Multi-Objective Optimization and Performance Evaluation of Manifold-Based Cooling Systems for Battery Thermal Management Using RSM and NSGA-II

Abstract

Efficient thermal management is critical to the safety, performance, and longevity of lithium-ion battery (LIB) energy storage systems. In this study, a novel manifold cold plate featuring an overflow channel with a triangular ridge at the bottom is proposed for a liquid-cooled Battery Thermal Management System (BTMS). A comprehensive multi-objective optimization framework is developed by integrating Response Surface Methodology (RSM), the Non-dominated Sorting Genetic Algorithm II (NSGA-II), and the Linear Programming Technique forMultidimensional Analysis of Preference (LINMAP) decision-making method to minimize the maximum temperature difference (Tcell) and pressure drop (P) across the cooling plate.Thedesign variables include the manifold channel width ratio (λ), the height ratio (φ), the inlet velocity (u), and the triangular ridge angle (θ). Second-order polynomial regression models are constructed and validated using Analysis ofVariance (ANOVA), yielding high coefficients of determination (R2 = 0.9926 forTcell and 0.9600 forP), confirming strong predictive accuracy. Sensitivity analysis reveals that the inlet velocity and channel angle are the primary factors influencing system performance. The LINMAP-based decision-making approach identifies an optimal configuration with λ = 1.031, φ = 1.47, u = 1.671 m/s, and θ = 29.8°, achieving a Tcell of 12.61°C and a P of 6742.99 Pa, with validation errors below 3%. Transient simulations at 0.5 and 1C discharge rates show that the LINMAP-optimized design reduces the maximumcell temperature by 13.12°C and 11.77°C, respectively, compared to the natural convection baseline, and by 1.42°C and 0.76°C compared to the prototype design, while maintaining comparable hydraulic resistance. This work offers valuable guidance for designing and optimizing liquid-cooled battery

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