A new method to perform Lithium-ion battery pack fault diagnostics – Part 1: Algorithm development and its performance analysis

Singh, Anubhav; Lodge, Andrew; Li, Yi; Widanage, Widanalage Dhammika; Barai, Anup

doi:10.1016/j.egyr.2023.11.008

Cited by 1 publication

(1 citation statement)

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“…Reference [21] proposed an online fault detection method for lithium-ion battery PACKs based on a combination of Hausdorff distance and modified Z-score. The methods above do not require the establishment of a cell model or the identification of complex parameters [22][23][24], which has the advantages of high efficiency and reliability, but suffers from poor real-time performance and a large data processing burden [25], making it difficult to obtain widespread application [26].…”

Section: Introductionmentioning

confidence: 99%

SOC estimation and fault identification strategy of energy storage battery PACK: Based on adaptive sliding mode observer

Xueyi,

Tinglong

2024

The Journal of Engineering

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Accurate state of charge (SOC) estimation and fault identification and localization are crucial in the field of battery system management. This article proposes an innovative method based on sliding mode observation theory for SOC estimation and short‐circuit fault location. The core of this new method is the design of an adaptive sliding mode observer, which reduces jitter by introducing adaptive switching gain, establishes an internal loop of gain and error, and improves the performance of SOC estimation. In addition, recursive least squares method was used to identify the key parameters of the model. Secondly, based on obtaining the SOC of each battery cell in series with the energy storage PACK, the specificity of the faulty battery cell in SOC change trend is utilized to identify and locate the short‐circuit fault of the energy storage PACK. The simulation and test results show that the designed adaptive sliding mode observer can significantly improve the estimation accuracy of SOC and has better stability. Compared to the commonly used Kalman estimation and BP neural network estimation methods, the designed method has improved accuracy by 5.53% and 3.42%, respectively. In addition, based on the accurate identification of SOC, the short‐circuit fault diagnosis results of the battery PACK have a high accuracy, confirming the feasibility and effectiveness of the designed strategy that includes SOC estimation and short‐circuit fault identification and positioning, and has broad application prospects.

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