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

Abstract

A large proportion of electric vehicle accidents are attributed to lithium-ion battery failure recently, which demands the time-efficient diagnosis and safety warning in advance of severe fault occurrence to ensure reliable operation of electric vehicles. However, serious battery system faults are often not caused by easily-observed cell state inconsistency, but derived from a certain cell failure with precursory signals untended, or occasional abuse, thus eventually thermal runaway. In this paper, a signal-based fault diagnosis method is presented, including signal analysis to eliminate the impact of state inconsistency on time-series feature extraction, feature fusion, and dimensionality reduction by manifold learning, with clustering-based outlier detection to identify abnormal signal features. The challenges in threshold determination of fused features can be effectively resolved by supplementary correction to largely reduce the amount of false alarms. Compared with the judgments from actual battery management systems, and other signal-based methods with single features, earlier detections can be achieved with robustness, verified by real-world pre-fault operation data of electric vehicles that suffered thermal runaway.

A Fault Diagnosis Method for Lithium-Ion Battery Packs Using Improved RBF Neural Network

“…Then a developed cross-cell monitoring algorithm was used to carry out the fault diagnosis. Jiang et al (2021) proposed a new signal-based fault diagnosis model for lithium-ion batteries. Then this model was used to verify the data from the thermal runaway of electric vehicles.…”

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

“…In this way, corresponding functions will be realized on signal components in different frequency ranges. To our best knowledge, using VMD in signal analysis can attain some satisfying performance [30], compared with using other signal process methods such as empirical mode decomposition [23] that suffers from end effect and mode mixing, and wavelet decomposition [31] of which the wavelet basis becomes hard to determine, due to coupled performances and occurrence of different types of faults; (2) Influenced by multiple external factors, it is hard to make robust judgments on abnormal signals by directly employing methods on raw signal data. Therefore, a generalized dimensionless indicator (GDI) extraction formula with moving-window observation is proposed to reduce the impact of the quality and the quantity of training data, and to effectively balance the sensitivity and stability of the signal features depending on the applied situation.…”

A Study on the Anomaly Detection of Engine Clutch Engagement/Disengagement Using Machine Learning for Transmission Mounted Electric Drive Type Hybrid Electric Vehicles

“…Studies on fault or anomaly detection for vehicle powertrains have been carried out by various approaches. They can be classified by rule-based methods [6][7][8][9][10][11][12], mathematical 2 of 21 model-based methods [13][14][15][16][17][18][19][20][21][22][23][24][25][26], and data-driven methods that use signal processing or machine learning [27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43].…”

“…Meyer proposed an inter-turn short circuit fault detection and fault degree identification method using moving horizon observer for the Toyota Prius traction motor [19]. In the case of using data-driven techniques, methods using frequency analysis [27,31,34,40], methods using frequency analysis and neural networks together [41], and methods using machine learning such as one-class SVM, Hidden Markov model, and Gaussian mixture model [28,33,36,42], etc., were proposed. Akin proposed a frequency analysis-based fault detection method used at the motor's zero speed [27].…”

“…Kordes detected faults of sensor data in a controller area network (CAN) bus using machine learning with the features by extracting features using cause and effect rules [36]. Jiang conducted fault diagnosis for an EV battery using variational mode decomposition and clustering [42]. In particular, in [44], the authors diagnosed faults of vehicle powertrain by using mathematical models and a neuro-fuzzy network together.…”