Power battery electromagnetic exposure and human body physiological signal measuring device and analysis method

Abstract

The invention discloses a power battery electromagnetic exposure and human body physiological signal measuring device and analysis method, and relates to the field of power battery analysis and calculation, and the method comprises the steps of obtaining power battery parameter information, and obtaining starting electromagnetic field information based on electromagnetic field monitoring according to the power battery parameter information. According to the method, the difference condition between the calculated value and the actual value of the electromagnetic field intensity is accurately evaluated through the electromagnetic field difference coefficient, and the speed electromagnetic field change correlation in the constant speed state is analyzed through the speed electromagnetic field correlation coefficient; by accurately analyzing the influence relationship between the electromagnetic exposure of the power battery and the human physiological signal during the starting state and the constant-speed driving of the electric vehicle, the state change of the power battery is adjusted by taking the change condition of the electromagnetic field during the constant-speed driving as a reference, and the influence of the state change of the power battery on the human physiological signal is accurately analyzed; and a reference basis is provided for making safety standards and policies in the electric vehicle industry.

Description

Technical Field

[0001] The present invention relates to the technical field of power battery analysis, and in particular to a power battery electromagnetic exposure and human physiological signal measurement device and analysis method. Background Art

[0002] With the popularity of electric vehicles, electromagnetic exposure has become a focus of attention, especially during long-term driving. The electromagnetic field generated by the battery may have potential effects on the physiological and mental health of drivers and passengers. In terms of international research, many 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 the electromagnetic field intensity of electric vehicles can be higher than that of traditional fuel vehicles when accelerating and charging, 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 effects, through long-term monitoring of the electromagnetic field 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 research on the specific connection between electromagnetic exposure of electric vehicles 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 from 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 electromagnetic exposure of electric vehicles and human health has gradually increased, but it mainly focuses on the measurement of electromagnetic field strength and the assessment of environmental impact, lacking a systematic analysis of emotions and mental health. Although some studies have explored the effects of electromagnetic exposure on health, especially the physiological reactions found in animal experiments (such as cell damage, changes in immune response, etc.), there is no in-depth empirical research on the specific effects of electromagnetic exposure of electric vehicles on the emotions and mental health of drivers and passengers.

[0003] At present, the analysis of the impact of electromagnetic exposure of power batteries and human physiological signals still has the problem of being unable to accurately analyze the electromagnetic field of power batteries under different motion states. Under uniform speed, the linear regression model can be used to accurately analyze the change state of the electromagnetic field of the power battery. However, when the power battery is started, accelerated and charged, the change of the electromagnetic field is not linear, resulting in reduced accuracy of the analysis results. When the power battery state is changed, it is impossible to ensure that the change state of the electromagnetic field remains unchanged, but only the power battery state is changed, resulting in too many variables in the test data, failing to achieve the effect of controlling the variables, and making the analysis results unreliable. Summary of the invention

[0004] In order to solve the above technical problems, a power battery electromagnetic exposure and human physiological signal measurement device and analysis method are provided. The technical solution solves the problem that the electromagnetic field of the power battery under different motion states cannot be accurately analyzed in the above background technology. Under uniform speed, the electromagnetic field change state of the power battery can be accurately analyzed through a linear regression model. However, when the power battery is started, accelerated and charged, the change of the electromagnetic field is not linear, resulting in reduced accuracy of the analysis results. When the power battery state is changed, it is impossible to ensure that the change state of the electromagnetic field remains unchanged, but only the power battery state is changed, resulting in too many variables in the test data, failing to achieve the effect of controlling the variables, and making the analysis results unreliable.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0006] The measurement and analysis methods of power battery electromagnetic exposure and human physiological signals include:

[0007] Acquiring power battery parameter information, wherein the power battery parameter information includes power battery parameter information and power battery placement information;

[0008] According to the power battery parameter information, based on the electromagnetic field monitoring, the starting electromagnetic field information is obtained, wherein the starting electromagnetic field information represents the electromagnetic field data when the power battery is started;

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

[0010] According to the basic electromagnetic field information and power battery parameter information, based on electromagnetic monitoring, the electromagnetic field difference coefficient is obtained;

[0011] Based on the working state of the power battery, the electric vehicle is driven in different states, the human physiological signals of the driver are monitored, and the human physiological signal measurement data are obtained, wherein the electric vehicle state includes the starting state, the constant speed driving state, the accelerated driving state and the charging state, and the human physiological signal measurement data includes the heart rate data, the heart rate variability data, the blood pressure data and the skin electrical response data;

[0012] According to the human physiological signal measurement data, the influence relationship between the electromagnetic exposure of the power battery and the human physiological signals when the electric vehicle is started and the electric vehicle is running at a constant speed is analyzed to obtain the first impact information of the human physiological electromagnetic exposure;

[0013] Based on the first impact information of human physiological electromagnetic exposure, the impact relationship between the electromagnetic exposure of the power battery and the human physiological signals during the acceleration state and the charging state of the electric vehicle is analyzed to obtain the second impact information of human physiological electromagnetic exposure;

[0014] According to the first impact information of human physiological electromagnetic exposure and the second impact information of human physiological electromagnetic exposure, the human physiological electromagnetic exposure impact information is obtained.

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

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

[0017] According to the power battery parameter information, the electromagnetic field and power battery status of the electric vehicle running at different speeds are monitored respectively to obtain the uniform speed test electromagnetic field monitoring data and power battery test monitoring data;

[0018] According to the electromagnetic field monitoring data of the uniform speed test, based on the linear regression equation, the speed electromagnetic field correlation coefficient is obtained, and the speed electromagnetic field correlation coefficient represents the proportional relationship between the speed and the electromagnetic field intensity when the electric vehicle is traveling at a uniform speed;

[0019] Obtain the power battery status difference index based on the power battery test monitoring data;

[0020] According to the power battery status difference index, obtain difference speed information;

[0021] Among them, if the power battery state difference index Q(V) ≥ 0.912, then the speed V is the normal speed at this time; if Q(V) < 0.912, then the speed V is the difference speed at this time;

[0022] The minimum speed value in the difference speed information is used as the power battery reference speed;

[0023] According to the power battery reference speed, speed electromagnetic field correlation coefficient and basic electromagnetic field information, the power battery test characteristic speed is obtained;

[0024] According to the characteristic speed of the power battery test, the electromagnetic field difference coefficient is obtained;

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

[0026]

[0027] Where Q(V) represents the power battery state difference index when the speed is V, ω i (V) represents the theoretical value of the i-th power battery status monitoring index obtained from the speed V, represents the monitoring value of the i-th power battery status monitoring indicator, and n is the total number of power battery status monitoring indicator types;

[0028] The power battery test characteristic speed is:

[0029]

[0030] Where V1 is the characteristic speed of the power battery test, B1 is the basic electromagnetic field strength, μ is the speed electromagnetic field correlation coefficient, V0 is the power battery benchmark speed, D is a constant, and D min Indicates the minimum value of D.

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

[0032] The electric vehicle is driven at a constant speed at a characteristic speed of a power battery test, and the electromagnetic field of the driving position of the electric vehicle and the state of the electric vehicle are monitored respectively to obtain first electromagnetic field test data and first electric vehicle test data;

[0033] According to the first electromagnetic field test data and the power battery test characteristic speed, it is determined whether the first electromagnetic field test data is abnormal. If so, the first electromagnetic field test data is unavailable, and the electromagnetic field of the driving position of the electric vehicle is monitored again. If not, the first electromagnetic field test data is available;

[0034] Among them, if or Then the first electromagnetic field test data is not available, if Then the first electromagnetic field test data is available, and B2 is the first electromagnetic field test data;

[0035] According to the first test data of the electric vehicle, first test data of the power battery and first test data of the electric motor are obtained;

[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, obtaining the electromagnetic field difference coefficient;

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

[0038]

[0039] Where K is the electromagnetic field difference coefficient, σ is the vacuum magnetic permeability, and σ = 4π×10 -7 H / m, I2 is the motor current, N is the number of motor winding turns, P1 is the power battery power, δ1 is the power battery voltage, r1 is the radial distance between the electric vehicle driving position and the power battery, and r2 is the radial distance between the electric vehicle driving position and the motor.

[0040] Preferably, the analysis of the influence relationship between the electromagnetic exposure of the power battery and the physiological signals of the human body when the electric vehicle is started and the electric vehicle is traveling at a constant speed to obtain the first influence information of the electromagnetic exposure of the human body specifically includes:

[0041] Obtaining human body's regular walking speed information;

[0042] The battery car was driven at a constant speed at the normal walking speed of the human body, and the human physiological signals of the volunteers sitting in the co-pilot seat were monitored before and after the battery car was started to obtain the start-up data;

[0043] The battery car was driven at a constant speed at the normal walking speed of the human body, and the human physiological signals of the volunteers who joined the co-pilot during the constant speed driving of the battery car were monitored to obtain the secondary start data;

[0044] Based on the one-time startup data, the human physiological signal monitoring data from the time when the battery car is started to the time when the battery car is driven at a constant speed for one minute is obtained as the startup impact data;

[0045] Based on the analysis of human physiological signals, a corresponding weight is set for each human physiological signal measurement data;

[0046] According to the startup impact data and the secondary startup data, based on the set weights, the startup impact index is obtained;

[0047] According to the human physiological signal measurement data, obtaining physiological signal uniform speed measurement data, wherein the physiological signal uniform speed measurement data represents the human physiological information measurement data of the driver when the battery car is driven at different uniform speeds;

[0048] According to the start-up impact index and the uniform speed measurement data of physiological signals, the first impact information of human physiological electromagnetic exposure is obtained;

[0049] The calculation formula of the startup impact index is:

[0050]

[0051] Where, E is the startup impact index, α s represents the weight of the sth human physiological signal, A max (s) represents the maximum value of the sth human physiological signal in the startup impact data, Represents the average value of the sth human physiological signal in the secondary startup data.

[0052] Preferably, obtaining the first impact information of human physiological electromagnetic exposure according to the startup impact index and the uniform speed measurement data of the physiological signal specifically includes:

[0053] Get the maximum speed of the electric vehicle;

[0054] Based on the maximum driving speed of the electric vehicle, uniform test speed information is obtained, wherein the uniform test speed information represents a uniform driving speed set of the electric vehicle, and the speed interval of the uniform test speed is 5 m / s;

[0055] Based on the uniform speed test speed information, the electric vehicle is made to travel at different uniform speeds, the driver's human physiological signals are monitored, and uniform speed measurement data of the physiological signals are obtained;

[0056] According to the start-up impact index and the uniform speed measurement data of the physiological signal, the abnormal index of the human physiological signal is obtained;

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

[0058] According to the human physiological signal abnormality index and the human physiological signal abnormality index threshold, whether the human physiological signal is abnormal is judged; if the human physiological signal abnormality index exceeds the human physiological signal abnormality index threshold, the human physiological signal measurement data is marked to obtain the human physiological signal abnormality data;

[0059] According to the startup impact index and the abnormal data of human physiological signals, first impact information of human physiological electromagnetic exposure is obtained, wherein the first impact information of human physiological electromagnetic exposure represents the impact relationship between the electromagnetic exposure of the power battery and the human physiological signals when the electric vehicle is in the startup state and driving at a constant speed;

[0060] The calculation formula of the abnormal index of human physiological signals is:

[0061]

[0062] Where R(V) represents the abnormal index of human physiological signals when the uniform speed is V, B max (V, s) represents the maximum value of the sth human physiological signal in the uniform speed measurement data of the physiological signal when the uniform speed is V, and B0(s) is the standard value of the sth human physiological signal.

[0063] Preferably, the analysis of the influence relationship between the electromagnetic exposure of the power battery and the physiological signals of the human body when the electric vehicle is accelerating and charging based on the first influence information of the physiological electromagnetic exposure of the human body to obtain the second influence information of the physiological electromagnetic exposure of the human body specifically includes:

[0064] Based on the first impact information of human physiological electromagnetic exposure, abnormal data of human physiological signals are obtained;

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

[0066] Get the uniform speed test speed information;

[0067] Based on the uniform speed test speed information, uniform speed test electromagnetic field information is obtained, wherein the uniform speed test electromagnetic field information represents the electromagnetic field information corresponding to the uniform speed of the electric vehicle;

[0068] According to the electromagnetic field information and electromagnetic field difference coefficient of the uniform speed test, the state data of the electric vehicle under the accelerated test is obtained;

[0069] Based on the acceleration test electric vehicle state data, the electric vehicle is accelerated at different accelerations until the speed of the electric vehicle reaches the calibrated speed of the electric vehicle, the driver's human physiological signals are monitored, and physiological signal acceleration measurement data is obtained;

[0070] According to the start-up impact index and the physiological signal acceleration measurement data, the human physiological signal acceleration abnormality index is obtained;

[0071] Based on the health analysis of human physiological signals, the threshold of the abnormal acceleration index of human physiological signals is obtained;

[0072] According to the human physiological signal acceleration abnormality index and the human physiological signal acceleration abnormality index threshold, it is judged whether the human physiological signal is abnormal; if the human physiological signal acceleration abnormality index exceeds the human physiological signal acceleration abnormality index threshold, the physiological signal acceleration measurement data is marked to obtain the human physiological signal acceleration abnormality data;

[0073] According to the electromagnetic field information of the uniform speed test, the charging current information is obtained;

[0074] Based on the charging current information, the electric vehicle is charged with the charging current, the driver's human physiological signals are monitored, and the physiological signal charging measurement data is obtained;

[0075] According to the physiological signal charging measurement data, the human body physiological signal charging abnormality index is obtained;

[0076] According to the charging index of the human physiological signal, it is judged whether the human physiological signal is abnormal. If so, the charging measurement data of the physiological signal is marked to obtain the abnormal charging data of the human physiological signal;

[0077] According to the abnormal acceleration data of the human physiological signal and the abnormal charging data of the physiological signal, the second impact information of the human physiological electromagnetic exposure is obtained, wherein the second impact information of the human physiological electromagnetic exposure represents the impact relationship between the electromagnetic exposure of the power battery and the human physiological signal when the electric vehicle is in an accelerated driving state and a charging state;

[0078] The acceleration test electric vehicle state data (P, δ, I) are specifically:

[0079]

[0080] In the formula, P is the power of the power battery, δ is the power battery voltage, I is the motor current, σ is the vacuum magnetic permeability, N is the number of turns of the motor winding, K is the electromagnetic field difference coefficient, μ is the speed electromagnetic field correlation coefficient, V is the uniform driving speed, and w is the current conversion coefficient;

[0081] The charging current is:

[0082]

[0083] Where I0 is the charging current.

[0084] Furthermore, a power battery electromagnetic exposure and human physiological signal measurement device is proposed to implement the above analysis method, including:

[0085] A main control module, the main control module is used to obtain difference 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 abnormal index and the human physiological signal abnormal index threshold, determine 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, determine whether the human physiological signal is abnormal according to the human physiological signal charging 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 battery car starting to the battery car driving at a constant speed for one minute based on one-time startup data as the startup impact data, set a corresponding weight for each human physiological signal measurement data based on the human physiological signal analysis, obtain the first impact information of human physiological electromagnetic exposure according to the startup impact index and the human physiological signal abnormal data, and obtain the second impact information of human physiological electromagnetic exposure according to the human physiological signal acceleration abnormal data and the physiological signal charging abnormal data;

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

[0087] An evaluation module, wherein the evaluation module is used to obtain a speed electromagnetic field correlation coefficient based on a linear regression equation according to electromagnetic field monitoring data of a uniform speed test, obtain a power battery state difference index according to power battery test monitoring data, obtain an electromagnetic field difference coefficient according to a power battery test characteristic speed, obtain a startup influence index based on a set weight according to startup influence data and secondary startup data, obtain a human physiological signal abnormality index according to the startup influence index and physiological signal uniform speed measurement data, obtain a human physiological signal acceleration abnormality index according to the startup influence index and physiological signal acceleration measurement data, and obtain a human physiological signal charging abnormality index according to 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 impact information.

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

[0090] A control unit, the control unit is used to obtain a power battery test characteristic speed according to a power battery reference speed, a speed electromagnetic field correlation coefficient and basic electromagnetic field information, obtain human physiological signal monitoring data from the time the battery vehicle is started to the time the battery vehicle is driven at a constant speed for one minute based on one start-up data, as start-up impact data, set a corresponding weight for each type of human physiological signal measurement data based on human physiological signal analysis, obtain first impact information on human physiological electromagnetic exposure according to a start-up impact index and abnormal human physiological signal data, and obtain second impact information on human physiological electromagnetic exposure according to abnormal acceleration data of human physiological signals and abnormal charging data of physiological signals;

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

[0092] A judgment unit, wherein the judgment unit is used to obtain 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 power battery test characteristic speed, 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 index.

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

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

[0095] The second acquisition unit is used to acquire basic electromagnetic field information based on the electromagnetic exposure analysis of human daily life, drive the electric vehicle in different states based on the working state of the power battery, monitor the driver's human physiological signals, and acquire human physiological signal measurement data.

[0096] Optionally, the evaluation module specifically includes:

[0097] a first evaluation unit, the first evaluation unit being used to obtain a speed electromagnetic field correlation coefficient based on a linear regression equation according to electromagnetic field monitoring data of a uniform speed test, to obtain a power battery state difference index according to power battery test monitoring data, and to obtain an electromagnetic field difference coefficient according to a power battery test characteristic speed;

[0098] The second evaluation unit is used to obtain a startup influence index based on the startup influence data and the secondary startup data based on the set weight, obtain a human physiological signal abnormality index based on the startup influence index and the physiological signal uniform speed measurement data, obtain a human physiological signal acceleration abnormality index based on the startup influence index and the physiological signal acceleration measurement data, and obtain a human physiological signal charging abnormality index based on the physiological signal charging measurement data.

[0099] Compared with the prior art, the present invention has the following beneficial effects:

[0100] The present invention proposes a device and an analysis method for measuring electromagnetic exposure of a power battery and human physiological signals. The device accurately evaluates the difference between the calculated value and the actual value of the electromagnetic field intensity through the electromagnetic field difference coefficient. The speed electromagnetic field correlation coefficient is used to analyze the correlation between the speed electromagnetic field changes under a uniform speed state. The influence of the electromagnetic exposure of the power battery and human physiological signals during the start-up state and uniform speed driving of an electric vehicle is accurately analyzed. Based on the electromagnetic field change during uniform speed driving, the power battery state change is adjusted, and the influence of the power battery state change on human physiological signals is accurately analyzed, providing a reference for the formulation of safety standards and policies for the electric vehicle industry. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0102] Figure 2 A flow chart for obtaining the electromagnetic field difference coefficient in the present invention;

[0103] Figure 3 This is a flow chart for obtaining uniform speed measurement data of physiological signals in the present invention;

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

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

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

[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 may think of other obvious variations.

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

[0109] Acquiring power battery parameter information, wherein the power battery parameter information includes power battery parameter information and power battery placement information;

[0110] According to the power battery parameter information, based on the electromagnetic field monitoring, the starting electromagnetic field information is obtained, wherein the starting electromagnetic field information represents the electromagnetic field data when the power battery is started;

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

[0112] According to the basic electromagnetic field information and power battery parameter information, based on electromagnetic monitoring, the electromagnetic field difference coefficient is obtained;

[0113] Specifically, according to the basic electromagnetic field information and power battery parameter information, based on electromagnetic monitoring, the electromagnetic field difference coefficient is obtained, including:

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

[0115] According to the power battery parameter information, the electromagnetic field and power battery status of the electric vehicle running at different speeds are monitored respectively to obtain the uniform speed test electromagnetic field monitoring data and power battery test monitoring data;

[0116] According to the electromagnetic field monitoring data of the uniform speed test, based on the linear regression equation, the speed electromagnetic field correlation coefficient is obtained, and the speed electromagnetic field correlation coefficient represents the proportional relationship between the speed and the electromagnetic field intensity when the electric vehicle is traveling at a uniform speed;

[0117] Obtain the power battery status difference index based on the power battery test monitoring data;

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

[0119] Among them, if the power battery state difference index Q(V) ≥ 0.912, then the speed V is the normal speed at this time; if Q(V) < 0.912, then the speed V is the difference speed at this time;

[0120] The minimum speed value in the difference speed information is used as the power battery reference speed;

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

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

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

[0124]

[0125] Where Q(V) represents the power battery state difference index when the speed is V, ω i (V) represents the theoretical value of the i-th power battery status monitoring index obtained from the speed V, represents the monitoring value of the i-th power battery status monitoring indicator, and n is the total number of power battery status monitoring indicator types;

[0126] The power battery test characteristic speed is:

[0127]

[0128] Where V1 is the characteristic speed of the power battery test, B1 is the basic electromagnetic field strength, μ is the speed electromagnetic field correlation coefficient, V0 is the power battery benchmark speed, D is a constant, and D min Indicates the minimum value of D.

[0129] In this scheme, the electromagnetic field monitoring data is tested at a uniform speed, and the speed electromagnetic field correlation coefficient is obtained based on the linear regression equation. The speed electromagnetic field correlation coefficient is used to accurately analyze the proportional relationship between the speed and the electromagnetic field strength when the electric vehicle is traveling at a uniform speed. The difference speed is selected through the power battery state difference index. It can be understood that with the increase of speed, the energy loss of electric vehicles is also increasing, that is, the difference between the output of the power battery and the actual application is also increasing. By using the minimum speed value in the difference speed information as the power battery benchmark speed, the accuracy of the data is improved. The power battery test characteristic speed is obtained through the power battery benchmark speed, the speed electromagnetic field correlation coefficient and the basic electromagnetic field information. The electromagnetic field difference condition is accurately analyzed through the power battery test characteristic speed.

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

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

[0132] According to the first electromagnetic field test data and the power battery test characteristic speed, it is determined whether the first electromagnetic field test data is abnormal. If so, the first electromagnetic field test data is unavailable, and the electromagnetic field of the driving position of the electric vehicle is monitored again. If not, the first electromagnetic field test data is available;

[0133] Among them, if or Then the first electromagnetic field test data is not available, if Then the first electromagnetic field test data is available, and B2 is the first electromagnetic field test data;

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

[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, obtaining the electromagnetic field difference coefficient;

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

[0137]

[0138] Where K is the electromagnetic field difference coefficient, σ is the vacuum magnetic permeability, and σ = 4π×10 -7H / m, I2 is the motor current, N is the number of motor winding turns, P1 is the power battery power, δ1 is the power battery voltage, r1 is the radial distance between the electric vehicle driving position and the power battery, and r2 is the radial distance between the electric vehicle driving position and the motor.

[0139] In this scheme, the first electromagnetic field test data and the power battery test characteristic speed are used to determine whether the first electromagnetic field test data is abnormal, thereby avoiding errors in the analysis results caused by failure of the electromagnetic field monitoring equipment. The electromagnetic field difference coefficient is obtained by comparing the calculated value of the electromagnetic field strength with the actual value, which facilitates the subsequent analysis of the battery exposure status.

[0140] Based on the working state of the power battery, the electric vehicle is driven in different states, the human physiological signals of the driver are monitored, and the human physiological signal measurement data are obtained, wherein the electric vehicle state includes the starting state, the constant speed driving state, the accelerated driving state and the charging state, and the human physiological signal measurement data includes the heart rate data, the heart rate variability data, the blood pressure data and the skin electrical response data;

[0141] According to the human physiological signal measurement data, the influence relationship between the electromagnetic exposure of the power battery and the human physiological signals when the electric vehicle is started and the electric vehicle is running at a constant speed is analyzed to obtain the first impact information of the human physiological electromagnetic exposure;

[0142] Specifically, the influence relationship between the electromagnetic exposure of the power battery and the physiological signals of the human body when the electric vehicle is started and the electric vehicle is running at a constant speed is analyzed to obtain the first impact information of the physiological electromagnetic exposure of the human body, including:

[0143] Obtaining human body's regular walking speed information;

[0144] The battery car was driven at a constant speed at the normal walking speed of the human body, and the human physiological signals of the volunteers sitting in the co-pilot seat were monitored before and after the battery car was started to obtain the start-up data;

[0145] The battery car was driven at a constant speed at the normal walking speed of the human body, and the human physiological signals of the volunteers who joined the co-pilot during the constant speed driving of the battery car were monitored to obtain the secondary start data;

[0146] Based on the one-time startup data, the human physiological signal monitoring data from the time when the battery car is started to the time when the battery car is driven at a constant speed for one minute is obtained as the startup impact data;

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

[0148] According to the startup impact data and the secondary startup data, based on the set weights, the startup impact index is obtained;

[0149] According to the human physiological signal measurement data, obtaining physiological signal uniform speed measurement data, wherein the physiological signal uniform speed measurement data represents the human physiological information measurement data of the driver when the battery car is driven at different uniform speeds;

[0150] According to the start-up impact index and the uniform speed measurement data of physiological signals, the first impact information of human physiological electromagnetic exposure is obtained;

[0151] The calculation formula of the startup impact index is:

[0152]

[0153] Where, E is the startup impact index, α s represents the weight of the sth human physiological signal, A max (s) represents the maximum value of the sth human physiological signal in the startup impact data, Represents the average value of the sth human physiological signal in the secondary startup data.

[0154] In this scheme, the human physiological signal monitoring data from the time the battery car is started to the time the battery car is running at a constant speed for one minute is obtained as the starting impact data. The starting impact index is obtained based on the set weights according to the starting impact data and the secondary starting data. The human physiological signals of the volunteers in the two situations, when they were in the co-pilot seat before and after the battery car was started and when they were added to the co-pilot seat while the battery car was running at a constant speed after starting, are compared through the starting impact index. This ensures the control of variables during the experiment and accurately analyzes the impact of the power battery starting on the human physiological signals.

[0155] Specifically, according to the startup impact index and the uniform speed measurement data of the physiological signal, the first impact information of the physiological electromagnetic exposure of the human body is obtained, which specifically includes:

[0156] Get the maximum speed of the electric vehicle;

[0157] Based on the maximum driving speed of the electric vehicle, uniform test speed information is obtained, wherein the uniform test speed information represents a uniform driving speed set of the electric vehicle, and the speed interval of the uniform test speed is 5 m / s;

[0158] Based on the uniform speed test speed information, the electric vehicle is made to travel at different uniform speeds, the driver's human physiological signals are monitored, and uniform speed measurement data of the physiological signals are obtained;

[0159] According to the start-up impact index and the uniform speed measurement data of the physiological signal, the abnormal index of the human physiological signal is obtained;

[0160] Based on the health analysis of human physiological signals, the threshold of abnormal index of human physiological signals is obtained;

[0161] According to the human physiological signal abnormality index and the human physiological signal abnormality index threshold, whether the human physiological signal is abnormal is judged; if the human physiological signal abnormality index exceeds the human physiological signal abnormality index threshold, the human physiological signal measurement data is marked to obtain the human physiological signal abnormality data;

[0162] According to the startup impact index and the abnormal data of human physiological signals, first impact information of human physiological electromagnetic exposure is obtained, wherein the first impact information of human physiological electromagnetic exposure represents the impact relationship between the electromagnetic exposure of the power battery and the human physiological signals when the electric vehicle is in the startup state and driving at a constant speed;

[0163] The calculation formula of the abnormal index of human physiological signals is:

[0164]

[0165] Where R(V) represents the abnormal index of human physiological signals when the uniform speed is V, B max (V, s) represents the maximum value of the sth human physiological signal in the uniform speed measurement data of the physiological signal when the uniform speed is V, and B0(s) is the standard value of the sth human physiological signal.

[0166] In this scheme, based on the maximum driving speed of the electric vehicle, the uniform speed test speed information is obtained, the electric vehicle is driven at different speeds, the driver's human physiological signals are monitored, and the uniform speed measurement data of the physiological signals are obtained. According to the startup impact index and the uniform speed measurement data of the physiological signals, the abnormality index of the human physiological signals is obtained. According to the abnormality index of the human physiological signals, it is determined whether the human physiological signals are abnormal, and the abnormality data of the human physiological signals are obtained. Through the startup impact index and the abnormality data of the human physiological signals, the first impact information of the human physiological electromagnetic exposure is obtained;

[0167] It is understandable that when driving at a constant 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 human physiological signals at a constant test speed with a speed interval of 5m / s ensures the stability and reliability of the data and improves analysis efficiency.

[0168] Based on the first impact information of human physiological electromagnetic exposure, the impact relationship between the electromagnetic exposure of the power battery and the human physiological signals during the acceleration state and the charging state of the electric vehicle is analyzed to obtain the second impact information of human physiological electromagnetic exposure;

[0169] Specifically, based on the first impact information of human physiological electromagnetic exposure, the impact relationship between the electromagnetic exposure of the power battery and the human physiological signals during the acceleration state of the electric vehicle and the charging state of the electric vehicle is analyzed to obtain the second impact information of human physiological electromagnetic exposure, which specifically includes:

[0170] Based on the first impact information of human physiological electromagnetic exposure, abnormal data of human physiological signals are obtained;

[0171] The minimum value of the battery car speed in the abnormal data of human physiological signals is used as the calibrated speed of the electric vehicle;

[0172] Get the uniform speed test speed information;

[0173] Based on the uniform speed test speed information, uniform speed test electromagnetic field information is obtained, wherein the uniform speed test electromagnetic field information represents the electromagnetic field information corresponding to the uniform speed of the electric vehicle;

[0174] According to the electromagnetic field information and electromagnetic field difference coefficient of the uniform speed test, the state data of the electric vehicle under the accelerated test is obtained;

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

[0176] According to the start-up impact index and the physiological signal acceleration measurement data, the human physiological signal acceleration abnormality index is obtained;

[0177] Based on the health analysis of human physiological signals, the threshold of the abnormal acceleration index of human physiological signals is obtained;

[0178] According to the human physiological signal acceleration abnormality index and the human physiological signal acceleration abnormality index threshold, it is judged whether the human physiological signal is abnormal; if the human physiological signal acceleration abnormality index exceeds the human physiological signal acceleration abnormality index threshold, the physiological signal acceleration measurement data is marked to obtain the human physiological signal acceleration abnormality data;

[0179] According to the electromagnetic field information of the uniform speed test, the charging current information is obtained;

[0180] Based on the charging current information, the electric vehicle is charged with the charging current, the driver's human physiological signals are monitored, and the physiological signal charging measurement data is obtained;

[0181] According to the physiological signal charging measurement data, the human body physiological signal charging abnormality index is obtained;

[0182] According to the charging index of the human physiological signal, it is judged whether the human physiological signal is abnormal. If so, the charging measurement data of the physiological signal is marked to obtain the abnormal charging data of the human physiological signal;

[0183] According to the abnormal acceleration data of the human physiological signal and the abnormal charging data of the physiological signal, the second impact information of the human physiological electromagnetic exposure is obtained, wherein the second impact information of the human physiological electromagnetic exposure represents the impact relationship between the electromagnetic exposure of the power battery and the human physiological signal when the electric vehicle is in an accelerated driving state and a charging state;

[0184] The acceleration test electric vehicle state data (P, δ, I) are specifically:

[0185]

[0186] In the formula, P is the power of the power battery, δ is the power battery voltage, I is the motor current, σ is the vacuum magnetic permeability, N is the number of turns of the motor winding, K is the electromagnetic field difference coefficient, μ is the speed electromagnetic field correlation coefficient, V is the uniform driving speed, and w is the current conversion coefficient;

[0187] The charging current is:

[0188]

[0189] Where I0 is the charging current.

[0190] In this scheme, the minimum value of the battery car speed in the abnormal data of human physiological signals is used as the calibration speed of the electric vehicle to obtain the uniform test speed information, and the uniform test electromagnetic field information is obtained based on the uniform test speed information. The accelerated test electric vehicle state data is obtained according to the uniform test electromagnetic field information and the electromagnetic field difference coefficient. Based on the accelerated test electric vehicle state data, the electric vehicle is accelerated at different accelerations until the electric vehicle speed reaches the calibration speed of the electric vehicle, the driver's human physiological signals are monitored, and the physiological signal acceleration measurement data is obtained;

[0191] It is understandable that if the battery exposure of electric vehicles during acceleration is analyzed only by setting the acceleration, the change state of the electromagnetic field intensity and the change state of the power battery state are not consistent with those of constant speed driving. Therefore, there are two variables, and it is impossible to accurately analyze the impact of the change of the power battery state on the human physiological signals.

[0192] In this embodiment, the power battery state during accelerated driving is restricted by taking the change in electromagnetic field strength during uniform speed driving as a reference to ensure that the change in electromagnetic field strength is consistent with that during uniform speed driving. It should be noted that if the power battery state is restricted by electromagnetic field monitoring equipment, there are certain defects. First, there is a delay. When adjusting through the electromagnetic field monitoring equipment, there is a reaction time and signal generation and transmission time in the middle, which affects the accuracy of the test data. Second, in the accelerated state, the change in electromagnetic field strength is not linear. When the power battery state is restricted by the electromagnetic field monitoring equipment, it cannot be quickly and accurately adjusted to the target effect.

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

[0194]

[0195] Where R′(P, δ, I) is the acceleration abnormality index of human physiological signals, B max (P, δ, I, s) represents the maximum value of the sth human physiological signal in the uniform speed measurement data of the physiological signal when the power battery power is P, the power battery voltage is δ, and the motor current is I;

[0196] The specific abnormal charging index of human physiological signals is:

[0197]

[0198] Where, R”(P, δ, I) is the charging abnormality index of human physiological signal, B max (I0, s) represents the maximum value of the sth human physiological signal in the uniform speed measurement data of the physiological signal when the power battery charging current is I0.

[0199] According to the first impact information of human physiological electromagnetic exposure and the second impact information of human physiological electromagnetic exposure, the human physiological electromagnetic exposure impact information is obtained.

[0200] Reference Figure 6 As shown, further, in combination with the above-mentioned power battery electromagnetic exposure and human physiological signal measurement and analysis method, a power battery electromagnetic exposure and human physiological signal measurement device is proposed, including:

[0201] A main control module, the main control module is used to obtain difference 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 abnormal index and the human physiological signal abnormal index threshold, determine 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, determine whether the human physiological signal is abnormal according to the human physiological signal charging 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 battery car starting to the battery car driving at a constant speed for one minute based on one-time startup data as the startup impact data, set a corresponding weight for each human physiological signal measurement data based on the human physiological signal analysis, obtain the first impact information of human physiological electromagnetic exposure according to the startup impact index and the human physiological signal abnormal data, and obtain the second impact information of human physiological electromagnetic exposure according to the human physiological signal acceleration abnormal data and the physiological signal charging abnormal data;

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

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

[0204] 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 impact information.

[0205] Main control module, specifically including:

[0206] A control unit, the control unit is used to obtain a power battery test characteristic speed according to a power battery reference speed, a speed electromagnetic field correlation coefficient and basic electromagnetic field information, obtain human physiological signal monitoring data from the time the battery vehicle is started to the time the battery vehicle is driven at a constant speed for one minute based on one start-up data, as start-up impact data, set a corresponding weight for each type of human physiological signal measurement data based on human physiological signal analysis, obtain first impact information on human physiological electromagnetic exposure according to a start-up impact index and abnormal human physiological signal data, and obtain second impact information on human physiological electromagnetic exposure according to abnormal acceleration data of human physiological signals and abnormal charging data of physiological signals;

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

[0208] A judgment unit, wherein the judgment unit is used to obtain 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 power battery test characteristic speed, 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 index.

[0209] Information acquisition module, specifically including:

[0210] A first acquisition unit, the first acquisition unit is used to acquire power battery parameter information, power battery parameter information and power battery placement information, and acquire starting electromagnetic field information based on the power battery parameter information and electromagnetic field monitoring;

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

[0212] Assessment modules include:

[0213] a first evaluation unit, the first evaluation unit being used to obtain a speed electromagnetic field correlation coefficient based on a linear regression equation according to electromagnetic field monitoring data of a uniform speed test, to obtain a power battery state difference index according to power battery test monitoring data, and to obtain an electromagnetic field difference coefficient according to a power battery test characteristic speed;

[0214] The second evaluation unit is used to obtain a startup influence index based on the startup influence data and the secondary startup data based on the set weight, obtain a human physiological signal abnormality index based on the startup influence index and the physiological signal uniform speed measurement data, obtain a human physiological signal acceleration abnormality index based on the startup influence index and the physiological signal acceleration measurement data, and obtain a human physiological signal charging abnormality index based on the physiological signal charging measurement data.

[0215] In summary, the advantages of the present invention are: through basic electromagnetic field information and power battery parameter information, based on electromagnetic monitoring, the electromagnetic field difference coefficient is obtained, and the difference between the calculated value and the actual value of the electromagnetic field intensity is accurately evaluated through the electromagnetic field difference coefficient; through uniform speed test electromagnetic field monitoring data, based on the linear regression equation, the speed electromagnetic field correlation coefficient is obtained, and the speed electromagnetic field change correlation under the uniform speed state is analyzed; through the startup influence index and the uniform speed measurement data of the physiological signal, the human physiological signal abnormality index is obtained; through the human physiological signal abnormality index, the influence relationship between the electromagnetic exposure of the power battery and the human physiological signal during the startup state and uniform speed driving of the electric vehicle is accurately analyzed; by taking the electromagnetic field change state during uniform speed driving as a benchmark, the power battery state change is adjusted, and the influence of the power battery state change on the human physiological signal is accurately analyzed, filling the gap in existing research, providing a reference basis for the formulation of safety standards and policies in the electric vehicle industry, and ensuring people's health and safety.

[0216] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for measuring and analyzing power battery electromagnetic exposure and human physiological signals, characterized in that: include: Acquiring power battery parameter information, wherein the power battery parameter information includes power battery parameter information and power battery placement information; According to the power battery parameter information, based on the electromagnetic field monitoring, the starting electromagnetic field information is obtained, wherein the starting electromagnetic field information represents the electromagnetic field data when the power battery is started; Based on the electromagnetic exposure analysis of human daily life, basic electromagnetic field information is obtained, where the basic electromagnetic field information represents the average electromagnetic field information of human daily life; According to the basic electromagnetic field information and power battery parameter information, based on electromagnetic monitoring, the electromagnetic field difference coefficient is obtained; Based on the working state of the power battery, the electric vehicle is driven in different states, the human physiological signals of the driver are monitored, and the human physiological signal measurement data are obtained, wherein the electric vehicle state includes the starting state, the constant speed driving state, the accelerated driving state and the charging state, and the human physiological signal measurement data includes the heart rate data, the heart rate variability data, the blood pressure data and the skin electrical response data; According to the human physiological signal measurement data, the influence relationship between the electromagnetic exposure of the power battery and the human physiological signals when the electric vehicle is started and the electric vehicle is running at a constant speed is analyzed to obtain the first impact information of the human physiological electromagnetic exposure; Based on the first impact information of human physiological electromagnetic exposure, the impact relationship between the electromagnetic exposure of the power battery and the human physiological signals during the acceleration state and the charging state of the electric vehicle is analyzed to obtain the second impact information of human physiological electromagnetic exposure; According to the first impact information of human physiological electromagnetic exposure and the second impact information of human physiological electromagnetic exposure, the human physiological electromagnetic exposure impact information is obtained.

2. The power battery electromagnetic exposure and human physiological signal measurement and analysis method according to claim 1 is characterized in that: The obtaining of the electromagnetic field difference coefficient based on electromagnetic monitoring according to the basic electromagnetic field information and the power battery parameter information specifically includes: Turn off the wireless communication function of the electric vehicle and output it in the power battery standard mode; According to the power battery parameter information, the electromagnetic field and power battery status of the electric vehicle running at different speeds are monitored respectively to obtain the uniform speed test electromagnetic field monitoring data and power battery test monitoring data; According to the electromagnetic field monitoring data of the uniform speed test, based on the linear regression equation, the speed electromagnetic field correlation coefficient is obtained, and the speed electromagnetic field correlation coefficient represents the proportional relationship between the speed and the electromagnetic field intensity when the electric vehicle is traveling at a uniform speed; Obtain the power battery status difference index based on the power battery test monitoring data; According to the power battery status difference index, obtain difference speed information; Among them, if the power battery state difference index Q(V) ≥ 0.912, then the speed V is the normal speed; if Q(V) < 0.912, then the speed V is the difference speed; The minimum speed value in the difference speed information is used as the power battery reference speed; According to the power battery reference speed, speed electromagnetic field correlation coefficient and basic electromagnetic field information, the power battery test characteristic speed is obtained; According to the characteristic speed of the power battery test, the electromagnetic field difference coefficient is obtained; The calculation formula of the power battery state difference index is: Where Q(V) represents the power battery state difference index when the speed is V, ω i (V) represents the theoretical value of the i-th power battery status monitoring index obtained from the speed V, represents the monitoring value of the i-th power battery status monitoring indicator, and n is the total number of power battery status monitoring indicator types; The power battery test characteristic speed is: Where V1 is the characteristic speed of the power battery test, B1 is the basic electromagnetic field strength, μ is the speed electromagnetic field correlation coefficient, V0 is the power battery benchmark speed, D is a constant, and D min Indicates the minimum value of D.

3. The power battery electromagnetic exposure and human physiological signal measurement and analysis method according to claim 2 is characterized in that: The obtaining of the electromagnetic field difference coefficient according to the power battery test characteristic speed specifically includes: The electric vehicle is driven at a constant speed at a characteristic speed of a power battery test, and the electromagnetic field of the driving position of the electric vehicle and the state of the electric vehicle are monitored respectively to obtain first electromagnetic field test data and first electric vehicle test data; According to the first electromagnetic field test data and the power battery test characteristic speed, it is determined whether the first electromagnetic field test data is abnormal. If so, the first electromagnetic field test data is unavailable, and the electromagnetic field of the driving position of the electric vehicle is monitored again. If not, the first electromagnetic field test data is available; Among them, if or Then the first electromagnetic field test data is not available, if Then the first electromagnetic field test data is available, and B2 is the first electromagnetic field test data; According to the first test data of the electric vehicle, first test data of the power battery and first test data of the electric motor are obtained; 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, obtaining the electromagnetic field difference coefficient; The calculation formula of the electromagnetic field difference coefficient is: Where K is the electromagnetic field difference coefficient, σ is the vacuum magnetic permeability, and σ = 4π×10 -7 H / m, I2 is the motor current, N is the number of motor winding turns, P1 is the power battery power, δ1 is the power battery voltage, r1 is the radial distance between the electric vehicle driving position and the power battery, and r2 is the radial distance between the electric vehicle driving position and the motor.

4. The power battery electromagnetic exposure and human physiological signal measurement and analysis method according to claim 1, characterized in that: The analysis of the influence relationship between the electromagnetic exposure of the power battery and the physiological signals of the human body when the electric vehicle is started and the electric vehicle is running at a constant speed, and obtaining the first influence information of the physiological electromagnetic exposure of the human body, specifically includes: Obtaining human body's regular walking speed information; The battery car was driven at a constant speed at the normal walking speed of the human body, and the human physiological signals of the volunteers sitting in the co-pilot seat were monitored before and after the battery car was started to obtain the start-up data; The battery car was driven at a constant speed at the normal walking speed of the human body, and the human physiological signals of the volunteers who joined the co-pilot during the constant speed driving of the battery car were monitored to obtain the secondary start data; Based on the one-time startup data, the human physiological signal monitoring data from the time when the battery car is started to the time when the battery car is driven at a constant speed for one minute is obtained as the startup impact data; Based on the analysis of human physiological signals, a corresponding weight is set for each human physiological signal measurement data; According to the startup impact data and the secondary startup data, based on the set weights, the startup impact index is obtained; According to the human physiological signal measurement data, obtaining physiological signal uniform speed measurement data, wherein the physiological signal uniform speed measurement data represents the human physiological information measurement data of the driver when the battery car is driven at different uniform speeds; According to the start-up impact index and the uniform speed measurement data of physiological signals, the first impact information of human physiological electromagnetic exposure is obtained; The calculation formula of the startup impact index is: Where, E is the startup impact index, α s represents the weight of the sth human physiological signal, A max (s) represents the maximum value of the sth human physiological signal in the startup impact data, Represents the average value of the sth human physiological signal in the secondary startup data.

5. The power battery electromagnetic exposure and human physiological signal measurement and analysis method according to claim 4 is characterized in that: The obtaining of the first impact information of human physiological electromagnetic exposure according to the startup impact index and the uniform speed measurement data of the physiological signal specifically includes: Get the maximum speed of the electric vehicle; Based on the maximum driving speed of the electric vehicle, uniform test speed information is obtained, wherein the uniform test speed information represents a uniform driving speed set of the electric vehicle, and the speed interval of the uniform test speed is 5 m / s; Based on the uniform speed test speed information, the electric vehicle is made to travel at different uniform speeds, the driver's human physiological signals are monitored, and uniform speed measurement data of the physiological signals are obtained; According to the start-up impact index and the uniform speed measurement data of the physiological signal, the abnormal index of the human physiological signal is obtained; Based on the health analysis of human physiological signals, the threshold of abnormal index of human physiological signals is obtained; According to the human physiological signal abnormality index and the human physiological signal abnormality index threshold, whether the human physiological signal is abnormal is judged; if the human physiological signal abnormality index exceeds the human physiological signal abnormality index threshold, the human physiological signal measurement data is marked to obtain the human physiological signal abnormality data; According to the startup impact index and the abnormal data of human physiological signals, first impact information of human physiological electromagnetic exposure is obtained, wherein the first impact information of human physiological electromagnetic exposure represents the impact relationship between the electromagnetic exposure of the power battery and the human physiological signals when the electric vehicle is in the startup state and driving at a constant speed; The calculation formula of the abnormal index of human physiological signals is: Where R(V) represents the abnormal index of human physiological signals when the uniform speed is V, B max (V, s) represents the maximum value of the sth human physiological signal in the uniform speed measurement data of the physiological signal when the uniform speed is V, and B0(s) is the standard value of the sth human physiological signal.

6. The power battery electromagnetic exposure and human physiological signal measurement and analysis method according to claim 1, characterized in that: Based on the first impact information of human physiological electromagnetic exposure, the impact relationship between the electromagnetic exposure of the power battery and the human physiological signal when the electric vehicle is in the acceleration state and the charging state is analyzed to obtain the second impact information of human physiological electromagnetic exposure, specifically including: Based on the first impact information of human physiological electromagnetic exposure, abnormal data of human physiological signals are obtained; The minimum value of the battery car speed in the abnormal data of human physiological signals is used as the calibrated speed of the electric vehicle; Get the uniform speed test speed information; Based on the uniform speed test speed information, uniform speed test electromagnetic field information is obtained, wherein the uniform speed test electromagnetic field information represents the electromagnetic field information corresponding to the uniform speed of the electric vehicle; According to the electromagnetic field information and electromagnetic field difference coefficient of the uniform speed test, the state data of the electric vehicle under the accelerated test is obtained; Based on the acceleration test electric vehicle state data, the electric vehicle is accelerated at different accelerations until the speed of the electric vehicle reaches the calibrated speed of the electric vehicle, the driver's human physiological signals are monitored, and physiological signal acceleration measurement data is obtained; According to the start-up impact index and the physiological signal acceleration measurement data, the human physiological signal acceleration abnormality index is obtained; Based on the health analysis of human physiological signals, the threshold of the abnormal acceleration index of human physiological signals is obtained; According to the human physiological signal acceleration abnormality index and the human physiological signal acceleration abnormality index threshold, it is judged whether the human physiological signal is abnormal; if the human physiological signal acceleration abnormality index exceeds the human physiological signal acceleration abnormality index threshold, the physiological signal acceleration measurement data is marked to obtain the human physiological signal acceleration abnormality data; According to the electromagnetic field information of the uniform speed test, the charging current information is obtained; Based on the charging current information, the electric vehicle is charged with the charging current, the driver's human physiological signals are monitored, and the physiological signal charging measurement data is obtained; According to the physiological signal charging measurement data, the human body physiological signal charging abnormality index is obtained; According to the charging index of the human physiological signal, it is judged whether the human physiological signal is abnormal. If so, the charging measurement data of the physiological signal is marked to obtain the abnormal charging data of the human physiological signal; According to the abnormal acceleration data of the human physiological signal and the abnormal charging data of the physiological signal, the second impact information of the human physiological electromagnetic exposure is obtained, wherein the second impact information of the human physiological electromagnetic exposure represents the impact relationship between the electromagnetic exposure of the power battery and the human physiological signal when the electric vehicle is in an accelerated driving state and a charging state; The acceleration test electric vehicle state data (P, δ, I) are specifically: In the formula, P is the power of the power battery, δ is the power battery voltage, I is the motor current, σ is the vacuum magnetic permeability, N is the number of turns of the motor winding, K is the electromagnetic field difference coefficient, μ is the speed electromagnetic field correlation coefficient, V is the uniform driving speed, and w is the current conversion coefficient; The charging current is: Where I0 is the charging current.

7. A power battery electromagnetic exposure and human physiological signal measurement device, used to implement the analysis method according to any one of claims 1 to 6, characterized in that: include: A main control module, the main control module is used to obtain difference 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 abnormal index and the human physiological signal abnormal index threshold, determine 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, determine whether the human physiological signal is abnormal according to the human physiological signal charging 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 battery car starting to the battery car driving at a constant speed for one minute based on one-time startup data as the startup impact data, set a corresponding weight for each human physiological signal measurement data based on the human physiological signal analysis, obtain the first impact information of human physiological electromagnetic exposure according to the startup impact index and the human physiological signal abnormal data, and obtain the second impact information of human physiological electromagnetic exposure according to the human physiological signal acceleration abnormal data and the physiological signal charging abnormal data; An information acquisition module, the information acquisition module is used to acquire power battery parameter information, power battery parameter information and power battery placement information, acquire startup electromagnetic field information based on the power battery parameter information and electromagnetic field monitoring, acquire basic electromagnetic field information based on electromagnetic exposure analysis of human daily life, drive the electric vehicle in different states based on the working state of the power battery, monitor the driver's human physiological signals, and acquire human physiological signal measurement data; An evaluation module, wherein the evaluation module is used to obtain a speed electromagnetic field correlation coefficient based on a linear regression equation according to electromagnetic field monitoring data of a uniform speed test, obtain a power battery state difference index according to power battery test monitoring data, obtain an electromagnetic field difference coefficient according to a power battery test characteristic speed, obtain a startup influence index based on a set weight according to startup influence data and secondary startup data, obtain a human physiological signal abnormality index according to the startup influence index and physiological signal uniform speed measurement data, obtain a human physiological signal acceleration abnormality index according to the startup influence index and physiological signal acceleration measurement data, and obtain a human physiological signal charging abnormality index according to physiological signal charging measurement data; 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 impact information.

8. The power battery electromagnetic exposure and human physiological signal measurement device according to claim 7, characterized in that: The main control module specifically includes: A control unit, the control unit is used to obtain a power battery test characteristic speed according to a power battery reference speed, a speed electromagnetic field correlation coefficient and basic electromagnetic field information, obtain human physiological signal monitoring data from the time the battery vehicle is started to the time the battery vehicle is driven at a constant speed for one minute based on one start-up data, as start-up impact data, set a corresponding weight for each type of human physiological signal measurement data based on human physiological signal analysis, obtain first impact information on human physiological electromagnetic exposure according to a start-up impact index and abnormal human physiological signal data, and obtain second impact information on human physiological electromagnetic exposure according to abnormal acceleration data of human physiological signals and abnormal charging data of physiological signals; An information receiving unit, which interacts with the information acquisition module and the evaluation module to receive data and transmit it to the judgment unit; A judgment unit, wherein the judgment unit is used to obtain 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 power battery test characteristic speed, 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 index.

9. The power battery electromagnetic exposure and human physiological signal measurement device according to claim 7, characterized in that: The information acquisition module specifically includes: A first acquisition unit, the first acquisition unit is used to acquire power battery parameter information, power battery parameter information and power battery placement information, and acquire starting electromagnetic field information based on the power battery parameter information and electromagnetic field monitoring; The second acquisition unit is used to acquire basic electromagnetic field information based on the electromagnetic exposure analysis of human daily life, drive the electric vehicle in different states based on the working state of the power battery, monitor the driver's human physiological signals, and acquire human physiological signal measurement data.

10. The power battery electromagnetic exposure and human physiological signal measurement device according to claim 7, characterized in that: The evaluation module specifically includes: a first evaluation unit, the first evaluation unit being used to obtain a speed electromagnetic field correlation coefficient based on a linear regression equation according to electromagnetic field monitoring data of a uniform speed test, to obtain a power battery state difference index according to power battery test monitoring data, and to obtain an electromagnetic field difference coefficient according to a power battery test characteristic speed; The second evaluation unit is used to obtain a startup influence index based on the startup influence data and the secondary startup data based on the set weight, obtain a human physiological signal abnormality index based on the startup influence index and the physiological signal uniform speed measurement data, obtain a human physiological signal acceleration abnormality index based on the startup influence index and the physiological signal acceleration measurement data, and obtain a human physiological signal charging abnormality index based on the physiological signal charging measurement data.

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