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PCM-assisted battery thermal management systems with hybrid fin–rib geometry using numerical modeling and AI-based optimization framework

Yanping WuMathematical Center, Weifang University of Science and Technology, Weifang 262700, ChinaMohammad Jafar MokarramSchool of Electronic Engineering and Intelligent Manufacturing, Anhui Xinhua University, Hefei 230088, ChinaHyder H Abd BallaDepartment of Unmanned Aviation Technical Engineering, Najaf polytechnic College, Al Furat Al Awsat Technical University, Najaf, IraqHemn A.H. BARZANIDepartment of Medical Laboratory Science, College of Health Science, Lebanese French University, Erbil, IraqRaymond GhandourCollege of Engineering and Technology, American University of the Middle East, Egaila, 54200, KuwaitHanaa Abu-ZinadahDepartment of Mathematics and Statistics, College of science, University of Jeddah, Jeddah, Saudi ArabiaIlkhom KhaydarovSchool of Exact Sciences, National Pedagogical University of Uzbekistan named after NizamiHind AlbalawiDepartment of Physics, College of Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaXuan YangWeifang University
2026en
ABI

Annotatsiya

Effective thermal regulation remains a critical challenge in high-power lithium-ion battery packs, particularly under elevated discharge rates, where excessive temperature rise and non-uniform heat distribution can accelerate degradation and compromise safety. In this numerical study, a novel phase change material (PCM)-assisted battery thermal management system (BTMS) was proposed for a compact circular battery pack comprising 15 cylindrical LiCoO 2 –26,650 cells. The design introduced a hybrid thermal control strategy that simultaneously exploits latent heat storage, enhanced solid conduction, and increased external heat-transfer area for passive heat dissipation. In addition to aluminum fins embedded within the PCM to overcome its inherently low thermal conductivity, a distinctive outer-rib architecture was introduced to increase the external heat-transfer surface area available for heat rejection. Compared with conventional PCM-based systems, the proposed geometry enhances passive heat dissipation by providing larger external surfaces exposed to the ambient environment. To systematically assess the influence of key geometric parameters, including battery distance from the enclosure wall, tilted fins spacing, and outer rib length, an artificial neural network (ANN) model was developed to predict battery temperature rise across multiple configurations. The ANN was subsequently coupled with a genetic algorithm (GA) to identify an optimal design that minimizes the increase in battery temperature, offering an efficient alternative to extensive parametric simulations. Additionally, K-fold cross-validation was conducted to evaluate the generalization capability of the trained network and to ensure that the ANN model's high prediction accuracy was not due to overfitting. The GA-based optimization identified an optimal BTMS configuration with a battery-wall distance of 30 mm, outer rib length of 11.590 mm, and tilted fins spacing of 20 mm, yielding a maximum battery temperature of 304.884 K at 1800 s under a 2C discharge rate. The optimized system reduced the peak battery temperature by nearly 12.7 K relative to the PCM-less case and by approximately 3 K compared to the Base-designs. Moreover, the optimal design achieved a total thermal energy storage of 3605 J at 1800 s, corresponding to enhancements of about 19–24% in total stored energy and 30–33% in latent heat storage compared with the Base-design configurations.

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