TY - GEN
T1 - Capacity Estimation for Retired Electric Vehicle Batteries in Agricultural Renewable Energy Systems
AU - Lee, C. C.
AU - Hu, Panpan
AU - Lam, S. K.
AU - Li, C. Y.
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - This paper presents a novel approach for estimating the remaining capacity of retired electric vehicle (EV) batteries in agricultural renewable energy systems. The experimental setup for measuring battery parameters, particularly focusing on Lithium Iron Phosphate (LFP) batteries under different room temperatures, is outlined. Grey Relational Analysis (GRA) is utilized to identify significant parameters for input to a Long Short-Term Memory (LSTM) model, which accurately estimates the remaining capacity of retired batteries. Simulation results using a measured battery dataset demonstrate the effectiveness of the proposed GRA-LSTM approach. The findings highlight the potential of repurposing retired EV batteries for sustainable energy storage in agricultural applications, optimizing resource utilization and enhancing energy efficiency in farming practices.
AB - This paper presents a novel approach for estimating the remaining capacity of retired electric vehicle (EV) batteries in agricultural renewable energy systems. The experimental setup for measuring battery parameters, particularly focusing on Lithium Iron Phosphate (LFP) batteries under different room temperatures, is outlined. Grey Relational Analysis (GRA) is utilized to identify significant parameters for input to a Long Short-Term Memory (LSTM) model, which accurately estimates the remaining capacity of retired batteries. Simulation results using a measured battery dataset demonstrate the effectiveness of the proposed GRA-LSTM approach. The findings highlight the potential of repurposing retired EV batteries for sustainable energy storage in agricultural applications, optimizing resource utilization and enhancing energy efficiency in farming practices.
KW - GRA-LSTM
KW - Lithium-ion batteries
KW - agricultural renewable energy systems
KW - capacity estimation
UR - http://www.scopus.com/inward/record.url?scp=85215519109&partnerID=8YFLogxK
U2 - 10.1109/INDIN58382.2024.10774260
DO - 10.1109/INDIN58382.2024.10774260
M3 - Conference contribution
AN - SCOPUS:85215519109
T3 - IEEE International Conference on Industrial Informatics (INDIN)
BT - Proceedings - 2024 IEEE 22nd International Conference on Industrial Informatics, INDIN 2024
T2 - 22nd IEEE International Conference on Industrial Informatics, INDIN 2024
Y2 - 18 August 2024 through 20 August 2024
ER -
