A vehicle energy flow analysis method, device, equipment and storage medium

Abstract

The present application discloses a vehicle energy flow analysis method, device, equipment and storage medium, which relates to the field of vehicle test analysis, and includes: a processing device obtains data to be processed, the data to be processed includes multiple data files, each data file is collected from a sensor, and the sampling frequency of the sensor is different, and the number of data contained in each data file is also different. Based on the data file obtained by the sensor with the highest sampling frequency, the data corresponding to each moment in other data files is determined, and then energy flow analysis is performed based on the data in all data files corresponding to the same moment. This method can effectively analyze the collected data and reduce the error with the actual test results.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a method, device, equipment and storage medium for analyzing vehicle energy flow. Background Art

[0002] In the field of vehicle analysis, especially in the road analysis technology of new energy vehicles, new energy vehicle road analysis technology is an important means to evaluate the performance and safety of new energy vehicles in actual road environments. The new energy vehicle road analysis technology includes cruising range test, charging test, braking performance test and noise and vibration test.

[0003] At present, different sensors are used for testing different parts, and different sensors have different sampling frequencies, which leads to different data collected by different sensors. If the data is not collected at the same time point, effective analysis cannot be performed. For example, the speed sensor collects a set of data every second in one minute, and a total of 60 sets of data are collected at 60 time points in one minute, while the temperature sensor collects a set of data every two seconds in one minute, and a total of 30 sets of data are collected at 30 time points in one minute. Among them, there are 30 time points in the speed sensor where there is no data on the temperature sensor, resulting in the inability to ensure that the collected data is synchronized in time during the analysis process of new energy vehicles, resulting in a certain error between the final analysis results and the actual analysis results. Summary of the invention

[0004] The present application provides a vehicle energy flow analysis method, device, equipment and storage medium, which can effectively analyze the collected data during the road analysis process of new energy vehicles and reduce the error with the actual test results.

[0005] In order to achieve the above purpose, this application adopts the following technical solutions:

[0006] In a first aspect, the present application provides a method for analyzing energy flow of new energy vehicles, the method comprising:

[0007] Acquire data to be processed, the data to be processed at least comprising a first data file and a second data file, the first data file comprising a plurality of first data and a time corresponding to each first data, the second data file comprising a plurality of second data and a time corresponding to each second data; the number of the first data is greater than the number of the second data;

[0008] Determine the first data file time, determined in the second data file Moment Time and Moment At the moment, in the first data file There is a corresponding first data at the moment, in the second data file There is no corresponding second data at the time;

[0009] Determine from the second data file The second data corresponding to the time and Second data corresponding to the time;

[0010] According to the time, time, The second data corresponding to the time The time and The second data corresponding to the time is determined to be the second data file Second data corresponding to the time;

[0011] The energy flow analysis is performed according to the first data and the second data at the same time.

[0012] In some possible implementations, the time, time, The second data corresponding to the time The time and The second data corresponding to the time is determined to be the second data file The second data corresponding to the time includes:

[0013] The following formula is used to calculate and obtain the second data file in Second data at the moment:

[0014]

[0015]

[0016]

[0017] in, , as well as is a positive integer, Time is The moment before the moment, Time is The moment after the moment, is the first weight, is the second weight, for The second data corresponding to the time, for The second data corresponding to the time, for The second data corresponding to the time.

[0018] In some possible implementations, the determining of the first data file Moments, including:

[0019] The time corresponding to the first data in the first data file is traversed in the time corresponding to the second data in the second data file, and the time corresponding to the first data that has not been traversed is determined as time.

[0020] In some possible implementations, obtaining the data to be processed includes:

[0021] A first data file collected by a first sensor and a second data file collected by a second sensor are acquired, wherein a sampling frequency of the first sensor is higher than a sampling frequency of the second sensor.

[0022] In some possible implementations, the data to be processed includes multiple data files, and when determining the second data file After the second data corresponding to the time instant, the method further includes:

[0023] Generate multiple groups of sample index data and multiple groups of sample energy consumption data according to the data at the same time in multiple data files;

[0024] Determine the correlation between the sample indicator data and the sample energy consumption data;

[0025] The sample indicator data and the sample energy consumption data whose correlation is lower than the correlation threshold are removed from the multiple groups of sample indicator data to obtain the remaining sample indicator data and the remaining sample energy consumption data;

[0026] The remaining sample index data and the remaining sample energy consumption data are used to train a vehicle energy consumption prediction model.

[0027] In some possible implementations, determining the correlation between the sample indicator data and the sample energy consumption data includes:

[0028]

[0029] in, is the correlation between the i-th sample index data and the i-th sample energy consumption data, is the set of the i-th indicator data, is the set of energy consumption data of the i-th vehicle, for The j-th sample indicator variable in for The j-th sample energy consumption variable in for 、 The joint probability density of for The marginal probability density of for The marginal probability density of , and The product of is equal to or The number of variables in is the horizontal axis coordinate variable of the grid G, is the vertical axis coordinate variable of the grid G, and are all positive integers, , q is or The number of variables in , j is a positive integer, j is less than or equal to q.

[0030] In some possible implementations, the method further includes:

[0031] Obtain the indicator data to be predicted;

[0032] The index data to be predicted is input into the vehicle energy consumption prediction model to obtain the vehicle energy consumption corresponding to the index data to be predicted.

[0033] In a second aspect, the present application provides a vehicle energy flow analysis device, the device comprising:

[0034] an acquisition module, used to acquire data to be processed, the data to be processed at least comprising a first data file and a second data file, the first data file comprising a plurality of first data and a time corresponding to each first data, the second data file comprising a plurality of second data and a time corresponding to each second data; the number of the first data is greater than the number of the second data;

[0035] A determination module is used to determine the first data file time, determined in the second data file Moment Time and Moment At the moment, in the first data file There is a corresponding first data at the moment, in the second data file There is no corresponding second data at the moment; determine from the second data file The second data corresponding to the time and Second data corresponding to the time;

[0036] A calculation module is used to calculate the time, time, The second data corresponding to the time The time and The second data corresponding to the time is determined to be the second data file Second data corresponding to the time;

[0037] The analysis module is used to perform energy flow analysis according to the first data and the second data at the same time.

[0038] In a third aspect, the present application provides a computing device, including a memory and a processor;

[0039] One or more computer programs are stored in the memory, and the one or more computer programs include instructions; when the instructions are executed by the processor, the computing device executes the method as described in any one of the first aspects.

[0040] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium is used to store a computer program, and the computer program is used to execute the method as described in any one of the first aspects.

[0041] It can be seen from the above technical solution that the present application has at least the following beneficial effects:

[0042] In the present application, the processing device obtains the data to be processed, and the data to be processed includes multiple data files, each of which is collected from a sensor, and the sampling frequency of the sensor is different, and the number of data contained in each data file is also different. Based on the data file obtained by the sensor with the highest sampling frequency, the data corresponding to each moment in other data files is determined, and then the energy flow analysis is performed according to the data in all data files corresponding to the same moment. The prior art is to directly perform energy flow analysis on the data obtained from the sensor. Since the sampling frequency of the sensor is different, the number of data collected by each sensor is also different, so there will be a certain error with the actual test results when performing energy flow analysis. It can be seen that in the present application, according to the time corresponding to the data in the data file obtained by the sensor with the highest sampling frequency, the calculation of the data of other data files is determined, and then the energy flow analysis is performed according to the data in all data files corresponding to the same moment. It can be seen that the scheme of the present application effectively analyzes the collected data and reduces the error with the actual test results.

[0043] It should be understood that the description of technical features, technical solutions, beneficial effects or similar language in this application does not imply that all features and advantages can be realized in any single embodiment. On the contrary, it is understood that the description of features or beneficial effects means that specific technical features, technical solutions or beneficial effects are included in at least one embodiment. Therefore, the description of technical features, technical solutions or beneficial effects in this specification does not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions and beneficial effects described in the present embodiment can also be combined in any appropriate manner. Those skilled in the art will understand that the embodiment can be realized without one or more specific technical features, technical solutions or beneficial effects of a specific embodiment. In other embodiments, additional technical features and beneficial effects can also be identified in a specific embodiment that does not embody all embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 A flow chart of a vehicle energy flow analysis method provided in an embodiment of the present application;

[0045] Figure 2 A schematic diagram of a vehicle data testing method provided in an embodiment of the present application;

[0046] Figure 3 A schematic diagram of a vehicle energy consumption prediction model provided in an embodiment of the present application;

[0047] Figure 4 A flow chart of a vehicle data management system provided in an embodiment of the present application;

[0048] Figure 5 A schematic diagram of a vehicle energy flow analysis device provided in an embodiment of the present application;

[0049] Figure 6 A schematic diagram of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0050] The terms "first", "second", "third", etc. in the specification of this application and the accompanying drawings are used to distinguish different objects rather than to limit a specific order.

[0051] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0052] At present, the existing technology is to directly perform energy flow analysis on the data collected from the sensor. Since the sampling frequencies of the sensors are different, the number of data collected by each sensor is also different. Therefore, there will be certain errors between the actual test results and the energy flow analysis.

[0053] In view of this, the embodiment of the present application provides a method for analyzing vehicle energy flow, in which a processing device obtains data to be processed, and the data to be processed includes multiple data files, each of which is collected from a sensor, and the sampling frequency of the sensor is different, and the number of data contained in each data file is also different. Based on the data file obtained by the sensor with the highest sampling frequency, the data corresponding to each moment in other data files is determined, and then the energy flow analysis is performed according to the data in all data files corresponding to the same moment. The prior art directly performs energy flow analysis on the data obtained from the sensor. Since the sampling frequency of the sensor is different, the number of data collected by each sensor is also different, so there will be a certain error with the actual test result when performing energy flow analysis. It can be seen that in the present application, according to the time corresponding to the data in the data file obtained by the sensor with the highest sampling frequency, the calculation of the data in other data files is determined, and then the energy flow analysis is performed according to the data in all data files corresponding to the same moment. It can be seen that the scheme of the present application effectively analyzes the collected data and reduces the error with the actual test result.

[0054] In order to make the technical solution of the present application clearer and easier to understand, a vehicle energy flow analysis method provided in an embodiment of the present application is introduced. Figure 1 As shown, the figure is a flow chart of a vehicle energy flow analysis method provided in an embodiment of the present application. The vehicle energy flow analysis method can be executed by a processing device, and the method includes:

[0055] S101: A processing device obtains data to be processed.

[0056] A processing device is a hardware system or software system used to perform specific tasks or process data. In scenarios involving vehicle energy flow analysis, a processing device may refer to a computer, server, or specific software platform dedicated to calculating, simulating, or analyzing vehicle energy flow.

[0057] In this application, before the processing device obtains the data to be processed, high-voltage architecture research and sensor layout are required. First, the vehicle high-voltage system architecture and low-voltage main electrical components are determined, and the sensor layout scheme is determined, such as Figure 2As shown, the figure is a schematic diagram of a vehicle data testing method provided by an embodiment of the present application. In the figure, the vehicle architecture includes a transmission system architecture 201, a high-voltage component architecture 202, a low-voltage component architecture 203 and a CAN communication architecture 204. The sensor arrangement includes the arrangement of a current sensor 211, a voltage sensor 212, a temperature sensor 213, a pressure sensor 214, a flow sensor 215, a torque sensor 216 and a CAN communication interface 217. The purpose of arranging the current sensor and the voltage sensor is to calculate the power. However, some signals are difficult to obtain through the sensor arrangement, so it is necessary to use a signal analysis method to obtain information such as battery SOC, battery minimum temperature, battery maximum temperature, battery cell voltage, motor torque, motor speed, compressor speed, etc.

[0058] Specifically, the deployment locations of voltage sensors include the interfaces of high-voltage electrical components and low-voltage electrical components. Since high-voltage wires cannot be damaged, otherwise it will cause danger. Therefore, a set of test harnesses is customized according to the model of the electrical interface of the high-voltage component. The test harness can be connected in series to the high-voltage harness. The test harness includes a voltage sensor and a signal output harness. After the test harness is installed, the signal output line is connected to the data acquisition module.

[0059] The current sensor deployment location generally includes high-voltage components and low-voltage components. High-voltage components include power batteries, front electric drive modules, rear electric drive modules, high-voltage PTC, air-conditioning compressors, DCDC input terminals, etc. Low-voltage components include DCDC output terminals, cooling fans, motor water pumps, battery water pumps, air-conditioning water pumps, blowers, 12V batteries, etc. Since different components have different rated powers, when selecting the current sensor range, ensure that the sensor range does not exceed 1.5 times the maximum current of the high-voltage and low-voltage components.

[0060] The temperature sensor deployment locations mainly include the battery water inlet and outlet, motor water inlet and outlet, radiator water inlet and outlet, heat exchanger water inlet and outlet and other major heat-generating and heat-dissipating components, which are used to assist in analyzing the correlation between passenger compartment energy consumption, battery energy consumption and temperature.

[0061] Flow sensors are mainly arranged in the motor cooling circuit water pipes, battery cooling circuit water pipes, air conditioning circuit water pipes, and radiator water pipes. Combined with temperature sensors, they are used to analyze the heat dissipation and heat generation of different power components.

[0062] The CAN communication interface obtains the CAN signal through the twisted wire on the connector of key controllers such as the battery management system and battery controller, creates specific working conditions to identify data that changes with the working condition characteristics, and then calibrates the offset and proportional values ​​of the CAN communication interface and sensors, external devices and other instruments. For example, when analyzing the battery SOC, DC fast charging conditions are carried out, the CAN data change trend is determined, the signal ID and bytes are determined, and then the CAN data is calibrated according to the instrument values ​​and charging pile values.

[0063] The actual road energy consumption of the vehicle under the multi-factor matrix of different temperatures 221, different weather 222, different time periods 223 and different routes 224 is tested. Typical temperatures include below -10℃, -5 to -10℃, 0 to -5℃, 0 to 10℃, 10 to 25℃, 25 to 35℃, and above 35℃; weather factors mainly include rain, snow, fog, haze, frost, and clear weather, and wind speeds mainly include 0-2, 2-4, and above 4; time periods mainly include 7:00-9:00 morning peak, 17:00-19:30 evening peak, 10:00 to 14:00 flat peak, and 22:00-2:00 night. Routes mainly include urban congested routes, suburban express routes, and comprehensive routes. The same test conditions need to be tested continuously for more than two days to avoid accidental errors. The developed data acquisition equipment is used during the test to synchronously collect data from multiple vehicles. Specifically, the road energy consumption test adopts the test matrix shown in Table 1:

[0064] Table 1:

[0065] Temperature ℃ weather Wind speed Time route -10℃ or less Snow Level 4 and above Morning rush hour Urban congested routes -10℃ or less Snow Level 4 and above Evening rush hour Urban congested routes -10℃ or less Snow Level 4 and above Pingfeng Urban congested routes -10℃ or less Snow Level 4 and above at night Urban congested routes 0 to 10℃ clear Level 2-4 Morning rush hour Suburban Express Route 0 to 10℃ clear Level 2-4 Evening rush hour Suburban Express Route 0 to 10℃ clear Level 2-4 Pingfeng Suburban Express Route 0 to 10℃ clear Level 2-4 at night Suburban Express Route …… …… …… …… ……

[0066] The test index calculation includes vehicle energy consumption 231, high voltage component energy consumption 232, low voltage component energy consumption 233 and motor efficiency 234.

[0067] S102: The processing device determines the first data file time, determined in the second data file Moment Time and Moment time.

[0068] The data to be processed includes at least a first data file and a second data file, but is not limited to two data files. The following is calculated and analyzed by using the data collected from the voltage sensor and the current sensor.

[0069] A first data file collected by a first sensor and a second data file collected by a second sensor are acquired, wherein a sampling frequency of the first sensor is higher than a sampling frequency of the second sensor.

[0070] Exemplarily, the first sensor is assumed to be a voltage sensor, the second sensor is assumed to be a current sensor, the first data file is voltage data collected from the voltage sensor and the time corresponding to the voltage data, and the second data file is current data collected from the current sensor and the time corresponding to the current data.

[0071] In some embodiments, the following may be used to determine Moment: traverse the moment corresponding to the first data in the first data file in the moment corresponding to the second data in the second data file, and determine the moment corresponding to the first data that has not been traversed to the result as time.

[0072] The following is shown in Table 2 Position relationship at time:

[0073] Table 2:

[0074] Voltage data (V) Current data (A) Time(s) 1 0.1 1 2 / 2 3 / 3 4 0.4 4 5 / 5 6 / 6 7 0.7 7 ··· ··· ···

[0075] The time corresponding to the current data in the current data file includes the 1st second, the 4th second, and the 7th second; the time corresponding to the voltage data in the voltage data file includes the 1st second, the 2nd second, the 3rd second, the 4th second, the 5th second, the 6th second, and the 7th second; the current data file and the voltage data file are compared according to the time, and the time corresponding to the current data file that does not exist is determined as At the moment, it can be seen in the table that the voltage data file has a value at every moment, while the current data file does not have a value at the 2nd, 3rd, 5th and 6th seconds, so the 2nd, 3rd, 5th and 6th seconds are determined as time.

[0076] according to The time is determined in the current data file The moment before and after the moment, determine The moment before the moment is Moment, sure The moment after the moment is In the table, if the second second is determined as time, then the first second is time, the 4th second is time.

[0077] S103: The processing device determines from the second data file The second data corresponding to the time and The second data corresponding to the time.

[0078] Determine according to the second data collected on the second sensor and the time corresponding to the second data The second data corresponding to the time and The second data corresponding to the time.

[0079] For example, it can be seen in the table, The second data corresponding to the moment is the current data and the value is 0.1A. The second data corresponding to the moment is current data and the value is 0.4A.

[0080] S104, processing equipment according to time, time, The second data corresponding to the time, Time and The second data corresponding to the time is determined to determine the second data file The second data corresponding to the time.

[0081] Use the following formula to calculate and get the second data file in Second data at the moment:

[0082]

[0083]

[0084]

[0085] in, , as well as is a positive integer, Time is The moment before the moment, Time is The moment after the moment, is the first weight, is the second weight, for The second data corresponding to the time, for The second data corresponding to the time, for The second data corresponding to the time.

[0086] Exemplarily, the current data determined in S102 and S103 and the time corresponding to the current data are calculated by the above formula to obtain The second data corresponding to the time , which is the missing value of the current data in the table, as shown in Table 3 below:

[0087] Table 3:

[0088] Voltage data (V) Current data (A) Time(s) 1 0.1 1 2 0.2 2 3 0.3 3 4 0.4 4 5 0.5 5 6 0.6 6 7 0.7 7 ··· ··· ···

[0089] S105: The processing device performs energy flow analysis according to the first data and the second data at the same time.

[0090] Energy flow analysis includes the power consumption of high-voltage and low-voltage components in new energy vehicles, the driving efficiency and braking efficiency of the motor, battery internal resistance analysis, external heating capacity of the battery, and external cooling capacity of the battery.

[0091] The following is a calculation of the power consumption of high-voltage and low-voltage components in new energy vehicles:

[0092]

[0093]

[0094] in, The power consumption of high-voltage components in new energy vehicles, is the voltage of high-voltage components in new energy vehicles, is the current of high-voltage components in new energy vehicles, The power consumption of low-voltage components in new energy vehicles. is the voltage of low-voltage components in new energy vehicles, For low-voltage components in new energy vehicles, For test time.

[0095] For example, according to the table summarized in S104, the voltage data and current data at the same time are analyzed for energy flow. At the second second, the voltage data is 2V and the current data is 0.2A. The formula for calculating power consumption is:

[0096] The motor efficiency is calculated by using the motor speed torque on the CAN bus and the motor current and bus voltage collected by the sensor, and the following formula is used to calculate the motor's driving efficiency and braking efficiency:

[0097]

[0098]

[0099] in, is the motor drive efficiency, is the motor braking efficiency, is the motor torque, is the motor speed, is the motor bus voltage, is the motor current. When I is a negative value, it is the braking current, which is used to calculate the braking efficiency. When I is a positive value, it is the driving current, which is used to calculate the driving efficiency.

[0100] The battery internal resistance analysis is calculated based on the heat generated by the battery itself. Since the driving process is divided into driving and braking, the battery discharges during driving and charges during braking. Since the charging internal resistance and discharging internal resistance of the battery are different, the data is first divided into battery charging data and battery discharging data. Then, since the battery internal resistance is highly correlated with the battery temperature, the test data is classified and processed according to the battery temperature, with a certain temperature interval as the segmentation threshold, such as every interval Separate the data, and then fit it according to the following formula based on the current and voltage distribution of the battery. The slope of the fitting curve is the internal resistance of the battery. The internal resistance fitted by the charging data is the charging internal resistance, and the internal resistance fitted by the discharging data is the discharging internal resistance.

[0101]

[0102] in, is the battery output voltage, is the battery output current, and are the coefficients to be fitted, is the fitted battery internal resistance, is the fitted open circuit voltage.

[0103] Based on the fitted battery internal resistance, a table of battery internal resistance that changes with temperature can be obtained, and then linear difference is performed to finally obtain a battery charging internal resistance map and battery discharging internal resistance map over the full temperature range.

[0104] According to the formula , the power consumption of the battery's internal resistance can be calculated. is the power consumption of the battery's internal resistance, is the battery output current, is the fitted battery internal resistance.

[0105] According to the battery water inlet temperature, water outlet temperature, and battery thermal management circuit flow, the battery heating capacity and battery cooling capacity are calculated using the following formula:

[0106]

[0107] in, The heating and cooling capacity outside the battery, is the specific heat capacity of the coolant, is the battery thermal management circuit flow, is the outlet temperature at the kth second, is the water inlet temperature at the kth second, For test time.

[0108] The external heating and cooling efficiency of the battery can be calculated based on the external heating amount of the battery and the external cooling amount of the battery, the external heating amount of the battery or the external heat dissipation of the battery.

[0109]

[0110] in, For the heating and cooling efficiency of the battery, It is the heating power of the external heat source or the cooling power of the external heat source, wherein the heating power of the external heat source is the power consumption of the PTC, and the cooling power of the external heat source is the power consumption of the compressor.

[0111] For example, the summary vehicle energy flow indicators are shown in Table 4 below:

[0112] Table 4:

[0113]

[0114] The energy flow data obtained according to the above method and the vehicle energy consumption data are correlated, wherein the energy flow data are multiple groups of sample index data, and the vehicle energy consumption data are multiple groups of sample energy consumption data.

[0115] Determine the correlation between the sample indicator data and the sample energy consumption data according to the following formula:

[0116]

[0117] in, is the correlation between the i-th sample index data and the i-th sample energy consumption data, is the set of the i-th indicator data, is the set of energy consumption data of the i-th vehicle, for The j-th sample indicator variable in for The j-th sample energy consumption variable in for 、 The joint probability density of for The marginal probability density of for The marginal probability density of , and The product of is equal to or The number of variables in is the horizontal axis coordinate variable of the grid G, is the vertical axis coordinate variable of the grid G, and are all positive integers, , q is or The number of variables in , j is a positive integer, j is less than or equal to q.

[0118] The sample indicator data and the sample energy consumption data whose correlation is lower than the correlation threshold are removed from the multiple groups of sample indicator data to obtain the remaining sample indicator data and the remaining sample energy consumption data.

[0119] Exemplarily, the correlation threshold is set to 0.7, the correlation between the first sample indicator data and the first sample energy consumption data is 0.8, and the correlation between the second sample indicator data and the second sample energy consumption data is 0.6. Depending on the size of the correlation threshold, the first sample indicator data and the first sample energy consumption data can be retained, and the second sample indicator data and the second sample energy consumption data can be removed.

[0120] The remaining sample index data and remaining sample energy consumption data are used to train the vehicle energy consumption prediction model. Figure 3 As shown in the figure, this figure is a schematic diagram of a vehicle energy consumption prediction model provided by an embodiment of the present application. In the figure, the vehicle energy consumption prediction model is divided into 7 layers. The first layer is the input layer, and the input quantity is Key energy flow indicators, the number of nodes is The second layer represents the fuzzy subspace, which is divided into 7 fuzzy subsets, namely positive large, positive medium, positive small, zero, negative small, negative medium, and negative large. Each node represents the membership function of the fuzzy set. (in, r is an integer, and , )The membership function adopts a bell-shaped function, and the formula is as follows:

[0121]

[0122] in, is the center of membership, is the width of the membership, and the number of nodes in this layer is , the third layer is the reasoning layer, each node output corresponds to the applicability of each rule, the number of nodes in this layer is The fourth layer is normalized calculation, and the number of nodes is The fifth layer is the output layer, and the number of nodes is 1.

[0123] Set the number of training times to 100. After completing the training, a vehicle energy consumption prediction model can be obtained.

[0124] Based on the above description, the embodiment of the present application provides a method for analyzing vehicle energy flow, in which a processing device obtains data to be processed, and the data to be processed includes multiple data files, each of which is collected from a sensor, and the sampling frequency of the sensor is different, and the number of data contained in each data file is also different. Based on the data file obtained by the sensor with the highest sampling frequency, the data corresponding to each moment in other data files is determined, and then the energy flow analysis is performed according to the data in all data files corresponding to the same moment. The prior art directly performs energy flow analysis on the data obtained from the sensor. Since the sampling frequency of the sensor is different, the number of data collected by each sensor is also different, so there will be a certain error with the actual test results when performing energy flow analysis. It can be seen that in the present application, according to the time corresponding to the data in the data file obtained by the sensor with the highest sampling frequency, the calculation of the data of other data files is determined, and then the energy flow analysis is performed according to the data in all data files corresponding to the same moment. It can be seen that the scheme of the present application can effectively analyze the collected data and reduce the error with the actual test results.

[0125] Exemplarily, the present application also proposes a portable, low-cost, vehicle-mounted high-precision data management system, such as Figure 4 As shown, this figure is a flow chart of a vehicle data management system provided in an embodiment of the present application, which can realize the energy flow testing of the same road by multiple vehicles at the same time, avoiding the phenomenon that the data cannot be compared due to different test times and different actual traffic conditions.

[0126] S401. Arrangement of vehicle test data and test indicators.

[0127] The data management system includes temperature measurement module, analog acquisition module, high voltage acquisition module, CAN acquisition module, and data recording module, which supports the acquisition and recording of temperature, current, voltage, flow, CAN, CANFD and other signals. The vehicle test data and test indicators are sorted according to the test matrix below.

[0128] The actual road energy consumption of the test vehicle in a multi-factor matrix of different temperatures, different weather, different time periods, different routes, etc. Typical temperatures include below -10℃, -10℃ to -5℃, -5℃ to 0℃, 0℃ to 10℃, 10℃ to 25℃, 25℃ to 35℃, and above 35℃; weather factors mainly include rain, snow, fog, haze, frost, and clear weather, and wind speeds mainly include 0-2, 2-4, and above 4; time periods mainly include 7:00-9:00 morning peak, 17:00-19:30 evening peak, 10:00 to 14:00 flat peak, 22:00-2:00 night, and routes mainly include urban congested routes, suburban express routes, and comprehensive routes. The same test conditions need to be tested continuously for more than two days to avoid accidental errors. During the test, the developed data acquisition equipment is used to simultaneously collect data from multiple vehicles.

[0129] S402, uploading the sorted data to the data management system.

[0130] The data management system can input vehicle energy flow data and indicators with one click, and has the functions of automatically analyzing performance indicators and process data between different vehicles, analyzing the development trend of vehicle indicators, and generating analysis reports with one click.

[0131] Specifically, the test data is classified according to test items, test conditions, test conditions, and test methods, and the classified data is stored separately in two categories: test indicators and test process data. Standard indicator names and parameter names are used to record various implementation indicators and process data. Then all the data of a car model is compressed and packaged and uploaded to the data management system. The system can automatically identify the data according to the data classification, and then display the vehicle performance indicators and process data on the interface. At the same time, it supports switching the test data on the interface according to test items, test conditions, test conditions, and test methods.

[0132] S403. Vehicle performance display.

[0133] You can select a certain vehicle model, determine the test items, test conditions, test conditions, and test methods. The system can intuitively display the vehicle energy consumption, the energy consumption and proportion of each component, and the test process data.

[0134] S404, comparison of multiple models.

[0135] You can select multiple vehicles in the data management system to compare the size of different indicators and the size of test process data under the same test items, test conditions, test conditions, and test methods. By comparing multiple vehicle indicators, you can intuitively see the gap between indicators of different models, and thus find out the performance indicators that need to be optimized.

[0136] S405. Intelligent analysis.

[0137] At the same time, you can perform intelligent analysis on a single test indicator, select the vehicle model that needs intelligent analysis, then select the test item, determine the test indicator to be analyzed, screen the test conditions, test methods, and test conditions, and use the maximum, minimum, average, median, standard deviation, kurtosis, and skewness of the indicator data of all vehicle models to reflect the degree of dispersion and distribution trend of vehicle test indicators. Display indicator data in the form of scatter plots, bar charts, and normal distribution graphs. In addition, it also supports trend analysis of vehicle indicators according to vehicle parameters such as vehicle wheelbase, vehicle year, and vehicle price. Through intelligent analysis, you can see the ranking of the test indicators of a certain model among many models, so as to determine whether the indicator needs to be optimized.

[0138] Based on the above description, the present application obtains the data to be processed through the processing device, and the data to be processed includes multiple data files, each of which is collected from the sensor, and the sampling frequency of the sensor is different, and the number of data contained in each data file is also different. Based on the data file obtained by the sensor with the highest sampling frequency, the data corresponding to each moment in other data files is determined, and then the energy flow analysis is performed according to the data in all data files corresponding to the same moment. The prior art is to directly perform energy flow analysis on the data obtained from the sensor. Since the sampling frequency of the sensor is different, the number of data collected by each sensor is also different, so there will be a certain error with the actual test results when performing energy flow analysis. It can be seen that in the present application, according to the time corresponding to the data in the data file obtained by the sensor with the highest sampling frequency, the calculation of the data of other data files is determined, and then the energy flow analysis is performed according to the data in all data files corresponding to the same moment. It can be seen that the scheme of the present application effectively analyzes the collected data and reduces the error with the actual test results.

[0139] like Figure 5 As shown, the embodiment of the present application also provides a schematic diagram of a vehicle energy flow analysis device, the vehicle energy flow analysis device includes: Figure 5 The illustrated embodiment includes an acquisition module 501 , a determination module 502 , a calculation module 503 and an analysis module 504 .

[0140] The acquisition module 501 is used to acquire data to be processed, wherein the data to be processed includes at least a first data file and a second data file, wherein the first data file includes a plurality of first data and a time corresponding to each first data, and the second data file includes a plurality of second data and a time corresponding to each second data; the number of the first data is greater than the number of the second data;

[0141] Determination module 502, used to determine the first data file time, determined in the second data file Moment Time and Moment At the moment, in the first data file There is a corresponding first data at the moment, in the second data file There is no corresponding second data at the moment; determine from the second data file The second data corresponding to the time and Second data corresponding to the time;

[0142] The calculation module 503 is used to calculate the time, time, The second data corresponding to the time The time and The second data corresponding to the time is determined to be the second data file Second data corresponding to the time;

[0143] The analysis module 504 is used to perform energy flow analysis according to the first data and the second data at the same time.

[0144] Optionally, the acquisition module 501 is used for the processing device to acquire the data to be processed, including:

[0145] A first data file collected by a first sensor and a second data file collected by a second sensor are acquired, wherein a sampling frequency of the first sensor is higher than a sampling frequency of the second sensor.

[0146] Optionally, the determining module 502 is used to determine the first data file Moments, including:

[0147] The time corresponding to the first data in the first data file is traversed in the time corresponding to the second data in the second data file, and the time corresponding to the first data that has not been traversed is determined as time.

[0148] Optionally, the analysis module 504 is used for determining that the data to be processed includes multiple data files. After the second data corresponding to the time is obtained, multiple groups of sample indicator data and multiple groups of sample energy consumption data are generated according to the data at the same time in multiple data files;

[0149] Determine the correlation between the sample indicator data and the sample energy consumption data;

[0150] The sample indicator data and the sample energy consumption data whose correlation is lower than the correlation threshold are removed from the multiple groups of sample indicator data to obtain the remaining sample indicator data and the remaining sample energy consumption data;

[0151] The remaining sample index data and the remaining sample energy consumption data are used to train a vehicle energy consumption prediction model.

[0152] Optionally, the calculation module 503 is used to calculate time, time, The second data corresponding to the time The time and The second data corresponding to the time is determined to be the second data file The second data corresponding to the time includes:

[0153] The following formula is used to calculate and obtain the second data file in Second data at the moment:

[0154]

[0155]

[0156]

[0157] in, , as well as is a positive integer, Time is The moment before the moment, Time is The moment after the moment, is the first weight, is the second weight, for The second data corresponding to the time, for The second data corresponding to the time, for The second data corresponding to the time.

[0158] Optionally, the analysis module 504 is used to determine the correlation between the sample indicator data and the sample energy consumption data, including:

[0159]

[0160] in, is the correlation between the i-th sample index data and the i-th sample energy consumption data, is the set of the i-th indicator data, is the set of energy consumption data of the i-th vehicle, for The j-th sample indicator variable in for The j-th sample energy consumption variable in for 、 The joint probability density of for The marginal probability density of for The marginal probability density of , and The product of is equal to or The number of variables in is the horizontal axis coordinate variable of the grid G, is the vertical axis coordinate variable of the grid G, and are all positive integers, , q is or The number of variables in , j is a positive integer, j is less than or equal to q.

[0161] Optionally, the analysis module 504 is used to obtain the indicator data to be predicted;

[0162] The index data to be predicted is input into the vehicle energy consumption prediction model to obtain the vehicle energy consumption corresponding to the index data to be predicted.

[0163] The present application also provides a computing device. Figure 6 As shown, this figure is a schematic diagram of a computing device provided in an embodiment of the present application, and the computing device 600 includes a bus 801, a processor 802, a communication interface 803 and a memory 804. The processor 802, the memory 804 and the communication interface 803 communicate through the bus 801.

[0164] The bus 801 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0165] The processor 802 may be any one or more of a central processing unit (CPU), a graphics processing unit (GPU), a microprocessor (MP), or a digital signal processor (DSP).

[0166] The communication interface 803 is used for communicating with the outside.

[0167] The memory 804 may include a volatile memory, such as a random access memory (RAM). The memory 804 may also include a non-volatile memory, such as a read-only memory (ROM), a flash memory, a hard disk drive (HDD), or a solid state drive (SSD).

[0168] The memory 804 stores executable codes, and the processor 802 executes the executable codes to perform a vehicle energy flow analysis method.

[0169] The embodiment of the present application also provides a computer-readable storage medium. The computer-readable storage medium can be any available medium that can be stored by a computing device or a data storage device such as a data center containing one or more available media. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid-state hard disk). The computer-readable storage medium includes instructions that instruct the computing device to execute a vehicle energy flow analysis method.

[0170] The embodiment of the present application further provides a computer program product, which includes one or more computer instructions. When the computer instructions are loaded and executed on a computing device, the process or function described in the embodiment of the present application is generated in whole or in part.

[0171] The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer or data center to another website, computer or data center via wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means.

[0172] When the computer program product is executed by a computer, the computer executes any one of the methods for analyzing a vehicle energy flow. The computer program product may be a software installation package, and when any one of the methods for analyzing a vehicle energy flow needs to be used, the computer program product may be downloaded and executed on a computer.

[0173] The descriptions of the processes or structures corresponding to the above-mentioned figures have different emphases. For parts that are not described in detail in a certain process or structure, please refer to the relevant descriptions of other processes or structures.

[0174] The above description is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present application should be included in the protection scope of the present application.

Claims

1. A method for analyzing vehicle energy flow, characterized in that: The method comprises: Acquire data to be processed, the data to be processed at least comprising a first data file and a second data file, the first data file comprising a plurality of first data and a time corresponding to each first data, the second data file comprising a plurality of second data and a time corresponding to each second data; the number of first data is greater than the number of second data; acquire a first data file collected by a first sensor and a second data file collected by a second sensor, the sampling frequency of the first sensor being higher than the sampling frequency of the second sensor; Determine the first data file time, traverse the time corresponding to the first data in the first data file in the time corresponding to the second data in the second data file, and determine the time corresponding to the first data that has not been traversed to the result as time; In the second data file, determine Moment Time and Moment At the moment, in the first data file The corresponding first data exists at the same time, and the second data file There is no corresponding second data at the time; determine from the second data file The second data corresponding to the time and Second data corresponding to the time; According to the time, time, The second data corresponding to the time The time and The second data corresponding to the time is determined to be the second data file Second data corresponding to the time; The energy flow analysis is performed according to the first data and the second data at the same time.

2. The method according to claim 1, characterized in that According to the time, time, The second data corresponding to the time The time and The second data corresponding to the time is determined to be the second data file The second data corresponding to the time includes: The following formula is used to calculate and obtain the second data file in Second data at the moment: in, , as well as is a positive integer, Time is The moment before the moment, Time is The moment after the moment, is the first weight, is the second weight, for The second data corresponding to the time, for The second data corresponding to the time, for The second data corresponding to the time.

3. The method according to claim 1, characterized in that The data to be processed includes a plurality of data files. After the second data corresponding to the time instant, the method further includes: Generate multiple groups of sample index data and multiple groups of sample energy consumption data according to the data at the same time in multiple data files; Determine the correlation between the sample indicator data and the sample energy consumption data; The sample indicator data and the sample energy consumption data whose correlation is lower than the correlation threshold are removed from the multiple groups of sample indicator data to obtain the remaining sample indicator data and the remaining sample energy consumption data; The remaining sample index data and the remaining sample energy consumption data are used to train a vehicle energy consumption prediction model.

4. The method according to claim 3, characterized in that: Determining the correlation between the sample indicator data and the sample energy consumption data includes: in, is the correlation between the i-th sample index data and the i-th sample energy consumption data, is the set of the i-th indicator data, is the set of energy consumption data of the i-th vehicle, for The j-th sample indicator variable in for The j-th sample energy consumption variable in for 、 The joint probability density of for The marginal probability density of for The marginal probability density of , and The product of is equal to or The number of variables in is the horizontal axis coordinate variable of the grid G, is the vertical axis coordinate variable of the grid G, and are all positive integers, , q is or The number of variables in , j is a positive integer, j is less than or equal to q.

5. The method according to claim 3, characterized in that: The method further comprises: Obtain the indicator data to be predicted; The index data to be predicted is input into the vehicle energy consumption prediction model to obtain the vehicle energy consumption corresponding to the index data to be predicted.

6. A vehicle energy flow analysis device, characterized in that: The device comprises: an acquisition module, used to acquire data to be processed, the data to be processed at least comprising a first data file and a second data file, the first data file comprising a plurality of first data and a time corresponding to each first data, the second data file comprising a plurality of second data and a time corresponding to each second data; the number of first data is greater than the number of second data; acquiring a first data file acquired by a first sensor and a second data file acquired by a second sensor, the sampling frequency of the first sensor being higher than the sampling frequency of the second sensor; A determination module is used to determine the first data file time, traverse the time corresponding to the first data in the first data file in the time corresponding to the second data in the second data file, and determine the time corresponding to the first data that has not been traversed to the result as time; determined in the second data file Moment Time and Moment At the moment, in the first data file The corresponding first data exists at the same time, and the second data file There is no corresponding second data at the time; determine from the second data file The second data corresponding to the time and Second data corresponding to the time; A calculation module is used to calculate the time, time, The second data corresponding to the time The time and The second data corresponding to the time is determined to be the second data file Second data corresponding to the time; The analysis module is used to perform energy flow analysis according to the first data and the second data at the same time.

7. A computing device, characterized in that including memory and processor; One or more computer programs are stored in the memory, and the one or more computer programs include instructions; when the instructions are executed by the processor, the computing device executes the method as claimed in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store a computer program, and the computer program is used to execute the method according to any one of claims 1 to 5.

Images