Pareto-based multi-objective design of battery cold plates with leaf-inspired bifurcations using arithmetic optimization algorithm and WASPAS technique

Efficient thermal management is essential for ensuring the safety, reliability, and longevity of Li-ion batteries, yet liquid-cooled plate (LCP) designs face persistent challenges in balancing cooling efficiency with hydraulic performance. This study presents an AI-based integrated workflow for the design and optimization of LCPs featuring leaf-inspired bifurcation geometries. The framework consists of four sequential stages: data analysis, predictive modeling using GMDH-type ANN, Pareto-based optimization via multi-objective arithmetic optimization algorithm (MOAOA) and multi-objective particle swarm optimization (MOPSO), and final design ranking with the WASPAS decision-making method. The GMDH models demonstrated high predictive accuracy, achieving R 2 > 0.99 for thermal resistance and pressure drop. Both MOAOA and MOPSO produced nearly identical Pareto fronts, confirming robustness in capturing trade-offs between thermal and hydraulic performance. Optimized inputs revealed balanced designs at mass flow rates of 0.8–1.5 g/s, channel widths of ∼3.7–3.9 mm, and heights of 2.4–2.5 mm, achieving thermal resistance of 0.25–0.35 K/W with pressure drops of 10–25 Pa, ensuring efficient cooling without excessive hydraulic penalties. Decision analysis revealed context-specific optimal designs, ranging from ultra-low thermal resistance (0.1793 K/W) at the cost of high pressure drop (106.81 Pa) to energy-efficient solutions with minimal pumping penalties (ΔP = 2.14 Pa).