Data-driven optimization of nano-PCM arrangements for battery thermal management based on Lattice Boltzmann simulation

An efficient Battery Thermal Management System (BTMS) is vital for maximizing electric vehicle effectiveness and extending service life, essential for sustainable transportation. This study proposes a novel non-uniform nano-PCM distribution strategy within BTMS to tackle battery overheating challenges and further proposes a multi-objective optimization framework combining Back Propagation Neural Networks (BPNN) and Genetic Algorithm (GA) to achieve optimal design solutions for BTMS. Optimization data is derived from the well-validated Lattice Boltzmann Method (LBM) results across 343 cases. Initial evaluations show that a negative gradient distributed nano-PCM (Type 2) improves melting rate, heat dissipation power, and temperature uniformity by 4.67%, 4.87%, 19%, and 7.0%, respectively. The BPNN-GA optimization framework satisfactorily correlates nanoparticle distribution with four evaluation metrics, achieving R 2 values from 0.9469 to 0.9987. Optimization improves melting rate, heat dissipation power, and regional and inter-regional temperature uniformity by 9.13%, 9.94%, 7.77%, and 29.73%, respectively. The BPNN-GA also demonstrates reasonable generalizability for the other two practical case scenarios with improvements in certain criteria up to 49.19%. This study highlights the potential of uneven nano-PCM configurations and the efficiency of the LBM-BPNN-GA framework in achieving superior thermal management for BTMS, which is expected to provide insights for future BTMS designs and implementations. • A novel non-uniform distributed Nano-PCM configuration with BTMS is proposed. • Cooling performance of Nano-PCM is assessed via LBM studies across 343 cases. • A novel multi-objective optimization framework using BPNN and GA is proposed. • A negatively gradient-distributed Nano-PCM shows superior cooling performance. • BPNN-GA framework successfully improved multiple evaluation met-rics up to 29.73%.