Based on the analysis on the influencing factors of urban logistics demand, this paper, taking into account the logistics demand with non-linear and small sample modeling characteristics from the perspective of urban freight volume, introduces the ant colony algorithm into the modeling process to optimize the penalty parameter “c” and “g” parameter of Radial Basis Function in support vector machine, and has made a prediction to the logistics demand of Qingdao with the optimized support vector machine model. The experimental results show that the prediction results of the improved support vector machine can bring the prediction closer to the reality with their more accuracy, stronger stability and less error rate, thus providing a guarantee for the logistics demand forecast of Qingdao.

Aiming at the impact of heat generation and temperature rise on the driving performance of a permanent magnet (PM) motor, taking the PM in-wheel motor (IWM) for electric vehicles as an object, research is conducted into the temperature distribution of the electromagnetic–thermal effect and cooling structure optimization. Firstly, the electromagnetic–thermal coupling model considering electromagnetic harmonics is established using the subdomain model and Bertotti’s iron loss separation theory. Combined with the finite element (FE) simulation model established by Ansoft Maxwell software platform, the winding copper loss, stator core loss and PM eddy current loss under the action of complex magnetic flux are analyzed, and the transient temperature distribution of each component is obtained through coupling. Secondarily, the influence of the waterway structure parameters on the heat dissipation effect of the PM-IWM is analyzed by the thermal-fluid coupled relationship. On the basis, the optimization design of waterway structure parameters is carried out to improve the heat dissipation effect of the cooling system based on the proposed chaotic mapping ant colony algorithm with metropolis criterion. The comparison before and after optimization shows that the temperature of key components is significantly improved, the average convection heat transfer coefficient (CHTC) is increased by 23.57%, the peak temperature of stator is reduced from 95.47 °C to 82.73 °C, and the peak temperature of PM is decreased by 14.26%, thus the demagnetization risk in the PM is improved comprehensively. The research results can provide some theoretical and technical support for the structural optimization of water-cooled dissipation in the PM motor.

The permanent magnet (PM) of synchronous motors is susceptible to factors such as high temperature, reverse magnetic field, and impact vibration, resulting in demagnetization faults that directly affect the operational performance of machine. To address the prediction problem of time-dependent demagnetization, an improved subdomain analytical model is proposed and reapplied to predict magnetic fields considering demagnetization effects. The degree and distribution of demagnetization are innovatively described and defined using orthogonal design and statistical methods. Taking the 20/24 PM in-wheel motor (IWM) as an object, the variation of magnetic field and back electromotive force (EMF) under different demagnetization cases and degrees are compared and analyzed according to the Maxwell’s theory and field-circuit coupling time-stepping finite element (FE) method. And the evolution rules and reconstruction characteristics of unbalanced electromagnetic force (UEF), cogging torque, and output torque are explored. The results demonstrate that the improved model predictions agree well with that of simulation results, and the amplitude of flux density and back EMF in demagnetized region are significantly lower compared to healthy conditions. Under the uniform demagnetization, the waveform of UEF, cogging torque, and output torque generated by the residual magnetic field is almost consistent with the healthy condition, with relatively lower peaks. In other cases, with the expansion of demagnetization degrees and demagnetized PM’s numbers, the resulting UEF increases in a stepped pattern, and the cogging torque and output torque both increase to varying degrees.

The design of the hub-and-spoke network has wide applications in the freight transportation system. This design involves the location of a group of hubs as well as the allocation between nonhub nodes and the hubs after the location. On the basis of the traditional single distribution hub-and-spoke network, the congestion flow waiting model (CFWM) and the congestion flow redistribution model (CFRM) are proposed in this paper after considering traffic waiting and traffic diversion, respectively, in the case of hub congestion. The presented models focus on the design of single distribution hub-and-spoke logistics network under traffic congestion. The objective function minimizes the total cost of the road network on the premise of ensuring the normal operation of the logistics network, which effectively balances the contradiction between the economic benefits of traffic scale and the congestion cost. Given the complexity of the problem, the congestion cost function is linearized, and the mutational particle swarm optimization (MPSO) is employed for the solution. Additionally, certain calculation experiments and sensitivity analysis of the congestion optimization model are conducted to verify the effectiveness and applicability of the constructed hub-and-spoke network and the congestion solutions. The results indicate that the optimized logistics network may effectively alleviate congestion, balance the network freight flow, and improve the stability of the hub-and-spoke network.

With the development of e-commerce logistics in China, online shopping has become increasingly popular among consumers, and the fresh e-commerce industry has shifted to a pre-positioning warehouse distribution model closer to consumers. This study starts from consumer demands and considers the spatial distribution characteristics of consumers. Through cluster analysis, demand points with high consumer density are identified, and potential locations are determined through two rounds of clustering. Drawing insights from the warehousing and distribution models of Freshippo and MissFresh in Wuhan and Nanjing, four classification algorithms are compared to determine the optimal pre-positioning warehouse distribution model for the enterprise in this study. A multi-objective optimization model is established, aiming to minimize enterprise costs and maximize customer satisfaction, and the optimal site selection plan is obtained. By determining the number and locations of pre-positioning warehouses through the optimal solution, the enterprise can achieve maximum investment returns in the spatial layout of these warehouses.

In recent years, the technology of Internet of Vehicles has developed rapidly, among which vehicle to everything (V2X) technology is provided with a quite important promotion value in the intelligent transportation field. In V2X environment, the driver can receive real-time information of the motion status of adjacent vehicles and the signal duration of the intersection, and take the decision-making action of manipulating the vehicle in advance, which has a certain impact on the evolution process of queue dissipation. This paper studies the improved OV model based on V2X environment and considering the driver’s early response time selects the intersection queue dissipation efficiency as the index to analyze the intersection traffic efficiency and studies the impact of different initial headway, maximum speed, and safety distance on the intersection traffic flow. The simulation results show that compared with the traditional OV model and the OV model considering driver’s reaction delay, the model proposed in this paper greatly improves the efficiency of traffic flow recovery after green light, so the model proposed in this paper can promote the stability of traffic flow. The linear stability of the proposed model was analyzed by using the linear stability theory to obtain the stability conditions of the improved model. Furthermore, a simulation environment was established to analyze the vehicle queue dissipation at intersections and to simulate the improved V2X car-following model as well as the platoon dissipation state under different traffic parameters. The research results show that vehicle drivers can obtain traffic flow operation status in real-time through V2X equipment and change the operation of the vehicle itself in a targeted manner, which can significantly improve the safety and stability of traffic flow operation, and greatly raise the dissipation efficiency of the platoon. The method proposed in this paper can increase the queue dissipation rate and shorten the recovery time of the traffic system, respectively.