Abstract
This paper proposes a new hybrid scheme using the EV battery and the local battery as a unit, taking an active part in the grid services. Both electric vehicles and grid-scale battery energy storage have been growing fast in recent years. The active combination of these two kinds of energy sectors is challenging but will unlock extra flexibility at the distribution level. Therefore, the EV battery (EVB) and local battery (LB) are studied in a hybrid scheme for the first time. The scheme delivers an improved optimal power schedule for the fast frequency regulation (FFR) in the microgrid. A hybrid power management strategy based on an improved model predictive control (IMPC) in a microgrid is developed. The IMPC is advanced by adding the battery degradation prediction and EV capacity prediction in the loop and designed for optimal power-sharing with the minimum effect on battery lifetime. The EV battery status, which is critical for both the IMPC and the battery degradation quantification, is predicted by the deep learning approach. The proposed hybrid power management with IMPC is verified to be very effective with optimal power-sharing and battery anti-aging control in the microgrid application.
| Original language | English |
|---|---|
| Article number | 100151 |
| Journal | eTransportation |
| Volume | 11 |
| Early online date | 14 Dec 2021 |
| DOIs | |
| Publication status | Published - Feb 2022 |
Bibliographical note
Funding Information:This work was supported by the National Natural Science Foundation of China under Grant No. 52172354 .
Publisher Copyright:
© 2021
Funding
| Funders | Funder number |
|---|---|
| National Natural Science Foundation of China | 52172354 |
Keywords
- Battery anti-aging control
- Battery energy storage
- Electric vehicle
- Frequency regulation
- Hybrid energy storage
ASJC Scopus subject areas
- Automotive Engineering
- Electrical and Electronic Engineering
- Energy Engineering and Power Technology
- Transportation