Analysis of vehicle-mounted capacitive weighing sensor based on virtual instrument technology

Virtual Instrument (VI) is a new achievement that is closely combined with testing technology and instrumentation technology in the process of the increasingly developed computer hardware, software and bus technology penetrating into other related technical fields. Since the specialized functions and panel controls of the instrument are formed by software, this new type of instrument is called "virtual instrument" internationally. It uses the data processing and graphics processing functions of the microcomputer to software the specialized functions and panel controls of traditional physical instruments, and the interface with the test data is also realized through computer software. From the virtual instrument display panel (such as virtual display screen, digital display and indicator light and oscilloscope, etc., which correspond to various physical instruments in function), you can understand the status of the instrument and read the test results for analysis.

1 Overview of vehicle weighing sensor

Based on the characteristics of virtual instruments, this paper uses the vehicle load detection device of the capacitive weighing sensor, and uses the leaf spring in the vehicle buffering and shock absorbing mechanism as the elastic body of the weighing sensor, which can be used for static or dynamic detection anytime and anywhere. Under the action of load, the buffering and shock absorbing mechanism (leaf spring) of the car is deformed, the distance between the two plates of the capacitive sensor installed at a specific position changes, the capacitance value changes, and the output voltage of the sensor changes accordingly. Based on the virtual instrument, starting from the parameter analysis of the static performance test, the law of the voltage change between the two plates of the capacitive sensor when the cargo changes when the vehicle is stationary is analyzed, which serves as a reference for the dynamic performance test. The voltage value corresponding to the specific load is analyzed, and the error analysis and curve fitting are performed using the virtual instrument program subVI, which is convenient and intuitive. The installation of the capacitive weighing sensor is shown in Figure 1.

The upper plate component of the capacitor is installed at the bottom of the frame, centered left and right; the lower plate component of the capacitor is installed above the middle of the axle, aligned with the upper plate of the capacitor. A set of capacitive sensors is installed above each axle of the vehicle.

The relationship between the various elements of the vehicle weighing system is as follows:

Under the action of load, the car's buffer shock absorbing mechanism (leaf spring) deforms, the distance d between the two plates of the capacitive sensor changes, and the capacitance value of the sensor also changes accordingly. The relationship between the output voltage value of the sensor circuit and the load value of the axle is pre-calibrated, and the load mass of the axle can be obtained based on the voltage value of each axle sensor circuit. By adding the load mass of each axle, the load mass of the entire vehicle can be obtained.

2 Experiment and data analysis

The static analysis test of the capacitance method for detecting vehicle load based on virtual instruments was carried out on the Black Panther SM1010 vehicle. The vehicle is a two-axle leaf spring structure with a rated load of 500 kg. During the static experiment, the vehicle was kept in a horizontal state, and the two wheels were vertically pressed on the SCS-2 electronic digital platform scale. The vehicle was loaded or unloaded with a 100 kg weight as the standard unit load. The test was divided into two strokes (each stroke includes two directions, forward and reverse), and the loading or unloading was carried out in the following order:

Forward 1-when the vehicle is in a free state (no hysteresis), gradually load from no load until the sensor output reaches the full scale;

Reverse 1: On the basis of forward 1, gradually unload to no load;

Forward 2: Based on Reverse 1 (with hysteresis), gradually load from no load until the output reaches full scale again;

Reverse 2: Based on forward 2, gradually unload until no load.

According to the test data, the arithmetic mean of the output of the two forward and reverse sensors is calculated respectively, and then the arithmetic mean of the total process is calculated. After programming and display under the virtual instrument programming software LabVIEW platform, the front panel of the program flowchart is as follows.

The software programming adopts modular design, mainly including curve fitting module, straight line fitting module and error analysis module. The least square linearity, hysteresis error and repeatability error in the error analysis module are all in the form of subVI, which provides great convenience for the analysis and programming of dynamic parameters. The error processing module mainly analyzes and processes the repeatability error, maximum standard deviation and hysteresis error of the data during the data analysis process. These data are used as the basis for subsequent data processing and programming of the capacitance weighing sensor compensation system, such as the repeatability error programming shown in Figure 4. In order to understand the influence of acceleration on the capacitance method vehicle load detection, according to the correspondence between the pre-calibrated load mass and the output voltage of the capacitance sensor, the load mass of the front and rear axles and the whole vehicle under a certain acceleration (aH) is obtained, and the results are shown in Table 1.

From the table, we can see that during braking, when the acceleration aH=4 m/s2 is compared with aH=0, the front axle load mass detected by the capacitive sensor increases by 122.5%, the rear axle decreases by 60.7%, and the vehicle load mass increases by 14.9%; during acceleration, when the acceleration aH=1.78 m/s2 is compared with aH=0, the front axle load mass decreases by 55.7%, the rear axle increases by 14.6%, and the vehicle load mass decreases by 14.4%. It can be seen that acceleration has a great influence on vehicle load detection. In order to ensure the accuracy of the detection results, software compensation must be used.

The relationship curve between load mass and acceleration drawn according to the data in Table 1 is shown in Figure 5.

From the above analysis system display results, it can be seen that the capacitive vehicle weighing device has good repeatability when static, but there are also certain nonlinear errors and large hysteresis, which directly affect the load detection results. The main reason for the nonlinear error is the nonlinear relationship between the relative change of capacitance and the plate. There are two main reasons for the hysteresis (including the reverse stroke not returning to zero): 1) real materials have hysteresis to a certain extent; 2) when the vehicle load size is different, the height and length of the leaf spring change with the load, friction is generated between the spring leaves, and friction is also generated at the connection between the two ends of the spring leaf and the frame. The use of leaf springs made of highly elastic materials, improving mechanical design, and reducing friction can reduce the hysteresis effect. The effect of nonlinear compensation and hysteresis compensation using software is very obvious.

3 Conclusion

The vehicle weighing analysis system based on virtual instruments has the characteristics of easy operation, user-friendly interface, and easy programming. Although virtual instruments do not have real instrument panels, they are far superior to traditional physical instruments in terms of functionality. Practice has proved that the vehicle weighing system based on virtual instruments is not only suitable for static measurement and analysis of vehicle loads detected by the capacitance method, but is also more suitable for dynamic measurement and analysis with larger data volumes. The innovation of this paper is to use the software panel of the virtual instrument to perform error analysis on static measurement of vehicle loads detected by the capacitance method, which not only gets rid of the shortcomings of list statistics that are prone to errors, but also makes the results convenient, fast and intuitive.