Electric vehicle energy consumption estimation method, device, equipment and program product

Abstract

The present invention discloses a method, device, equipment, and program product for estimating energy consumption of an electric vehicle, comprising: obtaining the voltages corresponding to different residual capacities of the battery of the electric vehicle during a target discharge process, and obtaining an actual correlation curve between the residual capacities and the voltages; screening out target curve segments in the actual correlation curve whose voltage fluctuation amplitudes exceed a preset fluctuation amplitude; performing curve smoothing on the target curve segments to obtain smooth curve segments; determining a target correlation curve based on the smooth curve segments and the actual correlation curve; inputting the target correlation curve and the motor efficiency curve into a pre-built battery simulation module to estimate the energy consumption data of the electric vehicle during the target discharge process. The present invention improves the accuracy of energy consumption simulation by dividing the curve in which the voltage changes with the residual capacities into multiple target curve segments, calculating the characteristic voltage of the battery when it is operating relatively stably, and then estimating the energy consumption of the electric vehicle using the motor efficiency curve corresponding to the characteristic voltage.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicles, and in particular to an electric vehicle energy consumption estimation method, device, equipment and program product. Background Art

[0002] With the rapid development of electric vehicles, accurately predicting and evaluating their range has become increasingly important. Traditional simulation methods typically use a fixed efficiency estimate of the electric drive assembly at the rated voltage of the power battery (a single characteristic voltage) to calculate the energy consumption and range of the entire battery during the entire discharge process. This method cannot accurately reflect the energy consumption characteristics of the battery at different remaining charge (SOC) levels, resulting in inaccurate energy consumption estimates for electric vehicles. Therefore, improving the accuracy of energy consumption estimates for electric vehicles is an urgent problem that needs to be solved. Summary of the Invention

[0003] The embodiments of the present application solve the technical problem of inaccurate energy consumption estimation of electric vehicles in the prior art by providing a method, device, equipment and program product for estimating energy consumption of electric vehicles, and achieve the technical effect of improving the accuracy of energy consumption estimation of electric vehicles by combining the energy consumption characteristics of the battery at different remaining power SOCs.

[0004] In a first aspect, the present application provides a method for estimating energy consumption of an electric vehicle, the method comprising:

[0005] Obtain the voltage corresponding to different remaining capacities of the battery of the electric vehicle during the target discharge process, and obtain the actual correlation curve between the remaining capacity and the voltage;

[0006] Filter out target curve segments whose voltage fluctuation amplitude exceeds a preset fluctuation amplitude in the actual correlation curve;

[0007] Performing curve smoothing processing on the target curve segment to obtain a smooth curve segment;

[0008] Obtaining a motor efficiency curve of an electric drive assembly of an electric vehicle at a characteristic voltage corresponding to a target curve segment during a target discharge process;

[0009] Determine the target correlation curve according to the smooth curve segment and the actual correlation curve;

[0010] The characteristic voltage corresponding to the target correlation curve and the motor efficiency curve corresponding to the characteristic voltage are input into a pre-built battery simulation module to estimate the energy consumption data of the electric vehicle during the target discharge process.

[0011] Furthermore, the voltages corresponding to different remaining capacities of the battery of the electric vehicle during the target discharge process are obtained, and an actual correlation curve between the remaining capacities and the voltage is obtained, including:

[0012] Obtain the voltage corresponding to different remaining capacities of the electric vehicle battery during the target discharge process, and obtain actual scattered data on the relationship between remaining capacity and voltage;

[0013] According to the actual scatter point data, an actual scatter point curve is obtained in which the voltage changes as the remaining power decreases. The actual scatter point curve is an actual correlation curve.

[0014] Furthermore, the target curve segment is subjected to curve smoothing processing to obtain a smooth curve segment, including:

[0015] Determine the average eigenvalue corresponding to every two point data in the target curve segment;

[0016] According to the average characteristic value corresponding to every two point data in the target curve segment, the process curve segment is obtained;

[0017] Determine whether the slope of the process curve segment is within a preset slope range;

[0018] When the slope of the process curve segment is within a preset slope range, determining the process curve segment as a smooth curve segment;

[0019] If the slope of the process curve segment is not within the preset slope range, the process curve segment is used as a new target curve segment, and the process returns to the step of determining the average value corresponding to every two point data in the target curve segment until the slope of the obtained process curve segment is within the preset slope range, and the process curve segment is determined to be a smooth curve segment.

[0020] Furthermore, obtaining a motor efficiency curve of the electric drive assembly of the electric vehicle at a characteristic voltage corresponding to a target curve segment during a target discharge process includes:

[0021] Obtaining the input electric power and output mechanical power corresponding to the target curve segment of the electric drive assembly of the electric vehicle during the target discharge process;

[0022] According to the input electric power and output mechanical power of the electric drive assembly corresponding to the target curve segment during the target discharge process, a motor efficiency curve of the electric drive assembly corresponding to the characteristic voltage of the target curve segment during the target discharge process is determined.

[0023] Furthermore, the voltages corresponding to different remaining capacities of the battery of the electric vehicle during the target discharge process are obtained, and an actual correlation curve between the remaining capacities and the voltage is obtained, including:

[0024] Obtain the voltages corresponding to different remaining charges of the electric vehicle during driving;

[0025] According to the target remaining power range corresponding to the target discharge process, the voltages corresponding to different remaining powers during driving are screened out to obtain the actual correlation curve between the remaining power and the voltage.

[0026] Furthermore, the target curve segment whose voltage fluctuation amplitude exceeds the preset fluctuation amplitude in the actual correlation curve is screened out, including:

[0027] Screening out multiple curve segments to be adjusted whose voltage fluctuation amplitudes exceed a preset fluctuation amplitude in the actual correlation curve;

[0028] According to the maximum number of motor efficiency curves allowed to be input by the pre-built battery simulation module, adjacent curve segments to be adjusted in the multiple curve segments to be adjusted are merged to obtain a target number of target curve segments, where the target number is less than or equal to the maximum number.

[0029] In a second aspect, the present application provides an electric vehicle energy consumption estimation device, the device comprising:

[0030] An actual curve acquisition module is used to obtain the voltages corresponding to different remaining capacities of the battery of the electric vehicle during the target discharge process, and obtain an actual correlation curve between the remaining capacities and the voltage;

[0031] A curve segmentation module is used to filter out target curve segments whose voltage fluctuation amplitude exceeds a preset fluctuation amplitude in the actual correlation curve;

[0032] A curve smoothing module is used to perform curve smoothing on the target curve segment to obtain a smooth curve segment;

[0033] A motor efficiency curve acquisition module is used to obtain a motor efficiency curve of an electric drive assembly of an electric vehicle at a characteristic voltage corresponding to a target curve segment during a target discharge process;

[0034] A target correlation curve determination module is used to determine the target correlation curve based on the smooth curve segment and the actual correlation curve;

[0035] The energy consumption estimation module is used to input the target correlation curve and the motor efficiency curve into the pre-built battery simulation module to estimate the energy consumption data of the electric vehicle during the target discharge process.

[0036] Furthermore, the actual curve acquisition module is used to:

[0037] Obtain the voltage corresponding to different remaining capacities of the electric vehicle battery during the target discharge process, and obtain actual scattered data on the relationship between remaining capacity and voltage;

[0038] According to the actual scatter point data, an actual scatter point curve is obtained in which the voltage changes as the remaining power decreases. The actual scatter point curve is an actual correlation curve.

[0039] Furthermore, the curve smoothing module is used to:

[0040] Determine the average eigenvalue corresponding to every two point data in the target curve segment;

[0041] According to the average characteristic value corresponding to every two point data in the target curve segment, the process curve segment is obtained;

[0042] Determine whether the slope of the process curve segment is within a preset slope range;

[0043] When the slope of the process curve segment is within a preset slope range, determining the process curve segment as a smooth curve segment;

[0044] If the slope of the process curve segment is not within the preset slope range, the process curve segment is used as a new target curve segment, and the process returns to the step of determining the average value corresponding to every two point data in the target curve segment until the slope of the obtained process curve segment is within the preset slope range, and the process curve segment is determined to be a smooth curve segment.

[0045] Furthermore, the motor efficiency curve acquisition module is used to:

[0046] Obtaining the input electric power and output mechanical power of the electric drive assembly of the electric vehicle during the target discharge process, corresponding to the characteristic voltage of the target curve segment;

[0047] The motor efficiency curve of the electric drive assembly corresponding to the characteristic voltage of the target curve segment during the target discharge process is determined based on the input electric power and output mechanical power of the electric drive assembly corresponding to the characteristic voltage of the target curve segment during the target discharge process.

[0048] Furthermore, the actual curve acquisition module is used to:

[0049] Obtain the voltages corresponding to different remaining charges of the electric vehicle during driving;

[0050] According to the target remaining power range corresponding to the target discharge process, the voltages corresponding to different remaining powers during driving are screened out to obtain the actual correlation curve between the remaining power and the voltage.

[0051] Furthermore, the curve segmentation module is used to:

[0052] Screening out multiple curve segments to be adjusted whose voltage fluctuation amplitudes exceed a preset fluctuation amplitude in the actual correlation curve;

[0053] According to the maximum number of motor efficiency curves allowed to be input by the pre-built battery simulation module, adjacent curve segments to be adjusted in the multiple curve segments to be adjusted are merged to obtain a target number of target curve segments, where the target number is less than or equal to the maximum number.

[0054] In a third aspect, the present application provides an electronic device, comprising:

[0055] processor;

[0056] a memory for storing processor-executable instructions;

[0057] The processor is configured to execute to implement an electric vehicle energy consumption estimation method provided in the first aspect.

[0058] In a fourth aspect, the present application provides a non-temporary computer-readable storage medium. When the instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to implement an electric vehicle energy consumption estimation method as provided in the first aspect.

[0059] In a fifth aspect, the present application provides a computer program product, comprising computer instructions, which are executed by a processor to implement an electric vehicle energy consumption estimation method as provided in the first aspect.

[0060] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0061] The embodiment of the present application obtains the voltage corresponding to different remaining capacities of the battery of an electric vehicle during a target discharge process to obtain an actual correlation curve between the remaining capacity and the voltage; selects a target curve segment in the actual correlation curve whose voltage fluctuation amplitude exceeds a preset fluctuation amplitude; performs curve smoothing on the target curve segment to obtain a smooth curve segment; obtains a motor efficiency curve of the electric drive assembly of the electric vehicle during the target discharge process corresponding to the characteristic voltage of the target curve segment; determines a target correlation curve based on the smooth curve segment and the actual correlation curve; inputs the target correlation curve and the motor efficiency curve into a pre-built battery simulation module to estimate the energy consumption data of the electric vehicle during the target discharge process. It can be seen that the embodiment of the present application divides the curve of voltage variation with remaining capacity into multiple target curve segments, takes into account the energy consumption characteristics of the battery at different remaining capacities, can better adapt to various complex working conditions, calculates the motor efficiency curve corresponding to the characteristic voltage of each target curve segment, and then estimates the energy consumption of the electric vehicle during the entire discharge process, thereby improving the accuracy of the energy consumption simulation of the electric vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following is a brief introduction to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0063] Figure 1 A flow chart of a method for estimating energy consumption of an electric vehicle provided in an embodiment of the present application;

[0064] Figure 2 A schematic diagram of an actual correlation curve between remaining power and voltage provided in an embodiment of the present application;

[0065] Figure 3 For the embodiment of this application Figure 2 Schematic diagram of the distribution of target curve segments after segmentation;

[0066] Figure 4 For the embodiment of this application Figure 2 Schematic diagram of the curve after smoothing the curve;

[0067] Figure 5 Schematic diagram of the corresponding relationship between the remaining power-voltage curve, the remaining power-time curve, and the speed-time curve in the embodiment of the present application;

[0068] Figure 6 A schematic diagram of the structure of an electric vehicle energy consumption estimation device provided in an embodiment of the present application;

[0069] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0070] The embodiment of the present application solves the technical problem of inaccurate energy consumption estimation of electric vehicles in the prior art by providing a method for estimating energy consumption of electric vehicles.

[0071] The technical solution of the embodiment of the present application is to solve the above technical problems, and the overall idea is as follows:

[0072] The embodiment of the present application obtains the voltage corresponding to different remaining capacities of the battery of an electric vehicle during a target discharge process to obtain an actual correlation curve between the remaining capacity and the voltage; selects a target curve segment in the actual correlation curve whose voltage fluctuation amplitude exceeds a preset fluctuation amplitude; performs curve smoothing on the target curve segment to obtain a smooth curve segment; obtains a motor efficiency curve of the electric drive assembly of the electric vehicle during the target discharge process corresponding to the characteristic voltage of the target curve segment; determines a target correlation curve based on the smooth curve segment and the actual correlation curve; inputs the target correlation curve and the motor efficiency curve into a pre-built battery simulation module to estimate the energy consumption data of the electric vehicle during the target discharge process. It can be seen that the embodiment of the present application divides the curve of voltage variation with remaining capacity into multiple target curve segments, takes into account the energy consumption characteristics of the battery at different remaining capacities, can better adapt to various complex working conditions, calculates the motor efficiency curve corresponding to the characteristic voltage of each target curve segment, and then estimates the energy consumption of the electric vehicle during the entire discharge process, thereby improving the accuracy of the energy consumption simulation of the electric vehicle.

[0073] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0074] First, the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. Furthermore, the character " / " in this document generally indicates an "or" relationship between the associated objects.

[0075] The present application provides an embodiment of Figure 1 A method for estimating energy consumption of an electric vehicle is shown, and the method includes steps S11 to S16.

[0076] Step S11, obtaining voltages corresponding to different remaining capacities of the battery of the electric vehicle during a target discharge process, and obtaining an actual correlation curve between the remaining capacities and the voltages;

[0077] Step S12, screening out target curve segments whose voltage fluctuation amplitude exceeds a preset fluctuation amplitude in the actual correlation curve;

[0078] Step S13, performing curve smoothing processing on the target curve segment to obtain a smooth curve segment;

[0079] Step S14, obtaining a motor efficiency curve of the electric drive assembly of the electric vehicle at a characteristic voltage corresponding to a target curve segment during a target discharge process;

[0080] Step S15, determining a target correlation curve based on the smooth curve segment and the actual correlation curve;

[0081] Step S16: input the target correlation curve and the motor efficiency curve into a pre-built battery simulation module to estimate the energy consumption data of the electric vehicle during the target discharge process.

[0082] The electric vehicle energy consumption estimation method provided in an embodiment of the present application can be executed by the controller of the electric vehicle, or by a cloud server or other terminal communicating with the controller, and the embodiment of the present application does not impose any restrictions on this.

[0083] Regarding step S11, the voltages corresponding to different remaining capacities of the battery of the electric vehicle during the target discharge process are obtained to obtain an actual correlation curve between the remaining capacities and the voltages.

[0084] First, obtain the voltages corresponding to the different remaining charges of the electric vehicle during driving. For example, the remaining charge corresponding to the electric vehicle at the beginning of driving is 100%. As the mileage of the car continues to increase, the remaining charge will start to decrease from 100%. During driving, collect the voltages corresponding to the different remaining charges, that is, collect the voltages corresponding to the process of the remaining charge decreasing from 100% to the minimum remaining charge threshold (for example, 10%). The acquisition frequency can be determined according to actual needs, and the embodiment of the present application does not limit this. For example, when the accuracy of the energy consumption estimation of the electric vehicle is required to be higher, the acquisition frequency can be higher.

[0085] According to the state of the battery installed in the electric vehicle, a target discharge process with relatively stable discharge can be specified, specifically a remaining power range is specified. In the embodiment of the present application, it is recorded as the target remaining power range, for example, it can refer to the remaining power range of 80%-20%.

[0086] Based on the target remaining capacity range corresponding to the target discharge process, the voltages corresponding to the different remaining capacities during driving are filtered out to obtain the actual correlation curve between the remaining capacity and voltage. For example, if the target remaining capacity range is 80%-20%, and the remaining capacities during driving are 100%-10%, then the voltage data corresponding to 80%-20% is filtered out from the voltage data corresponding to 100%-10%. The actual correlation curve between the remaining capacity and voltage is then obtained based on the voltage data corresponding to 80%-20%.

[0087] Specifically, after obtaining the voltages corresponding to different remaining capacities of the battery of an electric vehicle during the target discharge process, actual scattered data (i.e., discrete data) related to the remaining capacity and voltage can be obtained. Then, based on the actual scattered data, an actual scattered curve is obtained showing how the voltage changes as the remaining capacity decreases. The actual scattered curve is the actual correlation curve. For example, Figure 2The figure shows an actual correlation curve of the remaining power and voltage provided by the embodiment of the present application, with the horizontal axis being the remaining power and the vertical axis being the voltage. Figure 2 The curve in is a continuous curve, but in fact it is a discontinuous curve composed of multiple discrete points.

[0088] Regarding step S12, a target curve segment whose voltage fluctuation amplitude exceeds a preset fluctuation amplitude in the actual correlation curve is screened out.

[0089] Multiple curve segments to be adjusted are selected where the voltage fluctuation amplitude in the actual correlation curve exceeds a preset fluctuation amplitude. The voltage fluctuation amplitude can be the voltage change amplitude between two adjacent points in the actual correlation curve. When the voltage fluctuation between the two points exceeds the preset fluctuation amplitude, the two points are identified as curve segments to be adjusted. By determining the voltage fluctuation amplitude between any two adjacent points in the actual correlation curve, multiple curve segments to be adjusted can be obtained.

[0090] The maximum number of motor efficiency curves that the pre-built battery simulation module allows to be input is mainly determined by the capability of the simulation software used to build the battery simulation module. The higher the capability, the larger the maximum number, and the lower the capability, the smaller the maximum number.

[0091] Based on the maximum number of motor efficiency curves allowed by the pre-built battery simulation module, adjacent curve segments to be adjusted are merged among the multiple curve segments to be adjusted to obtain a target number of target curve segments, where the target number is less than or equal to the maximum number. In other words, within the maximum number limit, several adjacent curve segments to be adjusted can be merged into one target curve segment, so that the target number of the target curve segments in the middle is less than or equal to the maximum number.

[0092] For example, when the maximum number is 4, you can Figure 2 The actual correlation curve shown in FIG is obtained by obtaining a target curve segment less than or equal to 4, such as Figure 3 As shown, three target curve segments can be obtained, namely a, b, and c.

[0093] Regarding step S13, curve smoothing processing is performed on the target curve segment to obtain a smooth curve segment.

[0094] Curve smoothing is performed on each target curve segment to obtain a corresponding smooth curve segment. The specific process includes steps S131 to S135.

[0095] Step S131, determining the average characteristic value corresponding to every two point data in the target curve segment;

[0096] Step S132, obtaining a process curve segment according to the average characteristic value corresponding to every two point data in the target curve segment;

[0097] Step S133, determining whether the slope of the process curve segment is within a preset slope range;

[0098] Step S134: if the slope of the process curve segment is within a preset slope range, the process curve segment is determined to be a smooth curve segment;

[0099] In step S135, if the slope of the process curve segment is not within the preset slope range, the process curve segment is used as a new target curve segment, and the process returns to the step of determining the average value corresponding to every two point data in the target curve segment until the slope of the obtained process curve segment is within the preset slope range, and the process curve segment is determined to be a smooth curve segment.

[0100] The data between every two points in the target curve segment are averaged, the average value is used as the characteristic value, and a new curve segment is reconstructed. In the embodiment of the present application, it is recorded as a process curve segment.

[0101] Then, it is determined whether the slope between each two points in the process curve segment is within the preset slope range. If it is within the preset slope range, the current process curve segment is determined to be a smooth curve segment. If it is not within the preset slope range, the current process curve segment is used as a new target curve segment, and then the process returns to step S131 until the slope of the final process curve segment is within the preset slope range. The process curve segment with a slope within the preset slope range is determined to be a smooth curve segment. Figure 4 As shown, for Figure 3 Schematic diagram of the curve obtained after smoothing.

[0102] Regarding step S14, a motor efficiency curve of the electric drive assembly of the electric vehicle with a characteristic voltage corresponding to a target curve segment during a target discharge process is obtained.

[0103] First, the input electric power and output mechanical power of the electric drive assembly of the electric vehicle corresponding to the characteristic voltage of the target curve segment during the target discharge process are obtained.

[0104] The input electric power is obtained in the following way: during the driving of the electric vehicle, the current and voltage data corresponding to the target curve segment and input to the motor are recorded in real time through the electric power measuring instrument. The product of the current and voltage is the input electric power of the motor.

[0105] The output mechanical power is obtained by using a torque sensor and a speed sensor to obtain the torque and speed data of the motor output shaft corresponding to the target curve segment. The product of the torque and the speed divided by 9550 is the output mechanical power of the motor.

[0106] Based on the electric drive assembly's input electrical power and output mechanical power at the voltage corresponding to the target curve segment during the target discharge process, the motor efficiency curve corresponding to the characteristic voltage of the target curve segment during the target discharge process is determined. Specifically, the motor efficiency curve corresponding to the characteristic voltage of the target curve segment is obtained by dividing the output mechanical power corresponding to the target curve segment by the input electrical power.

[0107] Regarding step S15, a target correlation curve is determined based on the smooth curve segment and the actual correlation curve.

[0108] The corresponding target curve segment in the actual association curve is replaced by the smooth curve segment to obtain a relatively smooth target association curve.

[0109] Regarding step S16, the target correlation curve and the motor efficiency curve are input into a pre-built battery simulation module to estimate the energy consumption data of the electric vehicle during the target discharge process.

[0110] The pre-built battery simulation module can estimate the energy consumption data of the electric vehicle during the target discharge process based on the target correlation curve and motor efficiency curve.

[0111] like Figure 5 As shown in the figure, the U-SOC curve represents the actual relationship between remaining charge and voltage, the SOC-T curve shows the gradual decrease in remaining charge over time, and the VT curve shows the change in the electric vehicle's speed over time. These three curves correspond to each other. The U-SOC curve includes three target curve segments: U1, U2, and U3. The corresponding characteristic voltage of each target curve segment corresponds to a motor efficiency MAP. Based on these multiple target curve segments, the energy consumption data of the electric vehicle during the target discharge process can be estimated.

[0112] In summary, the embodiment of the present application obtains the voltage corresponding to different residual capacities of the battery of the electric vehicle during the target discharge process to obtain the actual correlation curve between the residual capacity and the voltage; screens out the target curve segment whose voltage fluctuation amplitude exceeds the preset fluctuation amplitude in the actual correlation curve; performs curve smoothing on the target curve segment to obtain a smooth curve segment; obtains the motor efficiency curve of the electric drive assembly of the electric vehicle during the target discharge process corresponding to the characteristic voltage of the target curve segment; determines the target correlation curve based on the smooth curve segment and the actual correlation curve; inputs the target correlation curve and the motor efficiency curve into a pre-built battery simulation module to estimate the energy consumption data of the electric vehicle during the target discharge process. It can be seen that the embodiment of the present application divides the curve of voltage change with residual capacity into multiple target curve segments, takes into account the energy consumption characteristics of the battery at different residual capacities, can better adapt to various complex working conditions, calculates the motor efficiency curve corresponding to the characteristic voltage of each target curve segment, and then estimates the energy consumption of the electric vehicle during the entire discharge process, thereby improving the accuracy of the energy consumption simulation of the electric vehicle.

[0113] Based on the same inventive concept, the present application provides the following embodiments: Figure 6 An electric vehicle energy consumption estimation device is shown, the device comprising:

[0114] The actual curve acquisition module 61 is used to obtain the voltages corresponding to different remaining capacities of the battery of the electric vehicle during the target discharge process, and obtain an actual correlation curve between the remaining capacities and the voltages;

[0115] The curve segmentation module 62 is used to screen out target curve segments in which the voltage fluctuation amplitude exceeds a preset fluctuation amplitude in the actual correlation curve;

[0116] A curve smoothing module 63 is used to perform curve smoothing processing on the target curve segment to obtain a smooth curve segment;

[0117] A motor efficiency curve acquisition module 64 is used to acquire a motor efficiency curve of the electric drive assembly of the electric vehicle at a characteristic voltage corresponding to a target curve segment during a target discharge process;

[0118] a target correlation curve determining module 65, configured to determine a target correlation curve based on the smooth curve segment and the actual correlation curve;

[0119] The energy consumption estimation module 66 is used to input the target correlation curve and the motor efficiency curve into the pre-built battery simulation module to estimate the energy consumption data of the electric vehicle during the target discharge process.

[0120] Furthermore, the actual curve acquisition module 61 is used to:

[0121] Obtain the voltage corresponding to different remaining capacities of the electric vehicle battery during the target discharge process, and obtain actual scattered data on the relationship between remaining capacity and voltage;

[0122] According to the actual scatter point data, an actual scatter point curve is obtained in which the voltage changes as the remaining power decreases. The actual scatter point curve is an actual correlation curve.

[0123] Furthermore, the curve smoothing module 63 is used to:

[0124] Determine the average eigenvalue corresponding to every two point data in the target curve segment;

[0125] According to the average characteristic value corresponding to every two point data in the target curve segment, the process curve segment is obtained;

[0126] Determine whether the slope of the process curve segment is within a preset slope range;

[0127] When the slope of the process curve segment is within a preset slope range, determining the process curve segment as a smooth curve segment;

[0128] If the slope of the process curve segment is not within the preset slope range, the process curve segment is used as a new target curve segment, and the process returns to the step of determining the average value corresponding to every two point data in the target curve segment until the slope of the obtained process curve segment is within the preset slope range, and the process curve segment is determined to be a smooth curve segment.

[0129] Furthermore, the motor efficiency curve acquisition module 64 is used to:

[0130] Obtaining the input electric power and output mechanical power corresponding to the target curve segment of the electric drive assembly of the electric vehicle during the target discharge process;

[0131] The motor efficiency curve of the electric drive assembly corresponding to the characteristic voltage of the target curve segment during the target discharge process is determined based on the input electric power and output mechanical power of the electric drive assembly corresponding to the characteristic voltage of the target curve segment during the target discharge process.

[0132] Furthermore, the actual curve acquisition module 61 is used to:

[0133] Obtain the voltages corresponding to different remaining charges of the electric vehicle during driving;

[0134] According to the target remaining power range corresponding to the target discharge process, the voltages corresponding to different remaining powers during driving are screened out to obtain the actual correlation curve between the remaining power and the voltage.

[0135] Furthermore, the curve segmentation module 62 is used to:

[0136] Screening out multiple curve segments to be adjusted whose voltage fluctuation amplitudes exceed a preset fluctuation amplitude in the actual correlation curve;

[0137] According to the maximum number of motor efficiency curves allowed to be input by the pre-built battery simulation module, adjacent curve segments to be adjusted in the multiple curve segments to be adjusted are merged to obtain a target number of target curve segments, where the target number is less than or equal to the maximum number.

[0138] Based on the same inventive concept, the present application provides the following embodiments: Figure 7 An electronic device as shown includes:

[0139] Processor 71;

[0140] a memory 72 for storing instructions executable by the processor 71;

[0141] The processor 71 is configured to execute to implement the electric vehicle energy consumption estimation method provided above.

[0142] Based on the same inventive concept, an embodiment of the present application provides a non-temporary computer-readable storage medium. When the instructions in the storage medium are executed by the processor 71 of the electronic device, the electronic device can execute an electric vehicle energy consumption estimation method as provided above.

[0143] Based on the same inventive concept, an embodiment of the present application provides a computer program product, including computer instructions, which are executed by the processor 71 to implement an electric vehicle energy consumption estimation method as provided above.

[0144] Since the electronic device described in this embodiment is an electronic device used to implement the information processing method in the embodiment of this application, based on the information processing method described in the embodiment of this application, those skilled in the art will be able to understand the specific implementation of the electronic device of this embodiment and its various variations, so how the electronic device implements the method in the embodiment of this application will not be described in detail here. As long as those skilled in the art implement the electronic device used by the information processing method in the embodiment of this application, it falls within the scope of protection to be provided by this application.

[0145] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0146] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0147] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0148] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0149] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0150] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for estimating energy consumption of an electric vehicle, characterized in that: The method comprises: Obtain the voltage corresponding to different remaining capacities of the battery of the electric vehicle during the target discharge process, and obtain the actual correlation curve between the remaining capacity and the voltage; Screening out a target curve segment whose voltage fluctuation amplitude exceeds a preset fluctuation amplitude in the actual correlation curve; Performing curve smoothing processing on the target curve segment to obtain a smooth curve segment; Obtaining a motor efficiency curve of the electric drive assembly of the electric vehicle at a characteristic voltage corresponding to the target curve segment during the target discharge process; determining a target correlation curve according to the smooth curve segment and the actual correlation curve; The target correlation curve and the motor efficiency curve are input into a pre-built battery simulation module to estimate energy consumption data of the electric vehicle during the target discharge process.

2. The method according to claim 1, wherein The step of obtaining voltages corresponding to different remaining capacities of the battery of the electric vehicle during the target discharge process and obtaining an actual correlation curve between the remaining capacities and the voltages includes: Obtaining voltages of the battery of the electric vehicle corresponding to different remaining capacities during the target discharge process, and obtaining actual scattered point data associated with the remaining capacities and the voltages; According to the actual scatter point data, an actual scatter point curve showing the voltage changing as the remaining power decreases is obtained, and the actual scatter point curve is the actual correlation curve.

3. The method according to claim 1, wherein The step of performing curve smoothing on the target curve segment to obtain a smooth curve segment includes: Determine the average characteristic value corresponding to every two point data in the target curve segment; Obtaining a process curve segment according to the average characteristic value corresponding to every two point data in the target curve segment; Determining whether the slope of the process curve segment is within a preset slope range; When the slope of the process curve segment is within the preset slope range, determining the process curve segment as the smooth curve segment; If the slope of the process curve segment is not within the preset slope range, the process curve segment is used as a new target curve segment, and the process returns to the step of determining the average value corresponding to every two point data in the target curve segment until the obtained slope of the process curve segment is within the preset slope range, and the process curve segment is determined as the smooth curve segment.

4. The method according to claim 1, wherein The step of obtaining a motor efficiency curve of the electric drive assembly of the electric vehicle at a characteristic voltage corresponding to the target curve segment during the target discharge process includes: Obtaining the input electric power and output mechanical power of the electric drive assembly of the electric vehicle corresponding to the target curve segment during the target discharge process; The motor efficiency curve of the electric drive assembly corresponding to the target curve segment during the target discharge process is determined according to the input electric power and output mechanical power of the electric drive assembly corresponding to the target curve segment during the target discharge process.

5. The method according to claim 1, wherein The step of obtaining voltages corresponding to different remaining capacities of the battery of the electric vehicle during the target discharge process and obtaining an actual correlation curve between the remaining capacities and the voltages includes: Obtaining voltages corresponding to different remaining capacities of the electric vehicle during driving; According to the target remaining power range corresponding to the target discharge process, the voltages corresponding to the different remaining powers during the driving process are screened out to obtain the actual correlation curve between the remaining power and the voltage.

6. The method according to claim 1, wherein The step of screening out a target curve segment having a voltage fluctuation amplitude exceeding a preset fluctuation amplitude in the actual correlation curve includes: Screening out a plurality of curve segments to be adjusted whose voltage fluctuation amplitudes exceed the preset fluctuation amplitudes in the actual correlation curve; According to the maximum number of motor efficiency curves allowed to be input by the pre-built battery simulation module, adjacent curve segments to be adjusted in the multiple curve segments to be adjusted are merged to obtain a target number of target curve segments, which is less than or equal to the maximum number.

7. An electric vehicle energy consumption estimation device, characterized in that: The device comprises: An actual curve acquisition module is used to obtain the voltages corresponding to different remaining capacities of the battery of the electric vehicle during the target discharge process, and obtain an actual correlation curve between the remaining capacities and the voltage; A curve segmentation module, configured to screen out target curve segments in the actual correlation curve whose voltage fluctuation amplitude exceeds a preset fluctuation amplitude; A curve smoothing module, configured to perform curve smoothing processing on the target curve segment to obtain a smooth curve segment; a motor efficiency curve acquisition module, configured to acquire a motor efficiency curve of the electric drive assembly of the electric vehicle at a characteristic voltage corresponding to the target curve segment during the target discharge process; a target correlation curve determining module, configured to determine a target correlation curve based on the smooth curve segment and the actual correlation curve; The energy consumption estimation module is used to input the target correlation curve and the motor efficiency curve into a pre-built battery simulation module to estimate the energy consumption data of the electric vehicle during the target discharge process.

8. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute to implement an electric vehicle energy consumption estimation method according to any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium, which, when the instructions in the storage medium are executed by a processor of an electronic device, enables the electronic device to implement the electric vehicle energy consumption estimation method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises computer instructions, wherein the computer instructions are executed by a processor to implement an electric vehicle energy consumption estimation method according to any one of claims 1 to 6.

Images