Modelling and analysis of the Coulomb Counting SOC Estimation method for 18650 Li-ion batteries under varying charge-discharge protocols

Modelling and analysis of the Coulomb Counting SOC Estimation method for 18650 Li-ion batteries under varying charge-discharge protocols

Hossein Nemati Bandkohan1, Patrick C. Howlett1

1 Institute for Frontier Materials, Deakin University, Burwood, Victoria 3125, Australia

Abstract

Among the various types of batteries, lithium-ion batteries (LIBs) are currently considered the most suitable technology for energy storage systems such as electric vehicles (EVs), and smart gadgets because of their good performance, high specific energy and specific power, and competitive pricing [1]. However, Li-ion batteries require sophisticated battery management system (BMS) technology to ensure safety and sustained lifetimes. In this respect, monitoring and prediction of the cell state of charge (SOC) are critical BMS capabilities. Accurate SoC estimation is necessary for battery protection to prevent over charge/discharge and to avoid accelerated aging of cells due to operation outside of the battery’s prescribed operational limits such as temperature, voltage, etc [2]. In some cases, current SOC prediction models have been found to provide poor prediction [3], particularly under partial state of charge (PSOC) duties such as those increasingly being employed for stationary applications such as back-up power and home storage, etc. The Coulomb Counting (CC) method has initially been selected for this work because of its low cost, easy implementation, and relatively high accuracy among the many SoC estimation methods that have been developed [4]. A MATLAB – Simulink simulation model and two model battery cycling regimes were established to investigate the impact of the test protocol’s complexity on the accuracy of the CC method. Comparing the experimental results obtained for two different commercial 18650 NMC cells with the simulation results showed that applying a more realistic cycling regime decreased the accuracy of the CC SOC estimation method.

Keywords: Lithium-ion Battery, State of Charge (SOC), Battery Management System (BMS), Coulomb Counting (CC) Method

References

[1] Hu X, Zou C, Zhang C, Li Y, Technological developments in batteries: a survey of principal roles, types, and management needs. IEEE Power Energy Mag 15 (2017) 20–31.

[2] W.-Y. Chang, „The State of Charge Estimating Methods for Battery: A Review,“ ISRN Applied Mathematics, vol. 2013, pp. 1-7, 2013, doi: 10.1155/2013/953792._

[3] J. Jia, J. Liang, Y. Shi, J. Wen, X. Pang, and J. Zeng, „SOH and RUL Prediction of Lithium-Ion Batteries Based on Gaussian Process Regression with Indirect Health Indicators,“ Energies, vol. 13, no. 2, 2020, doi: 10.3390/en13020375._

[4] M. P. Sabine Piller*, Andreas Jossen, „Methods for SOC determination and their applications,“ Journal of Power Sources, pp. 113-120, 2001.