Fault detection and diagnosis of electric vehicle battery pack combining neural network and CRDAN algorithm | International Journal of Metrology and Quality Engineering (IJMQE)

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Table 2

Comparison of RMSE and precision of different models on different types of faults.

Model	RMSE				Precision/%
Fault type				Fault type
	Short-circuit	Overcharging	Overdischarge	Capacity fade	Short-circuit	Overcharging	Overdischarge	Capacity fade
SVM	0.497**	0.509**	0.485**	0.477**	87.42**	87.70**	86.90**	87.56**
Transformer	0.350**	0.404**	0.353**	0.400**	90.12**	88.68**	89.04**	90.54**
Combination model 3	0.236**	0.202**	0.228**	0.205**	93.37**	94.54**	94.86**	93.33**
Combination model 4	0.188**	0.189**	0.185**	0.167**	94.69**	96.36**	95.24**	95.45**
Based on CRDAN (manuscript)	0.005**	0.013**	0.015**	0.019**	97.60**	98.85**	98.44**	98.59**

Note: ** indicates that the difference between groups is highly statistically significant (p < 0.01, analysis of variance results of FDI).

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