An Internal Short Circuit Fault Detecting of Battery Pack Based on Spearman Rank Correlation Combined with Neural Network

GAO Mingyu; CAI Linhui; SUN Changcheng; LIU Caiming; ZHANG Zhaowei; DONG Zhekang; HE Zhiwei; GAO Weiwei

doi:10.11999/JEIT210975

Volume 44 Issue 11

Nov. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(11): 3734-3747

GAO Mingyu, CAI Linhui, SUN Changcheng, LIU Caiming, ZHANG Zhaowei, DONG Zhekang, HE Zhiwei, GAO Weiwei. An Internal Short Circuit Fault Detecting of Battery Pack Based on Spearman Rank Correlation Combined with Neural Network[J]. Journal of Electronics & Information Technology, 2022, 44(11): 3734-3747. doi: 10.11999/JEIT210975

Citation:

GAO Mingyu, CAI Linhui, SUN Changcheng, LIU Caiming, ZHANG Zhaowei, DONG Zhekang, HE Zhiwei, GAO Weiwei. An Internal Short Circuit Fault Detecting of Battery Pack Based on Spearman Rank Correlation Combined with Neural Network[J]. Journal of Electronics & Information Technology, 2022, 44(11): 3734-3747. doi: 10.11999/JEIT210975

Citation:

GAO Mingyu, CAI Linhui, SUN Changcheng, LIU Caiming, ZHANG Zhaowei, DONG Zhekang, HE Zhiwei, GAO Weiwei. An Internal Short Circuit Fault Detecting of Battery Pack Based on Spearman Rank Correlation Combined with Neural Network[J]. Journal of Electronics & Information Technology, 2022, 44(11): 3734-3747. doi: 10.11999/JEIT210975

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An Internal Short Circuit Fault Detecting of Battery Pack Based on Spearman Rank Correlation Combined with Neural Network

doi: 10.11999/JEIT210975

GAO Mingyu^{1, 2},
CAI Linhui^{1, 2},
SUN Changcheng^{1, 2},
LIU Caiming^{1, 2},
ZHANG Zhaowei^{1, 2},
DONG Zhekang^{1, 2, 3
,
,},
HE Zhiwei^{1, 2},
GAO Weiwei⁴

1.
School of Electronic Information, Hangzhou Dianzi University, Hangzhou 310018, China
2.
Key Laboratory of Equipment Electronics Research, Hangzhou 310018, China
3.
College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
4.
Tianneng Battery Group Co., Ltd, Changxing 313100, China

Funds: The National Key R & D Program of China (2020YFB1710600), The National Natural Science Foundation of China (62171170), The Key R&D Program of Zhejiang Province (2021C01111)

Received Date: 2021-09-14
Accepted Date: 2022-03-10
Rev Recd Date: 2022-03-22

Available Online: 2022-03-21

Publish Date: 2022-11-14

Abstract

Abstract

Battery pack is an important part of the energy system of electric vehicles. Ensuring its safety is of great significance to the intelligent development of electric vehicles and human life and property. Detecting and guaranteeing the safety of battery pack in the energy system has become a research hotspot in the field of power batteries. Neural network is widely used in battery data detection, but the signal processing method based on correlation coefficient is still widely used in battery short circuit fault, and its implementation scheme often has some problems, such as targeting specific objects, requiring specific environment, and poor performance in general use. Based on this, this paper combines the characteristics of correlation coefficient and neural network, a neural network fault detection algorithm for internal short circuit in battery packs based on Three-channel parallel Bidirectional Gating Recurrent Unit (TBi-GRU) is proposed. Firstly, based on Spearman's rank correlation coefficient, the sliding window is combined with dimensionless and standardized multi-dimensional battery pack operating characteristics. Then, the TBi-GRU neural network is trained by using the extracted operating characteristics of the battery in the normal state. Then, based on the trained TBi-GRU model, the operating characteristics of the battery packs under the internal short circuit state are detected, and the condition of the battery string is detected by combining the prediction results with the dynamic thresholds of each channel. Through simulation analysis of ideal conditions and platform verification of actual environment, it is proved that this method can fully combine the strong robustness of Szpilman's rank correlation coefficient and the strong universality of TBI-GRU neural network to identify accurately the battery pack's internal short circuit fault.
- Internal short circuit detecing,
- Battery pack,
- Correlation coefficient,
- Dynamic threshold

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References(26)

References

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