Lithium Battery SOC Estimation Based on Multi-Head Attention Mechanism and GRU Algorithm
Keywords:Machine Learning, Lithium battery, Attention Mechanisms, State of Charge, GRU.
Pure electric vehicles have been widely used due to their non-pollution, low noise, high energy conversion efficiency and other advantages. SOC (State of Charge) is a crucial indicator for lithium batteries and pure electric vehicles. SOC cannot be directly measured. This article designs a new network structure. It is the GRU-Attention network structure. The stacked GRU algorithm in GRU-Attention network extracts the temporal characteristics of lithium battery test data, and the stacked multi-head self-attention network extracts the global information. The GRU-Attention network can avoid long-term dependency and gradient disappearance problems. The proposed network utilizes Stacked FFN as the dense layer. This article will test the network designed in the public data set at the University of Maryland. Simultaneously, this article compares the effects of different BatchSize on the performance of the algorithm. The network training process converges more effectively with a smaller BatchSize. Both too large and too small BatchSize have a negative impact on the generalization performance of the network. The extraction of the time-order character, however, may be hampered if the timestamp is too small. At the same time, the paper also compares the GRU-Attention network horizontally with the GRU and Attention networks. Eventually, the GRU-Attention network proposed in this article could better meet the estimate of the lithium battery SOC.
Pan, C., et al., Adaptive Neural Network-Based Prescribed-Time Observer for Battery State-of-Charge Estimation. IEEE Transactions on Power Electronics, 2023. 38(1): p. 165-176.
Yang, X., et al., Battery state of charge estimation using temporal convolutional network based on electric vehicles operating data. Journal of Energy Storage, 2022. 55.
Li, X. and M.F. Dumlao, Deep Learning Based Electric Vehicle BMS Intelligent Cloud Monitoring System, in 2022 3rd Asia-Pacific Conference on Image Processing, Electronics and Computers. 2022. p. 452-457.
Brieske D M, Warnecke A, Sauer D U. Modeling the volumetric expansion of the lithium-sulfur battery considering charge and discharge profiles. Energy Storage Materials, 2023, 55: 289-300.
Park, Minjun, et al. "Data-Driven Capacity Estimation of Li-Ion Batteries Using Constant Current Charging at Various Ambient Temperatures." IEEE Access 11 (2023): 2711-2720.
Hendricks C, Sood B, Pecht M. Lithium-ion battery strain gauge monitoring and depth of discharge estimation. Journal of Electrochemical Energy Conversion and Storage, 2023, 20(1): 011008.
Huang C S. An Online Condition-Based Parameter Identification Switching Algorithm for Lithium-Ion Batteries in Electric Vehicles. IEEE Transactions on Vehicular Technology, 2022.
de Souza A K, Hileman W, Trimboli M S, et al. A Control-Oriented Reduced-Order Model for Lithium-Metal Batteries. IEEE Control Systems Letters, 2022.
Li, X., et al., SOC Estimation of Lithium-Ion Battery for Electric Vehicle Based on Deep Multi-layer Perceptron. Comput Intell Neurosci, 2022. 2022: p. 3920317.
Çelikten, B., O. Eren, and Y.S. Karataş, An execution time optimized state of charge estimation method for lithium-ion battery. Journal of Energy Storage, 2022. 51.
Han B, Harding J R, Goodman J K S, et al. End-of-Charge Temperature Rise and State-of-Health Evaluation of Aged Lithium-Ion Battery. Energies, 2022, 16(1): 405.
Wassiliadis N, Kriegler J, Gamra K A, et al. Model-based health-aware fast charging to mitigate the risk of lithium plating and prolong the cycle life of lithium-ion batteries in electric vehicles. Journal of Power Sources, 2023, 561: 232586.
He J, Meng S, Li X, et al. Partial charging-based health feature extraction and state of health estimation of lithium-ion batteries. IEEE Journal of Emerging and Selected Topics in Power Electronics, 2022.
Konz Z M, Wirtz B M, Verma A, et al. High-throughput Li plating quantification for fast-charging battery design. Nature Energy, 2023: 1-12.
Guo, S. and X. Li, Computer Aided Art Design and Production Based on Video Stream. Computer-Aided Design and Applications, 2020. 18(S3): p. 70-81.
Zhou L, Lai X, Li B, et al. State Estimation Models of Lithium-Ion Batteries for Battery Management System: Status, Challenges, and Future Trends. Batteries, 2023, 9(2): 131.
Wadi A, Abdel-Hafez M, Hashim H A, et al. An Invariant Method for Electric Vehicle Battery State-of-Charge Estimation Under Dynamic Drive Cycles. IEEE Access, 2023, 11: 8663-8673.
Martyushev N V, Malozyomov B V, Sorokova S N, et al. Mathematical Modeling of the State of the Battery of Cargo Electric Vehicles. Mathematics, 2023, 11(3): 536.
Wong K L, Chou K S, Tse R, et al. A Novel Fusion Approach Consisting of GAN and State-of-Charge Estimator for Synthetic Battery Operation Data Generation. Electronics, 2023, 12(3): 657.
Chen S, Pan T, Jin B. State of Charge Estimation of Lithium-Ion Battery Using Energy Consumption Analysis. International Journal of Automotive Technology, 2023, 24(2): 445-457.
Lee J, Won J. Enhanced Coulomb Counting Method for SoC and SoH Estimation Based on Coulombic Efficiency. IEEE Access, 2023, 11: 15449-15459.
Wu L, Zhang Y. Attention-based encoder-decoder networks for state of charge estimation of lithium-ion battery. Energy, 2023: 126665.
Zou, R., et al., A novel convolutional informer network for deterministic and probabilistic state-of-charge estimation of lithium-ion batteries. Journal of Energy Storage, 2023. 57.
Li, J., et al., The state-of-charge predication of lithium-ion battery energy storage system using data-driven machine learning. Sustainable Energy, Grids and Networks, 2023. 34.
Liu, Y., et al., A review of lithium-ion battery state of charge estimation based on deep learning: Directions for improvement and future trends. Journal of Energy Storage, 2022. 52.
Mi Y Q, Deng W, He C, et al. In Situ Polymerized 1, 3‐Dioxolane Electrolyte for Integrated Solid‐State Lithium Batteries. Angewandte Chemie, 2023, 135(12): e202218621.
Zhao, L. and P. Qin, Accurate SOC Prediction and Monitoring of Each Cell in a Battery Pack Considering Various Influencing Factors. IEEE Transactions on Industrial Electronics, 2023. 70(1): p. 1025-1035.
Xiao, L., et al., Online state-of-charge estimation refining method for battery energy storage system using historical operating data. Journal of Energy Storage, 2023. 57.
Wang, Z., et al., Adaptive self-attention LSTM for RUL prediction of lithium-ion batteries. Information Sciences, 2023.
Park, M., et al., Data-Driven Capacity Estimation of Li-Ion Batteries Using Constant Current Charging at Various Ambient Temperatures. IEEE Access, 2023. 11: p. 2711-2720.
Khaleghi Rahimian, S. and Y. Tang, A Practical Data-Driven Battery State-of-Health Estimation for Electric Vehicles. IEEE Transactions on Industrial Electronics, 2023. 70(2): p. 1973-1982.
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