• The Transactions of the Korean Institute of Power Electronics
• /
• v.23 no.3
• /
• pp.174-181
• /
• 2018

This study introduces a systematic approach to selecting the internal parameters applied to the equivalent electrical-circuit model (EECM) of a lithium titanium oxide ($Li_4Ti_5O_{12}$; LTO) rechargeable cell. Based on the dynamic characteristic of the cell, a simplified EECM consisting of an open-circuit voltage (OCV), an ohmic resistance, and an RC ladder is fabricated. To select the internal parameters of a simplified EECM, experiments on discharge capacity, OCV, and discharge/charge resistances are performed using hybrid pulse power characterization and direct current internal resistance (DCIR) measurements over the full state-of-charge (SOC) range. The experimental results of the LTO rechargeable cell highlight the importance of correct selection of internal parameters that can reduce EECM errors. This study clearly provides experimental procedures, internal parameters results, and EECM guidelines for adaptive control-based SOC estimation for LTO rechargeable cells.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.24 no.1
• /
• pp.16-24
• /
• 2019

The performance of the battery management system (BMS) algorithm is important for ensuring the stability and efficient operation of battery packs. Such a performance is determined by the internal parameters of the electrical equivalent circuit model (EECM). This study proposes a performance improvement and verification of battery parameters for the BMS algorithm using electrical experiments and tools. The parameters were extracted through electrical characteristic experiments, and an EECM based on Ah counting was designed. Simulation results using the EECM were compared with actual experimental data to determine the best parameter extraction method.

• Journal of Electrical Engineering and Technology
• /
• v.11 no.6
• /
• pp.1664-1673
• /
• 2016

Lithium rechargeable cells are used in many industrial applications, because they have high energy density and high power density. For an effective use of these lithium cells, it is essential to build a reliable battery management system (BMS). Therefore, the state of charge (SOC) estimation is one of the most important techniques used in the BMS. An appropriate modeling of the battery characteristics and an accurate algorithm to correct the modeling errors in accordance with the simplified model are required for practical SOC estimation. In order to implement these issues, this approach presents the comparative analysis of the SOC estimation performance using equivalent electrical circuit modeling (EECM) and noise suppression technique (NST) in three representative $LiCoO_2/LiFePO_4/LiNiMnCoO_2$ cells extensively applied in electric vehicles (EVs), hybrid electric vehicles (HEVs) and energy storage system (ESS) applications. Depending on the difference between some EECMs according to the number of RC-ladders and NST, the SOC estimation performances based on the extended Kalman filter (EKF) algorithm are compared. Additionally, in order to increase the accuracy of the EECM of the $LiFePO_4$ cell, a minor loop trajectory for proper OCV parameterization is applied to the SOC estimation for the comparison of the performances among the compared to SOC estimation performance.

• Journal of IKEEE
• /
• v.23 no.2
• /
• pp.667-674
• /
• 2019

The recent incidents of Nuclear Power Plant(NPP) gave rise to a total power outage caused by the loss of the functions of the off-site and the emergency power supply. Currently, emergency power supply of NPP have been taken into account by Li-ion batteries instead of existing lead-acid batteries. In order to judge the applicability of the cylindrical Li-ion battery, it is necessary to analysis the results of various electrical tests. This paper investigates the basic electrical characteristics test of three types of cylindrical batteries in order to select the most suitable battery and estimate state of battery through equivalent circuit model and propose method to solve the problem.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.26 no.6
• /
• pp.381-389
• /
• 2021

Most diagnosis approaches rely on historical failure data that might not be feasible in real operating conditions because the battery voltage and internal parameters are nonlinear according to various operating conditions, such as cell-to-cell configuration and initial condition. To overcome this issue, the estimator and the predictor require integrated approaches that consider comprehensive data, with the degradation process and measured data taken into account. In this paper, vector autoregressive models (VAR) with various parameters that affect overdischarge to the cell in the battery pack were constructed, and the cell-to-cell parameters were identified using an adaptive model to analyze the influence of failure prognosis. The theoretical analysis is validated using experimental results in terms of the feasibility and advantages of fault prognosis.