ANN-GA-based optimization of a hybrid battery thermal regulation system with helical liquid cooling, PCM, and copper foam

Effective thermal management is essential for ensuring the safety, performance, and longevity of Lithium-ion batteries (LIBs). In this study, an optimized hybrid Battery Thermal Regulation System (BTRS) is proposed, integrating helical liquid cooling mini-channels and phase change material (PCM) embedded in a copper foam matrix (CPCM) to enhance heat dissipation. To efficiently explore the complex design space, a coupled framework integrating numerical simulations, a Genetic Algorithm (GA), and Artificial Neural Networks (ANN) was developed. The approach successfully identified an ideal configuration (OCD1) characterized by a 10 mm PCM thickness, a 0.08 m/s inlet velocity, and a \(65^\circ \) helical angle. Under a severe 3C discharge condition, this optimal design achieved a maximum temperature reduction of 21.27 K, representing an 82.25% improvement over a standalone cell. Furthermore, the optimized hybrid system demonstrated superior cooling efficacy compared to individual strategies, outperforming case A (liquid cooling) by 33.67%, case B (pure PCM) by 69.35%, and case C (CPCM) by 45%. The developed ANN model exhibited high predictive accuracy, with R 2 values exceeding 0.99 for both thermal responses. These findings provide a high fidelity and scalable framework for advanced thermal management of next-generation electric vehicle battery systems.