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Electrochemical Model-based Fault Diagnostics for Internal Short Circuit Detection in Li-ion Batteries

Li-ion batteries have proven to be a reliable power source in various applications from consumer electronics to electrified transportation. A key factor to widespread adoption of Li-ion batteries is to increase their operational safety. The majority of commercial Li-ion battery management systems rely on sensor measurements and limit checking to detect faulty conditions. However, battery faults which can potentially lead to catastrophic failures are typically subtle and undetectable through only sensor measurement, until it is too late. In this research proposal, we aim to investigate devising a model-based fault diagnostic algorithm capable of estimating some of the important internal electrochemical battery states, especially internal resistance, to detect faults which can lead to serious failures such as internal short circuits and therefore, catastrophic accidents. Successful completion of this simulation project is expected to significantly improve Li-ion battery systems.

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