Lithium-ion Battery Modeling Using Equivalent Circuit Model
Keywords:Lithium-ion Battery, Battery Modelling, Equivalent circuit model
With modern technologies, most devices such as electric vehicles are powered by lithium batteries. This kind of battery is advantageous to other types of batteries, such as higher energy density, reliability, etc. to work effectively, it is necessary to handle them using a battery management system BMS, which guarantees their safety and optimizes their performances in normal conditions. One of the things that a BMS must do is to estimate the state of charge SOC of the battery because it is the most critical indicator of battery state. This task is very challenging because the lithium-ion battery is a highly time-invariant, nonlinear, and complex electrochemical system. In this paper, we present a cell model that can be used in the state of the charge estimation process. This model is based on an electrical approach where we build an electrical circuit that has the same behavior as the real cell, this approach is called the Equivalent circuit model ECM. using a set of laboratory data, we will determine the model parameters using multiple techniques. Those parameters will be used in process of estimating the state of charge and other internal parameters.
Wang, Y., Tian, J., Sun, Z., Wang, L., Xu, R., Li, M., & Chen, Z. (2020), "A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems, " Renewable and Sustainable Energy Reviews, Volume 131, 110015.
Zhang, R.; Xia, B.; Li, B.; Lai, Y.; Zheng, W.; Wang, H.; Wang, W.; Wang, M. " Study on the Characteristics of a High-Capacity Nickel Manganese Cobalt Oxide (NMC) Lithium-Ion Battery—An Experimental Investigation," Energies 2018, 11, 2275.
Xiaosong Hu, Haifu Jiang, Fei Feng, Bo Liu, " An enhanced multi-state estimation hierarchy for advanced lithium-ion battery management, " Applied Energy, Volume 257, 114019, 2020.
Prashant Shrivastava, Tey Kok Soon, Mohd Yamani Idna Bin Idris, Saad Mekhilef, "Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries, " Renewable and Sustainable Energy Reviews, Volume 113, 109233, 2019.
Cheng Chen, Rui Xiong, Ruixin Yang, Weixiang Shen, Fengchun Sun, "State-of-charge estimation of lithium-ion battery using an improved neural network model and extended Kalman filter, " Journal of Cleaner Production, Volume 234, Pages 1153-1164, 2019.
C. Wei, M. Benosman, and T. Kim, "Online Parameter Identification for State of Power Prediction of Lithium-ion Batteries in Electric Vehicles Using Extremum Seeking, " Int. J. Control Autom. Syst., vol. 17, no. 11, pp. 2906–2916, Nov. 2019.
A. Wen, J. Meng, J. Peng, L. Cai, and Q. Xiao, "Online Parameter Identification of the Lithium-Ion Battery with Refined Instrumental Variable Estimation, " Complexity, vol. 2020, p. 8854618, 2020.
Huo, YT, Hu, W, Li, Z, Rao, Z, "Research on parameter identification and state of charge estimation of improved equivalent circuit model of Li?ion battery based on temperature effects for battery thermal management, " Int J Energy Res, Volume 44, Pages 11583– 11596, 2020.
Qi Zhang, Yunlong Shang, Yan Li, Naxin Cui, Bin Duan, Chenghui Zhang, "A novel fractional variable-order equivalent circuit model and parameter identification of electric vehicle Li-ion batteries," ISA Transactions, Volume 97, Pages 448-457, 2020.
A. Gismero, D. -I. Stroe and E. Schaltz, "Comparative Study of State of Charge Estimation Under Different Open Circuit Voltage Test Conditions for Lithium-Ion Batteries," IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society, Singapore, 2020, pp. 1767-1772
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