Abstract
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.
| Original language | English |
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| Title of host publication | Proceedings of 2017 IEEE Manchester PowerTech |
| Number of pages | 6 |
| Publisher | IEEE |
| Publication date | 2017 |
| DOIs | |
| Publication status | Published - 2017 |
| Externally published | Yes |
| Event | 12th IEEE Power and Energy Society PowerTech Conference: Towards and Beyond Sustainable Energy Systems - University Place, University of Manchester., Manchester, United Kingdom Duration: 18 Jun 2017 → 22 Jun 2017 |
Conference
| Conference | 12th IEEE Power and Energy Society PowerTech Conference |
|---|---|
| Location | University Place, University of Manchester. |
| Country/Territory | United Kingdom |
| City | Manchester |
| Period | 18/06/2017 → 22/06/2017 |
Keywords
- Battery energy storage systems (BESS)
- Capacity
- Equivalent circuit model (ECM)
- Internal resistance
- Model-based method
- Parameter identification
- State of charge (SOC)
- State of health (SOH)
