Model-based fault diagnosis approach on external short circuit of lithium-ion battery used in electric vehicles

Zeyu Chen, Rui Xiong, Jinpeng Tian, Xiong Shang and Jiahuan Lu

Applied Energy, 2016, vol. 184, issue C, 365-374

Abstract: This study investigates the external short circuit (ESC) fault characteristics of lithium-ion battery experimentally. An experiment platform is established and the ESC tests are implemented on ten 18650-type lithium cells considering different state-of-charges (SOCs). Based on the experiment results, several efforts have been made. (1) The ESC process can be divided into two periods and the electrical and thermal behaviors within these two periods are analyzed. (2) A modified first-order RC model is employed to simulate the electrical behavior of the lithium cell in the ESC fault process. The model parameters are re-identified by a dynamic-neighborhood particle swarm optimization algorithm. (3) A two-layer model-based ESC fault diagnosis algorithm is proposed. The first layer conducts preliminary fault detection and the second layer gives a precise model-based diagnosis. Four new cells are short-circuited to evaluate the proposed algorithm. It shows that the ESC fault can be diagnosed within 5s, the error between the model and measured data is less than 0.36V. The effectiveness of the fault diagnosis algorithm is not sensitive to the precision of battery SOC. The proposed algorithm can still make the correct diagnosis even if there is 10% error in SOC estimation.

Keywords: Electric vehicles; Battery safety; External short circuit; Fault diagnostics; Equivalent circuit model; Particle swarm optimization (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (32)

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DOI: 10.1016/j.apenergy.2016.10.026

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