Advancements in battery thermal management system for fast charging/ discharging applications

作者：Khan, Shahid Ali; Hussain, Iftikhar; Thakur, Amrit Kumar; Yu, Shi; Lau, Kwun Ting; He, Sihong; Dong, Kejian; Chen, Jingtan; Li, Xiangrong; Ahmad, Muhammad; Zhao, Jiyun^*

来源：Energy Storage Materials, 2024, 65: 103144.

DOI：10.1016/j.ensm.2023.103144

• Battery energy storage systems
• Thermal issues
• Thermal management system
• Li -ion battery
• Machine learning

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 perfor-mance, 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, of-fering 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.

• 单位
  西安电子科技大学; 上海大学