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Artificial intelligence-based integration technology applications in battery energy storage systems
- 1 Shenzhen Technology University
- 2 The Chinese University of Hong Kong
* Author to whom correspondence should be addressed.
Abstract
Battery Energy Storage Systems (BESS) are the backbone of modern power grids. They allow for the increase of energy storage, peak shaving, or backup power. Due to their complexity and dynamics, BESS require high-advanced management methods to optimise its performance. This paper focuses on the integration of Artificial Intelligence (AI) into BESS, discussing three main pillars: system stability, battery usage optimisation, and predictive maintenance. The emergence of Artificial Intelligence and in particular deep learning, reinforcement learning, and neural networks, brings significant improvements in the modelling of complex reaction mechanisms, the adaptation to real-time data, and predictive maintenance. By analysing large datasets from various sources, AI can increase the precision of State of Charge (SOC) estimation, reduce maintenance costs, and improve the reliability of the system. The comparison with different case studies underlines the potential implementation of AI in real-life applications, which brings cost savings and increased system efficiency. This paper concludes that the power of AI enables new techniques for BESS management, and it would bring major benefits in the construction of more powerful and resilient energy systems as a whole.
Keywords
Artificial Intelligence, Battery Energy Storage Systems, Predictive Maintenance, State of Charge Estimation, System Stability
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Cite this article
Cai,Z.;Ma,N. (2024). Artificial intelligence-based integration technology applications in battery energy storage systems. Advances in Engineering Innovation,12,41-46.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Journal：Advances in Engineering Innovation
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