State of Charge Estimation of Lithium-Ion Batteries for Electric Vehicle Application Using Gaussian Process Regression Approach
Vinoth Kumar P
Vinoth Kumar P
Department of Electrical and Electronics Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, India and INTI International University, Nilai, Negeri Sembilan, Malaysia.
For the purpose of ensuring a secure, dependable and affordable performancealong with clean energy in electric vehicles, the estimation of the precise state of charge of LIB is very important. In this article, Gaussian Process Regression with different kernel functions-based SOC prediction is proposed and their performance with good health and well-beingare evaluated and analyzed. A useful benefit of employing GPR is the ability to quantify and estimate uncertainties, allowing for the evaluation of the SOC estimate's dependability. The kernel function serves as a crucial hyperparameter that improves GPR performance. GPR considers the temperature and voltage of the battery, which are independent of one another, as their respective input parametersthat relates Industry, innovation and infrastructure where target-dependent variable is battery SOC. Initially, the training process involves determining the ideal hyperparameters of a kernel function to accurately represent the characteristics of the data. The accuracy of predicting SOC of the battery is evaluated using test data. According to the simulation outcomes, the squared exponential kernel function-based GPR estimates SOC with high accuracy and lower RMSE and MAE which ensures energy efficiency and quality education.
Keywords
State of Charge, GPR, Kernel Function, RMSE, LIB-Lithium Ion Battery, Energy Efficiency and Quality Education.
“Indicators for CO2 emissions (Edition 2023),” IEA CO2 Emissions from Fuel Combustion Statistics. OECD, Oct. 17, 2023. doi: 10.1787/1076c592-en.
K. Singh et al., “India’s renewable energy research and policies to phase down coal: Success after Paris agreement and possibilities post-Glasgow Climate Pact,” Biomass and Bioenergy, vol. 177, p. 106944, Oct. 2023, doi: 10.1016/j.biombioe.2023.106944.
S. Vedhanayaki and V. Indragandhi, “A Bayesian Optimized Deep Learning Approach for Accurate State of Charge Estimation of Lithium Ion Batteries Used for Electric Vehicle Application,” IEEE Access, vol. 12, pp. 43308–43327, 2024, doi: 10.1109/access.2024.3380188.
W. Zhou, Y. Zheng, Z. Pan, and Q. Lu, “Review on the Battery Model and SOC Estimation Method,” Processes, vol. 9, no. 9, p. 1685, Sep. 2021, doi: 10.3390/pr9091685.
M. Adaikkappan and N. Sathiyamoorthy, “Modeling, state of charge estimation, and charging of lithium‐ion battery in electric vehicle: A review,” International Journal of Energy Research, vol. 46, no. 3, pp. 2141–2165, Oct. 2021, doi: 10.1002/er.7339.
P. Pillai, S. Sundaresan, P. Kumar, K. R. Pattipati, and B. Balasingam, “Open-Circuit Voltage Models for Battery Management Systems: A Review,” Energies, vol. 15, no. 18, p. 6803, Sep. 2022, doi: 10.3390/en15186803.
Z. Lei, T. Liu, X. Sun, H. Xie, and Q. Sun, “Extended state observer assisted Coulomb counting method for battery state of charge estimation,” International Journal of Energy Research, vol. 45, no. 2, pp. 3157–3169, Nov. 2020, doi: 10.1002/er.6011.
D. Sun et al., “State of charge estimation for lithium-ion battery based on an Intelligent Adaptive Extended Kalman Filter with improved noise estimator,” Energy, vol. 214, p. 119025, Jan. 2021, doi: 10.1016/j.energy.2020.119025.
M. Hossain, M. E. Haque, and M. T. Arif, “Kalman filtering techniques for the online model parameters and state of charge estimation of the Li-ion batteries: A comparative analysis,” Journal of Energy Storage, vol. 51, p. 104174, Jul. 2022, doi: 10.1016/j.est.2022.104174.
X. Wang, Y. Gao, D. Lu, Y. Li, K. Du, and W. Liu, “Lithium Battery SoC Estimation Based on Improved Iterated Extended Kalman Filter,” Applied Sciences, vol. 14, no. 13, p. 5868, Jul. 2024, doi: 10.3390/app14135868.
Z. He, X. Zhang, X. Fu, C. Pan, and Y. Jin, “Research on battery state of charge estimation based on variable window adaptive extended Kalman filter,” International Journal of Electrochemical Science, vol. 19, no. 1, p. 100440, Jan. 2024, doi: 10.1016/j.ijoes.2023.100440.
J. Zhang, B. Xiao, G. Niu, X. Xie, and S. Wu, “Joint estimation of state-of-charge and state-of-power for hybrid supercapacitors using fractional-order adaptive unscented Kalman filter,” Energy, vol. 294, p. 130942, May 2024, doi: 10.1016/j.energy.2024.130942.
P. Kuang, F. Zhou, S. Xu, K. Li, and X. Xu, “State-of-charge estimation hybrid method for lithium-ion batteries using BiGRU and AM co-modified Seq2Seq network and H-infinity filter,” Energy, vol. 300, p. 131602, Aug. 2024, doi: 10.1016/j.energy.2024.131602.
P. Shrivastava, P. A. Naidu, S. Sharma, B. K. Panigrahi, and A. Garg, “Review on technological advancement of lithium-ion battery states estimation methods for electric vehicle applications,” Journal of Energy Storage, vol. 64, p. 107159, Aug. 2023, doi: 10.1016/j.est.2023.107159.
S.-L. Lin, “Deep learning-based state of charge estimation for electric vehicle batteries: Overcoming technological bottlenecks,” Heliyon, vol. 10, no. 16, p. e35780, Aug. 2024, doi: 10.1016/j.heliyon.2024.e35780.
S. Sharma, A. Garg, and B. K. Panigrahi, “Predicting State-of-Charge Using Gradient-Boosted SVR Ensemble Technique for Lithium Ion Battery Used in EVs,” IEEE Transactions on Transportation Electrification, vol. 10, no. 2, pp. 4441–4454, Jun. 2024, doi: 10.1109/tte.2023.3310159.
V. Selvaraj and I. Vairavasundaram, “A comprehensive review of state of charge estimation in lithium-ion batteries used in electric vehicles,” Journal of Energy Storage, vol. 72, p. 108777, Nov. 2023, doi: 10.1016/j.est.2023.108777.
Y. Zhang, Y. Dai, R. Yang, Z. Li, J. Zhao, and Q. Wu, “Noise-resistant state of charge estimation of Li-ion battery using the outlier robust extreme learning machine,” Energy Reports, vol. 9, pp. 1–8, Mar. 2023, doi: 10.1016/j.egyr.2022.10.367.
B. Aljafari, G. Devarajan, S. Arumugam, and I. Vairavasundaram, “Design and Implementation of Hybrid PV/Battery-Based Improved Single-Ended Primary-Inductor Converter-Fed Hybrid Electric Vehicle,” International Transactions on Electrical Energy Systems, vol. 2022, pp. 1–11, Aug. 2022, doi: 10.1155/2022/2934167.
D. Gunapriya et al., “An Exhaustive Investigation of Battery Management System (BMS),” 2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM), vol. i, pp. 1–5, Feb. 2023, doi: 10.1109/iciptm57143.2023.10117824.
V. K. P, “Novel Battery Management with Fuzzy Tuned Low Voltage Chopper and Machine Learning Controlled Drive for Electric Vehicle Battery Management: A Pathway Towards SDG,” Journal of Applied Data Sciences, vol. 5, no. 3, pp. 936–947, Sep. 2024, doi: 10.47738/jads.v5i3.236.
P. Vinoth Kumar, R. S. Athithya, R. Isai Valli, S. Abinaya and B. Hema, "Battery Management for Electric Vehicle Using Low Voltage DC-DC Converter," 2023 4th International Conference on Signal Processing and Communication (ICSPC), Coimbatore, India, 2023, pp. 62-67, doi: 10.1109/ICSPC57692.2023.10125922.
C. K. Chan, C. H. Chung, and J. Raman, “Optimizing Thermal Management System in Electric Vehicle Battery Packs for Sustainable Transportation,” Sustainability, vol. 15, no. 15, p. 11822, Aug. 2023, doi: 10.3390/su151511822.
Lai Qit Inn, A. N. Oumer, Azizuddin Abd Aziz, Januar Parlaungan Siregar, and Tezara Cionita, “Numerical Analysis of Battery Thermal Management System of Electric Vehicle,” Journal of Advanced Research in Numerical Heat Transfer, vol. 13, no. 1, pp. 106–114, Jul. 2023, doi: 10.37934/arnht.13.1.106114.
C. K. Chan, C. H. Chung, and J. Raman, “Optimizing Thermal Management System in Electric Vehicle Battery Packs for Sustainable Transportation,” Sustainability, vol. 15, no. 15, p. 11822, Aug. 2023, doi: 10.3390/su151511822.
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Selvarani N
Selvarani N
Department of ECE, PSNA College of Engineering and Technology, Dindigul, Tamil Nadu, India.
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Cite this article
Vinoth Kumar P, Selvarani N, Gunapriya D and Malathy Batumalay, “State of Charge Estimation of Lithium-Ion Batteries for Electric Vehicle Application Using Gaussian Process Regression Approach”, Journal of Machine and Computing, pp. 1107-1116, October 2024. doi:10.53759/7669/jmc202404102.
