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Overloading of trucks can easily cause damage to roads, bridges and other transportation facilities, and accelerate the fatigue loss of the vehicles themselves, and accidents are prone to occur under overload conditions. In recent years, various countries have formulated a series of management methods and governance measures for truck overloading. However, the detection method for overload behavior is not efficient and accurate enough. At present, the method of dynamic load identification is not perfect. No matter whether it is the dynamic weight measurement method of reconstructing the road surface or the non-contact dynamic weight measurement method, little attention is paid to the difference of different vehicles. Especially for different vehicles, there should be different load limits, and the current devices are not smart enough.

Magnetically coupled resonance wireless power transmission technology (MCR-WPT), as a technological innovation in the electric vehicle industry, is of great significance to promote the development of the electric vehicle industry chain. The current wireless charging technology is affected by the design of the vehicle itself, the distance between the vehicle-mounted part of the wireless charging and the ground is not fixed. And the changeable parking attitude will cause the projection of the transmitting coil and the receiving coil to deviate. Therefore, reasonable matching of transmission frequency, matching impedance and other parameters is of great significance for optimizing power transmission efficiency. This paper establishes a mathematical model of transmission frequency, matching impedance, distance between two coils and wireless power transmission efficiency.

As an important input parameter of intelligent vehicle active safety technology, road adhesion coefficient is of great significance in autonomous collision avoidance, emergency braking and collision avoidance, and variable adhesion road motion control. Traditional recognition methods based on vehicle dynamics require large data volume and low solution accuracy. This paper proposes an adhesion coefficient recognition method based on Elman neural network and Kalman filter. By establishing a seven-degree-of-freedom vehicle dynamics model, dynamic parameters such as yaw angular velocity, longitudinal velocity, lateral velocity, and angular velocity of each wheel, which are easy to measure and strongly related to the road adhesion coefficient, are analyzed as the input of the neural network model.