Monitoring Surface Strain Fields and Estimating State of Charge for Lithium Batteries

Junkai Wang; Wenjuan Sheng; Junfeng Pan; G. D. Peng²

doi:10.3788/CJL241147

Chinese Journal of Lasers, Volume. 52, Issue 7, 0710003(2025)

Monitoring Surface Strain Fields and Estimating State of Charge for Lithium Batteries

Junkai Wang¹, Wenjuan Sheng^1、*, Junfeng Pan¹, and G. D. Peng²²

Author Affiliations

¹School of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China

²School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney 2052, Australia

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Abstract Get PDF(in Chinese)

Figures & Tables(13)

Fig. 1. Fiber grating sensing principle

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Fig. 2. Experimental system setup

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Fig. 3. FBG arrangement diagram

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Fig. 4. Strains at different positions, voltage, and current under static working condition

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Fig. 5. SOC estimation results with different feature combinations under static working condition. (a) Estimated results; (b) distributions of errors

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Fig. 6. Strains at different positions, voltage, and current under dynamic working condition

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Fig. 7. SOC estimation results with different feature combinations under dynamic working condition. (a) Estimated results; (b) distributions of errors

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Table 1. Specifications and performance parameters of 18650 lithium battery

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Table 1. Specifications and performance parameters of 18650 lithium battery

Parameter	Value	Parameter	Value
Nominal capacity /（A·h）	3.2	Cut-off voltage in discharge stage /V	4.2
Nominal voltage /V	3.60	Diameter /mm	18±0.2
Discharge rate /C	3	Height /mm	65±2.0
Internal resistance /mΩ	<35	Weight /g	45±2

Table 2. Characteristic wavelengths
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Table 2. Characteristic wavelengths
FBG FBG0 FBG1 FBG2 FBG3
Characteristic wavelength /nm 1539.7 1538 1544 1550

Table 3. CNN-GRU network structure and parameters

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Table 3. CNN-GRU network structure and parameters

Layer	Type	Dimension	Filter/units	Activation function
Input		3×3
CNN	1D convolution	3×64	3 pexel×3 pexel	Relu
	Max pooling	3×64	2 pexel×2 pexel
	1D convolution	3×32	3 pexel×3 pexel	Relu
	Max pooling	3×32	2 pexel×2 pexel
Flatten		4×128
GRU		4×64	128 neurons	Tanh
Output	Dense			Linear

Table 4. Feature combinations
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Table 4. Feature combinations
Name Input feature combination
S0 Voltage， current
S1 Strain of FBG1， voltage， current
S2 Strain of FBG2， voltage， current
S3 Strain of FBG3， voltage， current

Table 5. Statistical error of SOC estimation accuracy under static working condition
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Table 5. Statistical error of SOC estimation accuracy under static working condition
Feature combination RMSE /% MAE /%
S0 1.01 0.72
S1 0.59 0.46
S2 0.78 0.61
S3 0.72 0.55

Table 6. Statistical error of SOC estimation accuracy under dynamic working condition
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Table 6. Statistical error of SOC estimation accuracy under dynamic working condition
Feature combination RMSE /% MAE /%
S0 2.02 1.42
S1 1.17 0.81
S2 1.59 1.18
S3 1.54 1.08

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Junkai Wang, Wenjuan Sheng, Junfeng Pan, G. D. Peng². Monitoring Surface Strain Fields and Estimating State of Charge for Lithium Batteries[J]. Chinese Journal of Lasers, 2025, 52(7): 0710003

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