Conference paper
Battery energy storage systems modeling for online applications
ABB Corp. Res., Vasteras, Sweden1
Dept. of Electr. & Comput. Eng., Aristotle Univ. of Thessaloniki, Thessaloniki, Greece2
Over the last decade the use of battery energy storage systems (BESS) on different applications, such as smart grid and electric vehicles, has been increasing rapidly. Therefore, the development of an electrical model of a battery, capable to estimate the states and the parameters of a battery during lifetime is of critical importance.
To increase the lifetime, safety and energy usage, appropriate algorithms are used to estimate, with the lowest estimation error, the state of charge of the battery, the battery impedance, as well as its remaining capacity. This paper focuses on the development of model-based online condition monitoring algorithms for Li-ion battery cells, which can be extended to battery modules and systems.
The condition monitoring algorithms were implemented after considering an optimal trade-off between their accuracy and overall complexity.
| Language: | English |
|---|---|
| Publisher: | IEEE |
| Year: | 2017 |
| Pages: | 1-6 |
| Proceedings: | 2017 IEEE Manchester PowerTech |
| ISBN: | 1509042377 , 1509042385 , 9781509042371 and 9781509042388 |
| Types: | Conference paper |
| DOI: | 10.1109/PTC.2017.7980809 |
Batteries Battery charge measurement Battery energy storage systems (BESS) Estimation Impedance Integrated circuit modeling Li-ion battery cells Mathematical model State of charge battery energy storage systems battery storage plants capacity condition monitoring electric vehicles electrical model energy storage equivalent circuit model (ECM) estimation error internal resistance model-based method model-based online condition monitoring algorithms parameter identification power system measurement power system simulation secondary cells smart grid smart power grids state estimation state of charge (SOC) state of health (SOH)
