Abstract

Losses in energy storage systems (ESSs) result from losses in battery systems and power conversion systems (PCSs). Thus, the power difference between the input and output occurs as a loss, which is considered an operational cost. Additionally, since battery systems consist of modules, there is always a temperature difference. Even if voltage balancing is conducted, deviations between the state of health (SoH) and state of charge (SoC) always exist. Therefore, a battery characteristic should be considered in relation to the efficient operation of an ESS. In this paper, charging control is implemented based on the SoC. When errors occur in the beginning, the coulomb counting method (CCM) continues to produce errors; it also calculates the SoC through an improved equation. Thus, it can calculate the SoC by using high-accuracy initial values. Moreover, battery deterioration occurs during charging and discharging, which increases a battery’s internal resistance. This reduces the switching time to the battery cut-off voltage or constant voltage (CV) mode, so it becomes possible to calculate the SoH. Therefore, in this paper, the algorithms and equations are proposed to perform SoH operations according to the charging time that is able to reach CV after charging. A conventional battery is usually charged by using constant current (CC) charging until the voltage of the battery module reaches the cut-off area. A switch to CV then occurs when the cut-off area is reached and maintained. However, SoC-based selective charging control is carried out to prevent heat problems. In addition, the battery is charged safely and efficiently by conducting SoH prediction considering the battery thermal characteristics, which vary depending on the charging time and other characteristics. In this paper, a 3 kW ESS was produced, and the proposed algorithm’s feasibility was verified.

Highlights

  • An energy storage system (ESS) is comprised of a power conversion system (PCS) and a battery system (battery + battery management system (BMS)), which is used to increase energy efficiency by supplying power to peaks while storing dump power

  • state of health (SoH) mode is carried out when toerrors prevent thermal problems, and the state of charge (SoC)-based selective control continues to produce if errors occur in the beginning, it canincalculate thecharging

  • The overall hardware configuration consisted of the PCS (AC/DC converter + DC/DC converter) method according to the SoC state of the PCS module

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Summary

Introduction

An energy storage system (ESS) is comprised of a power conversion system (PCS) and a battery system (battery + battery management system (BMS)), which is used to increase energy efficiency by supplying power to peaks while storing dump power. This paper predicts the SoC calculation based on modeling and manages expected battery lifetime according to charging and discharging times [4,5,6]. A fault diagnosis algorithm calculated using a module charging time due to deterioration is proposed to increase battery safety with selective charging and discharging, and prevent the risk of explosion from unsecured insulation distances due to battery deterioration during charging. In order to achieve a high degree of reliability and safety of the ESS, this paper proposes the following: (1) SoC and SoH algorithms, (2) selective charging control of the ESS, and (3) a fault diagnosis algorithm to prevent damage due to deterioration

Configuration of Proposed ESS
Improved Operation Management and Control of ESS
Improved SoC and SoH Prediction Control Method
Battery
Charge Control Method According to the SoC of PCS
Proposed
11. Battery chargesequence time and temperature
The Experiment Result of ESS
18. Charging according to battery
Findings
Conclusions
Full Text
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