A quantized stochastic modeling approach for fault diagnosis of Lithium-ion batteries

Sara Mohon, Satadru Dey, Pierluigi Pisu, Beshah Ayalew

Research output: Contribution to journalConference articlepeer-review

3 Scopus citations


Safety and reliability are still key concerns for the Lithium-ion (Li-ion) battery systems in spite of their current popularity as energy storage solutions for transportation and other applications. To improve the overall reliability of the Li-ion batteries, the Battery Management Systems (BMS) should have the capabilities to detect different types of faults. Some of these faults can lead to catastrophic scenarios if they are not diagnosed early. In this paper, a stochastic approach of quantized systems is proposed for fault detection in Li-ion batteries. The scheme uses a quantized stochastic model derived from the equivalent circuit model of the battery to predict the most probable future states/outputs from the measured inputs and quantized outputs. Fault detection is achieved via comparison of the expected event and the actual event. To illustrate the effectiveness of the approach the model parameters for commercial Li-ion battery cell have been extracted from experiments, and then faults are injected in simulation studies.

Original languageEnglish (US)
Pages (from-to)970-975
Number of pages6
Issue number21
StatePublished - Sep 1 2015
Event9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2015 - Paris, France
Duration: Sep 2 2015Sep 4 2015

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering


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