서울시 서대문구 연세로 50
제3공학관 C126호
T. 02-2123-6740 ㅣ F. 02-312-4584
Research Area
YONSEI ENERGY INFORMATICS Lab.
Energy Storage System Management
Aging diagnostics for lithium-ion batteries using signal processing.
Objectives
- The lithium-ion battery energy storage system (ESS) is mainly a commercialized energy storage device to improve the reliability and system of modern power systems.
- Online diagnostic or condition estimation technology is required to monitor batteries operating in power grids without disconnection from the electrical systems.
- Lithium-ion battery condition and aging diagnosis technology using various signal processing techniques are developed to prevent failures and accidents.
- The harmonic analysis, a new indicator of battery aging, enables efficient battery monitoring and diagnostics.
Research Highlights
- Aging battery diagnosis technology through signal processing technology and harmonic analysis
- Aging and state estimation techniques for lithium-ion batteries in online systems
- Development of aging indicator using harmonic analysis based on the nonlinear properties of batteries for health monitoring
▲ Example of diagnostics for lithium-ion batteries *
▲ Voltage THD difference between a normal battery and an aging battery *
Energy Storage System Management
Energy management system of battery ESS based on reinforcement learning.
Objectives
- ESS for smart grids, microgrids, and electric vehicles (EVs) require optimization in a variety of operating conditions and environments to operate efficiently and reliably.
- One of the purposes of this research is to develop an ESS optimal operating algorithm to improve the economic efficiency and lifespan of microgrids and electric vehicles.
- The ESS scheduling techniques are proposed to address the energy management problem in a microgrid to minimize the electricity cost and the degradation cost of the ESS.
- An algorithm for maximizing profit from ESS arbitrage based on demand forecasting is proposed.
Research Highlights
- Optimization scheduling study of ESS based on reinforcement learning.
- Design the ESS degradation model by using aging index of the ESS.
- Based on demand forecasting, we propose algorithms to maximize profit from ESS arbitrage.
▲ Analysis of microgrid for developing of ESS optimization algorithm *
▲ Workflow of the energy management system based on deep reinforcement learning *
Energy Storage System Management
ESS interconnected system diagnosis.
Objectives
- Since conventional power infrastructure diagnostic research was limited to the diagnostic target itself, the target system was separated before diagnosis.
- However, since ESS cannot be used by itself and is used with battery management system (BMS), power conversion system (PCS) and electrical wiring system, each component should be regarded as one integrated system, not as a separate power facility.
- In this research, diagnostic technology for the ESS is studied for ESS connected with various power facilities and devices.
Research Content
- Diagnostics for ESS connected with various power facilities
- Electrical model design and fault condition simulation of interconnected systems.
- Condition monitoring of lithium-ion batteries in relation to thermal, electrical, and mechanical stress.
▲ Example of interconnected system diagnosis (ESS-cable interconnected system) *
▲ Modeling the ESS-cable interconnected system *