TY - GEN
T1 - Modeling lithium-ion Battery in Grid Energy Storage Systems
T2 - 6th IEEE International Conference on Industrial Cyber-Physical Systems, ICPS 2023
AU - Miao, Yong
AU - He, Xinyuan
AU - Gu, Chenghong
PY - 2023/5/24
Y1 - 2023/5/24
N2 - Grid energy storage system (GESS) has been widely used in smart homes and grids, but its safety problem has impacted its application. Battery is one of the key components that affect the performance of GESS. Its performance and working conditions directly affect the safety and reliability of the power grid. With the development of data analytics and machine learning, the accuracy and adaptability of the battery state estimation model can be greatly improved. This paper proposes a new method to model battery, with low-quality data. First, it designs a data cleaning method for GESS battery operating data, including missing data filling and outlier data repair. Then, the repaired data is used to model battery. A battery mathematical model is proposed based on a deep learning algorithm to realize accurate GESS state estimation. The performance of the developed deep learning method is compared with conventional BP neural network and generalized regression neural network to highlight the technical merits. Results derived in this paper provide a solid basis for high-efficiency GESS operation and energy management.
AB - Grid energy storage system (GESS) has been widely used in smart homes and grids, but its safety problem has impacted its application. Battery is one of the key components that affect the performance of GESS. Its performance and working conditions directly affect the safety and reliability of the power grid. With the development of data analytics and machine learning, the accuracy and adaptability of the battery state estimation model can be greatly improved. This paper proposes a new method to model battery, with low-quality data. First, it designs a data cleaning method for GESS battery operating data, including missing data filling and outlier data repair. Then, the repaired data is used to model battery. A battery mathematical model is proposed based on a deep learning algorithm to realize accurate GESS state estimation. The performance of the developed deep learning method is compared with conventional BP neural network and generalized regression neural network to highlight the technical merits. Results derived in this paper provide a solid basis for high-efficiency GESS operation and energy management.
KW - Artificial intelligence
KW - Battery modeling
KW - Battery state estimation
KW - Big data
KW - Deep learning algorithm
KW - Grid energy storage system
UR - http://www.scopus.com/inward/record.url?scp=85163102571&partnerID=8YFLogxK
U2 - 10.1109/ICPS58381.2023.10128104
DO - 10.1109/ICPS58381.2023.10128104
M3 - Chapter in a published conference proceeding
AN - SCOPUS:85163102571
T3 - Proceedings - 2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems, ICPS 2023
BT - Proceedings - 2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems, ICPS 2023
PB - IEEE
CY - U. S. A.
Y2 - 8 May 2023 through 11 May 2023
ER -
