Many electric vehicles connected to the grid will lead to problems such as poor stability of power grid generation. The key to solving these problems is to propose an efficient, stable, and economical valley-filling charging scheme for electric vehicles and grid users in the vehicle network system. Firstly, the convex optimization theory is used to make the grid achieve the optimization effect of valley filling. On this basis, the electricity price scheme with a time-varying coefficient as the variable is proposed to meet the single objective optimization of EV charging cost optimization, and its degree of influence on the grid valley-filling effect is analyzed. Secondly, based on the competitive relationship between EV charging cost and battery life, the P2D model is simplified and analyzed, and the attenuation law of battery capacity is quantitatively described. The multi-objective optimization problem is established to express in a Pareto matrix. Finally, the compatibility between the multi-objective optimization and grid valley charging is analyzed. The simulation results show that: (1) The convexity electricity price scheme can satisfy the requirements of various retention rates to achieve the valley-filling effect; (2) The filling effect is satisfied with the electricity price scheme that minimizes the charging cost, and the key factors affecting the filling effect are analyzed; (3) The multi-objective optimization scheme with charging cost and battery life is compatible with the valley-filling effect.

Gel polymer electrolytes (GPEs) have high safety and excellent electrochemical performance, so applying GPEs in lithium batteries has received much attention. However, their poor lithium ion transfer number, cycling stability, and low room temperature ionic conductivity seriously affect the utilization of gel polymer electrolytes. This paper successfully synthesized flexible poly (vinylidene fluoride-hexafluoropropylene)–lithium titanium aluminum phosphate (PVDF-HFP-LATP) gel polymer electrolytes using the immersion precipitation method. The resulting GPE has a porous honeycomb structure, which ensures that the GPE has sufficient space to store the liquid electrolyte. The GPE has a high ionic conductivity of 1.03 ×10⁻³ S cm⁻¹ at room temperature (25 °C). The GPE was applied to LiFePO₄/GPE/Li batteries with good rate performance at room temperature. The discharge specific capacity of 1C was as high as 121.5 mAh/g, and the capacity retention rate was 94.0% after 300 cycles. These results indicate that PVDF-HFP-LATP-based GPEs have the advantage of simplifying the production process and can improve the utility of gel polymer lithium metal batteries.

In this paper, the negative impact of the charging load generated by the disorderly charging scheme of large-scale pure electric vehicles on the operation performance of the power grid system and the problem of reducing its charging energy efficiency are studied and analyzed. First, based on Matlab 2022a simulation software and the Monte Carlo random sampling method, the probability density model of the factors affecting the charging load is constructed, and the total charging load of different quantities is simulated. Second, the IEEE33-node distribution network model is introduced to simulate the influence of charging load on the grid under different permeability schemes. Finally, the multi-objective genetic algorithm is used to optimize the charging cost and battery life. Taking the 20% permeability scheme as an example, the research results show that, compared with the disorderly charging scheme, the multi-objective optimization scheme reduces the peaking valley difference rate by 24.34%, the charging load power generation cost by 29.5%, and the charging cost by 23.9%. The power grid profit increased by 45.8%, and the research conclusion has practical significance for the energy efficiency optimization of pure electric vehicles.

To address the complex calibration parameters and low optimization efficiency of dual-fuel engines, this paper innovatively proposes an optimization calibration method based on a simulation model and the Non-Dominated Sorting Whale Optimization Algorithm (NSWOA). Taking the YC6K dual-fuel engine as the research object, a high-precision simulation model was constructed within the GT-Power environment, and its reliability was confirmed through the external characteristic curve (the maximum deviation of torque and specific fuel consumption rate is less than 5%). A total of 260 parameter samples were generated using a Sobol sequence space-filling experimental design, and a performance prediction model was established by combining the Crested Porcupine Optimization algorithm and the Back-Propagation Neural Network (CPO-BP). The experimental results show that the CPO-BP model exhibits excellent predictive capability, with the coefficient of determination (R²) of nitrogen oxides (NO_x) and brake-specific fuel consumption rate (BSFC) reaching 0.98964 and 0.99501 respectively. Based on this, the NSWOA algorithm was introduced to optimize key parameters such as speed, torque, main injection timing, and rail pressure, with the optimization objectives being NO_x emissions and BSFC. The optimization results show that under 100% load conditions, the reduction in BSFC ranges from 1.5% to 4.3%, and NO_x emissions are reduced by 48.6% to 67.1%. The effectiveness of the optimized parameters was also verified through bench tests, providing an efficient solution for complex engineering optimization problems.

Gel polymer electrolytes (GPEs) have emerged as promising electrolytes for lithium metal batteries owing to their high ionic conductivity, mechanical flexibility, and reduced risk of electrolyte leakage. However, PAN-based GPEs still suffer from limited ion transport caused by the semi-crystalline structure of PAN chains. In this work, polyacrylonitrile (PAN)/poly(methyl methacrylate) (PMMA)/lithium aluminum titanium phosphate (LATP)/lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) gel polymer electrolytes were fabricated via direct ink writing (DIW) 3D printing, where PMMA was introduced to regulate the PAN matrix and enhance Li⁺ transport. The results reveal that PMMA incorporation effectively reduces PAN crystallinity, increases the amorphous fraction, and modifies the local functional-group environment of the polymer matrix, while LATP fillers further improve ionic transport and mechanical stability. The optimized PPM8:2 gel polymer electrolyte delivers a room-temperature ionic conductivity of 4.22 × 10⁻⁴ S cm⁻¹, a Li⁺ transference number of 0.624, and an electrochemical stability window of 4.75 V. When applied in LiFePO₄|Li batteries, it maintains a discharge capacity of approximately 150 mAh g⁻¹ after 100 cycles at 0.1 C with excellent rate capability and cycling stability. This work provides an effective approach to developing PAN-based gel polymer electrolytes for high-performance lithium metal batteries.