Lithium-ion battery energy storage systems (Li-BESSs) are expected to adjust variable renewable energies such as photovoltaic energy. In the viewpoint of the energy management cost, profiles of round-trip efficiency (RTE) are important characteristics of Li-BESSs. The profiles of RTE are related to the characteristics of both LIBs and inverters installed on Li-BESSs. The characteristics of Li-BESSs would be various because of reusing and degradation of lithium-ion batteries (LIBs). And inverters made by a lot of manufacturers would be so on. Accordingly, it could be said that the understanding of the profile of each Li-BESS’s RTE is complex and difficult. Therefore, we studied the time series analysis of operation data and tried to estimate the characteristics of both LIBs and inverters. The important ones of LIB are full-charge capacity (FCC), open-circuit voltage (OCV), and internal resistance (R). In addition, characteristics of OCV and R can be expressed as function profiles with state of charge (SOC) as elements. Similarly, the important one of inverter is the typical efficiency curve. In this study, we proposed the estimation method of the profiles of OCV and R and inverter efficiency curve. On the other hand, FCC was given as assuming that general diagnosis results would be provided. Then, a RTE profile of Li-BESS (including 9.94kWh LIB and 10kW inverter) was tried to establish from the results of it. The profiles of OCV and R were estimated by the method called MGFFD (Modified Gaussian Free-Form Deformation) that we have been established [1]. The novelty of this study could be described as: Development of the estimation method of an efficiency curve of inverter installed on BESS during operation (Fig.1). Quantitatively visualized profile of RTE of Li-BESS by combining results of MGFFD estimation applied for LIBs and inverter (Fig.2). The profile described on Fig.2 is the relationship between SOC, input-output power rate of inverter, and RTE of Li-BESS. To understand Li-BESS’s charge-discharge efficiency of from state at that time to the end of an operation plan, it would be necessary information. This proposed method could clarify various RTE of Li-BESSs, consequently, be useful for its efficient operation by information communication technology.Reference[1] M. Arima, L. Lin, and M. Fukui, "Kalman-Filter-Based Learning of Characteristic Profiles of Lithium-Ion Batteries", Sensors, vol. 22, no. 14, 5156. Figure 1