The battery energy storage system (BESS) market is growing rapidly around the world. Lithium Nickel Cobalt Manganese Oxide (LiNiCoMnO₂) is attracting attention due to its excellent energy density, high output power, and fast response characteristics. It is being extensively researched and is finding use in many applications, such as in electric vehicles (EV) and energy storage systems (ESS). The performance and lifetime characteristics of a battery change for varying Ni contents. The consideration of these characteristics of a battery allow for a more reliable battery management system (BMS) design. In this study, various experiments and analyses were carried out using a lithium-ion battery (NCM LIB) with differing Ni contents. In particular, the following two combinations were studied: LiNi_0.5Co_0.2Mn_0.3O₂(NCM523) and LiNi_0.6Co_0.2Mn_0.2O₂ (NCM622). Various analyses were performed, such as C-rate (C-rate is the charge-discharge rate of a battery relative to nominal capacity) performance tests, hybrid pulse power characterization (HPPC), accelerated deterioration experiments, electrochemical impedance spectroscopy (EIS), parameter estimations of battery equivalent circuits through alternating current (AC) and direct current (DC) impedance, and comparative analyses of battery modeling.

Energy storage system (ESS) can play a positive role in the power system due to its ability to store, charge and discharge energy. Additionally, it can be installed in various capacities, so it can be used in the transmission and distribution system and even at home. In this paper, the proposed algorithm for economic optimal scheduling of ESS linked to transmission systems in the Korean electricity market is proposed and incorporated into the BESS (battery energy storage system) demonstration test center. The proposed algorithm considers the energy arbitrage operation through SMP (system marginal price) and operation considering the REC (renewable energy certification) weight of the connected wind farm and frequency regulation service. In addition, the proposed algorithm was developed so that the SOC (state-of-charge) of the ESS could be separated into two virtual SOCs to participate in different markets and generate revenue. The proposed algorithm was simulated and verified through Matlab and loaded into the demonstration system using the Matlab “Runtime” function.

This paper presents the results of an analysis using the direct current internal resistance (DCIR) method on a nickel-cobalt-manganese oxide (NCM)-based battery with a nominal capacity of 55.6 Ah. The accelerated degradation test was performed on V0G, V1G, and V2G patterns, representing existing simple power supply, smart charging control, and bi-directional charge/discharge control, respectively. We assumed V0G, V1G, and V2G patterns and conducted charging and discharging experiments according to the set conditions. According to the pattern repetition, changes in the internal resistance of DCIR and AC-impedance were analyzed and battery deterioration was diagnosed. By comparing DCIR and AC-impedance, we confirmed that the changes in internal resistance has a similar trend. In particular, we propose a new DCIR analysis method in the “stop-operation” part rather than the traditional DCIR method. In the case of traditional DCIR method, time is required for the battery to stabilize. However, the newly proposed DCIR analysis method has the advantage of diagnosing the deterioration of the battery during system operation by analyzing the internal resistance without the stabilization time of the battery.

Large-scale lithium-ion energy storage systems (ESSs) are indispensable for renewable energy integration and grid support, yet ensuring long-term reliability under field conditions remains challenging. This study investigates degradation trends in a 50 MW-class ESS deployed on Jeju Island, South Korea, focusing on two indicators: direct current internal resistance (DCIR) and state-of-health (SOH). Annual round-trip (capacity) and hybrid pulse power characterization (HPPC) tests conducted from 2023 to 2025 quantified capacity fade and resistance growth. A polynomial-regression-based temperature compensation was applied—compensating DCIR to 23 °C and SOH to 30 °C—which reduced environmental scatter and clarified year-to-year degradation trends. Beyond mean shifts, intra-bank variability increased over time, indicating rising internal imbalance. A focused case study (Bank 03-01) revealed concurrent SOH decline and DCIR escalation localized near specific racks; spatial maps linked this hotspot to heating, ventilation, and air conditioning (HVAC)-driven airflow asymmetry and episodic fan operation. These findings underscore the importance of combining temperature compensation, variability-based diagnostics, and spatial visualization in field ESS monitoring. The proposed methodology provides practical insights for the early detection of abnormal degradation and supports lifecycle management of utility-scale ESSs under real-world conditions.