With the increase in energy density of power batteries, the risk of thermal runaway significantly increases under extreme working conditions. Therefore, this article proposes a biomimetic liquid cooling plate design based on the fractal structure of fir needle leaf veins, combined with Murray’s mass transfer law, which has significantly improved the heat dissipation performance under extreme working conditions. A multi-field coupling model of electrochemistry fluid heat transfer was established using ANSYS 2022 Fluent, and the synergistic mechanism of environmental temperature, coolant parameters, and heating power was systematically analyzed. Research has found that compared to traditional serpentine channels, leaf vein biomimetic structures can reduce the maximum temperature of batteries by 11.78 °C at a flow rate of 4 m/s and 5000 W/m³. Further analysis reveals that there is a critical flow rate threshold of 2.5 m/s for cooling efficiency (beyond which the effectiveness of temperature reduction decreases by 86%), as well as a thermal saturation temperature of 28 °C (with a sudden increase in temperature rise slope by 284%). Under low-load conditions of 2600 W/m ³, the system exhibits a thermal hysteresis plateau of 40.29 °C. To predict the battery temperature in advance and actively intervene in cooling the battery pack, based on the experimental data and thermodynamic laws of the biomimetic liquid cooling system mentioned above, this study further constructed a support vector machine (SVM) prediction model to achieve real-time and accurate prediction of the highest temperature of the battery pack (validation set average relative error 1.57%), providing new ideas for intelligent optimization of biomimetic liquid cooling systems.

To address the thermal management challenges of electric vehicle power batteries under complex operating conditions, this study proposes a biomimetic honeycomb-shaped liquid cooling plate and conducts a systematic analysis of its cooling performance along with machine learning-based prediction for CTP lithium iron phosphate battery packs. A fluid–solid coupling numerical model was developed using ANSYS Fluent, employing the control variable method to investigate the effects of coolant flow rate (0.2–4.2 m/s), coolant inlet temperature (5–32 °C), ambient temperature (15–39 °C), and battery heating power (1000–5500 W/m³) on the maximum battery temperature. Simulation results demonstrate that the honeycomb structure leverages its hexagonal channel geometry and large specific surface area to achieve rapid and uniform heat transfer, with no localized hot spots observed across all operating conditions. The maximum battery temperature exhibits a marginal decreasing trend as coolant flow rate increases, with 1.4 m/s approaching the optimal flow rate; it rises approximately linearly with elevated inlet temperature, ambient temperature, and heating power—each 3 °C increase in inlet or ambient temperature raises the maximum temperature by approximately 1.98 °C and 3 °C, respectively, while a 500 W/m³ increase in heating power corresponds to an approximately 2.8 °C rise. Under standard conditions (heating power: 3000 W/m³; inlet temperature ≤23 °C; ambient temperature ≤27 °C), the maximum battery temperature remains below 45 °C; high-heating (≥3500 W/m³) or high-temperature (≥30 °C) scenarios require coordinated control strategies. Furthermore, based on simulation data, seven machine learning models—BPNN, GA-BP, PSO-BP, SVM, RBFNN, RF, and LSTM—were constructed and evaluated for their performance in predicting the maximum temperature of battery packs. The results showed that the LSTM model achieved the highest prediction accuracy on the validation set, with RMSE, MAE, MAPE, and R² values of 0.8068, 0.6891, 1.5653%, and 0.9865, respectively, while models such as SVM and RBFNN exhibited severe overfitting. This study validated the engineering effectiveness of the honeycomb structure liquid cooling plate and identified LSTM as the optimal model for predicting battery pack maximum temperature, providing a theoretical foundation and data support for the structural design and intelligent control of power battery thermal management systems.