A Comprehensive Signal-Based Fault Diagnosis Method for Lithium-Ion Batteries in Electric Vehicles

Xinwei Cong,Linjing Zhang,Jiuchun Jiang,Weige Zhang,Yan Jiang,Caiping Zhang

doi:10.3390/en14051221

Abstract

To enhance the operational reliability and safety of electric vehicles (EVs), big data platforms for EV supervision are rapidly developing, which makes a large quantity of battery data available for fault diagnosis. Since fault types related to lithium-ion batteries play a dominant role, a comprehensive fault diagnosis method is proposed in this paper, in pursuit of an accurate early fault diagnosis method based on voltage signals from battery cells. The proposed method for battery fault diagnosis mainly includes three parts: variational mode decomposition in the signal analysis part to separate the inconsistency of cell states, critical representative signal feature extraction by using a generalized dimensionless indicator construction formula and effective anomaly detection by sparsity-based clustering. The signal features of the majority of signal-based battery fault detection studies are found to be particular cases with a specific set of parameter values of the proposed indicator construction formula. With the sensitivity and stability balanced by appropriate moving-window size selection, the proposed signal-based method is validated to be capable of earlier anomaly detection, false-alarm reduction, and anomalous performance identification, compared with traditional approaches, based on actual pre-fault operating data from three different situations.

Highlights

Lithium-ion batteries are usually used in the power supply and storage of electric vehicles (EVs) because of their superior performance [1]
As for the model-based methods for lithium-ion batteries, by utilizing filters and observers, the equivalent circuit model [14], the electrochemical model [6], and other models [15] at battery cell level are applied to trace and update parameters related to battery states, with excellent real-time monitoring performance [16]
A large quantity of studies on fractional order models (FOMs) along with their derivatives [17,18], and models on battery pack level [19,20,21] have been dedicated to enhance the accuracy of depicting complicated electrochemical behaviors, material properties, constructions, and the nonlinear fading mechanism of lithium-ion batteries [22]

Summary

Introduction

Lithium-ion batteries are usually used in the power supply and storage of electric vehicles (EVs) because of their superior performance [1]. Even under relatively ideal working conditions, the collected signals in a battery pack could still be influenced by the differences in the state of charge (SOC), state of health (SOH), internal impedance, and other external environmental factors, so it is necessary to make an effort to effectively distinguish various actual faults from severe inconsistency [19]. A further reason that makes it unfeasible to adopt model-based methods for time-efficient battery fault diagnosis in practice is that, their ideal boundary conditions are hardly satisfied in actual operation of EVs

Methods

Results

Conclusion