In order to reduce flow resistance loss in EV thermal management systems, this research builds a comprehensive computational process. The study used an advanced three-dimensional topology optimization technology integrating detailed fluid flow analysis with an adjoint sensitivity solver. This integrated computational approach helps systematic analysis of the complete design region. Thus, the internal flow channels with high resistance can be rearranged. The optimization target was set to minimize total pressure drop under defined operational parameters in real driving conditions. Through iterative calculation, the study successfully created three flow manifolds with different geometric shapes; each flow channel has its own distinct source of high resistance. The results show that the optimization effect is quite good, compared with the traditional manifold developed based on engineering experience; these optimized designs have reduced the pressure drop by 27%, 41%, and 74%, respectively. Beyond these quantitative pressure reduction data, detailed flow field analysis revealed that the optimized manifolds promote substantially improved hydrodynamic characteristics. The optimized internal channels generate more uniform velocity profiles, effectively diminish spatial velocity variations, and restrain vortex formation and recirculation zones. These useful flow field enhancements collectively contribute to a dramatic reduction in energy dissipation. This improves the thermodynamic efficiency of the thermal management system effectively. In order to conduct a more comprehensive verification, the optimized manifold was evaluated under various non-design operating conditions. These three designs consistently maintained stable performance characteristics and their low resistance properties in operating scenarios different from the original conditions, compared to the original manifold. Its stable performance under variable conditions shows the effectiveness of the topology-optimized methods and shows its broad operational adaptability. This is of great significance for the automotive application field, as the operating conditions in this field are often changing.