# Research on EMC Optimization of Wireless Electric Field Sensor for Rail Vehicle

Authors: Xueming Liu; Ping Sun; Ta Zhao; Gang Song; Caixin Fu; Weiping Liu; Tewabe Chekole Workneh
Journal: Journal of Sensors
 (2022)

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## Abstract

In order to improve the electromagnetic compatibility of railway vehicle wireless electric field sensor, a research on electromagnetic compatibility optimization of railway vehicle wireless electric field sensor is proposed. The results show that the optimized RF radiation energy is greatly reduced from 60 to 20, which is completely lower than the standard limit of radiation disturbance, and meets the requirements of EMC design of electronic products. The feasibility and effectiveness of the optimal design are proved. The experimental results show that the test results meet the requirements of general technical conditions of electromagnetic compatibility of speed sensor, which indicates that the electromagnetic compatibility design proposed can be popularized and applied.


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## Body

## 1. Introduction

As an important support of the “sensing layer”, under various test conditions, especially the electromagnetic compatibility and environmental adaptability test, the accuracy of its induced signal and data transmission in the electromagnetic environment with interference and harsh installation and use environment has become an important link in sensor detection [1]. In other words, the input and output technical data and data transmission of various sensors are the important content of truly and effectively evaluating the qualification of sensors [2]. The electromagnetic compatibility of the sensor refers to the adaptability of the sensor in the electromagnetic environment, the ability to maintain its inherent performance and complete the specified functions [3]. At present, many researches on the electromagnetic compatibility performance of traditional railway track have been relatively mature and comprehensive, and the relevant standards are becoming more and more mature. In terms of electromagnetic compatibility of high-speed railway track vehicle system, although some relevant standards and regulations have been issued, they are still in the stage of finding and solving problems in engineering application [4]. Therefore, we should deeply understand the existing electromagnetic environment of high-speed railway rolling stock system, conduct qualitative and quantitative analysis on the electromagnetic interference between subsystems and equipment within subsystems, and consider the electromagnetic compatibility performance of the system at the beginning of system design [5]. Figure 1 shows the electromagnetic environment framework of high-speed railway rolling stock system.Figure 1
Electromagnetic environment diagram of high-speed railway rolling stock system.In this paper, a chaotic neural network (CHNN) optimization algorithm is proposed to solve the problem of electromagnetic compatibility of speed sensor, and the test results of electromagnetic compatibility are given. Practice shows that this algorithm can effectively reduce the peak point of sensor RF energy, weaken its radiation influence, and reduce the radiation range and the effect is satisfactory.


## 2. Literature Review

Many scholars have done relevant research on the electromagnetic compatibility of sensors. They mainly studied the electromagnetic compatibility of photoelectric sensor and improved the suppression of interference source, the design of circuit board, and the coding mode. Ji et al. Studied the electromagnetic compatibility of pressure sensor, which adopts a multilayer shielded circuit board, and high-precision components are also selected in components. In addition, it also processes the high-frequency ground in the circuit [6]. Berka et al. studied the piezoelectric carbon sensor, flexibly applied the transmission line theory, and obtained the relevant processing results of electromagnetic shielding efficiency [7]. Kosarev et al. studied the aircraft engine speed sensor. Through its flexible use of inductive coupling, they came to the conclusion that the high current coil will generate induced voltage on the adjacent road [8]. Davoudabadifarahani et al. studied the electromagnetic compatibility of displacement sensor. They believe that the random switching noise is the product of analog comparison circuit, so the displacement sensor has high requirements for anti-interference signal [9]. Liang and others have studied the wireless sensor. They mainly carry out simulation analysis with the help of simulation software. With the help of their simulation results, we can avoid many problems in the design of wireless sensor. Nowadays, there are few studies on the electromagnetic compatibility of EMU speed sensors. At present, there is also research on CRH2 EMU speed sensor [10]. Ovchinnikov et al. studied the protective grounding mode and installation mode of CRH2 EMU and considered that the change of pantograph catenary relationship will lead to offline pantograph catenary, lifting pantograph, etc., which will lead to the overvoltage of the vehicle body, which can reach kV, which may eventually lead to the breakdown and damage of the capacitance connected in series between the sensor shielding layer and the vehicle body, so the service life of the speed sensor will be reduced [11]. Wang et al. mainly studied the protective grounding mode. They believe that the surge pulse group will be generated during pantograph lifting and lowering. Once the insulation performance of the speed sensor is reduced to a certain extent, the signal line and power line will produce surge pulses. Under the propagation of the conductor, the interior of the monitoring system will also be affected by the surge pulse group [12]. Cai et al. believe that the development of foreign electric vehicles has been decades of history and automobile technology is relatively mature and has made some achievements. The main performance is as follows: There are effective electromagnetic compatibility testing, management, and certification institutions for the whole vehicle and parts; high-precision automotive EMC testing system; and professional electromagnetic simulation design software is being developed. Third-party testing and certification institutions have also established electromagnetic compatibility laboratories to actively carry out electromagnetic compatibility research of electric vehicles, and various technical indicators including electromagnetic compatibility disturbance suppression and anti-interference technology are relatively advanced [13].


## 3. Research Methods

### 3.1. Electromagnetic Compatibility Test Standard for Railway Vehicle Speed Radio Field Sensor

The test shall be conducted in two directions of vertical polarization and horizontal polarization of transmitting antenna [14]. The transmitting antenna shall test the four faces of the sensor, respectively. When the sensor can be placed in different directions, the test surface is increased to six. Relevant parameters of electromagnetic compatibility test items of speed sensor include the following:
(1)
Test items: RF electromagnetic field radiation immunity test(2)
Severity level: level 3, and the corresponding interference source intensity is 10 V/M(3)
Frequency range: 80~1000 MHz(4)
Qualification judgment basis: During the test, the sensor shall work normally under the rated voltage, and the change of sensor signal output shall not exceed 2%


### 3.2. RF Emission Theory of Track Speed Sensor

For most systems, RF energy is generated by the current flowing between devices and between the signal source and the 0 V reference voltage [15]. RF radiation can be described by a small loop antenna carrying RF current. The radiation principle belongs to differential mode radiation, as shown in Figure 2.Figure 2
Radiation mechanism of disturbance source.For most PCBs containing transmission lines, the loop size has radiation energy corresponding to the range up to megahertz [16].The maximum loop area not exceeding the specific specification level is shown in Formula (1), and the area is represented by A:
(1)A=380rE/f2Is,where r is the distance between the transmitting loop and the receiving antenna, m; E is the radiation limit, μV/m; f is the frequency, MHz; and Is is the current, mA.The maximum field strength formula established in a closed-loop boundary region obtained from Formula (1) is shown below:
(2)E=Af2Is/380r.Obviously, in free space, the radiation energy is inversely proportional to the distance between the signal source and the receiving antenna; inside the PCB, you should also know the total area of a loop between the signal source and the return path. These equations must be solved for each loop and each specific frequency, with a large amount of calculation, and the optimal solution needs to be sought with the help of intelligent optimization algorithm [17, 18].


### 3.3. Chaotic Neural Network Optimization Algorithm

The basic idea of chaos optimization method is to apply the characteristics of the universality, randomness, and regularity of chaos to the specific optimization search. It has a series of advantages, such as easy to jump out of the local optimal solution to reach the global optimal solution, high search efficiency, and low requirements for optimization conditions. Therefore, if the neural network and chaos algorithm can be organically combined, a new chaotic neural network can be obtained, which has the ability to deal with complex problems often encountered in the optimization design of electrical appliances, such as local minimum, multioptimization variables, and energy function [19, 20].


#### 3.3.1. Optimization Idea of Chaotic Neural Network (CHNN)

(1) The chaotic neural network based on the solution of the inverse problem of electric field adopts the optimization method of parallel search of multiple variables to be optimized. (2) The chaotic neural network adopts the feedback network mode so that each neuron can affect the optimization process of other neurons according to its own optimization trend. In this way, the weights of the chaotic neural network can be modified in real time, parallel, and in a full range, so as to effectively deal with the problems to be optimized [21, 22]


#### 3.3.2. Iterative Block Diagram

The specific steps of chaos optimization method are shown in Figure3.Figure 3
CHNN optimization method iterative block diagram.The convergence criterion in Figure3 is as follows: If the energy value of the energy function does not change in neighborhood Pk and there is always Pk≤Pk+1 after M cycles, it is determined that the optimal solution is in this neighborhood, that is, when Fk+1−Fk≤δ and Pk≤Pk+1Fk+1,Fk are the energy function after K +1 and K iterations, respectively, δ is a minimal positive value.


#### 3.3.3. Algorithm Comparison Between Chaotic Neural Network (CHNN) and Traditional Neural Network (HNN)

In order to verify the correctness and effectiveness of CHNN algorithm, a multivariable function of Formula (3) is used for test:
(3)Fx=πn10sin2πx1+∑i=1n−1x1−121+sin2πxi+1+xn−12.Because this test function is a multidimensional space function with many extreme points, the optimization performance of CHNN can be fully investigated. In Formula (3), when n takes 5, the number of extreme points of the function is about 105, the global optimal value is 0.0, and the corresponding global optimal value is xi=1i=1,2,⋯,5. The comparison of CHNN and HNN optimization results is shown in Figure 4.Figure 4
C Comparison of HNN and HNN convergence curves.


## 3.1. Electromagnetic Compatibility Test Standard for Railway Vehicle Speed Radio Field Sensor

The test shall be conducted in two directions of vertical polarization and horizontal polarization of transmitting antenna [14]. The transmitting antenna shall test the four faces of the sensor, respectively. When the sensor can be placed in different directions, the test surface is increased to six. Relevant parameters of electromagnetic compatibility test items of speed sensor include the following:
(1)
Test items: RF electromagnetic field radiation immunity test(2)
Severity level: level 3, and the corresponding interference source intensity is 10 V/M(3)
Frequency range: 80~1000 MHz(4)
Qualification judgment basis: During the test, the sensor shall work normally under the rated voltage, and the change of sensor signal output shall not exceed 2%


## 3.2. RF Emission Theory of Track Speed Sensor

For most systems, RF energy is generated by the current flowing between devices and between the signal source and the 0 V reference voltage [15]. RF radiation can be described by a small loop antenna carrying RF current. The radiation principle belongs to differential mode radiation, as shown in Figure 2.Figure 2
Radiation mechanism of disturbance source.For most PCBs containing transmission lines, the loop size has radiation energy corresponding to the range up to megahertz [16].The maximum loop area not exceeding the specific specification level is shown in Formula (1), and the area is represented by A:
(1)A=380rE/f2Is,where r is the distance between the transmitting loop and the receiving antenna, m; E is the radiation limit, μV/m; f is the frequency, MHz; and Is is the current, mA.The maximum field strength formula established in a closed-loop boundary region obtained from Formula (1) is shown below:
(2)E=Af2Is/380r.Obviously, in free space, the radiation energy is inversely proportional to the distance between the signal source and the receiving antenna; inside the PCB, you should also know the total area of a loop between the signal source and the return path. These equations must be solved for each loop and each specific frequency, with a large amount of calculation, and the optimal solution needs to be sought with the help of intelligent optimization algorithm [17, 18].


## 3.3. Chaotic Neural Network Optimization Algorithm

The basic idea of chaos optimization method is to apply the characteristics of the universality, randomness, and regularity of chaos to the specific optimization search. It has a series of advantages, such as easy to jump out of the local optimal solution to reach the global optimal solution, high search efficiency, and low requirements for optimization conditions. Therefore, if the neural network and chaos algorithm can be organically combined, a new chaotic neural network can be obtained, which has the ability to deal with complex problems often encountered in the optimization design of electrical appliances, such as local minimum, multioptimization variables, and energy function [19, 20].


### 3.3.1. Optimization Idea of Chaotic Neural Network (CHNN)

(1) The chaotic neural network based on the solution of the inverse problem of electric field adopts the optimization method of parallel search of multiple variables to be optimized. (2) The chaotic neural network adopts the feedback network mode so that each neuron can affect the optimization process of other neurons according to its own optimization trend. In this way, the weights of the chaotic neural network can be modified in real time, parallel, and in a full range, so as to effectively deal with the problems to be optimized [21, 22]


### 3.3.2. Iterative Block Diagram

The specific steps of chaos optimization method are shown in Figure3.Figure 3
CHNN optimization method iterative block diagram.The convergence criterion in Figure3 is as follows: If the energy value of the energy function does not change in neighborhood Pk and there is always Pk≤Pk+1 after M cycles, it is determined that the optimal solution is in this neighborhood, that is, when Fk+1−Fk≤δ and Pk≤Pk+1Fk+1,Fk are the energy function after K +1 and K iterations, respectively, δ is a minimal positive value.


### 3.3.3. Algorithm Comparison Between Chaotic Neural Network (CHNN) and Traditional Neural Network (HNN)

In order to verify the correctness and effectiveness of CHNN algorithm, a multivariable function of Formula (3) is used for test:
(3)Fx=πn10sin2πx1+∑i=1n−1x1−121+sin2πxi+1+xn−12.Because this test function is a multidimensional space function with many extreme points, the optimization performance of CHNN can be fully investigated. In Formula (3), when n takes 5, the number of extreme points of the function is about 105, the global optimal value is 0.0, and the corresponding global optimal value is xi=1i=1,2,⋯,5. The comparison of CHNN and HNN optimization results is shown in Figure 4.Figure 4
C Comparison of HNN and HNN convergence curves.


## 3.3.1. Optimization Idea of Chaotic Neural Network (CHNN)

(1) The chaotic neural network based on the solution of the inverse problem of electric field adopts the optimization method of parallel search of multiple variables to be optimized. (2) The chaotic neural network adopts the feedback network mode so that each neuron can affect the optimization process of other neurons according to its own optimization trend. In this way, the weights of the chaotic neural network can be modified in real time, parallel, and in a full range, so as to effectively deal with the problems to be optimized [21, 22]


## 3.3.2. Iterative Block Diagram

The specific steps of chaos optimization method are shown in Figure3.Figure 3
CHNN optimization method iterative block diagram.The convergence criterion in Figure3 is as follows: If the energy value of the energy function does not change in neighborhood Pk and there is always Pk≤Pk+1 after M cycles, it is determined that the optimal solution is in this neighborhood, that is, when Fk+1−Fk≤δ and Pk≤Pk+1Fk+1,Fk are the energy function after K +1 and K iterations, respectively, δ is a minimal positive value.


## 3.3.3. Algorithm Comparison Between Chaotic Neural Network (CHNN) and Traditional Neural Network (HNN)

In order to verify the correctness and effectiveness of CHNN algorithm, a multivariable function of Formula (3) is used for test:
(3)Fx=πn10sin2πx1+∑i=1n−1x1−121+sin2πxi+1+xn−12.Because this test function is a multidimensional space function with many extreme points, the optimization performance of CHNN can be fully investigated. In Formula (3), when n takes 5, the number of extreme points of the function is about 105, the global optimal value is 0.0, and the corresponding global optimal value is xi=1i=1,2,⋯,5. The comparison of CHNN and HNN optimization results is shown in Figure 4.Figure 4
C Comparison of HNN and HNN convergence curves.


## 4. Result Discussion

The speed sensor needs a high-speed device similar to the wheel to collect the speed signal in real time. High-speed rotating motor is an option, but the spindle size of the motor is too small, which requires complex adjustment when loading samples, and positioning is very difficult. According to the observation, the high-speed rotating grinding wheel driven by the motor is very close to the wheel from shape to speed, and it has a large disk surface, which is very simple for the sensor three-axis positioning. Considering the steadiness of motor speed, the difference of sensor, and the speed monitoring magnitude in different application occasions, the inverter is specially installed to adjust the motor speed, in order to meet the test purpose of speed requirements within a certain range [23].


### 4.1. Establishment of Test Model

The high-speed rotating motor can be an option, but the size of motor spindle is too small, complex adjustment is required during sample clamping, and positioning is very difficult. According to the observation, the high-speed rotating grinding wheel driven by the motor has a large disk surface, which is very simple for the three-axis positioning of the sensor [24]. Considering the fixity of motor speed and the difference of sensor and monitoring speed in different applications, a frequency converter is specially installed to adjust the motor speed in order to meet the speed requirements in a certain range. The test model of radio field sensor for rail vehicle is shown in Figure 5.Figure 5
Test model of radio field sensor for rail vehicle.


### 4.2. Comparison of Test Results Before and After Optimization

Figures6 and 7 are the quasi-peak test curves of radiation disturbance of ordinary vehicle speed sensor before structural optimization. Among them, Figure 6 shows the horizontal polarization test results of electromagnetic radiation, and Figure 7 shows the vertical polarization test results of electromagnetic radiation (when alternating voltage generates alternating current through network conductor, electromagnetic wave is generated, and E field and H field are orthogonal to each other and propagate at the same time). In the figure, the abscissa represents the test frequency (MHz), the ordinate represents the electric field intensity E (V · m-1), and the curve represents the actual test value [25].Figure 6
Horizontal polarization test curve of vehicle speed sensor before optimization.Figure 7
Vertical polarization test curve of vehicle speed sensor before optimization.The results of electromagnetic polarization perpendicular to the vehicle speed sensor are optimized. By comparison, it is not difficult to find that the optimized RF radiation energy is greatly reduced, completely lower than the standard limit of radiation harassment, and meet the requirements of electromagnetic compatibility design of electronic products.Figure8 shows the test results of electromagnetic radiation vertical polarization of the optimized vehicle speed sensor. By comparing with Figure 7, it is not difficult to find that the optimized RF radiation energy is greatly weakened from 60 to 20, which is completely lower than the standard limit of radiation disturbance and meets the requirements of EMC design of electronic products [26].Figure 8
Vertical polarization test curve of optimized vehicle speed sensor.The electromagnetic compatibility of speed sensor is very important because it is a common monitoring and control component in transportation vehicles. The electromagnetic compatibility design scheme of a velocity sensor is proposed, and the optimal solution of structure design is sought by simulated annealing algorithm, which provides the theoretical basis for the scheme. The E field and H field distribution of the sensor before and after the improvement are simulated and compared. The improved sensor will be sent to the entry-exit Inspection and Quarantine Bureau for GB/T17626.3 (IEC61000-4-3) standard electromagnetic compatibility test. Experimental results show that the electromagnetic compatibility design can significantly reduce the intensity of radiation disturbance, indicating that the proposed scheme is correct, effective, and satisfactory.


## 4.1. Establishment of Test Model

The high-speed rotating motor can be an option, but the size of motor spindle is too small, complex adjustment is required during sample clamping, and positioning is very difficult. According to the observation, the high-speed rotating grinding wheel driven by the motor has a large disk surface, which is very simple for the three-axis positioning of the sensor [24]. Considering the fixity of motor speed and the difference of sensor and monitoring speed in different applications, a frequency converter is specially installed to adjust the motor speed in order to meet the speed requirements in a certain range. The test model of radio field sensor for rail vehicle is shown in Figure 5.Figure 5
Test model of radio field sensor for rail vehicle.


## 4.2. Comparison of Test Results Before and After Optimization

Figures6 and 7 are the quasi-peak test curves of radiation disturbance of ordinary vehicle speed sensor before structural optimization. Among them, Figure 6 shows the horizontal polarization test results of electromagnetic radiation, and Figure 7 shows the vertical polarization test results of electromagnetic radiation (when alternating voltage generates alternating current through network conductor, electromagnetic wave is generated, and E field and H field are orthogonal to each other and propagate at the same time). In the figure, the abscissa represents the test frequency (MHz), the ordinate represents the electric field intensity E (V · m-1), and the curve represents the actual test value [25].Figure 6
Horizontal polarization test curve of vehicle speed sensor before optimization.Figure 7
Vertical polarization test curve of vehicle speed sensor before optimization.The results of electromagnetic polarization perpendicular to the vehicle speed sensor are optimized. By comparison, it is not difficult to find that the optimized RF radiation energy is greatly reduced, completely lower than the standard limit of radiation harassment, and meet the requirements of electromagnetic compatibility design of electronic products.Figure8 shows the test results of electromagnetic radiation vertical polarization of the optimized vehicle speed sensor. By comparing with Figure 7, it is not difficult to find that the optimized RF radiation energy is greatly weakened from 60 to 20, which is completely lower than the standard limit of radiation disturbance and meets the requirements of EMC design of electronic products [26].Figure 8
Vertical polarization test curve of optimized vehicle speed sensor.The electromagnetic compatibility of speed sensor is very important because it is a common monitoring and control component in transportation vehicles. The electromagnetic compatibility design scheme of a velocity sensor is proposed, and the optimal solution of structure design is sought by simulated annealing algorithm, which provides the theoretical basis for the scheme. The E field and H field distribution of the sensor before and after the improvement are simulated and compared. The improved sensor will be sent to the entry-exit Inspection and Quarantine Bureau for GB/T17626.3 (IEC61000-4-3) standard electromagnetic compatibility test. Experimental results show that the electromagnetic compatibility design can significantly reduce the intensity of radiation disturbance, indicating that the proposed scheme is correct, effective, and satisfactory.


## 5. Conclusion

With the development of high-speed railway in China, EMC has been paid more and more attention. Aiming at the large number of interference sources and disturbed equipment, different electromagnetic characteristics, and complex coupling ways of high-speed railway vehicle system, a chaotic neural network EMC optimization design algorithm is proposed. As an important branch of sensor, speed sensor is widely used in automobiles, trains, ships, and other vehicles and is one of the important control components to ensure the stable operation of the whole machine. In recent years, with the development of speed sensors towards networking, intelligence, and miniaturization, higher requirements are put forward for their ability to resist electromagnetic disturbance. Therefore, it is of long-term and important practical significance to study the electromagnetic compatibility optimization design of new speed sensors.Electromagnetic compatibility has become the key problem of electric vehicle technology. In view of this problem, researchers at home and abroad have done a lot of research and obtained a lot of valuable research results, forming a relatively perfect research system of automotive electromagnetic compatibility. In addition, the establishment of electromagnetic compatibility professional testing and certification institutions, as well as the development of professional electromagnetic simulation software, provides convenience for electromagnetic compatibility research, reduces the cost of electric vehicles in the development stage, and can more effectively promote the research and development of electric vehicles.



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