Model-based Stochastic Fault Detection and Diagnosis for Lithium-ion Batteries

TL;DR

This paper introduces a stochastic fault detection and diagnosis algorithm for lithium-ion batteries that uses physical models and measured temperature data to accurately identify faults despite uncertainties.

Contribution

It develops a two-step probabilistic FDD method combining model correction and fault identification, improving detection accuracy over traditional Monte Carlo approaches.

Findings

High fault detection rate for individual faults

Effective detection of simultaneous faults

Outperforms Monte Carlo simulations in efficiency

Abstract

Lithium-ion battery (Li-ion) is becoming the dominant energy storage solution in many applications such as hybrid electric and electric vehicles, due to its higher energy density and longer life cycle. For these applications, the battery should perform reliably and pose no safety threats. However, the performance of Li-ion batteries can be affected by abnormal thermal behaviors, defined as faults. It is essential to develop reliable thermal management system to accurately predict and monitor thermal behaviors of Li-ion battery. Using the first-principle models of batteries, this work presents a stochastic fault detection and diagnosis (FDD) algorithm to identify two particular faults in the Li-ion battery cells, using easily measured quantities such as temperatures. Models of Li-ion battery are typically derived from the underlying physical phenomena. To make model tractable and useful, it is common to make simplifications during model development, which may consequently introduce mismatch between models and battery cells. Further, FDD algorithms can be affected by uncertainty, which may originate from either intrinsic time varying phenomena or model calibration with noisy data. A two-step FDD algorithm is developed in this work to correct model of Li-ion battery cells and to identify faulty operations from a normal operating condition. An iterative optimization problem is proposed to correct the model by incorporating the errors between measured quantities and model predictions, which is followed by an optimization-based FDD to provide a probabilistic description of the occurrence of possible faults, while taking the uncertainty into account. The two-step stochastic FDD algorithm in this work is shown to be efficient in terms of fault detection rate for both individual and simultaneous faults in Li-ion batteries, as compared to Monte Carlo (MC) simulations.