Power Battery Electromagnetic Exposure and Human Physiological Signal Measuring Device and Analysis Method

Abstract

The present invention discloses a device and an analysis method for measuring the electromagnetic exposure of power batteries and human physiological signals, which relate to the field of power battery analysis and calculation, including obtaining power battery parameter information, and based on the power battery parameter information and electromagnetic field monitoring, obtaining startup electromagnetic field information. The present invention accurately evaluates the difference between the calculated value and the actual value of the electromagnetic field intensity through the electromagnetic field difference coefficient, analyzes the change correlation of the velocity electromagnetic field in the uniform motion state through the velocity electromagnetic field correlation coefficient, accurately analyzes the influence relationship between the electromagnetic exposure of the power battery and the human physiological signals when the electric vehicle is in the startup state and uniform driving, takes the change condition of the electromagnetic field during uniform driving as a reference, adjusts the change of the power battery state, accurately analyzes the influence of the change of the power battery state on the human physiological signals, and provides a reference basis for the safety standards and policy formulation of the electric vehicle industry.

Description

Technical Field

[0001] The present invention relates to the technical field of power battery analysis, and specifically to a device and analysis method for measuring the electromagnetic exposure of a power battery and human physiological signals. Background Art

[0002] With the popularization of electric vehicles, electromagnetic exposure has become the focus of people's attention. Especially during long driving or riding, the electromagnetic fields generated by the battery may have potential impacts on the physiological and mental health of drivers and passengers. In international research, a number of studies have shown that there are significant differences in the electromagnetic radiation of electric vehicles under different working conditions. For example, Moussa et al. (2019) found that when electric vehicles are accelerating and charging, the intensity of their electromagnetic fields can be higher than that of traditional fuel vehicles, and the impact on the surrounding environment is more significant. He et al. (2021) emphasized the potential risks of electromagnetic radiation to the health of drivers and passengers, including psychological and emotional impacts, through long-term monitoring of the electromagnetic fields of electric vehicles. In addition, García et al. (2020) pointed out that long-term electromagnetic exposure may lead to emotional problems such as anxiety and depression, but the research on the specific relationship between electric vehicle electromagnetic exposure and emotional health is still limited. Domestic research started relatively late, but there have been some progress in the characteristics and safety of electromagnetic radiation of electric vehicles. Zhang et al. (2020) analyzed the electromagnetic field characteristics of electric vehicles during driving and emphasized the impact of electromagnetic radiation on driving safety. In recent years, the research on the electromagnetic exposure of electric vehicles and human health has gradually increased, but mainly focuses on the measurement of electromagnetic field intensity and the evaluation of environmental impacts, lacking systematic analysis of emotions and mental health. Although some studies have explored the impact of electromagnetic exposure on health, especially the physiological responses (such as cell damage, changes in immune responses, etc.) found in animal experiments, there is no in-depth empirical research on the specific impact of electric vehicle electromagnetic exposure on the emotions and mental health of drivers and passengers.

[0003] Currently, in the analysis of the impact of power battery electromagnetic exposure on human physiological signals, it is impossible to accurately analyze the electromagnetic fields of power batteries under different motion states. In the uniform motion state, the change state of the electromagnetic field of the power battery can be accurately analyzed through a linear regression model. However, when the power battery is starting, accelerating, and charging, the change of the electromagnetic field is not linear, resulting in a decrease in the accuracy of the analysis results. When changing the state of the power battery, it is impossible to ensure that the change of the electromagnetic field remains unchanged while only changing the state of the power battery, resulting in too many variables in the test data and failing to achieve the effect of controlling variables, making the analysis results lack reliability. Summary of the Invention

[0004] To solve the above technical problems, a measurement device and analysis method for power battery electromagnetic exposure and human physiological signals are provided. The present technical solution solves the problem in the above-mentioned background technology that it is impossible to accurately analyze the electromagnetic field of a power battery under different motion states. In the uniform motion state, the change state of the electromagnetic field of the power battery can be accurately analyzed through a linear regression model. However, when the power battery is starting, accelerating, and charging, the change of the electromagnetic field does not show linearity, resulting in a decrease in the accuracy of the analysis results. When changing the state of the power battery, it is impossible to ensure that the change situation of the electromagnetic field remains unchanged while only changing the state of the power battery, resulting in too many variables in the test data and failing to achieve the effect of controlling variables, making the analysis results lack reliability.

[0005] To achieve the above objectives, the technical solution adopted in the present invention is as follows:

[0006] A measurement and analysis method for power battery electromagnetic exposure and human physiological signals, including:

[0007] Obtain power battery parameter information;

[0008] Based on the power battery parameter information and electromagnetic field monitoring, obtain startup electromagnetic field information, where the startup electromagnetic field information represents the electromagnetic field data when the power battery starts;

[0009] Based on the analysis of human daily electromagnetic exposure, obtain basic electromagnetic field information, where the basic electromagnetic field information represents the average electromagnetic field information in human daily life;

[0010] Based on the basic electromagnetic field information and power battery parameter information, obtain an electromagnetic field difference coefficient through electromagnetic monitoring;

[0011] Based on the working state of the power battery, drive the electric vehicle in different states, monitor the human physiological signals of the driver, and obtain human physiological signal measurement data. The electric vehicle states include startup state, uniform driving state, accelerating driving state, and charging state. The human physiological signal measurement data includes heart rate data, heart rate variability data, blood pressure data, and skin conductance response data;

[0012] Based on the human physiological signal measurement data, analyze the influence relationship between power battery electromagnetic exposure and human physiological signals when the electric vehicle is in the startup state and the uniform driving state of the electric vehicle, and obtain the first influence information on human physiological electromagnetic exposure;

[0013] Based on the first influence information on human physiological electromagnetic exposure, analyze the influence relationship between power battery electromagnetic exposure and human physiological signals when the electric vehicle is in the accelerating driving state and the charging state of the electric vehicle, and obtain the second influence information on human physiological electromagnetic exposure;

[0014] Obtain the human physiological electromagnetic exposure impact information based on the first impact information of human physiological electromagnetic exposure and the second impact information of human physiological electromagnetic exposure.

[0015] Preferably, the obtaining of the electromagnetic field difference coefficient based on the basic electromagnetic field information and the power battery parameter information through electromagnetic monitoring specifically includes:

[0016] Turn off the wireless communication function of the electric vehicle and output it in the standard mode of the power battery;

[0017] Monitor the electromagnetic field and the power battery status of the electric vehicle traveling at a constant speed at different speeds according to the power battery parameter information, and obtain the electromagnetic field monitoring data of the constant-speed test and the power battery test monitoring data;

[0018] Based on the electromagnetic field monitoring data of the constant-speed test, obtain the speed-electromagnetic field correlation coefficient through a linear regression equation, and the speed-electromagnetic field correlation coefficient represents the proportional relationship between the speed and the electromagnetic field intensity when the electric vehicle travels at a constant speed;

[0019] Obtain the power battery status difference index according to the power battery test monitoring data;

[0020] Obtain the difference speed information according to the power battery status difference index;

[0021] Among them, if the power battery status difference index , then the speed V at this time is the normal speed, if , then the speed V at this time is the difference speed;

[0022] Take the minimum value of the speed in the difference speed information as the reference speed of the power battery;

[0023] Obtain the power battery test characteristic speed according to the power battery reference speed, the speed-electromagnetic field correlation coefficient and the basic electromagnetic field information;

[0024] Obtain the electromagnetic field difference coefficient according to the power battery test characteristic speed;

[0025] The calculation formula of the power battery status difference index is:

[0026]

[0027] In the formula, represents the power battery status difference index when the speed is V, represents the theoretical value of the i-th power battery status monitoring index obtained from the speed V, represents the monitored value of the i-th power battery status monitoring index, and n is the total number of types of power battery status monitoring indexes;

[0028] The power battery test characteristic speed is:

[0029]

[0030] Wherein, is the characteristic speed of the power battery test, is the basic electromagnetic field intensity, is the correlation coefficient between the speed and the electromagnetic field, is the reference speed of the power battery, is a constant, represents the minimum value of.

[0031] Preferably, obtaining the electromagnetic field difference coefficient according to the characteristic speed of the power battery test specifically includes:

[0032] Drive the electric vehicle at a constant speed at the characteristic speed of the power battery test, monitor the electromagnetic field at the driver's seat of the electric vehicle and the state of the electric vehicle respectively, and obtain the first test data of the electromagnetic field and the first test data of the electric vehicle;

[0033] According to the first test data of the electromagnetic field and the characteristic speed of the power battery test, judge whether the first test data of the electromagnetic field is abnormal. If so, the first test data of the electromagnetic field is unavailable, and the electromagnetic field at the driver's seat of the electric vehicle is monitored again. If not, the first test data of the electromagnetic field is available;

[0034] Wherein, if or , the first test data of the electromagnetic field is unavailable. If , the first test data of the electromagnetic field is available. is the first test data of the electromagnetic field;

[0035] Obtain the first test data of the power battery and the first test data of the motor according to the first test data of the electric vehicle;

[0036] Based on the first test data of the power battery, the first test data of the motor and the first test data of the electromagnetic field, obtain the electromagnetic field difference coefficient;

[0037] The calculation formula of the electromagnetic field difference coefficient is:

[0038]

[0039] Wherein, K is the electromagnetic field difference coefficient, is the vacuum permeability, and , is the motor current, is the number of turns of the motor winding, is the power of the power battery, is the voltage of the power battery, is the radial distance between the driver's seat of the electric vehicle and the power battery, is the radial distance between the driver's seat of the electric vehicle and the motor.

[0040] Preferably, analyzing the influence relationship between the electromagnetic exposure of the power battery and the human physiological signals when the electric vehicle is in the starting state and the electric vehicle is traveling at a constant speed, and obtaining the first influence information of human physiological electromagnetic exposure, specifically including:

[0041] Obtaining the information of the human's normal walking speed;

[0042] Driving the battery vehicle at a constant speed at the human's normal walking speed, monitoring the human physiological signals of the volunteers who are located in the co-driver's seat before and after the battery vehicle starts, and obtaining the first start-up data;

[0043] Driving the battery vehicle at a constant speed at the human's normal walking speed, monitoring the human physiological signals of the volunteers who join the co-driver's seat during the battery vehicle's constant-speed driving after starting, and obtaining the second start-up data;

[0044] Based on the first start-up data, obtaining the human physiological signal monitoring data within one minute after the battery vehicle starts until it travels at a constant speed, as the start-up influence data;

[0045] Based on the analysis of human physiological signals, setting corresponding weights for each kind of human physiological signal measurement data;

[0046] According to the start-up influence data and the second start-up data, and based on the set weights, obtaining the start-up influence index;

[0047] According to the human physiological signal measurement data, obtaining the physiological signal constant-speed measurement data, where the physiological signal constant-speed measurement data represents the measurement data of the driver's human physiological information when the battery vehicle travels at different constant speeds;

[0048] According to the start-up influence index and the physiological signal constant-speed measurement data, obtaining the first influence information of human physiological electromagnetic exposure;

[0049] Among them, the calculation formula of the start-up influence index is:

[0050]

[0051] In the formula, is the start-up influence index, represents the weight of the s-th kind of human physiological signal, represents the maximum value of the s-th kind of human physiological signal in the start-up influence data, represents the average value of the s-th kind of human physiological signal in the second start-up data.

[0052] Preferably, obtaining the first influence information on human physiological electromagnetic exposure according to the start influence index and the uniformly measured data of physiological signals specifically includes:

[0053] Obtain the maximum driving speed of the electric vehicle;

[0054] Based on the maximum driving speed of the electric vehicle, obtain the uniformly tested speed information, where the uniformly tested speed information represents the set of uniformly driving speeds of the electric vehicle, and the speed interval of the uniformly tested speed is 5 m / s;

[0055] Based on the uniformly tested speed information, make the electric vehicle drive uniformly at different speeds, monitor the driver's human physiological signals, and obtain the uniformly measured data of physiological signals;

[0056] According to the start influence index and the uniformly measured data of physiological signals, obtain the abnormal index of human physiological signals;

[0057] Based on the health analysis of human physiological signals, obtain the threshold of the abnormal index of human physiological signals;

[0058] According to the abnormal index of human physiological signals and the threshold of the abnormal index of human physiological signals, judge whether the human physiological signals are abnormal. If the abnormal index of human physiological signals exceeds the threshold of the abnormal index of human physiological signals, mark the measured data of the human physiological signals to obtain the abnormal data of human physiological signals;

[0059] According to the start influence index and the abnormal data of human physiological signals, obtain the first influence information on human physiological electromagnetic exposure, where the first influence information on human physiological electromagnetic exposure represents the influence relationship between the electromagnetic exposure of the power battery during the start state and uniform driving of the electric vehicle and human physiological signals;

[0060] Among them, the calculation formula of the abnormal index of human physiological signals is:

[0061]

[0062] In the formula, represents the abnormal index of human physiological signals when the uniform driving speed is V, represents the maximum value of the s-th human physiological signal in the uniformly measured data of physiological signals when the uniform driving speed is V, is the standard value of the s-th human physiological signal.

[0063] Preferably, based on the first influence information on human physiological electromagnetic exposure, analyze the influence relationship between the electromagnetic exposure of the power battery during the acceleration state and charging state of the electric vehicle and human physiological signals, and obtain the second influence information on human physiological electromagnetic exposure, specifically including:

[0064] Obtain abnormal data of human physiological signals based on the first impact information of human physiological electromagnetic exposure;

[0065] Take the minimum value of the battery car speed in the abnormal data of human physiological signals as the calibrated speed of the electric vehicle;

[0066] Obtain the information of the constant-speed test speed;

[0067] Based on the information of the constant-speed test speed, obtain the constant-speed test electromagnetic field information, where the constant-speed test electromagnetic field information represents the electromagnetic field information corresponding to the constant driving speed of the electric vehicle;

[0068] Obtain the state data of the accelerating test electric vehicle according to the constant-speed test electromagnetic field information and the electromagnetic field difference coefficient;

[0069] Based on the state data of the accelerating test electric vehicle, make the electric vehicle accelerate at different accelerations until the speed of the electric vehicle reaches the calibrated speed of the electric vehicle, monitor the driver's human physiological signals, and obtain the physiological signal acceleration measurement data;

[0070] Obtain the human physiological signal acceleration abnormal index according to the starting impact index and the physiological signal acceleration measurement data;

[0071] Obtain the threshold value of the human physiological signal acceleration abnormal index based on the health analysis of the human physiological signals;

[0072] Judge whether the human physiological signals are abnormal according to the human physiological signal acceleration abnormal index and the threshold value of the human physiological signal acceleration abnormal index. If the human physiological signal acceleration abnormal index exceeds the threshold value of the human physiological signal acceleration abnormal index, mark the physiological signal acceleration measurement data to obtain the human physiological signal acceleration abnormal data;

[0073] Obtain the charging current information according to the constant-speed test electromagnetic field information;

[0074] Based on the charging current information, make the electric vehicle charge with the charging current, monitor the driver's human physiological signals, and obtain the physiological signal charging measurement data;

[0075] Obtain the human physiological signal charging abnormal index according to the physiological signal charging measurement data;

[0076] Judge whether the human physiological signals are abnormal according to the human physiological signal charging abnormal index. If so, mark the physiological signal charging measurement data to obtain the human physiological signal charging abnormal data;

[0077] According to the human physiological signals, accelerate the abnormal data and the abnormal data of physiological signal charging, and obtain the second influence information of human physiological electromagnetic exposure. The second influence information of human physiological electromagnetic exposure represents the influence relationship between the electromagnetic exposure of the power battery and the human physiological signals when the electric vehicle is accelerating and charging;

[0078] Among them, the state data of the accelerating test electric vehicle Specifically:

[0079]

[0080] In the formula, is the power of the power battery, is the voltage of the power battery, is the current of the motor, is the vacuum permeability, is the number of turns of the motor winding, K is the electromagnetic field difference coefficient, is the speed electromagnetic field correlation coefficient, V is the constant driving speed, is the current conversion coefficient;

[0081] The charging current is:

[0082]

[0083] In the formula, is the charging current.

[0084] Furthermore, a measuring device for the electromagnetic exposure of the power battery and the human physiological signals is proposed, which is used to implement the above analysis method, including:

[0085] The main control module is used to obtain differential speed information according to the power battery state difference index, determine whether the first electromagnetic field test data is abnormal according to the first electromagnetic field test data and the power battery test characteristic speed, determine whether the human physiological signal is abnormal according to the human physiological signal abnormality index and the human physiological signal abnormality index threshold, determine whether the human physiological signal is abnormal according to the human physiological signal acceleration abnormality index and the human physiological signal acceleration abnormality index threshold, determine whether the human physiological signal is abnormal according to the human physiological signal charging abnormality index, obtain the power battery test characteristic speed according to the power battery reference speed, the speed electromagnetic field correlation coefficient and the basic electromagnetic field information, obtain the human physiological signal monitoring data of the battery vehicle from startup to one minute of uniform driving as startup influence data based on the first startup data, set corresponding weights for each human physiological signal measurement data based on human physiological signal analysis, obtain the first human physiological electromagnetic exposure influence information according to the startup influence index and the human physiological signal abnormality data, and obtain the second human physiological electromagnetic exposure influence information according to the human physiological signal acceleration abnormality data and the physiological signal charging abnormality data;

[0086] The information acquisition module is used to acquire power battery parameter information, obtain startup electromagnetic field information based on electromagnetic field monitoring according to the power battery parameter information, obtain basic electromagnetic field information based on human daily electromagnetic exposure analysis, drive the electric vehicle in different states according to the power battery working state, monitor the human physiological signals of the driver, and obtain human physiological signal measurement data;

[0087] The evaluation module is used to obtain the speed electromagnetic field correlation coefficient based on the linear regression equation according to the uniform test electromagnetic field monitoring data, obtain the power battery state difference index according to the power battery test monitoring data, obtain the electromagnetic field difference coefficient according to the power battery test characteristic speed, obtain the startup influence index based on the set weights according to the startup influence data and the second startup data, obtain the human physiological signal abnormality index according to the startup influence index and the physiological signal uniform measurement data, obtain the human physiological signal acceleration abnormality index according to the startup influence index and the physiological signal acceleration measurement data, and obtain the human physiological signal charging abnormality index according to the physiological signal charging measurement data;

[0088] The display module interacts with the main control module and is used to output and display the startup electromagnetic field information, the electromagnetic field difference coefficient, the human physiological signal measurement data and the human physiological electromagnetic exposure influence information.

[0089] Optionally, the main control module specifically includes:

[0090] A control unit, which is used to obtain the test characteristic speed of the power battery according to the reference speed of the power battery, the speed-electromagnetic field correlation coefficient, and the basic electromagnetic field information, obtain the human physiological signal monitoring data within one minute after the battery vehicle starts until it travels at a constant speed based on the first startup data as the startup impact data, set corresponding weights for each human physiological signal measurement data based on the analysis of human physiological signals, obtain the first impact information of human physiological electromagnetic exposure according to the startup impact index and the abnormal data of human physiological signals, and obtain the second impact information of human physiological electromagnetic exposure according to the abnormal data of human physiological signal acceleration and the abnormal data of physiological signal charging;

[0091] An information receiving unit, which interacts with the information acquisition module and the evaluation module, and is used to receive data and transmit it to the judgment unit;

[0092] A judgment unit, which is used to obtain the differential speed information according to the power battery state difference index, judge whether the first electromagnetic field test data is abnormal according to the first electromagnetic field test data and the test characteristic speed of the power battery, judge whether the human physiological signal is abnormal according to the human physiological signal abnormal index and the human physiological signal abnormal index threshold, judge whether the human physiological signal is abnormal according to the human physiological signal acceleration abnormal index and the human physiological signal acceleration abnormal index threshold, and judge whether the human physiological signal is abnormal according to the human physiological signal charging abnormal index.

[0093] Optionally, the information acquisition module specifically includes:

[0094] A first acquisition unit, which is used to acquire the power battery parameter information and obtain the startup electromagnetic field information based on the electromagnetic field monitoring according to the power battery parameter information;

[0095] A second acquisition unit, which is used to obtain the basic electromagnetic field information based on the analysis of human daily electromagnetic exposure, monitor the human physiological signals of the driver by driving the electric vehicle in different states based on the working state of the power battery, and obtain the human physiological signal measurement data.

[0096] Optionally, the evaluation module specifically includes:

[0097] A first evaluation unit, which is used to obtain the speed-electromagnetic field correlation coefficient based on the linear regression equation according to the electromagnetic field monitoring data of the constant-speed test, obtain the power battery state difference index according to the power battery test monitoring data, and obtain the electromagnetic field difference coefficient according to the test characteristic speed of the power battery;

[0098] The second evaluation unit is configured to obtain a start - up impact index based on start - up impact data and secondary start - up data according to set weights, obtain an abnormal index of human physiological signals based on the start - up impact index and physiological signal uniform - speed measurement data, obtain an abnormal acceleration index of human physiological signals based on the start - up impact index and physiological signal acceleration measurement data, and obtain an abnormal charging index of human physiological signals based on physiological signal charging measurement data.

[0099] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0100] The present invention provides a power battery electromagnetic exposure and human physiological signal measurement device and analysis method. By means of the electromagnetic field difference coefficient, the difference between the calculated value and the actual value of the electromagnetic field intensity is accurately evaluated. By means of the velocity - electromagnetic field correlation coefficient, the change correlation of the velocity - electromagnetic field under the uniform - speed state is analyzed. By accurately analyzing the influence relationship between the power battery electromagnetic exposure and human physiological signals during the start - up state and uniform - speed driving of an electric vehicle, based on the electromagnetic field change condition during uniform - speed driving, the state change of the power battery is adjusted, and the influence of the power battery state change on human physiological signals is accurately analyzed, providing a reference basis for the safety standards and policy formulation in the electric vehicle industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0101] Figure 1 It is a flowchart of the power battery electromagnetic exposure and human physiological signal measurement and analysis method proposed by the present invention;

[0102] Figure 2 It is a flowchart for obtaining the electromagnetic field difference coefficient in the present invention;

[0103] Figure 3 It is a flowchart for obtaining physiological signal uniform - speed measurement data in the present invention;

[0104] Figure 4 It is a flowchart for obtaining the first influence information of human physiological electromagnetic exposure in the present invention;

[0105] Figure 5 It is a flowchart for obtaining the second influence information of human physiological electromagnetic exposure in the present invention;

[0106] Figure 6 It is a structural block diagram of the power battery electromagnetic exposure and human physiological signal measurement device proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0107] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art can think of other obvious variations.

[0108] Refer to Figure 1 -Figure 5 As shown in Figure 5 , the method for measuring and analyzing the electromagnetic exposure of a power battery and human physiological signals in an embodiment of the present invention includes:

[0109] Obtain power battery parameter information;

[0110] Based on the power battery parameter information and electromagnetic field monitoring, obtain startup electromagnetic field information, where the startup electromagnetic field information represents the electromagnetic field data when the power battery starts;

[0111] Based on the analysis of the electromagnetic exposure in human daily life, obtain basic electromagnetic field information, where the basic electromagnetic field information represents the average electromagnetic field information in human daily life;

[0112] Based on the basic electromagnetic field information and the power battery parameter information, obtain an electromagnetic field difference coefficient through electromagnetic monitoring;

[0113] Specifically, based on the basic electromagnetic field information and the power battery parameter information, obtaining an electromagnetic field difference coefficient through electromagnetic monitoring specifically includes:

[0114] Turn off the wireless communication function of the electric vehicle and output it in the standard mode of the power battery;

[0115] According to the power battery parameter information, monitor the electromagnetic field and the power battery status of the electric vehicle when driving at different constant speeds respectively, and obtain the electromagnetic field monitoring data for constant speed tests and the power battery test monitoring data;

[0116] Based on the electromagnetic field monitoring data for constant speed tests and using a linear regression equation, obtain a speed-electromagnetic field correlation coefficient, where the speed-electromagnetic field correlation coefficient represents the proportional relationship between the speed and the electromagnetic field intensity when the electric vehicle is driving at a constant speed;

[0117] According to the power battery test monitoring data, obtain a power battery status difference index;

[0118] According to the power battery status difference index, obtain difference speed information;

[0119] Among them, if the power battery status difference index , then the speed V at this time is the normal speed. If , then the speed V at this time is the difference speed;

[0120] Take the minimum value of the speed in the difference speed information as the reference speed of the power battery;

[0121] According to the reference speed of the power battery, the speed-electromagnetic field correlation coefficient, and the basic electromagnetic field information, obtain the test characteristic speed of the power battery;

[0122] According to the test characteristic speed of the power battery, obtain the electromagnetic field difference coefficient;

[0123] The calculation formula of the state difference index of the power battery is as follows:

[0124]

[0125] In the formula, represents the state difference index of the power battery when the speed is V, represents the theoretical value of the i-th power battery state monitoring index obtained from the speed V, represents the monitored value of the i-th power battery state monitoring index, and n is the total number of types of power battery state monitoring indexes;

[0126] The test characteristic speed of the power battery is:

[0127]

[0128] In the formula, is the test characteristic speed of the power battery, is the basic electromagnetic field intensity, is the speed-electromagnetic field correlation coefficient, is the reference speed of the power battery, is a constant, represents the minimum value of

[0129] In this solution, by uniformly testing the electromagnetic field monitoring data and based on the linear regression equation, the speed-electromagnetic field correlation coefficient is obtained. Through the speed-electromagnetic field correlation coefficient, the proportional relationship between the speed and the electromagnetic field intensity when the electric vehicle is traveling at a constant speed is accurately analyzed. Through the state difference index of the power battery, the differential speed is selected. It can be understood that as the speed increases, the energy loss of the electric vehicle also increases, that is, the difference between the output of the power battery and the actual application also increases. By taking the minimum value of the speed in the differential speed information as the reference speed of the power battery, the accuracy of the data is improved. Through the reference speed of the power battery, the speed-electromagnetic field correlation coefficient, and the basic electromagnetic field information, the test characteristic speed of the power battery is obtained. Through the test characteristic speed of the power battery, the electromagnetic field difference condition is accurately analyzed.

[0130] Specifically, according to the test characteristic speed of the power battery, the electromagnetic field difference coefficient is obtained, which specifically includes:

[0131] The electric vehicle is driven at a constant speed at the test characteristic speed of the power battery, and the electromagnetic field at the driver's seat of the electric vehicle and the state of the electric vehicle are monitored respectively to obtain the first electromagnetic field test data and the first electric vehicle test data;

[0132] Based on the first test data of the electromagnetic field and the characteristic speed of the power battery test, determine whether the first test data of the electromagnetic field is abnormal. If so, the first test data of the electromagnetic field is unavailable, and the electromagnetic field of the driver's seat of the electric vehicle is monitored again. If not, the first test data of the electromagnetic field is available;

[0133] Among them, if or , then the first test data of the electromagnetic field is unavailable. If , then the first test data of the electromagnetic field is available. is the first test data of the electromagnetic field;

[0134] According to the first test data of the electric vehicle, obtain the first test data of the power battery and the first test data of the motor;

[0135] Based on the first test data of the power battery, the first test data of the motor, and the first test data of the electromagnetic field, obtain the electromagnetic field difference coefficient;

[0136] The calculation formula of the electromagnetic field difference coefficient is:

[0137]

[0138] In the formula, K is the electromagnetic field difference coefficient, is the vacuum permeability, and , is the motor current, is the number of turns of the motor winding, is the power of the power battery, is the voltage of the power battery, is the radial distance between the driver's seat of the electric vehicle and the power battery, is the radial distance between the driver's seat of the electric vehicle and the motor.

[0139] In this solution, by using the first test data of the electromagnetic field and the characteristic speed of the power battery test, it is determined whether the first test data of the electromagnetic field is abnormal, avoiding errors in the analysis results caused by failures of electromagnetic field monitoring equipment. By comparing the calculated value and the actual value of the electromagnetic field intensity, the electromagnetic field difference coefficient is obtained, which is convenient for analyzing the exposed state of the battery later.

[0140] Based on the working state of the power battery, the electric vehicle is driven in different states, and the physiological signals of the driver are monitored to obtain the physiological signal measurement data. The states of the electric vehicle include the starting state, the constant-speed driving state, the accelerating driving state, and the charging state. The physiological signal measurement data includes heart rate data, heart rate variability data, blood pressure data, and skin conductance response data;

[0141] Analyze the relationship between the electromagnetic exposure of the power battery and the human physiological signals during the start-up state of the electric vehicle and the uniform driving of the electric vehicle according to the human physiological signal measurement data, and obtain the first influence information on human physiological electromagnetic exposure;

[0142] Specifically, analyze the relationship between the electromagnetic exposure of the power battery and the human physiological signals during the start-up state of the electric vehicle and the uniform driving of the electric vehicle, and obtain the first influence information on human physiological electromagnetic exposure, which specifically includes:

[0143] Obtain the information on the normal walking speed of the human body;

[0144] Drive the battery vehicle at a uniform speed at the normal walking speed of the human body, monitor the human physiological signals of the volunteers who are in the co-pilot before and after the start of the battery vehicle, and obtain the first start-up data;

[0145] Drive the battery vehicle at a uniform speed at the normal walking speed of the human body, monitor the human physiological signals of the volunteers who join the co-pilot midway during the uniform driving after the start of the battery vehicle, and obtain the second start-up data;

[0146] Based on the first start-up data, obtain the human physiological signal monitoring data within one minute after the start of the battery vehicle until it reaches a uniform speed as the start-up influence data;

[0147] Based on the analysis of human physiological signals, set corresponding weights for each human physiological signal measurement data;

[0148] According to the start-up influence data and the second start-up data, based on the set weights, obtain the start-up influence index;

[0149] According to the human physiological signal measurement data, obtain the physiological signal uniform measurement data, and the physiological signal uniform measurement data represents the human physiological information measurement data of the driver when the battery vehicle is driving at different uniform speeds;

[0150] According to the start-up influence index and the physiological signal uniform measurement data, obtain the first influence information on human physiological electromagnetic exposure;

[0151] Among them, the calculation formula of the start-up influence index is:

[0152]

[0153] In the formula, is the start-up influence index, represents the weight of the s-th human physiological signal, represents the maximum value of the s-th human physiological signal in the start-up influence data, represents the average value of the s-th human physiological signal in the second start-up data.

[0154] In this solution, by obtaining the human physiological signal monitoring data from when the battery-powered vehicle starts until it travels at a constant speed for one minute as the start-up impact data, based on the start-up impact data and the secondary start-up data, and according to the set weights, the start-up impact index is obtained. Through the start-up impact index, the human physiological signals in two cases are compared: when the volunteer is in the co-pilot seat before and after the battery-powered vehicle starts and when the volunteer joins the co-pilot seat during the constant-speed driving after the battery-powered vehicle starts, ensuring variable control during the experiment and accurately analyzing the impact of the power battery start-up on human physiological signals.

[0155] Specifically, according to the start-up impact index and the physiological signal constant-speed measurement data, the first impact information on human physiological electromagnetic exposure is obtained, specifically including:

[0156] Obtain the maximum driving speed of the electric vehicle;

[0157] Based on the maximum driving speed of the electric vehicle, obtain the constant-speed test speed information, where the constant-speed test speed information represents the set of constant-speed driving speeds of the electric vehicle, and the speed interval of the constant-speed test speed is 5 m / s;

[0158] Based on the constant-speed test speed information, make the electric vehicle travel at different constant speeds, monitor the driver's human physiological signals, and obtain the physiological signal constant-speed measurement data;

[0159] According to the start-up impact index and the physiological signal constant-speed measurement data, obtain the human physiological signal anomaly index;

[0160] Based on the health analysis of human physiological signals, obtain the threshold of the human physiological signal anomaly index;

[0161] According to the human physiological signal anomaly index and the threshold of the human physiological signal anomaly index, determine whether the human physiological signal is abnormal. If the human physiological signal anomaly index exceeds the threshold of the human physiological signal anomaly index, mark the measurement data of this human physiological signal to obtain the human physiological signal abnormal data;

[0162] According to the start-up impact index and the human physiological signal abnormal data, obtain the first impact information on human physiological electromagnetic exposure, where the first impact information on human physiological electromagnetic exposure represents the impact relationship between the electromagnetic exposure of the power battery during the start-up state and constant-speed driving of the electric vehicle and the human physiological signals;

[0163] Among them, the calculation formula of the human physiological signal anomaly index is:

[0164]

[0165] In the formula, represents the human physiological signal anomaly index when the constant-speed driving speed is V, Denote the maximum value of the s-th human physiological signal in the physiological signal uniform measurement data when the uniform driving speed is V, which is the standard value of the s-th human physiological signal.

[0166] In this solution, based on the maximum driving speed of the electric vehicle, obtain the uniform test speed information, drive the electric vehicle at different uniform speeds, monitor the driver's human physiological signals, obtain the physiological signal uniform measurement data, according to the start influence index and the physiological signal uniform measurement data, obtain the human physiological signal anomaly index, according to the human physiological signal anomaly index, judge whether the human physiological signal is abnormal, obtain the human physiological signal abnormal data, and through the start influence index and the human physiological signal abnormal data, obtain the first influence information of human physiological electromagnetic exposure;

[0167] It can be understood that when driving at a uniform speed, the greater the speed of the electric vehicle, the higher the output of the power battery, and correspondingly, the higher the electromagnetic field strength. Monitoring and evaluating the human physiological signals at a uniform test speed with a speed interval of 5 m / s ensures the stability and reliability of the data and improves the analysis efficiency.

[0168] Based on the first influence information of human physiological electromagnetic exposure, analyze the influence relationship between the electromagnetic exposure of the power battery and the human physiological signals when the electric vehicle is accelerating and when the electric vehicle is charging, and obtain the second influence information of human physiological electromagnetic exposure;

[0169] Specifically, based on the first influence information of human physiological electromagnetic exposure, analyze the influence relationship between the electromagnetic exposure of the power battery and the human physiological signals when the electric vehicle is accelerating and when the electric vehicle is charging, and obtain the second influence information of human physiological electromagnetic exposure, which specifically includes:

[0170] Based on the first influence information of human physiological electromagnetic exposure, obtain the human physiological signal abnormal data;

[0171] Take the minimum value of the battery vehicle speed in the human physiological signal abnormal data as the calibrated speed of the electric vehicle;

[0172] Obtain the uniform test speed information;

[0173] Based on the uniform test speed information, obtain the uniform test electromagnetic field information, and the uniform test electromagnetic field information represents the electromagnetic field information corresponding to the uniform driving speed of the electric vehicle;

[0174] According to the uniform test electromagnetic field information and the electromagnetic field difference coefficient, obtain the acceleration test electric vehicle state data;

[0175] Based on the state data of the electric vehicle during the acceleration test, the electric vehicle is accelerated at different accelerations until the speed of the electric vehicle reaches the calibrated speed of the electric vehicle, and the physiological signals of the driver are monitored to obtain the physiological signal acceleration measurement data;

[0176] According to the start influence index and the physiological signal acceleration measurement data, obtain the physiological signal acceleration anomaly index;

[0177] Based on the health analysis of the human physiological signals, obtain the threshold of the physiological signal acceleration anomaly index;

[0178] According to the physiological signal acceleration anomaly index and the threshold of the physiological signal acceleration anomaly index, judge whether the human physiological signal is abnormal. If the physiological signal acceleration anomaly index exceeds the threshold of the physiological signal acceleration anomaly index, mark the physiological signal acceleration measurement data to obtain the physiological signal acceleration anomaly data;

[0179] According to the electromagnetic field information during the constant-speed test, obtain the charging current information;

[0180] Based on the charging current information, the electric vehicle is charged with the charging current, and the physiological signals of the driver are monitored to obtain the physiological signal charging measurement data;

[0181] According to the physiological signal charging measurement data, obtain the physiological signal charging anomaly index;

[0182] According to the physiological signal charging anomaly index, judge whether the human physiological signal is abnormal. If so, mark the physiological signal charging measurement data to obtain the physiological signal charging anomaly data;

[0183] According to the physiological signal acceleration anomaly data and the physiological signal charging anomaly data, obtain the second influence information of human physiological electromagnetic exposure, and the second influence information of human physiological electromagnetic exposure represents the influence relationship between the electromagnetic exposure of the power battery and the human physiological signals when the electric vehicle is in the acceleration driving state and the charging state of the electric vehicle;

[0184] Among them, the state data of the electric vehicle during the acceleration test Specifically:

[0185]

[0186] In the formula, is the power of the power battery, is the voltage of the power battery, is the motor current, is the vacuum permeability, is the number of turns of the motor winding, K is the electromagnetic field difference coefficient, is the speed electromagnetic field correlation coefficient, V is the constant driving speed, is the current conversion coefficient;

[0187] The charging current is:

[0188]

[0189] In the formula, is the charging current.

[0190] In this solution, by taking the minimum value of the battery car speed in the abnormal data of the human physiological signal as the calibrated speed of the electric vehicle, obtaining the constant-speed test speed information, based on the constant-speed test speed information, obtaining the constant-speed test electromagnetic field information, according to the constant-speed test electromagnetic field information and the electromagnetic field difference coefficient, obtaining the state data of the accelerating test electric vehicle, and based on the state data of the accelerating test electric vehicle, making the electric vehicle accelerate at different accelerations until the speed of the electric vehicle reaches the calibrated speed of the electric vehicle, monitoring the driver's human physiological signal, and obtaining the physiological signal acceleration measurement data;

[0191] It can be understood that if only by setting the acceleration, analyzing the influence of the battery exposure during the acceleration of the electric vehicle on the human physiological signal, at this time, compared with the constant-speed driving, the change state of the electromagnetic field intensity and the change state of the power battery state do not match, so there are two variables, and it is impossible to accurately analyze the influence of the change of the power battery state on the human physiological signal;

[0192] In this embodiment, by taking the change condition of the electromagnetic field intensity during constant-speed driving as a reference, restricting the state of the power battery during accelerating driving to ensure that the change of the electromagnetic field intensity is consistent with that during constant-speed driving. It should be noted that if the state of the power battery is restricted through the electromagnetic field monitoring device, there are certain defects. One is the delay. When adjusting through the electromagnetic field monitoring device, there is a reaction time and a signal generation and transmission time in the middle, which affects the accuracy of the test data. The other is that during the acceleration state, the change of the electromagnetic field intensity is not a linear change. When restricting the state of the power battery through the electromagnetic field monitoring device, it is impossible to quickly and accurately adjust to the target effect.

[0193] In this embodiment, the human physiological signal acceleration abnormal index is specifically:

[0194]

[0195] In the formula, is the human physiological signal acceleration abnormal index, represents that the power of the power battery is P, the voltage of the power battery is , and the motor current is the maximum value of the s-th human physiological signal in the physiological signal constant-speed measurement data when;

[0196] The specific abnormal index of human physiological signal charging is as follows:

[0197]

[0198] In the formula, is the abnormal index of human physiological signal charging, represents the charging current of the power battery as the maximum value of the s-th human physiological signal in the uniformly measured physiological signal data when.

[0199] According to the first influence information of human physiological electromagnetic exposure and the second influence information of human physiological electromagnetic exposure, obtain the influence information of human physiological electromagnetic exposure.

[0200] Referring to Figure 6 shown, further, in combination with the above method for measuring and analyzing the electromagnetic exposure of power batteries and human physiological signals, a device for measuring the electromagnetic exposure of power batteries and human physiological signals is proposed, including:

[0201] The main control module is used to obtain the difference speed information according to the power battery state difference index, judge whether the first electromagnetic field test data is abnormal according to the first electromagnetic field test data and the test characteristic speed of the power battery, judge whether the human physiological signal is abnormal according to the human physiological signal abnormal index and the human physiological signal abnormal index threshold, judge whether the human physiological signal is abnormal according to the human physiological signal acceleration abnormal index and the human physiological signal acceleration abnormal index threshold, judge whether the human physiological signal is abnormal according to the human physiological signal charging abnormal index, obtain the test characteristic speed of the power battery according to the power battery reference speed, the speed electromagnetic field correlation coefficient and the basic electromagnetic field information, obtain the human physiological signal monitoring data within one minute after the battery vehicle starts to the battery vehicle travels at a constant speed based on the first start data as the start influence data, set corresponding weights for each human physiological signal measurement data based on the human physiological signal analysis, obtain the first influence information of human physiological electromagnetic exposure according to the start influence index and the human physiological signal abnormal data, and obtain the second influence information of human physiological electromagnetic exposure according to the human physiological signal acceleration abnormal data and the physiological signal charging abnormal data;

[0202] The information acquisition module is used to acquire the power battery parameter information, obtain the start electromagnetic field information based on the power battery parameter information and electromagnetic field monitoring, obtain the basic electromagnetic field information based on the analysis of human daily electromagnetic exposure, drive the electric vehicle to travel in different states based on the working state of the power battery, monitor the human physiological signals of the driver, and obtain the human physiological signal measurement data;

[0203] An evaluation module, which is used to obtain the velocity-electromagnetic field correlation coefficient based on the linear regression equation according to the electromagnetic field monitoring data of the uniform speed test, obtain the state difference index of the power battery according to the power battery test monitoring data, obtain the electromagnetic field difference coefficient according to the characteristic speed of the power battery test, obtain the starting influence index based on the starting influence data and the secondary starting data according to the set weight, obtain the abnormal index of the human physiological signal according to the starting influence index and the uniform measurement data of the physiological signal, obtain the abnormal index of the human physiological signal during acceleration according to the starting influence index and the acceleration measurement data of the physiological signal, and obtain the abnormal index of the human physiological signal during charging according to the charging measurement data of the physiological signal;

[0204] A display module, which interacts with the main control module and is used to output and display the starting electromagnetic field information, the electromagnetic field difference coefficient, the human physiological signal measurement data, and the human physiological electromagnetic exposure influence information.

[0205] The main control module specifically includes:

[0206] A control unit, which is used to obtain the characteristic speed of the power battery test according to the reference speed of the power battery, the velocity-electromagnetic field correlation coefficient, and the basic electromagnetic field information, obtain the human physiological signal monitoring data from the start of the battery vehicle to one minute after the battery vehicle travels at a uniform speed based on the primary starting data as the starting influence data, set corresponding weights for each human physiological signal measurement data based on the analysis of the human physiological signal, obtain the first influence information of the human physiological electromagnetic exposure according to the starting influence index and the abnormal data of the human physiological signal, and obtain the second influence information of the human physiological electromagnetic exposure according to the abnormal data of the human physiological signal during acceleration and the abnormal data of the human physiological signal during charging;

[0207] An information receiving unit, which interacts with the information acquisition module and the evaluation module and is used to receive data and transmit it to the judgment unit;

[0208] A judgment unit, which is used to obtain the difference speed information according to the state difference index of the power battery, judge whether the first electromagnetic field test data is abnormal according to the first electromagnetic field test data and the characteristic speed of the power battery test, judge whether the human physiological signal is abnormal according to the abnormal index of the human physiological signal and the threshold of the abnormal index of the human physiological signal, judge whether the human physiological signal is abnormal according to the abnormal index of the human physiological signal during acceleration and the threshold of the abnormal index of the human physiological signal during acceleration, and judge whether the human physiological signal is abnormal according to the abnormal index of the human physiological signal during charging.

[0209] The information acquisition module specifically includes:

[0210] A first acquisition unit, which is configured to acquire power battery parameter information and, based on the power battery parameter information and electromagnetic field monitoring, acquire startup electromagnetic field information;

[0211] A second acquisition unit, which is configured to acquire basic electromagnetic field information based on the analysis of the electromagnetic exposure in human daily life, drive the electric vehicle in different states based on the working state of the power battery, monitor the physiological signals of the driver, and acquire the measurement data of the human physiological signals.

[0212] An evaluation module, specifically including:

[0213] A first evaluation unit, which is configured to acquire the velocity-electromagnetic field correlation coefficient based on a linear regression equation according to the electromagnetic field monitoring data of the constant-speed test, acquire the power battery state difference index according to the power battery test monitoring data, and acquire the electromagnetic field difference coefficient according to the characteristic velocity of the power battery test;

[0214] A second evaluation unit, which is configured to acquire the startup impact index based on the set weight according to the startup impact data and the secondary startup data, acquire the human physiological signal abnormality index according to the startup impact index and the constant-speed measurement data of the physiological signals, acquire the human physiological signal acceleration abnormality index according to the startup impact index and the acceleration measurement data of the physiological signals, and acquire the human physiological signal charging abnormality index according to the charging measurement data of the physiological signals.

[0215] In summary, the advantages of the present invention are as follows: By using the basic electromagnetic field information and the power battery parameter information, and based on electromagnetic monitoring, the electromagnetic field difference coefficient is acquired. Through the electromagnetic field difference coefficient, the difference between the calculated value and the actual value of the electromagnetic field intensity is accurately evaluated. According to the electromagnetic field monitoring data of the constant-speed test and based on the linear regression equation, the velocity-electromagnetic field correlation coefficient is acquired to analyze the correlation between the velocity and the electromagnetic field change in the constant-speed state. Through the startup impact index and the constant-speed measurement data of the physiological signals, the human physiological signal abnormality index is acquired. Through the human physiological signal abnormality index, the influence relationship between the electromagnetic exposure of the power battery and the human physiological signals during the startup state and the constant-speed driving of the electric vehicle is accurately analyzed. By taking the electromagnetic field change condition during the constant-speed driving as a reference, the state change of the power battery is adjusted, and the influence of the power battery state change on the human physiological signals is accurately analyzed, filling the gap in the existing research, providing a reference basis for the safety standards and policy formulation of the electric vehicle industry, and ensuring people's health and safety.

[0216] The basic principles, main features and advantages of the present invention have been shown and described above. Those skilled in the art should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification is only the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. Method for measuring and analyzing electromagnetic exposure of power battery and human physiological signals, characterized in that Including: Obtain the parameter information of the power battery; Based on the parameter information of the power battery and electromagnetic field monitoring, obtain the starting electromagnetic field information, where the starting electromagnetic field information represents the electromagnetic field data when the power battery starts; Based on the analysis of the electromagnetic exposure in human daily life, obtain the basic electromagnetic field information, where the basic electromagnetic field information represents the average electromagnetic field information in human daily life; Based on the basic electromagnetic field information and the parameter information of the power battery, and based on electromagnetic monitoring, obtain the electromagnetic field difference coefficient; Based on the working state of the power battery, drive the electric vehicle in different states, monitor the physiological signals of the driver's body, and obtain the measured data of the physiological signals of the human body. The states of the electric vehicle include the starting state, the constant-speed driving state, the accelerating driving state, and the charging state. The measured data of the physiological signals of the human body include heart rate data, heart rate variability data, blood pressure data, and skin conductance response data; Based on the measured data of the physiological signals of the human body, analyze the influence relationship between the electromagnetic exposure of the power battery and the physiological signals of the human body when the electric vehicle is in the starting state and the constant-speed driving state of the electric vehicle, and obtain the first influence information on the electromagnetic exposure of the human body; Based on the first influence information on the electromagnetic exposure of the human body, analyze the influence relationship between the electromagnetic exposure of the power battery and the physiological signals of the human body when the electric vehicle is in the accelerating driving state and the charging state of the electric vehicle, and obtain the second influence information on the electromagnetic exposure of the human body; Based on the first influence information on the electromagnetic exposure of the human body and the second influence information on the electromagnetic exposure of the human body, obtain the influence information on the electromagnetic exposure of the human body.

2. The method for measuring and analyzing the electromagnetic exposure of a power battery and human physiological signals according to claim 1, characterized in that The step of obtaining the electromagnetic field difference coefficient based on the basic electromagnetic field information and the parameter information of the power battery and based on electromagnetic monitoring specifically includes: Turn off the wireless communication function of the electric vehicle and output it in the standard mode of the power battery; Based on the parameter information of the power battery, monitor the electromagnetic field and the state of the power battery when the electric vehicle is driving at a constant speed at different speeds respectively, and obtain the monitored data of the electromagnetic field during the constant-speed test and the monitored data of the power battery during the test; Based on the monitored data of the electromagnetic field during the constant-speed test and the linear regression equation, obtain the speed-electromagnetic field correlation coefficient, where the speed-electromagnetic field correlation coefficient represents the proportional relationship between the speed and the electromagnetic field intensity when the electric vehicle is driving at a constant speed; Based on the monitored data of the power battery during the test, obtain the power battery state difference index; Based on the power battery state difference index, obtain the difference speed information; Among them, if the power battery state difference index , then the speed V at this time is the normal speed. If , then the speed V at this time is the difference speed; Take the minimum speed in the difference speed information as the reference speed of the power battery; Based on the reference speed of the power battery, the speed-electromagnetic field correlation coefficient, and the basic electromagnetic field information, obtain the characteristic speed of the power battery during the test; Based on the characteristic speed of the power battery during the test, obtain the electromagnetic field difference coefficient; The calculation formula of the power battery state difference index is: In the formula, represents the state difference index of the power battery at speed V, represents the theoretical value of the i-th power battery state monitoring index obtained from speed V, represents the monitored value of the i-th power battery state monitoring index, and n is the total number of types of power battery state monitoring indexes; The characteristic speed of the power battery during the test is: In the formula, is the characteristic speed of the power battery test, is the basic electromagnetic field strength, is the correlation coefficient of the speed and the electromagnetic field, and is the reference speed of the power battery, is a constant, denotes the minimum value of.

3. The method for measuring and analyzing the electromagnetic exposure of a power battery and human physiological signals according to claim 2, wherein The step of obtaining the electromagnetic field difference coefficient based on the characteristic speed of the power battery during the test specifically includes: Drive the electric vehicle at a constant speed at the characteristic speed of the power battery during the test, monitor the electromagnetic field at the driver's seat of the electric vehicle and the state of the electric vehicle respectively, and obtain the first test data of the electromagnetic field and the first test data of the electric vehicle; Based on the first test data of the electromagnetic field and the characteristic speed of the power battery test of the electric vehicle, determine whether the first test data of the electromagnetic field is abnormal. If so, the first test data of the electromagnetic field is unavailable, and the electromagnetic field of the driver's seat of the electric vehicle is monitored again. If not, the first test data of the electromagnetic field is available; Among them, if or , the first test data of the electromagnetic field is unavailable. If , the first test data of the electromagnetic field is available. is the first test data of the electromagnetic field; Based on the first test data of the electric vehicle, obtain the first test data of the power battery and the first test data of the motor; Based on the first test data of the power battery, the first test data of the motor, and the first test data of the electromagnetic field, obtain the electromagnetic field difference coefficient; The calculation formula of the electromagnetic field difference coefficient is: Where K is the electromagnetic field difference coefficient, is the vacuum permeability, and , is the motor current, is the number of turns of the motor winding, is the power of the power battery, is the voltage of the power battery, is the radial distance between the driver's seat of the electric vehicle and the power battery, is the radial distance between the driver's seat of the electric vehicle and the motor.

4. The method for measuring and analyzing the electromagnetic exposure of a power battery and human physiological signals according to claim 3, wherein, Analyze the influence relationship between the electromagnetic exposure of the power battery and the human physiological signals when the electric vehicle is in the starting state and driving at a constant speed, and obtain the first influence information on human physiological electromagnetic exposure, specifically including: Obtain the information on the normal walking speed of the human body; Drive the battery vehicle at a constant speed at the normal walking speed of the human body, and monitor the human physiological signals of the volunteers who are in the co-pilot before and after the battery vehicle starts to obtain the first start data; Drive the battery vehicle at a constant speed at the normal walking speed of the human body, and monitor the human physiological signals of the volunteers who join the co-pilot during the constant speed driving after the battery vehicle starts to obtain the second start data; Based on the first start data, obtain the human physiological signal monitoring data within one minute after the battery vehicle starts until it drives at a constant speed as the start influence data; Based on the analysis of human physiological signals, set corresponding weights for each human physiological signal measurement data; According to the start influence data and the second start data, based on the set weights, obtain the start influence index; According to the human physiological signal measurement data, obtain the physiological signal constant speed measurement data, and the physiological signal constant speed measurement data represents the measurement data of the driver's human physiological information when the battery vehicle drives at different constant speeds; According to the start influence index and the physiological signal constant speed measurement data, obtain the first influence information on human physiological electromagnetic exposure; Among them, the calculation formula of the start influence index is: Wherein, is the startup influence index, represents the weight of the s-th human physiological signal, represents the maximum value of the s-th human physiological signal in the startup influence data, represents the average value of the s-th human physiological signal in the secondary startup data.

5. The method for measuring and analyzing the electromagnetic exposure of a power battery and human physiological signals according to claim 4, characterized in that, The obtaining of the first influence information on human physiological electromagnetic exposure according to the start influence index and the physiological signal constant speed measurement data specifically includes: Obtain the maximum driving speed of the electric vehicle; Based on the maximum driving speed of the electric vehicle, obtain the constant speed test speed information, and the constant speed test speed information represents the set of constant driving speeds of the electric vehicle, and the speed interval of the constant speed test speed is 5 m / s; Based on the constant speed test speed information, make the electric vehicle drive at different constant speeds, monitor the driver's human physiological signals, and obtain the physiological signal constant speed measurement data; According to the start influence index and the physiological signal constant speed measurement data, obtain the human physiological signal abnormality index; Based on the health analysis of human physiological signals, obtain the threshold of the human physiological signal abnormality index; According to the human physiological signal abnormality index and the threshold of the human physiological signal abnormality index, judge whether the human physiological signal is abnormal. If the human physiological signal abnormality index exceeds the threshold of the human physiological signal abnormality index, mark the measurement data of the human physiological signal to obtain the abnormal data of the human physiological signal; Obtain the first influence information of human physiological electromagnetic exposure according to the start influence index and abnormal data of human physiological signals. The first influence information of human physiological electromagnetic exposure represents the influence relationship between the electromagnetic exposure of the power battery during the start state and uniform driving of the electric vehicle and human physiological signals; Among them, the calculation formula of the human physiological signal abnormality index is: In the formula, represents the abnormal index of human physiological signals when the uniform driving speed is V, represents the maximum value of the s-th human physiological signal in the uniformly measured physiological signal data when the uniform driving speed is V, is the standard value of the s-th human physiological signal.

6. The method for measuring and analyzing the electromagnetic exposure of a power battery and human physiological signals according to claim 5, characterized in that Based on the first influence information of human physiological electromagnetic exposure, analyze the influence relationship between the electromagnetic exposure of the power battery during the acceleration state and charging state of the electric vehicle and human physiological signals, and obtain the second influence information of human physiological electromagnetic exposure, which specifically includes: Based on the first influence information of human physiological electromagnetic exposure, obtain abnormal data of human physiological signals; Take the minimum value of the battery vehicle speed in the abnormal data of human physiological signals as the calibrated speed of the electric vehicle; Obtain uniform test speed information; Based on the uniform test speed information, obtain uniform test electromagnetic field information, and the uniform test electromagnetic field information represents the electromagnetic field information corresponding to the uniform driving speed of the electric vehicle; According to the uniform test electromagnetic field information and the electromagnetic field difference coefficient, obtain the state data of the accelerating test electric vehicle; Based on the state data of the accelerating test electric vehicle, make the electric vehicle accelerate at different accelerations until the speed of the electric vehicle reaches the calibrated speed of the electric vehicle, monitor the human physiological signals of the driver, and obtain the physiological signal acceleration measurement data; According to the start influence index and the physiological signal acceleration measurement data, obtain the human physiological signal acceleration abnormality index; Based on the health analysis of human physiological signals, obtain the threshold of the human physiological signal acceleration abnormality index; According to the human physiological signal acceleration abnormality index and the threshold of the human physiological signal acceleration abnormality index, judge whether the human physiological signal is abnormal. If the human physiological signal acceleration abnormality index exceeds the threshold of the human physiological signal acceleration abnormality index, mark the physiological signal acceleration measurement data to obtain the human physiological signal acceleration abnormal data; According to the uniform test electromagnetic field information, obtain the charging current information; Based on the charging current information, make the electric vehicle charge with the charging current, monitor the human physiological signals of the driver, and obtain the physiological signal charging measurement data; According to the physiological signal charging measurement data, obtain the human physiological signal charging abnormality index; According to the human physiological signal charging abnormality index, judge whether the human physiological signal is abnormal. If so, mark the physiological signal charging measurement data to obtain the human physiological signal charging abnormal data; According to the human physiological signal acceleration abnormal data and the physiological signal charging abnormal data, obtain the second influence information of human physiological electromagnetic exposure. The second influence information of human physiological electromagnetic exposure represents the influence relationship between the electromagnetic exposure of the power battery during the acceleration state and charging state of the electric vehicle and human physiological signals; Among them, the state data of the accelerated test electric vehicle Specifically: Wherein, is the power of the power battery, is the voltage of the power battery, is the current of the motor, is the vacuum permeability, is the number of turns of the motor winding, K is the electromagnetic field difference coefficient, is the speed electromagnetic field correlation coefficient, V is the constant driving speed, is the current conversion coefficient; The charging current is: In the formula, is the charging current.

7. A power battery electromagnetic exposure and human physiological signal measurement device for implementing the analysis method as described in claim 6, characterized in that, Including: The main control module is used to obtain differential speed information according to the power battery state difference index, judge whether the first electromagnetic field test data is abnormal according to the first electromagnetic field test data and the power battery test characteristic speed, judge whether the human physiological signal is abnormal according to the human physiological signal abnormality index and the human physiological signal abnormality index threshold, judge whether the human physiological signal is abnormal according to the human physiological signal acceleration abnormality index and the human physiological signal acceleration abnormality index threshold, judge whether the human physiological signal is abnormal according to the human physiological signal charging abnormality index, obtain the power battery test characteristic speed according to the power battery reference speed, the speed electromagnetic field correlation coefficient and the basic electromagnetic field information, obtain the human physiological signal monitoring data from the start of the battery vehicle to one minute after the battery vehicle travels at a constant speed based on the primary start data as the start influence data, set corresponding weights for each human physiological signal measurement data based on the human physiological signal analysis, obtain the first human physiological electromagnetic exposure influence information according to the start influence index and the human physiological signal abnormality data, and obtain the second human physiological electromagnetic exposure influence information according to the human physiological signal acceleration abnormality data and the physiological signal charging abnormality data; The information acquisition module is used to acquire power battery parameter information, obtain the start electromagnetic field information based on the power battery parameter information and electromagnetic field monitoring, obtain the basic electromagnetic field information based on the analysis of human daily electromagnetic exposure, drive the electric vehicle in different states based on the power battery working state, monitor the human physiological signal of the driver, and obtain the human physiological signal measurement data; The evaluation module is used to obtain the speed electromagnetic field correlation coefficient based on the linear regression equation according to the electromagnetic field monitoring data during the constant speed test, obtain the power battery state difference index according to the power battery test monitoring data, obtain the electromagnetic field difference coefficient according to the power battery test characteristic speed, obtain the start influence index based on the set weights according to the start influence data and the secondary start data, obtain the human physiological signal abnormality index according to the start influence index and the physiological signal constant speed measurement data, obtain the human physiological signal acceleration abnormality index according to the start influence index and the physiological signal acceleration measurement data, and obtain the human physiological signal charging abnormality index according to the physiological signal charging measurement data; The display module interacts with the main control module and is used to output and display the start electromagnetic field information, the electromagnetic field difference coefficient, the human physiological signal measurement data and the human physiological electromagnetic exposure influence information.

8. The electromagnetic exposure measurement device for power batteries and the human physiological signal measurement device according to claim 7, characterized in that, The main control module specifically includes: A control unit, which is used to obtain the test characteristic speed of the power battery according to the reference speed of the power battery, the speed-electromagnetic field correlation coefficient, and the basic electromagnetic field information, obtain the human physiological signal monitoring data within one minute after the battery vehicle starts until it travels at a constant speed based on the first start-up data as the start-up influence data, set corresponding weights for each human physiological signal measurement data based on the analysis of human physiological signals, obtain the first influence information of human physiological electromagnetic exposure according to the start-up influence index and the abnormal data of human physiological signals, and obtain the second influence information of human physiological electromagnetic exposure according to the abnormal data of human physiological signal acceleration and the abnormal data of physiological signal charging; An information receiving unit, which interacts with the information acquisition module and the evaluation module, and is used to receive data and transmit it to the judgment unit; A judgment unit, which is used to obtain the differential speed information according to the power battery state difference index, judge whether the first electromagnetic field test data is abnormal according to the first electromagnetic field test data and the test characteristic speed of the power battery, judge whether the human physiological signal is abnormal according to the human physiological signal abnormal index and the human physiological signal abnormal index threshold, judge whether the human physiological signal is abnormal according to the human physiological signal acceleration abnormal index and the human physiological signal acceleration abnormal index threshold, and judge whether the human physiological signal is abnormal according to the human physiological signal charging abnormal index.

9. The electromagnetic exposure of the power battery and the human physiological signal measuring device according to claim 7, characterized in that The information acquisition module specifically includes: A first acquisition unit, which is used to acquire the power battery parameter information and obtain the start-up electromagnetic field information based on the electromagnetic field monitoring according to the power battery parameter information; A second acquisition unit, which is used to obtain the basic electromagnetic field information based on the analysis of human daily electromagnetic exposure, monitor the human physiological signals of the driver by driving the electric vehicle in different states based on the working state of the power battery, and obtain the human physiological signal measurement data.

10. The power battery electromagnetic exposure and human physiological signal measuring device according to claim 7, characterized in that The evaluation module specifically includes: A first evaluation unit, which is used to obtain the speed-electromagnetic field correlation coefficient based on the linear regression equation according to the constant-speed test electromagnetic field monitoring data, obtain the power battery state difference index according to the power battery test monitoring data, and obtain the electromagnetic field difference coefficient according to the test characteristic speed of the power battery; A second evaluation unit, which is used to obtain the start-up influence index based on the set weights according to the start-up influence data and the second start-up data, obtain the human physiological signal abnormal index according to the start-up influence index and the physiological signal constant-speed measurement data, obtain the human physiological signal acceleration abnormal index according to the start-up influence index and the physiological signal acceleration measurement data, and obtain the human physiological signal charging abnormal index according to the physiological signal charging measurement data.

Images