Smoothing of the Noisy Lithium-ion Battery Surface Temperature Data Based on Savitzky–Golay (SG) Filter
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https://doi.org/10.37284/eaje.7.1.2135
Résumé
As the charging current and surrounding temperatures rise concurrently in an electric vehicle's (EV) battery pack, the battery temperature increases abruptly beyond safe limits. The battery is susceptible to triggering thermal runaway at higher temperatures, leading to battery damage and even an explosion. The control system depends on these measurement data from the sensors to prevent dangerous situations and maximize the performance and cycle life of batteries. However, traditional noisy temperature measurement sensors in a real application, such as thermistors and thermocouples, are extensively used. In this paper, the experimental setup, the heat pipe-based battery thermal management system (BTMS) was designed and experimented with high input power. The battery was sandwiched with heat pipes, and heated at 30, 40, 50, and 60 W. The Savitzky-Golay filter technique is applied to the noisy temperature data of the surface temperature of the lithium-ion batteries (LIBs) used to estimate the condition of the battery. According to this study, the start-up time parameter in battery thermal management can be controlled by the Savitzky-Golay filter. This will attenuate random temperature fluctuations of battery temperature noise and avoid the cooling system from being falsely triggered. The results are measured by the signal-to-noise (SNR) to demonstrate the ability of the Savitzky-Golay filter to eliminate noise
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Références
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[37] Ruffin, C., R.L. King, and N.H. Younan, A combined derivative spectroscopy and Savitzky-Golay filtering method for the analysis of hyperspectral data. GIScience & Remote Sensing, 2008. 45(1): p. 1-15.
[2] Tran, M.-K., et al., A review of range extenders in battery electric vehicles: Current progress and future perspectives. World Electric Vehicle Journal, 2021. 12(2): p. 54.
[3] Deng, Z., et al., Sensitivity analysis and joint estimation of parameters and states for all-solid-state batteries. IEEE Transactions on Transportation Electrification, 2021. 7(3): p. 1314-1323.
[4] Akhoundzadeh, M.H., et al., Investigation and simulation of electric train utilizing hydrogen fuel cell and lithium-ion battery. Sustainable Energy Technologies and Assessments, 2021. 46: p. 101234.
[5] Mbulu, H., Y. Laoonual, and S. Wongwises, Experimental study on the thermal performance of a battery thermal management system using heat pipes. Case Studies in Thermal Engineering, 2021. 26: p. 101029.
[6] Zhang, Y., et al., Lithium-ion battery pack state of charge and state of energy estimation algorithms using a hardware-in-the-loop validation. IEEE Transactions on Power Electronics, 2016. 32(6): p. 4421-4431.
[7] Zhang, W., et al., A novel heat pipe assisted separation type battery thermal management system based on phase change material. Applied Thermal Engineering, 2020. 165: p. 114571.
[8] Qin, P., et al., Experimental and numerical study on a novel hybrid battery thermal management system integrated forced-air convection and phase change material. Energy Conversion and Management, 2019. 195: p. 1371-1381.
[9] Liu, S., G. Zhang, and C.-Y. Wang, Challenges and Innovations of Lithium-Ion Battery Thermal Management Under Extreme Conditions: A Review. ASME Journal of Heat and Mass Transfer, 2023. 145(8): p. 080801.
[10] Yang, J., Y. Cai, and C. Mi, Lithium-ion battery capacity estimation based on battery surface temperature change under constant-current charge scenario. Energy, 2022. 241: p. 122879.
[11] Ma, S., et al., Temperature effect and thermal impact in lithium-ion batteries: A review. Progress in Natural Science: Materials International, 2018. 28(6): p. 653-666.
[12] Katoch, S.S. and M. Eswaramoorthy. A detailed review on electric vehicles battery thermal management system. in IOP conference series: materials science and engineering. 2020. IOP Publishing.
[13] Xiong, R., L. Li, and J. Tian, Towards a smarter battery management system: A critical review on battery state of health monitoring methods. Journal of Power Sources, 2018. 405: p. 18-29.
[14] Panchal, S., et al., Cycling degradation testing and analysis of a LiFePO4 battery at actual conditions. International Journal of Energy Research, 2017. 41(15): p. 2565-2575.
[15] Guo, Y., et al., Online estimation of SOH for lithium-ion battery based on SSA-Elman neural network. Protection and Control of Modern Power Systems, 2022. 7(3): p. 1-17.
[16] Liu, C., et al., Strong robustness and high accuracy in predicting remaining useful life of supercapacitors. APL Materials, 2022. 10 (6).
[17] Yang, S., et al., Passive and Active Balancing, in Advanced Battery Management System for Electric Vehicles. 2022, Springer. p. 165-187.
[18] Pesaran, A., S. Santhanagopalan, and G. Kim, Addressing the impact of temperature extremes on large format li-ion batteries for vehicle applications (presentation). 2013, National Renewable Energy Lab.(NREL), Golden, CO (United States).
[19] Richardson, R.R., P.T. Ireland, and D.A. Howey, Battery internal temperature estimation by combined impedance and surface temperature measurement. Journal of Power Sources, 2014. 265: p. 254-261.
[20] Siddique, A.R.M., S. Mahmud, and B. Van Heyst, A comprehensive review on a passive (phase change materials) and an active (thermoelectric cooler) battery thermal management system and their limitations. Journal of Power Sources, 2018. 401: p. 224-237.
[21] Triwijaya, S., A. Pradipta, and T. Wati. Automatic Design of Battery Charging System Power Supply from Photovoltaic Sources Base on Voltage. in Journal of Physics: Conference Series. 2021. IOP Publishing.
[22] Jin, S., M.-S. Youn, and Y.-J. Kim. Optimization of air-cooling system for a lithium-ion battery pack. in E3S Web of Conferences. 2021. EDP Sciences.
[23] Du, J., et al., Thermal management of air-cooling lithium-ion battery pack. Chinese Physics Letters, 2021. 38(11): p. 118201.
[24] Balasingam, B., Robust Battery Management System Design With MATLAB. 2023: Artech House.
[25] Dong, G., et al., Online state of charge estimation and open circuit voltage hysteresis modeling of LiFePO4 battery using invariant imbedding method. Applied Energy, 2016. 162: p. 163-171.
[26] Li, X., C. Yuan, and Z. Wang, Multi-time-scale framework for prognostic health condition of lithium battery using modified Gaussian process regression and nonlinear regression. Journal of Power Sources, 2020. 467: p. 228358.
[27] Rahimi-Eichi, H., F. Baronti, and M.-Y. Chow, Online adaptive parameter identification and state-of-charge coestimation for lithium-polymer battery cells. IEEE Transactions on Industrial Electronics, 2013. 61(4): p. 2053-2061.
[28] Dong, G., et al., Remaining dischargeable time prediction for lithium-ion batteries using unscented Kalman filter. Journal of Power Sources, 2017. 364: p. 316-327.
[29] Liang, J., Y. Gan, and Y. Li, Investigation on the thermal performance of a battery thermal management system using heat pipe under different ambient temperatures. Energy conversion and management, 2018. 155: p. 1-9.
[30] De Oliveira, M.A., et al., Use of savitzky–golay filter for performances improvement of SHM systems based on neural networks and distributed PZT sensors. Sensors, 2018. 18(1): p. 152.
[31] Savitzky, A. and M.J. Golay, Smoothing and differentiation of data by simplified least squares procedures. Analytical chemistry, 1964. 36(8): p. 1627-1639.
[32] Schmid, M., D. Rath, and U. Diebold, Why and how Savitzky–Golay filters should be replaced. ACS Measurement Science Au, 2022. 2(2): p. 185-196.
[33] Liu, Y., et al., Applications of savitzky-golay filter for seismic random noise reduction. Acta Geophysica, 2016. 64: p. 101-124.
[34] Chen, J., et al., A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter. Remote sensing of Environment, 2004. 91(3-4): p. 332-344.
[35] Bian, J.-h., et al., Reconstruction of NDVI time-series datasets of MODIS based on Savitzky-Golay filter. Journal of Remote Sensing, 2010. 14(4): p. 725-741.
[36] Li, M. and J. Liu. Reconstructing vegetation temperature condition index based on the Savitzky–Golay filter. in Computer and Computing Technologies in Agriculture IV: 4th IFIP TC 12 Conference, CCTA 2010, Nanchang, China, October 22-25, 2010, Selected Papers, Part III 4. 2011. Springer.
[37] Ruffin, C., R.L. King, and N.H. Younan, A combined derivative spectroscopy and Savitzky-Golay filtering method for the analysis of hyperspectral data. GIScience & Remote Sensing, 2008. 45(1): p. 1-15.
Publiée
25 août, 2024
Comment citer
Hossea, J., & Greyson, K. (2024). Smoothing of the Noisy Lithium-ion Battery Surface Temperature Data Based on Savitzky–Golay (SG) Filter. East African Journal of Engineering, 7(1), 262-271. https://doi.org/10.37284/eaje.7.1.2135
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Articles
Copyright (c) 2024 Jordan H. Hossea, Kenedy Greyson
Ce travail est disponible sous la licence Creative Commons Attribution 4.0 International .
