Lithium-ion batteries fault diagnostic for electric vehicles using sample entropy analysis method
Journal article, 2020

Fault detection plays a vital role in the operation of lithium-ion batteries in electric vehicles. Typically, during the operation of battery systems, voltage signals are susceptible to noise interference. In this paper, a novel fault detection method based on the Empirical Mode Decomposition and Sample Entropy is proposed to identify battery faults under various operating conditions. Firstly, effective fault features are extracted through the proposed Empirical Mode Decomposition method by decomposing battery voltage signals and removing the noise interference during the voltage sampling process. Experiments are conducted to quantitatively illustrate the fault features extracted by the Empirical Mode Decomposition. Then, based on these extracted fault features, the Sample Entropy values are calculated to help accurately detect and locate the battery faults. Moreover, an evaluation strategy of the detected faults is designed to indicate the battery fault level. Finally, the effectiveness of the proposed approach is verified against real-world data measured from electric vehicles in the presence of regular and sudden faults.

Lithium-ion batteries

Fault detection

Electric vehicles

Sample entropy


Xiaoyu Li

Beijing Institute of Technology

Kangwei Dai

Beijing Institute of Technology

Zhenpo Wang

Beijing Institute of Technology

Weiji Han

Chalmers, Electrical Engineering, Systems and control

Journal of Energy Storage

2352-152X (eISSN)

Vol. 27 101121

Subject Categories

Analytical Chemistry

Other Chemical Engineering

Other Electrical Engineering, Electronic Engineering, Information Engineering



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