Vehicle energy flow analysis method, device, equipment and medium based on technical bench

Abstract

The application discloses a vehicle energy flow analysis method and device based on a technical bench, equipment and a medium, relates to the technical field of vehicles, and comprises the following steps: a processing device acquires test conditions, the test conditions comprising a test mode and a simulated opening degree of an accelerator pedal; according to the test mode and the simulated opening degree, a target voltage input to a vehicle control unit (VCU) is determined; the target voltage is converted into an analog voltage by a digital-to-analog conversion module and output to the VCU, so that the VCU controls a motor according to the analog voltage; vehicle energy data is collected during the operation of the motor controlled by the VCU according to the analog voltage; and vehicle energy flow is analyzed according to the vehicle energy data. The method can improve the accuracy of vehicle energy flow analysis.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicles, in particular to a vehicle energy flow analysis method and device based on a technical bench, equipment and a medium. BACKGROUND

[0002] In the field of vehicle analysis, vehicle energy flow analysis based on a technical bench is an important test method, which helps to comprehensively understand the energy consumption distribution of the vehicle under different working conditions, so as to provide effective guidance for optimizing the matching of each component, improving the operation efficiency of each component, and the selection and design of each component.

[0003] At present, when testing the electric drive efficiency under different conditions on the technical bench, the driver can control the opening degree of the accelerator pedal with the foot. Since the vehicle will vibrate during the test process, the stability of the manual control of the accelerator pedal is poor. Therefore, the data obtained by testing in this way will have a large error, which will further lead to poor accuracy of vehicle energy flow analysis. SUMMARY

[0004] The present application provides a vehicle energy flow analysis method and device based on a technical bench, which can improve the accuracy of vehicle energy flow analysis.

[0005] To achieve the above-mentioned purpose, the present application adopts the following technical solutions:

[0006] In a first aspect, the present application provides a vehicle energy flow analysis method based on a technical bench, which comprises:

[0007] Obtaining test conditions, the test conditions including a test mode and a simulated opening degree of an accelerator pedal;

[0008] According to the test mode and the simulated opening degree, determining a target voltage input to a vehicle control unit VCU;

[0009] Through a digital-to-analog conversion module, the target voltage is converted into an analog voltage and output to the VCU, so that the VCU controls the electric motor according to the analog voltage;

[0010] During the operation of the electric motor controlled by the VCU according to the analog voltage, collecting vehicle energy data;

[0011] According to the vehicle energy data, analyzing the vehicle energy flow.

[0012] In some possible implementations, the test conditions are obtained by:

[0013] Presenting a test interface to the user, the test interface including a first configuration area of the test mode and a second configuration area of the simulated opening degree of the accelerator pedal;

[0014] obtaining a test mode configured by the user in the first configuration area and a simulated opening degree of the accelerator pedal configured in the second configuration area.

[0015] In some possible implementation manners, the accelerator pedal comprises a first positive output end, a first negative output end, a second positive output end and a second negative output end, the VCU comprises a first positive input end, a first negative input end, a second positive input end and a second negative input end, and the digital-to-analog conversion module comprises a third positive output end and a fourth positive output end.

[0016] The first positive output end is disconnected from the first positive input end, the second positive output end is disconnected from the second positive input end, the third positive output end is connected to the first positive input end, the fourth positive output end is connected to the second positive input end, the first negative output end is connected to the first negative input end, and the second negative output end is connected to the second negative input end.

[0017] In some possible implementation manners, after the vehicle energy data is collected, the method further comprises:

[0018] generating a plurality of sets of sample index data and a plurality of sets of sample energy consumption data according to the vehicle energy data;

[0019] determining a correlation between the sample index data and the sample energy consumption data;

[0020] removing, from the plurality of sets of sample index data, sample index data with a correlation lower than a correlation threshold, to obtain remaining sample index data and remaining sample energy consumption data;

[0021] training a vehicle energy consumption prediction model by using the remaining sample index data and the remaining sample energy consumption data.

[0022] In some possible implementation manners, the determining of the correlation between the sample index data and the sample energy consumption data comprises:

[0023]

[0024] wherein, is a correlation between ith sample index data and ith sample energy consumption data, is a set of ith index data, is a set of ith vehicle energy consumption data, is is a jth sample index variable in the set, is a jth sample energy consumption variable in the set, is a jth sample energy consumption variable in the set, is 、 the joint probability density of is the marginal probability density of is the marginal probability density of , and the product of or the number of variables in a is a horizontal axis coordinate variable of the grid G, b is a vertical axis coordinate variable of the grid G, and are both positive integers, q is or the number of variables in

[0025] In some possible implementation manners, the method further includes:

[0026] acquiring to-be-predicted index data;

[0027] inputting the to-be-predicted index data into the vehicle energy consumption prediction model, to obtain vehicle energy consumption corresponding to the to-be-predicted index data.

[0028] In some possible implementation manners, the method further includes:

[0029] presenting the analysis result to a user.

[0030] In a second aspect, the present application provides a vehicle energy flow analysis device based on a technical bench, the device comprising:

[0031] an acquisition module configured to acquire test conditions, the test conditions comprising a test mode and a simulated opening degree of an accelerator pedal;

[0032] a determination module configured to determine a target voltage input to a vehicle control unit VCU according to the test mode and the simulated opening degree; and convert the target voltage into an analog voltage through a digital-to-analog conversion module, and output the analog voltage to the VCU, so that the VCU controls an electric motor according to the analog voltage;

[0033] a collection module configured to collect vehicle energy data during operation of the electric motor controlled by the VCU according to the analog voltage;

[0034] an analysis module configured to analyze the vehicle energy flow according to the vehicle energy data.

[0035] In a third aspect, the present application provides a computing device comprising a memory and a processor.

[0036] Wherein, the memory stores one or more computer programs, the one or more computer programs include instructions; when the instructions are executed by the processor, the computing device executes the method as any one of the first aspect.

[0037] In a fourth aspect, the present application provides a computer readable storage medium for storing a computer program for executing the method as any one of the first aspect.

[0038] From the above technical solutions, the present application has at least the following beneficial effects:

[0039] In the present application, the processing device acquires a test condition, the test condition includes a test mode and a simulated opening degree of an accelerator pedal, according to the test mode and the simulated opening degree, a target voltage input to a vehicle control unit VCU is determined, the target voltage is converted into an analog voltage through a digital-to-analog conversion module, and the analog voltage is output to the VCU, so that the VCU controls the electric motor according to the analog voltage, vehicle energy data is collected in the process that the VCU controls the electric motor according to the analog voltage, and vehicle energy flow is analyzed according to the vehicle energy data. At present, when testing the electric drive efficiency under different conditions on a technical bench, the opening degree of the accelerator pedal can be controlled by the driver with the foot. Since the vehicle will vibrate during the test process, the stability of the manual control of the accelerator pedal is poor. Therefore, the data obtained in this way has a large error, and the accuracy of the vehicle energy flow analysis is poor. It can be seen that the scheme of the present application can improve the accuracy of the vehicle energy flow analysis.

[0040] It should be understood that the description of technical features, technical solutions, beneficial effects or similar language in the present application does not imply that all features and advantages can be achieved in any single embodiment. On the contrary, it can be understood that the description of a feature or a beneficial effect means that the specific technical feature, technical solution or beneficial effect is included in at least one embodiment. Therefore, the description of technical features, technical solutions or beneficial effects in the specification does not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions and beneficial effects described in the embodiments can be combined in any appropriate manner. Those skilled in the art will understand that the embodiments can be implemented without one or more specific technical features, technical solutions or beneficial effects of a particular embodiment. In other embodiments, additional technical features and beneficial effects can be identified in specific embodiments that do not embody all embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0041] Figure 1 A flowchart of a vehicle energy flow analysis method based on a technical bench provided by the embodiments of the present application is shown in the figure;

[0042] Figure 2 A schematic diagram of a test interface provided for an embodiment of the present application;

[0043] Figure 3 A schematic diagram of a vehicle test flow provided for an embodiment of the present application;

[0044] Figure 4 A schematic diagram of a test method for vehicle data provided for an embodiment of the present application;

[0045] Figure 5 A schematic diagram of a vehicle energy consumption prediction model provided for an embodiment of the present application;

[0046] Figure 6 A flowchart of a vehicle data management system provided for an embodiment of the present application;

[0047] Figure 7 A schematic diagram of a vehicle energy flow analysis device based on a technical bench provided for an embodiment of the present application;

[0048] Figure 8 A schematic diagram of a computing device provided for an embodiment of the present application. DETAILED DESCRIPTION

[0049] The terms "first", "second", and "third" and the like in the specification and the appended claims of the present application are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances such that the descriptive terms used herein are to be interpreted in the context as exercised by those of ordinary skill in the art.

[0050] In the embodiments of the present application, the words "exemplary" and "for example" are used to mean serving as an example, instance, or illustration, at 99 times not necessarily a preference over other examples. Any implementation described herein as "exemplary" or "for example" is not necessarily to be construed as preferred or advantageous over other implementations. Rather, the exemplary or for example implementations are intended to convey a particular manner of implementing an aspect of the application to one of ordinary skill in the art, and are not necessarily intended to convey that only those implementations are to be employed from among all of the possible implementations that can be employed to implement that described aspect of the application.

[0051] At present, when testing the electric drive efficiency under different conditions on a technical bench, the opening degree of the accelerator pedal can be controlled by the driver using the foot. However, due to the shaking of the vehicle during the test process, the stability of the manual control of the accelerator pedal is poor. Therefore, the data obtained by testing in this way has a large error, which further leads to poor accuracy of the vehicle energy flow analysis. Another solution is to control the opening degree of the accelerator pedal by a pedal robot. However, the pedal robot is not only complicated to install and time-consuming, but also expensive.

[0052] Therefore, the embodiment of the present application provides a vehicle energy flow analysis method based on a technical bench, in which a processing device acquires test conditions, the test conditions include a test mode and a simulated opening degree of an accelerator pedal, according to the test mode and the simulated opening degree, a target voltage input to a vehicle control unit VCU is determined, the target voltage is converted into an analog voltage through a digital-to-analog conversion module, and the analog voltage is output to the VCU, so that the VCU controls an electric motor according to the analog voltage, vehicle energy data is collected in the process that the VCU controls the electric motor to operate according to the analog voltage, and vehicle energy flow is analyzed according to the vehicle energy data.

[0053] In order to make the technical solutions of the present application clearer and easier to understand, a vehicle energy flow analysis method based on a technical bench provided by the embodiment of the present application is introduced. As shown in Figure 1 The vehicle energy flow analysis method provided by the embodiment of the present application can be executed by a processing device, and the method includes the following steps.

[0054] S101, the processing device acquires test conditions.

[0055] The processing device refers to a hardware system or a software system used for executing specific tasks or processing data. In the scenario related to vehicle energy flow analysis, the processing device can refer to a computer, a server or a specific software platform specially used for calculating, simulating or analyzing vehicle energy flow.

[0056] The test conditions include a test mode and a simulated opening degree of an accelerator pedal, the test mode includes a sport mode, a comfort mode and a snow mode, etc., and the test interface is as shown in Figure 2 The embodiment of the present application provides a test interface, and in the figure, it can be seen that the test interface includes a first configuration area 301 of a test mode, a second configuration area 302 of a simulated opening degree of an accelerator pedal and a submission control 303. The user can configure the test mode in the first configuration area 301 and the simulated opening degree of the accelerator pedal in the second configuration area 302, after the user completes the above configuration, the submission control 303 can be triggered, and then the processing device can acquire the content configured by the user in the first configuration area 301 and the second configuration area 302 based on the triggering operation.

[0057] S102, the processing device determines a target voltage input to a vehicle control unit VCU according to the test mode and the simulated opening degree.

[0058] The mapping relationship between the opening degree of the accelerator pedal and the voltage input to the VCU is different in different test modes. For example, the mapping relationship between the opening degree of the accelerator pedal and the voltage input to the VCU is a first mapping relationship when the test mode is a sports mode, and the mapping relationship between the opening degree of the accelerator pedal and the voltage input to the VCU is a second mapping relationship when the test mode is an energy-saving mode.

[0059] It should be noted that the above mapping relationship is obtained by fitting based on actual measurement data in advance.

[0060] After the processing device determines the test mode, the mapping relationship corresponding to the test mode can be obtained based on the test mode. Then the processing device brings the user configured simulation opening degree into the above mapping relationship to obtain the corresponding target voltage, thereby determining the target voltage input to the vehicle control unit VCU.

[0061] S103, the processing device converts the target voltage into an analog voltage through a digital-to-analog conversion module and outputs it to the VCU.

[0062] The processing device disconnects the 2-way hard-wired connection between the accelerator pedal and the VCU, connects the 2-way output voltage of the digital-to-analog conversion module to the VCU input harness, and then controls the digital-to-analog conversion module to convert the target voltage into an analog voltage through the test interface of the upper computer, thereby realizing precise control of vehicle acceleration. The principle diagram of the above process is shown in Figure 3 The diagram includes an accelerator pedal 401, a VCU 402, a digital-to-analog conversion module 403, an upper computer 404, and a power supply module 405. The power supply module 405 is used to provide a power supply voltage to the digital-to-analog conversion module 403, which can be 12V.

[0063] The accelerator pedal 401 includes a first positive output end 411, a first negative output end 412, a second positive output end 413, and a second negative output end 414. The VCU 402 includes a first positive input end 415, a first negative input end 416, a second positive input end 417, and a second negative input end 418. The digital-to-analog conversion module includes a third positive output end 419 and a fourth positive output end 420.

[0064] The first positive output end 411 is disconnected from the first positive input end 415, the second positive output end 413 is disconnected from the second positive input end 417, the third positive output end 419 is connected to the first positive input end 411, the fourth positive output end 420 is connected to the second positive input end 417, the first negative output end 412 is connected to the first negative input end 416, and the second negative output end 414 is connected to the second negative input end 418.

[0065] The two groups of ports can ensure stable transmission of signals and form redundant backup to improve reliability. After a fault occurs in one of the groups of ports, the voltage signal output by the accelerator pedal can still be transmitted to the VCU through the other group of ports, thereby determining the maneuverability of the vehicle.

[0066] In S104, the VCU collects vehicle energy data during control of the motor by the motor.

[0067] Before the processing device in the present application acquires the data to be processed, high-voltage architecture research and sensor arrangement are required. First, the high-voltage system architecture of the vehicle and the main low-voltage electrical components are determined, and thus the sensor arrangement scheme is determined, as shown in the following table. Figure 4 As shown in the following 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 current sensors 211, voltage sensors 212, temperature sensors 213, pressure sensors 214, flow sensors 215, torque sensors 216, and CAN communication interfaces 217. The purpose of arranging current sensors and voltage sensors is to calculate the electric quantity. However, some signals are difficult to obtain through sensor arrangement, so signal analysis methods are required to obtain information such as battery SOC, battery minimum temperature, battery maximum temperature, battery cell voltage, motor torque, motor speed, compressor speed, and the like.

[0068] Specifically, the deployment position of the voltage sensor includes the interface of the high-voltage electrical component and the interface of the low-voltage electrical component. Since the high-voltage wire cannot be damaged, otherwise it will cause danger, therefore, according to the type of the high-voltage component electrical interface, a set of test wiring harness is customized, which can be connected in series to the high-voltage wiring harness. The test wiring harness includes voltage sensors and signal output wiring harness. After installing the test wiring harness, connect the signal output wiring to the data acquisition module.

[0069] The deployment position of the current sensor generally includes high-voltage components and low-voltage components. The high-voltage components include power battery, front electric drive module, rear electric drive module, high-voltage PTC (Positive Temperature Coefficient), air conditioner compressor, DCDC input end, etc. The low-voltage components include DCDC output end, cooling fan, motor water pump, battery water pump, air conditioner water pump, air blower, 12V storage battery, etc. Since the rated power of different components is different, therefore, when selecting the current sensor range, the sensor range should be ensured not to exceed 1.5 times the maximum current of the high-voltage component and the low-voltage component.

[0070] The temperature sensor deployment position mainly includes the inlet and outlet of the battery, the inlet and outlet of the motor, the inlet and outlet of the radiator, the inlet and outlet of the heat exchanger and other main heat generating and heat dissipating components, for assisting in analyzing the correlation between the passenger compartment energy consumption, the battery energy consumption and the temperature.

[0071] The flow sensor is mainly arranged in the motor cooling circuit water pipe, the battery cooling circuit water pipe, the air conditioning circuit water pipe and the radiator water pipe, combined with the temperature sensor, for analyzing the heat dissipation and heat generation of different power components.

[0072] The CAN communication interface obtains CAN signals through the twisted pair on the connector of the battery management system, the battery controller and other key controllers, identifies data changing with the working condition characteristics under specific working conditions, and then calibrates the offset and proportion value with sensors, external devices and other instruments. For example, when analyzing the battery SOC, the DC fast charging working condition is carried out, the CAN data trend is determined, the ID and byte of the signal are determined, and then the CAN data is calibrated according to the instrument value and the charging pile value.

[0073] The following is the test of the vehicle bench, the endurance mileage test under different temperature conditions 221, the charging performance test 222, the resistance decomposition test 223 and the electric drive efficiency test 224. The endurance mileage test 221 includes tests of the vehicle under high temperature, low temperature, ultra-low temperature, normal temperature and other temperature points, the charging performance test 222 includes tests of the vehicle under normal temperature, high temperature, low temperature and ultra-low temperature, the resistance decomposition test 223 includes the mechanical resistance, caliper resistance and bearing resistance of the transmission system under different temperatures and different speeds. The electric drive efficiency test 224 mainly includes the electric drive efficiency under different temperatures, such as normal temperature and low temperature working conditions. Sensor data and CAN bus data are collected synchronously during the test.

[0074] Specifically, the endurance mileage test 221 adopts the test matrix in Table 1, and records all sensor data and CAN bus data during the test.

[0075] Table 1:

[0076]

[0077] The charging performance test 222 adopts the following test matrix, considering that the air conditioner may be turned on during actual charging, the test matrix is more close to the actual demand, and all sensor data and CAN bus data are recorded during the test.

[0078] Table 2:

[0079]

[0080] The resistance decomposition test 223 includes a road sliding resistance test, a drum drag test, and a four-motor bench test. The road sliding resistance test can test the total resistance of the vehicle running, including wind resistance, rolling resistance, and mechanical internal resistance; the drum drag test can test the total resistance excluding air resistance; and the four-motor bench test can test the mechanical internal resistance. The vehicle is disassembled from the tires and installed on the four-motor bench, the vehicle is powered on, the neutral gear is engaged, and then the vehicle is fully warmed up, such as 40 minutes of reverse drag at 80 km / h. After fully warming up, the formal test is carried out, the test speed is 10 km / h, 20 km / h, 30 km / h, …, the highest speed, each speed point is stable for half a minute, and three groups of tests are continuously completed to obtain the average value of the vehicle mechanical resistance T1 at each speed point. Then the driving half shaft is disassembled, and the same, the test speed and method are the same as above, the resistance and T2 of the caliper and bearing are obtained, then the caliper is disassembled, and the test is carried out, the method is the same as above, the bearing resistance T3 is obtained, and then the bearing resistance is obtained according to T2 and T3.

[0081] The electric drive efficiency test 224 needs to collect the input current, voltage, wheel end speed and torque of the electric drive. Because there are many test points, only the efficiency map under normal temperature conditions is tested. The test bench is a four-motor bench, and the test speed is 10 km / h, 20 km / h, 30 km / h, …, the highest speed.

[0082] The test index calculation includes the endurance mileage index 231, the charging performance index 232, the resistance index 233, and the efficiency map index 234.

[0083] The processing device collects the test data obtained during the test process through the data acquisition device, in order to subsequently analyze the energy flow.

[0084] S105, according to the vehicle energy data, the vehicle energy flow is analyzed.

[0085] The energy flow analysis includes calculating the energy consumption of high-voltage components and low-voltage components in the charging test and endurance mileage test, and calculating the transmission efficiency of the electric drive system in the efficiency test.

[0086] Specifically, the endurance mileage test calculates the endurance mileage, the power consumption per 100 kilometers, the endurance mileage decline rate, the instrument endurance mileage estimation accuracy, the energy consumption of high and low voltage components, and the heat generation and heat dissipation of each component. The charging test calculates the charging time, the charging capacity, the average charging power, the low temperature charging time increase rate, the energy consumption of high and low voltage components, and the heat generation and heat dissipation of each component. The resistance decomposition test calculates the vehicle resistance, the transmission system resistance, the caliper resistance, and the bearing resistance at each speed.

[0087] Specifically, the high and low voltage component energy consumption of the endurance mileage test includes the battery discharge amount, the front electric drive system power consumption, the rear electric drive system power consumption, the compressor, the PTC, the DCDC, and the like high voltage components, and the DCDC low voltage end power consumption, the fan, the blower, the battery water pump, and the like low voltage component power consumption. The high and low voltage component energy consumption of the charging test includes the battery charging amount, the front electric drive system power consumption, the rear electric drive system power consumption, the compressor, the PTC, the DCDC, and the like high voltage components, and the DCDC low voltage end power consumption, the fan, the blower, the battery water pump, and the like low voltage component power consumption.

[0088] The following is the calculation of the power consumption of the high voltage components and the low voltage components in the electric vehicle:

[0089]

[0090]

[0091] wherein, is the power consumption of the high voltage components in the electric vehicle, is the voltage of the high voltage components in the electric vehicle, is the current of the high voltage components in the electric vehicle, is the power consumption of the low voltage components in the electric vehicle, is the voltage of the low voltage components in the electric vehicle, is the low voltage components in the electric vehicle, is the test time.

[0092] For example, according to the test data obtained in S104, the voltage data and the current data at the same time are analyzed for energy flow, and at the 2nd second, the voltage data is 2V, and the current data is 0.2A, which are brought into the formula for calculating the power consumption to obtain

[0093] The motor efficiency is calculated, and the electric drive efficiency and the power supply efficiency are calculated using the following formula:

[0094]

[0095]

[0096]

[0097]

[0098]

[0099]

[0100] wherein, is the total efficiency of the motor drive of the whole vehicle, is the motor front axle drive efficiency, is the motor rear axle drive efficiency, is the whole vehicle motor feeding total efficiency, is the motor front axle feeding efficiency, is the motor rear axle feeding efficiency, is the wheel edge total torque, is the front axle total torque, is the rear axle total torque, is the motor speed, is the motor bus voltage, is the motor total output current, is the front motor output current, is the rear motor output current.

[0101] According to the battery self-heat production, the battery internal resistance analysis is calculated. Since the driving process is divided into driving and braking, the battery is discharged during driving, and the battery is charged during braking. Since the charging internal resistance and the 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 closely related to the battery temperature, the test data is classified and processed according to the battery temperature, and a certain temperature interval is used as the segmentation threshold, such as every interval Separate the data, and then according to the current and voltage distribution of the battery, fit according to the following formula, and the slope of the fitted curve is the battery internal resistance. 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.

[0102]

[0103] wherein, is the battery output voltage, is the battery output current, and is the coefficient to be fitted, is the fitted battery internal resistance, is the fitted open circuit voltage.

[0104] According to the fitted battery internal resistance, a battery internal resistance table varying with temperature can be obtained, and then linear difference is performed to finally obtain a battery charging internal resistance map and a battery discharging internal resistance map in the whole temperature range.

[0105] According to the formula , the power consumption of the battery internal resistance itself can be calculated. Wherein, is the power consumption of the battery internal resistance itself, is the battery output current, is the fitted battery internal resistance.

[0106] According to the water inlet temperature, water outlet temperature of the battery, the battery thermal management loop flow, the battery heating capacity and the battery cooling capacity are calculated, and the following formula is used:

[0107]

[0108] wherein, is the heating capacity and the cooling capacity of the battery exterior, is the specific heat capacity of the cooling liquid, is the battery thermal management loop flow, is the outlet temperature of the kth second, is the inlet temperature of the kth second, is the test time.

[0109] According to the heating capacity of the battery exterior and the cooling capacity of the battery exterior, the heating capacity of the battery exterior or the cooling capacity of the battery exterior, the heating and cooling efficiency of the battery exterior can be calculated.

[0110]

[0111] wherein, is the heating and cooling efficiency of the battery, 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.

[0112] For example, the vehicle energy flow indicators are shown in Table 3 as follows:

[0113] Table 3:

[0114]

[0115] According to the correlation between the energy flow data obtained by the above method and the vehicle energy consumption data, the energy flow data is a plurality of sample indicator data, and the vehicle energy consumption data is a plurality of sample energy consumption data.

[0116] The correlation between the sample indicator data and the sample energy consumption data is determined according to the following formula:

[0117]

[0118] wherein, is the correlation between the ith sample indicator data and the ith sample energy consumption data, is the set of ith indicator data, is the set of ith vehicle energy consumption data, is the jth sample indicator variable in is the jth sample energy consumption variable in is the jth sample indicator variable in is the jth sample energy consumption variable in for 、 The joint probability density, for marginal probability density, for marginal probability density, , and The product equals or The number of variables in a Let G be the horizontal axis coordinate variable. b Let G be the y-axis coordinate variable. and All are positive integers. q is or The number of variables in q, where j is a positive integer, and j is less than or equal to q.

[0119] Sample indicator data with correlation below the correlation threshold are removed from multiple sets of sample indicator data to obtain the remaining sample indicator data and the remaining sample energy consumption data.

[0120] For example, if 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, the first sample indicator data and the first sample energy consumption data can be kept according to the magnitude of the correlation threshold, while the second sample indicator data and the second sample energy consumption data can be removed.

[0121] A vehicle energy consumption prediction model is trained using the remaining sample index data and the remaining sample energy consumption data. For example... Figure 5 As shown in the figure, this is a schematic diagram of a vehicle energy consumption prediction model provided in an embodiment of this application. The vehicle energy consumption prediction model is divided into 7 layers, with the first layer being the input layer and the input quantity being... Key energy flow indicators, number of nodes: The second layer represents the fuzzy subspace, divided into 7 fuzzy subsets: 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 It is an integer, and , The membership function uses a bell-shaped function, and the formula is as follows:

[0122]

[0123] in, As the center of membership, The width of the membership degree, the number of nodes of the layer is The third layer is an inference layer, each node outputs a degree of applicability corresponding to each rule, the number of nodes of the layer is The fourth layer is a normalization calculation, the number of nodes is The fifth layer is an output layer, the number of nodes is 1.

[0124] The number of training times is set to 100, and after the training is completed, a vehicle energy consumption prediction model can be obtained.

[0125] S106, presenting the analysis result to the user.

[0126] The analysis result can include the power consumption of the electric drive efficiency, the high-voltage components, the low-voltage components, and the power consumption of the battery internal resistance, etc. The processing device displays the energy consumption values of each component according to the test results through the monitoring interface, so that it can be directly observed whether the energy consumption of each component meets the requirements or exceeds the standard.

[0127] Based on the above description, the embodiment of the application provides a vehicle energy flow analysis method based on a technical bench, in which a processing device acquires test conditions, the test conditions include a test mode and a simulated opening degree of an accelerator pedal, according to the test mode and the simulated opening degree, a target voltage input to a vehicle control unit VCU is determined, the target voltage is converted into an analog voltage through a digital-to-analog conversion module, and the analog voltage is output to the VCU, so that the VCU controls the electric motor according to the analog voltage, in the process that the VCU controls the electric motor according to the analog voltage, vehicle energy data is collected, and vehicle energy flow is analyzed according to the vehicle energy data. At present, when the electric drive efficiency under different conditions is tested on the technical bench, the opening degree of the accelerator pedal can be controlled by the driver with the foot, and since the vehicle will vibrate during the test process, the stability of the manual control of the accelerator pedal is poor. Therefore, the data obtained by testing in this way has a large error, and further leads to poor accuracy of vehicle energy flow analysis. It can be seen that the scheme of the application can improve the accuracy of vehicle energy flow analysis.

[0128] Exemplarily, the application also provides a portable, low-cost, vehicle-mounted high-precision data management system, as shown in Figure 6 The figure is a flowchart of a vehicle data management system provided by the embodiment of the application, which can improve the accuracy of vehicle energy flow analysis.

[0129] S601, vehicle test data and test index arrangement.

[0130] The data management system includes a temperature measurement module, an analog quantity acquisition module, a high-voltage acquisition module, a CAN acquisition module, and a data recording module, supports acquisition and recording of temperature, current, voltage, flow, CAN, CANFD and other signals. According to the following test matrix, the vehicle test data and test indicators are sorted out.

[0131] The endurance mileage test 221 under different temperature conditions, the charging performance test 222, the resistance decomposition test 223, and the electric drive efficiency test 224. The endurance mileage test 221 includes tests of the vehicle at high temperature, low temperature, ultra-low temperature, normal temperature and other temperature points, the charging performance test 222 includes tests at normal temperature, high temperature, low temperature, ultra-low temperature and other temperature points, the resistance decomposition test 223 includes mechanical resistance, caliper resistance, bearing resistance and other resistances of the transmission system at different temperatures and different speeds. The electric drive efficiency test 224 mainly includes electric drive efficiency at different temperatures, such as normal temperature and low temperature conditions. Sensor data and CAN bus data are collected synchronously during the test.

[0132] S602, upload the sorted data to the data management system.

[0133] The data management system can one-key enter vehicle energy flow data and indicators, has the functions of automatically analyzing performance indicators and process data of different vehicles, analyzing the development trend of vehicle indicators, and one-key generating analysis reports.

[0134] Specifically, the test data is classified according to test items, test conditions, test conditions, test methods, the classified data is stored according to test indicators and test process data, standard indicator names and parameter names are used to record each implementation indicator and process data, then all the data of a vehicle model are compressed and packaged, uploaded to the data management system, the system can automatically identify according to data classification, then the vehicle performance indicators and process data are displayed on the interface, and the test data can be switched on the interface according to test items, test conditions, test conditions and test methods.

[0135] S603, vehicle performance display.

[0136] A certain vehicle model can be selected, test items, test conditions, test conditions and test methods can be determined, and vehicle energy consumption, component energy consumption and proportion, and test process data can be intuitively displayed in the system.

[0137] S604, multi-model comparison.

[0138] The multiple vehicles can be selected in the data management system, and the sizes of different indexes under the same test project, test condition, test working condition and test method are compared, and the test process data size is compared. Through the comparison of the indexes of the multiple vehicles, the difference between the indexes of different vehicle models can be directly observed, so that the performance index to be optimized can be found.

[0139] S605, intelligent analysis.

[0140] Meanwhile, intelligent analysis can be performed on a single test index, a vehicle model to be intelligently analyzed is selected, a test project is selected, a test index to be analyzed is determined, and test conditions, test methods and test working conditions are screened. The maximum value, minimum value, average value, median, standard deviation, kurtosis and skewness of the index data of all vehicle models can be used to reflect the dispersion degree and distribution trend of the vehicle test index. The index data is displayed in a scatter plot, a column chart and a normal distribution chart. In addition, trend analysis of the vehicle index according to vehicle wheelbase, vehicle year, vehicle price and other vehicle parameters is supported. Through intelligent analysis, the ranking of the test index of a vehicle model in a plurality of vehicle models can be seen, and whether the index needs to be optimized can be determined.

[0141] The above Figures 1 to 6 The vehicle energy flow analysis method based on a technical bench provided in the embodiments of the present application is described in detail, and the device and equipment provided in the embodiments of the present application will be described in combination with the accompanying drawings.

[0142] As Figure 7 shown, the figure is a schematic diagram of a vehicle energy flow analysis device based on a technical bench provided in the embodiments of the present application, and the device comprises:

[0143] The acquisition module 701 is configured to acquire a test condition, and the test condition comprises a test mode and a simulated opening degree of an accelerator pedal.

[0144] The determination module 702 is configured to determine a target voltage input to a vehicle control unit VCU according to the test mode and the simulated opening degree, convert the target voltage into an analog voltage through a digital-to-analog conversion module, and output the analog voltage to the VCU, so that the VCU controls the electric motor according to the analog voltage.

[0145] The acquisition module 703 is configured to acquire vehicle energy data during the operation of the electric motor controlled by the VCU according to the analog voltage.

[0146] The analysis module 704 is configured to analyze the vehicle energy flow according to the vehicle energy data.

[0147] Optionally, the acquisition of the test condition comprises:

[0148] presenting a test interface to a user, the test interface comprising a first configuration area of a test mode and a second configuration area of a simulated opening degree of an accelerator pedal;

[0149] obtaining the test mode configured by the user in the first configuration area and the simulated opening degree of the accelerator pedal configured by the user in the second configuration area.

[0150] Optionally, the accelerator pedal comprises a first positive output end, a first negative output end, a second positive output end and a second negative output end, the VCU comprises a first positive input end, a first negative input end, a second positive input end and a second negative input end, and the digital-to-analog conversion module comprises a third positive output end and a fourth positive output end.

[0151] The first positive output end is disconnected from the first positive input end, the second positive output end is disconnected from the second positive input end, the third positive output end is connected to the first positive input end, the fourth positive output end is connected to the second positive input end, the first negative output end is connected to the first negative input end, and the second negative output end is connected to the second negative input end.

[0152] Optionally, after collecting the vehicle energy data, the method further comprises:

[0153] generating a plurality of sets of sample index data and a plurality of sets of sample energy consumption data according to the vehicle energy data;

[0154] determining the correlation between the sample index data and the sample energy consumption data;

[0155] removing sample index data with a correlation lower than a correlation threshold from the plurality of sets of sample index data to obtain remaining sample index data and remaining sample energy consumption data;

[0156] training a vehicle energy consumption prediction model using the remaining sample index data and the remaining sample energy consumption data.

[0157] Optionally, the determining the correlation between the sample index data and the sample energy consumption data comprises:

[0158]

[0159] wherein, is the correlation between the ith sample index data and the ith sample energy consumption data, is a set of ith index data, is a set of ith vehicle energy consumption data, is is the jth sample index variable in the set of sample index data, is the jth sample energy consumption variable in the set of sample energy consumption data, is the jth sample index variable in the set of sample index data, is the jth sample energy consumption variable in the set of sample energy consumption data, 、 the joint probability density of X and Y, the marginal probability density of X, the marginal probability density of Y, , and the product of the joint probability density of X and Y, or the number of variables in X or Y, a is a horizontal axis coordinate variable of the grid G, b is a vertical axis coordinate variable of the grid G, and are positive integers, q is or the number of variables in X or Y, and j is a positive integer less than or equal to q.

[0160] Optionally, the method further comprises:

[0161] obtaining to-be-predicted index data;

[0162] inputting the to-be-predicted index data into the vehicle energy consumption prediction model to obtain vehicle energy consumption corresponding to the to-be-predicted index data.

[0163] Optionally, the method further comprises:

[0164] presenting the analysis result to a user.

[0165] The vehicle energy flow analysis device based on a technical bench according to the embodiments of the present application can correspond to the method described in the embodiments of the present application, and the above-mentioned other operations and / or functions of each module / unit of the vehicle energy flow analysis device based on a technical bench are respectively used to implement the corresponding flow of each method in the embodiments shown in the above-mentioned Figure 1 For brevity, they will not be described here.

[0166] The embodiments of the present application also provide a computing device. As shown in Figure 8 the figure is a schematic diagram of a computing device provided by the embodiments of the present application, which computing device 800 comprises 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.

[0167] ​​The 801 bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 8 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0168] The processor 802 can be any one or more of the following processors: central processing unit (CPU), graphics processing unit (GPU), microprocessor (MP), or digital signal processor (DSP).

[0169] The communication interface 803 is used for communication with external devices.

[0170] Memory 804 may include volatile memory, such as random access memory (RAM). Memory 804 may also include non-volatile memory, such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid state drive (SSD).

[0171] The memory 804 stores executable code, which is executed by the processor 802 to perform a vehicle energy flow analysis method based on a technical bench.

[0172] Specifically, in achieving Figure 7 In the case of the illustrated embodiment, and Figure 7 When the modules or units of the vehicle energy flow analysis device based on the technical bench described in the embodiments are implemented by software, the execution... Figure 7 The software or program code required for the functions of each module / unit can be partially or wholly stored in memory 804. Processor 802 executes the program code corresponding to each unit stored in memory 804 to execute the vehicle energy flow analysis method based on the technical bench.

[0173] The embodiments of the present application also provide a computer readable storage medium. The computer readable storage medium can be any available medium or data storage device that can store the instructions of the computer device. The available medium can be a magnetic medium (for example, floppy disk, hard disk, magnetic tape), an optical medium (for example, DVD), or a semiconductor medium (for example, solid state disk), etc. The computer readable storage medium comprises instructions for instructing the computer device to execute the technical bench based vehicle energy flow analysis method.

[0174] The embodiments of the present application also provide a computer program product comprising one or more computer instructions. When the computer instructions are loaded and executed on the computer device, the processes or functions described in the embodiments of the present application are generated in whole or in part.

[0175] The computer instructions can 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 can be transmitted from one website, computer or data center to another website, computer or data center through wired (for example, coaxial cable, optical fiber) or wireless (for example, infrared, wireless, microwave, etc.) mode.

[0176] The computer program product is executed by the computer, and the computer executes any method of the technical bench based vehicle energy flow analysis method. The computer program product can be a software installation package, and when any method of the technical bench based vehicle energy flow analysis method is needed, the computer program product can be downloaded and executed on the computer.

[0177] The descriptions of the processes or structures corresponding to the above respective figures are each focused on, and the parts not described in detail in a certain process or structure can be referred to the related descriptions of other processes or structures.

[0178] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited to this. Any change or replacement within the technical scope disclosed in the present application should be covered in the protection scope of the present application.

Claims

1. A method for vehicle energy flow analysis based on a technology bench, characterized in that, The method comprises: acquiring test conditions, the test conditions comprising a test mode and a simulated opening degree of an accelerator pedal; determining a target voltage input to a vehicle control unit VCU according to the test mode and the simulated opening degree; converting the target voltage into an analog voltage through a digital-to-analog conversion module and outputting the analog voltage to the VCU to control the electric motor according to the analog voltage; collecting vehicle energy data during the operation of the electric motor controlled by the VCU according to the analog voltage; analyzing the vehicle energy flow according to the vehicle energy data; wherein the analysis process at least comprises: The electric drive efficiency includes the total efficiency of the vehicle motor drive, the front axle drive efficiency of the motor and the rear axle drive efficiency of the motor, and the power supply efficiency includes the total efficiency of the vehicle motor power supply, the front axle power supply efficiency of the motor and the rear axle power supply efficiency of the motor, and the electric drive efficiency and the power supply efficiency are calculated by the following formula: wherein, is the total efficiency of the motor drive of the vehicle, is the efficiency of the motor front axle drive, is the efficiency of the motor rear axle drive, is the total efficiency of the motor feed of the vehicle, is the efficiency of the motor front axle feed, is the efficiency of the motor rear axle feed, is the total torque of the wheel, is the total torque of the front axle, is the total torque of the rear axle, is the motor speed, is the motor bus voltage, is the total motor output current, is the front motor output current, is the rear motor output current; The battery internal resistance and open circuit voltage are fitted by the following formula: wherein, is the battery output voltage, is the battery output current, and are coefficients to be fitted, is the fitted battery internal resistance, is the fitted open circuit voltage; According to the fitted battery internal resistance, a table of battery internal resistance changing with temperature is determined, and then linear interpolation is performed to obtain the battery charging internal resistance map and the battery discharging internal resistance map in the full temperature range; According to the battery water inlet temperature, the battery water outlet temperature and the battery thermal management loop flow, the battery heating capacity and the battery refrigeration capacity are calculated, and the following formula is used: wherein, Qheat is the heating amount outside the battery, and Qcooling is the cooling amount outside the battery, Ccooling is the specific heat capacity of the cooling liquid, Qbattery is the battery thermal management circuit flow rate, Tkout is the outlet temperature of the kth second, Tkint is the inlet temperature of the kth second, Ttest is the test time; According to the battery external heating capacity and the battery external refrigeration capacity, the battery external heating capacity or the battery external heat dissipation capacity, the battery external heating and refrigeration efficiency can be calculated: wherein, is the heating efficiency or the refrigeration efficiency of the battery, is the heating power of the external heat source or the refrigeration power of the external heat source, wherein the heating power of the external heat source is the power consumption of the PTC, and the refrigeration power of the external heat source is the power consumption of the compressor; The acquisition of the test conditions comprises: presenting a test interface to the user, the test interface comprising a first configuration area of the test mode and a second configuration area of the simulated opening degree of the accelerator pedal; acquiring the test mode configured by the user in the first configuration area and the simulated opening degree of the accelerator pedal configured by the user in the second configuration area; The accelerator pedal comprises a first positive output end, a first negative output end, a second positive output end and a second negative output end, the VCU comprises a first positive input end, a first negative input end, a second positive input end and a second negative input end, and the digital-to-analog conversion module comprises a third positive output end and a fourth positive output end; the first positive output end is disconnected from the first positive input end, the second positive output end is disconnected from the second positive input end, the third positive output end is connected to the first positive input end, the fourth positive output end is connected to the second positive input end, the first negative output end is connected to the first negative input end, and the second negative output end is connected to the second negative input end.

2. The method of claim 1, wherein, After collecting the vehicle energy data, the method further comprises: generating a plurality of sets of sample index data and a plurality of sets of sample energy consumption data according to the vehicle energy data; determining the correlation between the sample index data and the sample energy consumption data; removing sample index data with a correlation lower than a correlation threshold from the plurality of sets of sample index data to obtain remaining sample index data and remaining sample energy consumption data; training a vehicle energy consumption prediction model using the remaining sample index data and the remaining sample energy consumption data.

3. The method of claim 2, wherein, The determination of the correlation between the sample index data and the sample energy consumption data comprises: wherein, is the correlation between the i-th sample indicator data and the i-th sample energy consumption data, is the set of i-th indicator data, is the set of i-th vehicle energy consumption data, is is the j-th sample indicator variable in is is the j-th sample energy consumption variable in is 、 is the joint probability density of is the marginal probability density of is the marginal probability density of is the marginal probability density of , , and is the product of or is the number of variables in a is the horizontal axis coordinate variable of the grid G, b is the vertical axis coordinate variable of the grid G, and are both positive integers, , q is or is the number of variables in is the number of variables in 4. The method of claim 3, wherein, The method further comprises: acquiring to-be-predicted index data; Input the to-be-predicted index data into the vehicle energy consumption prediction model to obtain vehicle energy consumption corresponding to the to-be-predicted index data.

5. The method of claim 1, wherein, The method further comprises: presenting the analysis result to a user.

6. A technology bench based vehicle energy flow analysis apparatus, characterized by, The device comprises: an acquisition module configured to acquire a test condition, the test condition comprising a test mode and a simulated opening degree of an accelerator pedal; a determination module configured to determine a target voltage input to a vehicle control unit (VCU) according to the test mode and the simulated opening degree; and convert the target voltage into an analog voltage through a digital-to-analog conversion module and output the analog voltage to the VCU, so that the VCU controls an electric motor according to the analog voltage; a collection module configured to collect vehicle energy data during operation of the electric motor controlled by the VCU according to the analog voltage; wherein the analysis process at least comprises: The electric drive efficiency includes the total efficiency of the motor drive of the whole vehicle, the front axle drive efficiency of the motor, and the rear axle drive efficiency of the motor, and the power supply efficiency includes the total efficiency of the motor power supply of the whole vehicle, the front axle power supply efficiency of the motor, and the rear axle power supply efficiency of the motor, and the electric drive efficiency and the power supply efficiency are calculated by the following formulas: wherein, is the total efficiency of the motor drive of the vehicle, is the efficiency of the front axle drive of the motor, is the efficiency of the rear axle drive of the motor, is the total efficiency of the motor feed of the vehicle, is the efficiency of the front axle feed of the motor, is the efficiency of the rear axle feed of the motor, is the total torque of the wheel edge, is the total torque of the front axle, is the total torque of the rear axle, is the motor speed, is the motor bus voltage, is the total motor output current, is the front motor output current, is the rear motor output current; The battery internal resistance and the open circuit voltage are fitted by the following formulas: wherein, is the battery output voltage, is the battery output current, and are coefficients to be fitted, is the fitted battery internal resistance, is the fitted open circuit voltage; According to the fitted battery internal resistance, a table of the battery internal resistance changing with temperature is determined, and then linear interpolation is performed to obtain the battery charging internal resistance map and the battery discharging internal resistance map in the whole temperature range; According to the water inlet temperature and the water outlet temperature of the battery, the battery thermal management loop flow, the battery heating capacity and the battery refrigeration capacity are calculated, and the following formulas are used: wherein, Qheat is the heating amount outside the battery, and Ccooling is the specific heat capacity of the cooling liquid, Qbattery is the battery thermal management circuit flow, Tk is the outlet temperature of the kth second, Tk is the inlet temperature of the kth second, T is the test time; According to the external heating capacity of the battery and the external refrigeration capacity of the battery, the external heating capacity of the battery or the external heat dissipation capacity of the battery, the external heating and refrigeration efficiency of the battery can be calculated: wherein, is the heating efficiency or the refrigeration efficiency of the battery, is the heating power of the external heat source or the refrigeration power of the external heat source, wherein the heating power of the external heat source is the power consumption of the PTC, and the refrigeration power of the external heat source is the power consumption of the compressor; an analysis module configured to analyze the vehicle energy flow according to the vehicle energy data; The acquisition of the test condition comprises: presenting a test interface to a user, the test interface comprising a first configuration area of a test mode and a second configuration area of a simulated opening degree of an accelerator pedal; and acquiring the test mode configured by the user in the first configuration area and the simulated opening degree of the accelerator pedal configured by the user in the second configuration area. The accelerator pedal comprises a first positive output end, a first negative output end, a second positive output end, and a second negative output end, the VCU comprises a first positive input end, a first negative input end, a second positive input end, and a second negative input end, and the digital-to-analog conversion module comprises a third positive output end and a fourth positive output end; the first positive output end is disconnected from the first positive input end, the second positive output end is disconnected from the second positive input end, the third positive output end is connected to the first positive input end, the fourth positive output end is connected to the second positive input end, the first negative output end is connected to the first negative input end, and the second negative output end is connected to the second negative input end.

7. A computing device, comprising: comprises a memory and a processor; wherein one or more computer programs are stored in the memory, the one or more computer programs comprising instructions; when the instructions are executed by the processor, the computing device executes the method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium is configured to store a computer program for performing the method according to any one of claims 1 to 5.

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