Fault Diagnosis Method for Lithium-Ion Battery Packs in Real-World Electric Vehicles Based on K-Means and the Fréchet Algorithm

Abstract

Battery failure has traditionally been a major concern for electric vehicle (EV) safety, and early fault diagnosis will reduce many EV safety accidents. However, the short-circuit signal is generally very weak, so it is still a challenge to achieve a timely warning of battery failure. In this paper, an initial microfault diagnosis method is proposed for the data of electric vehicles in actual operation. First, a robust locally weighted regression data smoothing method is proposed that can effectively remove noisy data and retain fault characteristics. Second, an ordinary-least-squares-based voltage potential feature extraction method is proposed, which can effectively capture the small fault features of battery cells and achieve early warning. Third, a reference cell selection method based on K-means clustering is proposed, which can effectively reduce the false alarms caused by the inconsistency of each cell. Fourth, the Fréchet algorithm is introduced into the field of battery pack fault diagnosis and combined with thresholds for battery pack fault diagnosis and localization to accomplish the diagnosis and early warning of minor faults. Finally, the fault diagnosis method is validated by three actual running electric vehicles to verify the effectiveness, reliability, and robustness of the method.

Fault Diagnosis Method for Lithium-Ion Power Battery Incorporating Multidimensional Fault Features

“…The results show that the method proposed in this paper ensures the accuracy of detecting faults while improving robustness, which verifies the feasibility of applying the method in real working conditions. [28] 3610th/311th Yes Relatively Information entropy [30] -NO Poor Cosine similarity [32] 3621th/311th Yes Relatively Extended RMSE [33] 4114th/351th Yes Fair Ours 3880th/318th Yes Good…”

“…In an ideal state, conventional fault diagnosis methods such as the correlation [28] 3610th/311th Yes Relatively Poor Information entropy [30] -NO Poor Cosine similarity [32] 3621th/311th Yes Relatively Poor Extended RMSE [33] 4114th/351th Yes Fair Ours 3880th/318th Yes Good…”

“…In an ideal state, conventional fault diagnosis methods such as the correlation coefficient method and information entropy method are not affected by data fluctuations, and they can accurately determine the occurrence of faults. However, in a non-ideal state, the data fluctuation of the power ba ery will cause these methods to produce frequent false [28] 3610th/311th Yes Relatively Poor Information entropy [30] -NO Poor Cosine similarity [32] 3621th/311th Yes Relatively Poor Extended RMSE [33] 4114th/351th Yes Fair Ours 3880th/318th Yes Good…”

Comparative Analysis of Commonly Used Machine Learning Approaches for Li-Ion Battery Performance Prediction and Management in Electric Vehicles

“…Various machine learning techniques have been used in the literature to predict the states of LIBs. These techniques include support vector machines (SVMs) [11], decision trees (DTs) [12], random forests (RFs) [9], artificial neural networks (ANNs), k-means clustering [13], deep learning [14][15][16], genetic algorithms [14], gradient boosting machines (GBMs) [17], automatic regression, and nonlinear regression models [11]. SVMs are used to solve classification and regression problems by making a distinction between datasets.…”

Anomaly Detection Method for Lithium-Ion Battery Cells Based on Time Series Decomposition and Improved Manhattan Distance Algorithm

“…Once an accurate mathematical model is established, model-based methods can be significantly effective . However, these methods have high requirements for model accuracy, hardware, and additional equipment . As the complexity of the system increases, a significant amount of time and effort is required to test and model different batteries and faults. , Meanwhile, due to the highly nonlinear nature of battery systems, most existing models are usually only suitable for detecting specific types of faults and do not have universality, making it difficult to establish accurate models.…”