Thermal Performance Study of a Bionic Sine‐Wave Fin‐Enhanced Phase‐Change Material Battery Thermal Management System

To enhance the thermal conductivity of conventional fins in phase‐change materials (PCMs), a novel sinusoidal fin structure was proposed in this study, drawing inspiration from the bionic serpentine configuration while building upon traditional straight fin designs case 0 (four fins, each 0.5 mm thick and 65 mm high, are evenly distributed around the battery's circumference). First, single‐factor analyses were conducted on the sinusoidal fins’ amplitude, number of outer rings, and number of branch fins at a 5C discharge rate. Results indicated that with an amplitude of 10 mm, three outer rings, and six branch fins, the maximum battery temperature was reduced by 4.39 K compared to the conventional fin structure. Subsequently, the impact of LM (Levenberg‐Marquardt), SCG (Scaled Conjugate gradient), and RProp (Resilient backpropagation) optimization algorithms, along with varying neuron counts, on the accuracy of the BP (Back Propagation) neural network was evaluated. The LM algorithm with 10 neurons was found to yield the optimal model accuracy. Finally, a genetic algorithm was employed for single‐objective optimization of the sine‐wave branch fin thickness ( D 1 ), first‐layer ring radius ( R 1 ), second‐layer ring radius ( R 2 ), and first‐/second‐layer ring thickness ( D 2 ). The genetic algorithm predicted the battery temperature to be 319.49 K, while the simulated battery reached a maximum temperature of 319.50 K, with an error margin of only 0.0012%. The maximum battery temperature was reduced to 319.50 K, a decrease of 0.26 K compared to the preoptimization state. Compared to case 0, the maximum temperature was lowered by 4.65 K (17.88%). The operational time at the same temperature was extended by 900 s (125%), and the melting uniformity of the PCM was significantly improved.