well-known trend filter. This eliminates requiring knowledge of battery model parameter values, or initial state-of-charge (SOC). The proposed approach overcomes the need for extensive training when compared to using neural-networks based techniques. Also, the developed trend filter when used with a UAS, eliminates the need for selecting windows of data to be processed. This is advantageous compared to earlier work, which uses a different trend filtering mechanism, because selection of window sizes is not straightforward. Further, the approach used in this letter shows that the UAS based technique is implementable on a cell phone. Associated mathematical results, and experimental data from such an implementation are presented. Additionally, the technique is also applied to other larger capacity Li-ion batteries showing its versatility. The developed technique can also be used to detect when the state-of-health (SOH) of a Li-ion battery is about to enter an unsafe region.