**2. Battery Efficiency for Predicting Battery State**

Figure 1 illustrates the factors affecting the performance of a battery.

As the number of charge-discharge cycles increases, a chemical reaction occurs in the battery, causing aging, which reduces the SoH of the battery. Aging increases the internal resistance of a battery and decreases its charge-discharge capacity. As the capacity of a battery decreases, its charge voltage reaches the maximum value.

Identifying the occurrence of aging during the charge-discharge operation of a battery requires determination of the magnitude by which its capacity decreases by calculating its internal resistance or efficiency.

Although accurate modeling of a battery is required to understand its state, it is difficult to perform accurate modeling because of the nonlinear characteristics. Furthermore, given the various factors for batteries, the system costs increase because the roles of the

BMS managing the battery vary and the number of computations increases. This paper proposes a battery efficiency calculation formula that considers the internal resistance, which significantly affects the performance of a battery, as well as a system that considers the nonlinear characteristics.

**Figure 1.** Factors affecting the state of a battery.

Figure 2 shows an ESS system, in which the proposed algorithm was implemented. The ESS consisted of a battery system and a power conversion system (PCS). The battery system consisted of a battery and a BMS. The ac-dc of the PCS comprised a two-level converter that was easy to control with high efficiency, while the dc-dc comprised a fullbridge converter [68].

**Figure 2.** Proposed ESS configuration diagram.

Figure 3 illustrates the BMS configuration of the battery system. The BMS received data regarding the battery voltage, current, and temperature and predicted the SoC and SoH. Furthermore, the data were transmitted using controller area network (CAN) communication. When any abnormalities occurred in the battery voltage, current, or state, the charge-discharge state of the battery was cut off to protect it. Furthermore, the BMS provided a protection function to secure the battery safety when an abnormality in the battery temperature occurred [69]. By applying the proposed algorithm, the BMS sensed the battery voltage, current, and temperature; accumulated data; calculated the battery efficiency; and predicted the SoC and SoH. In addition, battery's efficiency protected it in from faults.

**Figure 3.** Proposed BMS configuration diagram.
