Comprehensive Review on State of Charge Estimation in Battery Management System

Comprehensive Review on State of Charge Estimation in Battery Management System

© 2022 by IJETT Journal
Volume-70 Issue-7
Year of Publication : 2022
Authors : R Sivapriyan, Sakshi N, C V Mohan, Lavan Raj
DOI : 10.14445/22315381/IJETT-V70I7P218

How to Cite?

R Sivapriyan, Sakshi N, C V Mohan, Lavan Raj, "Comprehensive Review on State of Charge Estimation in Battery Management System" International Journal of Engineering Trends and Technology, vol. 70, no. 7, pp. 169-179, 2022. Crossref,

This paper describes the latest methods and enhanced techniques used to determine the precise State of Charge (SOC). The three primary factors influencing SOC accuracy are the environmental temperature, current, and open-circuit voltage for a typical battery. It is essential to know the SOC that judges the battery’s life. This paper analyzes five different methods to estimate the SOC using different Algorithms and Neural networks. These methods are state-of-the-art methods that can be used to check the correctness of the measurement of SOC in batteries. This paper discusses and analyses the Regression algorithm, time series algorithm, K nearest neighbor algorithm, AGA-based RBF neural network, and Back Propagation neural network to determine the précised SOC. Each method's advantages and disadvantages were discussed and compared with other models to show their superiority. A sample of data was fed to these models, and the result was noted for all five methods. Later, the data were analyzed for their accuracy.

AGA-based RBF neural network, Backpropagation neural network, K nearest neighbor algorithm, Regression algorithm, State of charge, Time series algorithm.

[1] Uma Ravi Sankar Yalavarthy, Venkata Siva Krishna Rao Gadi, “ PEM Fuel Cell Powered Electric Vehicle Propelled By PMSM Using Fuzzy PID Controller- A Research,” International Journal of Engineering Trends and Technology, Vol.70, No.1, Pp.63-74, 2022.
[2] Manav Bansal, Arpit Chhabra, Niraj Singhal, “Smart City-Shrewd Vehicle Versatility Utilizing IOT,” International Journal of Engineering Trends and Technology, Vol.70, No.3, Pp.29-36, 2022.
[3] H. Lipu, M. Hannan, A. Hussain, A. Ayob, M. H. Saad, and K. M. Muttaqi, “State of Charge Estimation in Lithium-Ion Batteries: A Neural Network Optimization Approach,” Electronics, Vol.9, No.9, Pp.1546, 2020.
[4] X. Shu, G. Li, J. Shen, W. Yan, Z. Chen, and Y. Liu, “An Adaptive Fusion Estimation Algorithm for State of Charge of Lithium-Ion Batteries Considering Wide Operating Temperature and Degradation,” Journal of Power Sources, Vol.462, No.228132, 2020.
[5] Y. Gao, C. Zhu, X. Zhang, and B. Guo, “Implementation and Evaluation of A Practical Electrochemical-Thermal Model of Lithium-Ion Batteries for EV Battery Management System," Energy, Vol. 221, No. 119688, 2021.
[6] X. Wang, X. Wei, J. Zhu, H. Dai, Y. Zheng, X. Xu, and Q. Chen, “A Review of Modeling, Acquisition, and Application of Lithium-Ion Battery Impedance for Onboard Battery Management," Etransportation, Vol.7, No.100093, 2021.
[7] H. Dai, B. Jiang, X. Hu, X. Lin, X. Wei, and M. Pecht, “Advanced Battery Management Strategies for A Sustainable Energy Future: Multilayer Design Concepts and Research Trends," Renewable and Sustainable Energy Reviews, Vol.138, No.110480, 2021.
[8] H. A. Gabbar, A. M. Othman, and M. R. Abdussami, “Review of Battery Management Systems (BMS) Development and Industrial Standards Technologies,” Vol.9, No.2, Pp.28, 2021.
[9] N. G. Panwar, S. Singh, A. Garg, A. K. Gupta, and L. Gao, “Recent Advancements in Battery Management System for Li-Ion Batteries of Electric Vehicles: Future Role of Digital Twin, Cyber-Physical Systems, Battery Swapping Technology and Non-Destructive Testing,” Energy Technology, 2021.
[10] N. Mohammed and A. M. Saif, “Programmable Logic Controller-Based Lithium-Ion Battery Management System for Accurate State of Charge Estimation,” Computers & Electrical Engineering, Vol.93, No.107306, 2021.
[11] C. A., J. B. Holm-Nielsen, P. Sanjeevikumar, and S. Himavathi,”Artificial Intelligent Techniques for Electric and Hybrid Electric Vehicles,” Wiley-Scrivener, 2020.
[12] C. Liu, Q. Li, and K. Wang, “State-of-Charge Estimation and Remaining Useful Life Prediction of Supercapacitors,” Renewable and Sustainable Energy Reviews, Vol.150, No.111408, 2021.
[13] H. F. Khan, A. Hanif, M. U. Ali, and A. Zafar, “A Lagrange Multiplier and Sigma Point Kalman Filter Based Fused Methodology for Online State of Charge Estimation of Lithium-Ion Batteries,” Journal of Energy Storage, Vol. 41, No.102843, 2021.
[14] C. Jiang, S. Wang, B. Wu, C. Fernandez, X. Xiong, and J. Coffie-Ken, “A State- of-Charge Estimation Method of the Power LithiumIon Battery in Complex Conditions Based on Adaptive Square Root Extended Kalman Filter,” Energy, Vol.219, No.119603, 2021.
[15] V. Chandran, C. K Patil, A. Karthick, D. Ganeshaperumal, R. Rahim, and A. Ghosh, “State of Charge Estimation of Lithium-Ion Battery for Electric Vehicles Using Machine Learning Algorithms,” World Electric Vehicle Journal, Vol.12, No.1, Pp.38, 2021.
[16] J. Tian, R. Xiong, W. Shen, and J. Lu, “State-of-Charge Estimation of Lifepo4 Batteries in Electric Vehicles: A Deep-Learning Enabled Approach,” Applied Energy, Vol. 291, No.116812, 2021.
[17] P. Xu, B. Liu, X. Hu, T. Ouyang, and N. Chen, “State-of-Charge Estimation for Lithium-Ion Batteries Based on Fuzzy Information Granulation and Asymmetric Gaussian Membership Function,” IEEE Transactions on Industrial Electronics, 2021.
[18] Poushali Pal, Devabalaji K. R, S. Priyadarshini "Design of Battery management system for Residential applications" International Journal of Engineering Trends and Technology 68.3(2020):12-17.
[19] M. H. Lipu, M. Hannan, T. F. Karim, A. Hussain, M. H. Saad, A. Ayob, M. S. Miah, and T. Mahlia, “Intelligent Algorithms and Control Strategies for Battery Management System in Electric Vehicles: Progress, Challenges and Future Outlook,” Journal of Cleaner Production, 2021.
[20] F. Xiao, C. Li, Y. Fan, G. Yang, and X. Tang, “State of Charge Estimation for Lithium-Ion Battery Based on Gaussian Process Regression with Deep Recurrent Kernel,” International Journal of Electrical Power & Energy Systems, Vol.124, No. 106369, 2021.
[21] P. Xu, J. Li, C. Sun, G. Yang, and F. Sun, “Adaptive State-of-Charge Estimation for Lithium-Ion Batteries By Considering Capacity Degradation,” Electronics, Vol.10, No.2, Pp.122, 2021.
[22] C. Bian, H. He, S. Yang, and T. Huang, “State-of-Charge Sequence Estimation of Lithium-Ion Battery Based on Bidirectional Long Short-Term Memory Encoder-Decoder Architecture,” Journal of Power Sources, Vol.449, No.227558, 2020.
[23] Euclidean Distance Formula - Derivation, Examples. Https://Www.Cuemath.Com/Euclidean-Distance-Formula/.
[24] X. Gu, K. See, Y. Wang, L. Zhao, and W. Pu, “The Sliding Window and SHAP Theory—An Improved System with A Long ShortTerm Memory Network Model for State of Charge Prediction in Electric Vehicle Application,” Energies, Vol.14, No.12, Pp. 3692, 2021.
[25] X. Yang, S. Wang, W. Xu, J. Qiao, C. Yu, and C. Fernandez, “Fuzzy Adaptive Singular Value Decomposition Cubature Kalman Filtering Algorithm for Lithium-Ion Battery State-of-Charge Estimation,” International Journal of Circuit Theory and Applications, 2021.
[26] W.-Y. Chang, ”Estimation of the State of Charge for An LFP Battery Using A Hybrid Method That Combines A RBF Neural Network, An OLS Algorithm and AGA,” International Journal of Electrical Power and Energy Systems, Vol.53, Pp.603–611, 2013.
[27] D. Sun, X. Yu, C. Wang, C. Zhang, R. Huang, Q. Zhou, T. Amietszajew, and R. Bhagat, “State of Charge Estimation for Lithium-Ion Battery Based on An Intelligent Adaptive Extended Kalman Filter with Improved Noise Estimator," Energy, Vol.214, No. 119025, 2021.
[28] Y. Guo, Z. Yang, K. Liu, Y. Zhang, and W. Feng, “A Compact and Optimized Neural Network Approach for Battery State-of-Charge Estimation of Energy Storage System," Energy, Vol.219, No.119529, 2021.
[29] M. Wei, M. Ye, J. B. Li, Q. Wang, and X. X. Xu, “State of Charge Estimation for Lithium-Ion Batteries Using Dynamic Neural Network Based on Sine Cosine Algorithm,” Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering: 09544070211018038, 2021.
[30] D. Cui, B. Xia, R. Zhang, Z. Sun, Z. Lao, W. Wang, W. Sun, Y. Lai, and M. Wang, “A Novel Intelligent Method for the State of Charge Estimation of Lithium-Ion Batteries Using A Discrete Wavelet Transform-Based Wavelet Neural Network,” Energies, Vol.11, No.4, Pp. 995, 2018.
[31] D. Johnson, “Back Propagation Neural Network: What Is Backpropagation Algorithm in Machine Learning?” in: Https://Www.Guru99.Com/Backpropogation-Neural-Network.Html
[32] Mazur, “A Step-By-Step Backpropagation Example.” in: Https://Mattmazur.Com/2015/03/17/A-Step-By-Step-BackpropagationExample/
[33] M. S. H. Lipu, M. A. Hannan, A. Hussain, M. H. Saad, A. Ayob, and M. N. Uddin, “Extreme Learning Machine Model for State-ofCharge Estimation of Lithium-Ion Battery Using Gravitational Search Algorithm,” IEEE Transactions on Industry Applications Vol.55, No.4, Pp. 4225–4234, 2019.
[34] G. Zhang, B. Xia, and J. Wang, “Intelligent State of Charge Estimation of Lithium-Ion Batteries Based on LM Optimized Backpropagation Neural Network,” Journal of Energy Storage, Vol.44, No.103442, 2021.
[35] “Linear Regression.” in: Https://En.Wikipedia.Org/Wiki/Linear_Regression.
[36] “Implementation of K Nearest Neighbors.” in: Https://Www.Geeksforgeeks.Org/Implementation-K-Nearest-Neighbors/
[37] “K-Nearest Neighbors (KNN) with Python.” in: Https://Datascienceplus.Com/K-Nearest-Neighbors-Knn-with-Python/
[38] “What Are the Advantages and Disadvantages of Regression Algorithms[Online].” in: Https://Www.I2tutorials.Com/What-Are-theAdvantages-and-Disadvantages-of- Regression-Algorithms/2019
[39] “Advantages and Disadvantages of Time Series.” in: Https://Rb.Gy/Aoxmml. 2020.
[40] “What are the Advantages and Disadvantages of KNN Classifier?” in: Https://Www.I2tutorials.Com/Advantages-and-Disadvantages-ofKnn-Classifier/2019.
[41] H. Yu, T. Xie, S. Paszczynski, and B. M. Wilamowski, “Advantages of Radial Basis Function Networks for Dynamic System Design,” IEEE Transactions on Industrial Electronics, Vol.58, No.12, Pp. 5438–5450, 2011. DOI: 10.1109/TIE.2011.2164773.
[42] D. Johnson, “Back Propagation Neural Network: What Is Backpropagation Algorithm in Machine Learning? [Online]”. in: Https://Www.Guru99.Com/Backpropagation-Neuralnetwork.Html. 2021.
[43] Y. Chen, C. Li, S. Chen, H. Ren, and Z. Gao, “A Combined Robust Approach Based on Auto-Regressive Long Short-Term Memory Network and Moving Horizon Estimation for State-of-Charge Estimation of Lithium-Ion Batteries,” International Journal of Energy Research, 2021.