Application of artificial intelligence in flywheel energy storage

doi:10.19799/j.cnki.2095-4239.2025.0658

Abstract

Abstract:

Flywheel energy storage systems (FESSs) offer outstanding advantages in grid frequency regulation, inertia support, high-frequency peak shaving, and other applications due to their high power density, long service life, rapid responses, and environmental friendliness. However, FESSs face challenges related to achieving cost effectiveness and design reliability, stability of high-speed permanent magnet motors, control of magnetic suspension control, online prediction of faults, and control of multimachine parallel arrays. This paper reviews recent literature on the application of artificial intelligence (AI) technologies in key areas of FESSs, including design optimization, motor control, magnetic suspension control, grid-connected control, and fault diagnosis. Particular focus is given to the use of algorithms such as neural networks in several technical domains: modeling and analysis of composite material rotors, multiparameter collaborative optimization of permanent magnet synchronous motors (PMSMs), efficiency optimization and speed estimation of PMSMs under varying conditions, electromagnetic bearing control algorithms, grid-connected system robustness and distributed collaborative control, frequency regulation strategies, and bearing fault diagnosis and early warning. Future development directions such as the integration of large AI models and multitechnology collaborative optimization are also discussed. These insights aim to support intelligent research and development in FESSs.

Key words: flywheel energy storage, artificial intelligence, frequency regulation, fault diagnosis

CLC Number:

TK 02

Lu WEI, Zhiyi LENG, Jia YE, Yujie XU, Haisheng CHEN. Application of artificial intelligence in flywheel energy storage[J]. Energy Storage Science and Technology, 2025, 14(8): 3019-3027.

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