Fault diagnosis of lithium-ion battery sensors using GAPSO-FNN

doi:10.19799/j.cnki.2095-4239.2022.0403

Abstract

Abstract:

Various sensors inside the power battery of novel energy vehicles are used to monitor the safety of the battery system, and sensor failure can cause errors in the charge state and other indicators, which can severely trigger the risk of thermal runaway. For effective and accurate battery sensor fault diagnosis, we propose a fault diagnosis method based on genetic algorithm-based particle swarm optimization and fuzzy neural network (GAPSO-FNN). The proposed method is applied to diagnose the sensor faults of lithium-ion batteries. We obtained data on battery sensor faults by combining the hardware platform and Matlab/Simulink environment, then preprocessed and extracted features from the fault data, and finally used the GAPSO-FNN-based method to diagnose battery sensor faults and compared the results with conventional neural network (NN)- and fuzzy neural network (FNN)-based methods. Simulation results show that the GAPSO-FNN-based method improves the accuracy by 25% and 10% compared with the conventional NN- and FNN-based methods and the fault diagnosis accuracy can reach 95%. Thus, the proposed method effectively improves fault diagnosis accuracy while reducing the amount of information required for fault diagnosis.

Key words: lithium-ion battery, sensor fault diagnosis, GAPSO-FNN, health monitoring, thermal runaway risk

CLC Number:

TM 912

Zhifu WANG, Wei LUO, Yuan YAN, Song XU, Wenmei HAO, Conglin ZHOU. Fault diagnosis of lithium-ion battery sensors using GAPSO-FNN[J]. Energy Storage Science and Technology, 2023, 12(2): 602-608.

Figures/Tables 12

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References 12

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性能参数	索尼543450
额定电压/V	3.7
额定容量/mAh	1000
充电截止电压/V	4.25
放电截止电压/V	2.75
最大放电电流/A	2
放电温度/℃	-20～60
过电流保护/A	3.0±1.0
循环寿命/次	≥1000

标签	状态(温度：23 ℃)
1	电流/电压传感器均正常
2	电流传感器故障、电压传感器正常
3	电压传感器故障、电流传感器正常
4	电流/电压传感器均故障

算法	查准率/%	特异度/%	准确率/%	收敛时间/s
传统的神经网络算法	75.3	74.9	70.0	3.76
模糊神经网络算法	89.6	86.7	85.0	1.52
GAPSO-FNN算法	94.7	92.3	95.0	1.23

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