A real-time path planning for reducing vehicles traveling time in cooperative-intelligent transportation systems

Order dispatching optimization in ride-sourcing market by considering cross service modes

In the field of logistics and transportation, balancing high-quality demand with economic benefits is a crucial issue. The introduction of electric vehicles offers new solutions to this challenge. However, the limited range of electric vehicles poses a significant challenge for logistics operations. To effectively address this problem, this paper proposes an electric vehicle path planning method based on convolutional neural networks and long-short-term memory (CNN-LSTM-EVPP). Specifically, the CNN-LSTM model is employed to predict the driving energy consumption for each route by combining traffic data and road data. Finally, the NSGA-II algorithm is used to optimize the predicted energy consumption values and find the optimal path. Experiments show that the method is effective. The algorithm has the potential to enhance distribution efficiency and reduce costs. The optimized path length is reduced by 8.87% and the driving energy consumption is reduced by 8.31%.

Research on Electric Vehicle Route Planning and Energy Consumption Prediction Based on CNN-LSTM Model

The present work deals with the development of a thermal composite insulation by using concrete as the main component with a number of thermal  insulation materials, including polyester, cork, chaff, rice husk, sheep manure, sheep wool, glass, cotton, wax and emulsion resin. Various mechanical and thermal properties of the composite insulation are evaluated. These properties help in estimating their suitability and potential for various applications in thermal insulation and thus utilizing waste and reduce environmental concerns.The results obtained from this study are useful for the prediction of thermal conductivity of thermal insulation building blocks. Experimental results showed that the best material is polyester with average thermal conductivity of (0.734557 W/m.oC) then cork, chaff, rice husk, sheep manure, sheep wool and wax are good insulators, because of their low conductivity. But poor thermal insulators are glass and emulsion resin, because of their high thermal conductivity results comparatively. Especially 2% emulsion resin which has high average thermal conductivity as it is (1.290638 W/m.oC). Glass and resin are not suitable less use for the validation of experimental results, because of their high thermal conductivity, and as resin which high cost. So, they are difficult to acquire for practical purpose. Addition of wax significantly decreases thermal conductivity. It was shown that addition 0.3 wt% wax and 0.5 wt% wax to the concrete resulted in (0.008342 W/m.oC) and (0.027716 W/m.oC) decrease in average thermal conductivity respectively. And addition emulsion resin to the concrete causes decrease in average thermal conductivity. Also it was shown 0.3 wt% and 0.5 wt% resulted in (0.131417 W/m.oC) and (0.156545 W/m.oC) decrease in average thermal conductivity respectively. The strongest material used in the present work is an emulsion resin, exactly 2.3 %wt emulsion resin, since it has compact strength (25.591 MPa). But the weakest one is cork and chaff which have compact strength of (1.4731Mpa and 1.3469 MPa) respectively.

Experimental Study for Selection of Composite Materials Used in Thermal Insulation

With the advancement of research on multi-agent reinforcement learning, an increasing number of studies have begun to apply reinforcement learning to multi-agent path planning. This paper focuses on using distributed reinforcement learning for path planning and proposes an RAFO-A3C (Random and fixed obstacles A3C) multi-agent path planning algorithm for maps with mixed forms of fixed and random obstacles. The algorithm combines reinforcement learning and imitation learning, with A3C as the main training method and odrM* as an auxiliary. The algorithm learns decentralized strategies to plan coordinated paths in a partially observable world without the need for inter-agent communication. By introducing curriculum learning and multi-step penalty mechanisms into the RAFO-A3C algorithm, the trained strategies can be applied to environments of different sizes and obstacle densities without the need for retraining, and at the same time, avoid agents getting stuck in oscillatory ineffective behaviors. Finally, the algorithm proposed in this paper is compared with other multi-agent path planning algorithms, and the experimental results show its superiority in terms of success rate, completion time, and path length.

Multi-Agent Reinforcement Learning for Path Planning with Various Random and Fixed Obstacle Configurations

Optimal path planning method of electric vehicles considering power supply

The hot summer and cold winter zone is a transition zone between the cold zone and hot zone, sweltering in summer and chilly in winter, of which climate is worse. In recent years, with people’s raised requirements on indoor living environments, the energy consumption of buildings in hot summer and cold winter zone has been greatly increased. However, the thermal performance of walls in this zone is worse, and thus a mass of energy is wasted. This paper thoroughly analyzes and compares some energy-saving wall materials and thermal insulation systems used in projects in general, according to the climate in the zone combined with the design standard for the walls of residential buildings in the hot summer and cold winter zone. The results indicate that reasonably selecting the applicable wall materials and thermal insulation systems according to the local energy consumption characteristics could optimize resource utilization and have a positive effect on energy efficiency.

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The Study on Thermal Performance and Applicability of Energy-saving Wall Materials in Hot Summer and Cold Winter Zones

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Optimal path planning method of electric vehicles considering power supply

The Study on Thermal Performance and Applicability of Energy-saving Wall Materials in Hot Summer and Cold Winter Zones

Optimal path planning method of electric vehicles considering power supply