Abstract
This study is about applications of nanofluids and various soft computing algorithms on designs of battery thermal management systems and their potential performance enhancement in cooling. Brief information on Li-ion batteries, energy storage process and cooling techniques such as passive, active and hybrid cooling techniques are presented. Basic knowledge on nanofluids and soft computing methods are explained to deep understanding the following chapters. Potential of using nanofluids on thermal management of battery packs and effect on their life cycles and performance improvements are discussed. Application of the most common soft computing methods in battery thermal management systems is presented. Li-ion batteries are a promising solution to energy storage issue with appropriate thermal management designs such as presented in this review. When different active and hybrid cooling battery thermal management systems are operated with nanofluids, their performances are increased. Different machine learning methods have been successfully used in battery thermal management systems and outputs from the modeling have been considered for further performance enhancement and optimization studies. Even though, they are excellent tools assisting in high fidelity simulations or expensive experimental testing of systems, deep learning and other advanced machine learning methods may be considered for future studies. Exergetic performance analysis of nano enhanced thermal management along with the cost of using nanofluids is needed as the extension of the current studies.
•Applications of nanofluids and soft computing algorithms on BTM systems are reviewed.•Brief information on Li-ion batteries, energy storage process and cooling are presented.•Li-ion batteries are a promising solution to energy storage with thermal management designs.