A parameters identification method of the equivalent circuit model of the supercapacitor cell module based on segmentation optimization

Abstract

In practical settings, the supercapacitor is often used as the storage battery, which is composed of several supercapacitor cells in series. In order to accurately estimate the State of Charge (SoC) in the supercapacitor cell module, an equivalent model of supercapacitor cell module is invoked, which is expected to reflect the characteristics of supercapacitor cell module, especially the self-discharge characteristics during standing. The results of parameter identification directly affect the model accuracy. Hitherto, most supercapacitor equivalent models have been proposed for supercapacitor cells, but if the module equivalent model is characterized by connecting many equivalent models of supercapacitor cells in series, it would lead to the cumulative errors and the additional errors, which would incur errors in the parameter identification, and directly affect the model accuracy. The paper aims to obtain the accurate equivalent model parameters, the supercapacitor cell module is regarded as the object, the three-branch equivalent circuit model is established for the supercapacitor cell module, a discussion is given on the parameter identification methods about Circuit Analysis Method (CA) and Recursive Least Squares Method (RLS). This paper establishes the Simulink simulation model for the multi-method parameter identification of supercapacitor cell module, the simulation and analysis are performed to illustrate the advantages and disadvantages of CA and Circuit Analysis-Recursive Least Squares Method (CA-RLS). Then, it proposes a parameters identification method of the equivalent circuit model of supercapacitor cell module based on Segmentation Optimization (SO). The effectiveness of SO is verified by simulation and error analysis, the results indicate that SO can more effectively reflect the charging characteristics and self-discharge characteristics of the supercapacitor cell module. In particular, the comprehensive error in the static self-discharge phase is 0.28%, which is 6.83% and 0.64% lower than CA and CA-RLS, respectively.

Characterization and Model Parameters of Large Commercial Supercapacitor Cells

“…A wide variety of phenomenological circuit models of the SC cells is published in the literature [16]- [32]. Several studies exist that analyze and compare the performance of the different models [2], [19], [28].…”

“…More in particular, experimental characterization of SC cells is required for evaluating their performance (and limits) under realistic operating conditions in view of practical applications and a reliable cell model, validated in realistic operating regimes, is also of utmost importance for the design and the optimization of the SC systems employing multiple cells. Intense research activities have been carried out so far concerning the characterization [2], [10]- [15] and the modelling [2], [16]- [32] of commercial SC cells available on the market. However, most of the characterization and modelling studies reported in the literature apply test currents that are very limited with respect to the practical operating currents of SC cells, that can reach up to 150 A.…”

Time-Domain Circuit Modelling for Hybrid Supercapacitors

“…Here, a decrease in the charging time allows a larger number of users to benefit from the sharing service per day, the energy stored in the pack being the same. Several SC models have already been investigated in the literature [22][23][24][25][26][27][28][29][30][31][32][33]. In Table 2 and Figure 1, most of the models available in the literature are summarized.…”

“…Models Commercial Supercap SC Technology [22] (a), (b), (c) -EDLC [23] (a), (b), (c), (d) -EDLC [24] (e) BCAP3000, by Maxwell EDLC [25] (d) 10 F, 2.7 V, by Maxwell EDLC [26] (b) -EDLC [27] (d) BCAP0650 P270, by Maxwell EDLC [28] (c), (d) -EDLC [29] (d) BCAP0350 E270 T11 350 F, by Maxwell EDLC [30] (b) -EDLC [31] (c) -EDLC [32] (d) -EDLC [33] (f) The validity of the models proposed for these previous technologies has not been yet verified for hybrid supercapacitors and is one of the core aspects of this work.…”

Estimation of state‐of‐energy for lithium batteries based on dual adaptive particle filters considering variable current and noise effects

“…23 Although all the above studies can achieve accurate estimation of SOE to some extent, there are still two problems. One is the low accuracy of the OCV-SOE expressions under the traditional fitting methods at the beginning and end of battery discharge process 24 ; the second is the neglect of the influence of different operating load currents 25 on the identification of model parameters. Therefore, this study use Gaussian polynomials to characterize the OCV-SOE relationship based on the DP model, and re-establish the improved state space equations based on the Gaussian function system to improve the accuracy of the OCV-SOE correspondence expression at the beginning and end of the discharging process.…”