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

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.

Advancing Smart Lithium-Ion Batteries: A Review on Multi-Physical Sensing Technologies for Lithium-Ion Batteries

“…Electrical parameters, such as voltage and current, form the foundational data collected by a BMS. Typically, BMS monitors the voltage across parallel-connected individual cells at the module level and the current through series-connected modules at the pack level [6]. Although current and voltage signals underpin most battery state estimation algorithms within BMS [7], these signals often fall short in accurately depicting the battery's internal state.…”

Fault Location for Distribution Smart Grids: Literature Overview, Challenges, Solutions, and Future Trends

“…The authors in [154] proposed a signal-based fault diagnosis method for lithiu batteries in EVs based on voltage signals. The variational mode decomposition (V algorithm is a signal-based technique that is used to identify voltage signal characte related to either long-term battery state variations or local responses to external excit Then a generalized dimensionless indicator (GDI) is used to reduce the impact of the ity and quantity of training data.…”

A New Methodology for Early Detection of Failures in Lithium-Ion Batteries

“…However, detecting failures in LIBs presents a practical challenge due to the ideal conditions that are not met in real electric vehicle activities and the lack of voltage signal based methods. Therefore, in order to have a method that can be implemented in fault diagnosis of batteries on the voltage signal, the following methodology has been proposed: (a) variational mode decomposition in the signal analysis part to separate the inconsistency of the cell states, (b) extraction of the critical characteristics of the signal by using a generalized dimension indicator construction formula, and (c) detection of standardized anomalies through scatterbased clustering [17].…”