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Due to the long-term effects of vehicle load and temperature, the occurrence and expansion of reflective cracks has become the most critical issue of pavement structure currently. In order to reduce the damage caused by reflective cracks on the road surface, one kind of pavement structures was adopted, in which the low-modulus HDPE modified asphalt stress absorbing layer was added between the old cement concrete pavement and the asphalt overlay. The finite element was used to establish the pavement structure model with the stress absorbing layer, and the vehicle load and temperature were applied to simulate the actual stress situation of the pavement structure. The stress analysis of the pavement structure under the effect of the two loads showed that HDPE stress absorbing layer has a better dissipative effect on the temperature stress, and the stress value of the pavement structure under the effect of vehicle loads was bigger than that under the effect of temperature, and the most reasonable thickness of HDPE stress absorbing layer to prevent reflective cracks was 2–4 cm.

To address the higher cooling requirement for embankments in permafrost regions, a novel active refrigerating method, which focuses on releasing permafrost heat in warm seasons, is presented. Based on the real situations in field, comparisons were made between thermally driven and electrically driven refrigeration methods in the roadway application. A prototype of the vapor compression refrigeration system (VCRS) was then designed and manufactured, and its practical performance experimentally investigated. The data shows that: (1) the refrigerating temperature can reach below 0 °C in warm seasons; (2) the heat reflux capacity and effective coefficient of performance is about 55.4 W and 0.41, respectively; (3) the predicted long term cooling scope is more than 6 m. The VCRS prototype can present good refrigerating performance in warm seasons. We conclude that the new structure is a potential method for preventing permafrost degradation and can ensure the long-term thermal stability of embankments under the effects of climate warming.

The current active cooling structures, which were successfully adopted in the construction of the Qinghai–Tibet Railway, sometimes cannot maintain the long–time foundation stability in warm permafrost regions. The objective of this study is to present and examine a novel solar assisted compression refrigeration system. A special refrigeration apparatus was designed and manufactured, and its practical performance was experimentally investigated. The test results indicated that the new structure demonstrates excellent refrigerating performance corresponding to negative temperatures in warm seasons, and the temperature can be automatically controlled at −5, −10 and −15 °C; in addition, the new structure has a low energy consumption with the corresponding coefficient of performance (COP) of 6.68, 4.91, 3.45. The study provides reference and guidance for railway design and construction in permafrost regions, such as for the Sichuan–Tibet Railway.

The demand of electrical vehicles for batteries with higher power has increased rapidly. The existed prediction method for power state involves composite pulse method, dynamic fusion state prediction algorithm based on multiparameters and SOC method based on battery, while the adaptation of the former two methods is competent for dynamic working conditions, and the last one may require high level hardware and huge amount of calculation when involved in batteries pack. So the research on power state of batteries is of significance in extending the service life, regenerative braking and energy recycling fields. BP neural network has an advantage of application and feasibility in tackling with the severe nonlinear characteristic of power state of battery at dynamic working conditions without any high level hardware. It has been approved by vehicle test that the factors that affect the power state of battery at dynamic working conditions involve road conditions, natural environments, and human factor, among which the main factor is the current and voltage value. Then this paper has analyzed the main factors that affect the power state of battery, established a prediction model for power state of Li-ion power battery based on BP neural network and probed into the influence on the precision of the network prediction model with training functions: trainscg, trainlm and traincgb, which proved that the output of function trainlm has the least relative error and rapid constringency, which highlights the incomparable superiority of trainlm training function in the middle-scale BP neural network in rapid approaching.