Brilliant plasmonic colors with long‐standing stability are generated via laser direct writing. This plasmonic coloring system is made of silver nanoparticles (Ag NPs) layer embedded in the quartz glass formed by ion implantation. The laser‐induced plasmonic heating merges the small Ag NPs into larger ones, which modifies the plasmon resonances. The plasmon resonances can be further tuned via changing the irradiation time and power, which shows scattering colors ranging from red to green and cyan. By scanning the laser across the Ag NPs layer, sophisticated plasmonic patterns and images with high resolution (≈105 DPI) can be obtained and preserved over long time (several months). This plasmonic coloring system via laser printing is facile, cost‐effective, accurate, and highly stable with rich hue, compared to other plasmonic color systems, which bears significant potentials for industrial applications such as optics, displays, decorations, data storage, and anti‐counterfeiting.

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In order to reduce the adverse effects of disordered charging of electric vehicles on the safe and stable operation of the distribution network, a multiobjective optimal scheduling method for the sequential charging software of networked electric vehicles is proposed. Aimed at minimizing charging costs and peak-to-valley differences in distribution network loads, its scheduling strategy will continuously roll and update EV charging schemes over time. The results show that the actual response data collected by the Internet of Vehicles app has corrected the probability distribution of the user’s choice of charging mode and response behavior. On the fifth day, the user’s actual charging response curve is close to the theoretical curve obtained by the optimization algorithm, and the expected charging is basically achieved. Calculations showed that the variance of the total load curve after charging decreased by 24.8 from 169.35 to 127.39. The proposed orderly charging strategy can effectively reduce the charging cost of electric vehicle users and the peak-to-valley difference of the distribution network load, play a good role in peak-valley filling, improve the convergence accuracy of the algorithm, and obtain the optimal solution of the problem.

The wheel-ground contact load characteristics of unmanned ground vehicles are an important foundation for vehicle design, structural parameter optimization, off-road performance evaluation, and control strategy formulation. The load characteristics of unmanned ground vehicles are mainly investigated based on traditional vehicle terramechanics theory, which cannot reflect wheel-ground contact. This study proposed a model integrated with qualitative theoretical analysis and quasi-quantitative simulation to evaluate wheel-ground contact load characteristics during the off-road movement of unmanned vehicles. Prediction and test models of system wheel contact load characteristics were built by multi-physical field coupling analysis. Flow and power characteristics during unilateral steering were discussed systematically through terramechanics theory. The accuracy of the models was verified by experiments. Results show that changes in the tire load affect the average stress on the ground contact surface of tire, which leads to the forward gravity center of the entire machine. The optimal combination of structural parameters under dynamic working conditions of the unmanned vehicles is determined based on multi-physics coupling analysis model to optimize the structural design. The load pressure of the system reaches 19.53 MPa in the accelerated start-up phase, and the error of simulation and test results is within 10%. This study provides tools for theoretical and simulation analysis for development of the optimized structure design and control strategy formulation of unmanned ground vehicles.