Lithium-ion batteries fault diagnostic for electric vehicles using sample entropy analysis method
Artikel i vetenskaplig tidskrift, 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


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Beijing Institute of Technology

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Beijing Institute of Technology

[Person b4b48e89-6abf-4511-838a-ce5f7f90aaa9 not found]

Beijing Institute of Technology

[Person 2b2033a6-c838-4d89-a43e-73e2f1ce2fc0 not found]

Chalmers, Elektroteknik, System- och reglerteknik

Journal of Energy Storage

2352-152X (eISSN)

Vol. 27 101121


Analytisk kemi

Annan kemiteknik

Annan elektroteknik och elektronik



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