Abstract
Battery energy storage systems (BESS) are essential for integrating renewable energy sources and enhancing grid stability and reliability. However, fast charging/discharging of BESS pose significant challenges to the performance, thermal issues, and lifespan. This paper provides not only an overview of the recent advancements of battery thermal management systems (BTMS) for fast charging/discharging of BESS but also machine learning (ML) approach to optimizing its design and operation. Various thermal management strategies are highlighted in this review, such as liquid-based, phase-change material-based, refrigerant-based, and ML-based methods, offering improved thermal performance and better safety for fast charge/discharge applications. Overall, this paper provides a comprehensive and critical analysis of the current advancements and prospects of BESS thermal management and identifies the current research gaps and future directions for developing a more efficient and reliable BESS. © 2023 Elsevier B.V.
| Original language | English |
|---|---|
| Article number | 103144 |
| Journal | Energy Storage Materials |
| Volume | 65 |
| Online published | 17 Dec 2023 |
| DOIs | |
| Publication status | Published - Feb 2024 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Research Keywords
- Battery energy storage systems
- Li-ion battery
- Machine learning
- Thermal issues
- Thermal management system
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