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.

The GM(1,1) model's prediction accuracy is significantly influenced by the accuracy of background value estimation. The traditional trapezoidal background value can only be applied to a specific data sequence. Therefore, this study proposes a GM(1,1) model background value reconstruction approach based on the combination of intelligent trapezoidal and variable weights in order to increase the model's application as well as its prediction accuracy. The trapezoidal background value function with slope and point position parameters is called model I. Then, a set of point position parameter sequences, with a new background value function is constructed, called model II. A genetic algorithm is utilized to seek for the values of the parameters to be determined in both models I and II. The results showed that for the exponential growth data series, model I and II have higher prediction accuracy compared to traditional models. For data sequences, taking the traffic volume series of a road from 2014 to 2023, the prediction accuracy of this paper's model I method can be improved by 0.3643 % and 0.2725 % compared with Deng's and Wang's models. The prediction accuracy of this paper's model II method has been further improved by 0.1075 % compared with that of model I.

The construction of urban expressways will significantly impact the travel of surrounding residents. Traffic flow assignment is a key method to address this issue. This study, therefore, addresses the impact of urban expressway construction on nearby residents’ travel by proposing an optimised traffic flow assignment method. Traditional methods rely on labour-intensive OD (origin-destination) matrix acquisition, but this research introduces an OD reverse derivation model that eliminates the need for a prior matrix. Key road sections are identified using the stepwise point placement method, with peak-hour traffic volumes surveyed. An incremental assignment method generates a distribution matrix, and the original OD matrix is derived using a maximum entropy-based model. A stochastic user equilibrium assignment model incorporating a path length-corrected logit is constructed, and a genetic algorithm solves the objective function. Using evening peak traffic data from Huai’an’s road network, including an expressway construction zone, the results show that total travel time decreased by 14.11% after applying the method, from 4,050,327.517 seconds to 3,478,967.635 seconds. This demonstrates the proposed method’s effectiveness in reducing congestion and improving travel efficiency for surrounding residents.

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.