Integrating heat transfer and control optimization: A comprehensive review of battery thermal management systems
Pouya Kabirzadeh, Zheng Liu, Mostafa Olyaei, Haoyun Qiu, Yashraj Gurumukhi, Harsh Tyagi, Yuan Jiang, Vivek S. Garimella, B. Singh, Yumeng Li, Pingfeng Wang, Nenad Miljkovic
Abstract
With advances in lithium-ion battery technology, electric vehicles (EVs) are becoming increasingly popular. Effective battery thermal management systems (BTMSs) are crucial for enhancing the performance and range of EVs. However, BTMS development is a multidisciplinary task requiring extensive domain knowledge across various fields, posing challenges for researchers to acquire this expertise simultaneously. This review comprehensively summarizes recent research on BTMS, considering aspects of battery electrochemistry , geometry, cooling methods, control techniques, and optimization algorithms. In terms of battery electrochemistry, principles of heat generation and commonly used models are discussed, along with survey of the adverse effects of temperature. For battery geometry, temperature gradients are analyzed within cells of different shapes and sizes, including cylindrical, pouch, and prismatic cells. The analysis of cooling methods includes air cooling, heat pipe-based cooling, indirect liquid cooling, phase change material cooling, single/two phase immersion cooling, and hybrid cooling for all mentioned battery variations, highlighting which methods are more practical in the industry. Additionally, system-level analyses of battery cooling methods have been investigated to understand their overall impact on EV performance. In addition to lithium-ion batteries, cooling methods for solid-state batteries are reviewed, along with strategies to mitigate thermal runaway. Furthermore, the review explores various control strategies for BTMS, including non-feedback control and feedback control . The analysis delves into different objective functions, constraints, design variables, and optimization algorithms relevant to BTMS optimization. This comprehensive analysis aims to provide a deep understanding of critical topics necessary for designing next-generation BTMS, ensuring enhanced performance, reliability, and safety for EVs.