Abstract
With the high population of electric vehicle adoption, precisely controlling the temperature of the battery modules is essential to provide long-term sustainability and reliability. In the amount of battery thermal management techniques, adding spoilers is a promising method for enhancing the heat transfer performance of cold plates, but the performance of the system is highly sensitive to different geometry parameters. In this study, heat transfer performance for the cold plates battery thermal management system with a tesla flow channel was numerically and experimentally investigated with a focus on the geometry parameters, including spoiler length (L), mounting position (X), and coolant flow rate (ν). A modified heuristic-based swarm intelligence multi-objective optimization algorithm is proposed to obtain the optimal spoiler parameters. The results show the maximum temperature of the battery module is reduced by 3.12 ℃ after adding the spoiler. The optimization spoilers maintain the maximum temperature of the battery module below 30.9 ℃, and the best spoiler parameters as L = 5.10 mm, X = 14.3 mm, and v = 1.186 × 10-2 m/s. This study uses a heuristic-based swarm intelligence multi-objective optimization algorithm to investigate the optimization process of the spoiler structural parameters and provides guidance for the application of advanced multi-objective optimization algorithms in cold plate design.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call