Comparative Study on Parameter Identification Methods for Dual-Polarization Lithium-Ion Equivalent Circuit Model

Abstract

Md.Sazzad.Hosen@vub.be (M.S.H.); Mohsen.Akbarzadeh.Sokkeh@vub.be (M.A.S.); Shovon.Goutam@vub.be (S.G); Joris.Jaguemont@vub.be (J.J.); Maitane.Berecibar@vub.be (M.B.); Joeri.van.mierlo@vub.be (J.V.M.) 2 Flanders Make,Abstract: A lithium-ion battery cell's electrochemical performance can be obtained through a series of standardized experiments, and the optimal operation and monitoring is performed when a model of the Li-ions is generated and adopted. With discrete-time parameter identification processes, the electrical circuit models (ECM) of the cells are derived. Over their wide range, the dual-polarization (DP) ECM is proposed to characterize two prismatic cells with different anode electrodes. In most of the studies on battery modeling, attention is paid to the accuracy comparison of the various ECMs, usually for a certain Li-ion, whereas the parameter identification methods of the ECMs are rarely compared. Hence in this work, three different approaches are performed for a certain temperature throughout the whole SoC range of the cells for two different load profiles, suitable for light-and heavy-duty electromotive applications. Analytical equations, least-square-based methods, and heuristic algorithms used for model parameterization are compared in terms of voltage accuracy, robustness, and computational time. The influence of the ECMs' parameter variation on the voltage root mean square error (RMSE) is assessed as well with impedance spectroscopy in terms of Ohmic, internal, and total resistance comparisons. Li-ion cells are thoroughly electrically characterized and the following conclusions are drawn: (1) All methods are suitable for the modeling, giving a good agreement with the experimental data with less than 3% max voltage relative error and 30 mV RMSE in most cases. (2) Particle swarm optimization (PSO) method is the best trade-off in terms of computational time, accuracy, and robustness. (3) Genetic algorithm (GA) lack of computational time compared to PSO and LS (4) The internal resistance behavior, investigated for the PSO, showed a positive correlation to the voltage error, depending on the chemistry and loading profile.

Research on online parameter identification and SOC estimation methods of lithium‐ion battery model based on a robustness analysis

“…In addition, polarization internal resistance is greatly affected by SOC, especially in the terminal area of SOC. 28,29 At this time, the model parameters will be disturbed and lead to abnormal jitter, and since the parameter identification method based on FFRLS does not track parameter changes, model identification accuracy is reduced. Thus, FF-AGLS is proposed in this paper in order to attempt to solve the above problems.…”

“…Moreover, ohmic internal resistance and polarization internal resistance are greatly affected by temperature and SOC, among which the ohmic resistance is very significantly affected by temperature interference, especially at low temperatures. In addition, polarization internal resistance is greatly affected by SOC, especially in the terminal area of SOC 28,29 . At this time, the model parameters will be disturbed and lead to abnormal jitter, and since the parameter identification method based on FFRLS does not track parameter changes, model identification accuracy is reduced.…”

“…[25][26][27] The model parameters of the battery significantly change at different temperatures, and the battery parameters sharply fluctuate at low temperatures and low SOC areas. 28,29 As a result, the algorithm's tracking effect on parameters is severely affected, and the accuracy of parameter identification is highly reduced, which also serves as the current bottleneck in the algorithm.…”

“…This is caused by the biased estimation used by FFRLS in reducing the number of calculations, making the algorithm very sensitive to the chattering of data and greatly weakening the robustness of the algorithm 25‐27 . The model parameters of the battery significantly change at different temperatures, and the battery parameters sharply fluctuate at low temperatures and low SOC areas 28,29 . As a result, the algorithm's tracking effect on parameters is severely affected, and the accuracy of parameter identification is highly reduced, which also serves as the current bottleneck in the algorithm.…”

Implications of the BATTERY 2030+ AI‐Assisted Toolkit on Future Low‐TRL Battery Discoveries and Chemistries

“…Hence, accurate states must be obtained to prevent extra degradation of the cells, inefficient operation outside the safe‐operation area, or worst‐case scenarios of thermal runaways. These are categorized into state‐of‐charge (SoC), [ 112 ] state‐of‐health (SoH) [ 113 ] in terms of capacity and power, and state‐of‐safety. In contrast, a general term known as State‐of‐X (SoX) describes all the accounted state estimation processes.…”