Energy consumption prediction method and device of low-speed unmanned vehicle, storage medium and equipment

Abstract

The invention discloses an energy consumption prediction method and device for a low-speed unmanned vehicle, a storage medium and equipment, and belongs to the technical field of automatic driving. Working condition characteristics of the low-speed unmanned vehicle are obtained through big data, wherein the working condition characteristics comprise test time, test mileage, average vehicle speed and environment information during test; vehicle parameters of the low-speed unmanned vehicle to be measured are obtained, wherein the vehicle parameters comprise the whole vehicle weight, the rolling resistance coefficient, driving system information, motor information, DC-DC information, battery information and a low-voltage load; obtaining a preset energy consumption prediction model, wherein the energy consumption prediction model is a model for performing digital calculation on the energy flow based on the law of conservation of energy; and processing the vehicle parameters and the working condition characteristics by using the energy consumption prediction model to obtain predicted energy consumption of the low-speed unmanned vehicle. The energy consumption of the low-speed unmanned vehicle is predicted based on the actual working condition, and the accuracy of energy consumption prediction is improved; energy consumption prediction is carried out from two dimensions of simulation prediction and actual test, and the precision of energy consumption prediction is improved.

Description

Technical Field

[0001] The present application relates to the field of autonomous driving technology, and in particular to a method, device, storage medium, and equipment for predicting energy consumption of a low-speed unmanned vehicle. Background Art

[0002] With the advancement of society and automotive technology, especially the widespread application of autonomous driving technology, the energy consumption and range of low-speed autonomous vehicles in urban conditions have become key parameters of great concern to companies and customers. Accurately testing and evaluating the energy consumption performance of low-speed autonomous vehicles is crucial for company design and development, and is also a key component in measuring product competitiveness and technological level. Low-speed autonomous vehicles can include low-speed unmanned logistics vehicles.

[0003] Existing vehicle energy consumption prediction methods are mainly based on the China Light-duty Vehicle Test Cycle (CLTC) operating conditions defined by national standards, which are suitable for predicting the energy consumption of passenger cars. However, passenger cars and low-speed unmanned vehicles differ in terms of maximum speed and speed conditions. Therefore, the CLTC operating conditions are not suitable for predicting the energy consumption of low-speed unmanned vehicles. Summary of the Invention

[0004] This application provides a method, device, storage medium, and equipment for predicting energy consumption of a low-speed unmanned vehicle, which is used to solve the problem that the CLTC working condition can predict the energy consumption of passenger cars but cannot predict the energy consumption of low-speed unmanned vehicles. The technical solution is as follows:

[0005] According to a first aspect of the present application, a method for predicting energy consumption of a low-speed unmanned vehicle is provided, the method comprising:

[0006] Obtaining operating characteristics of low-speed autonomous vehicles through big data, including test time, test mileage, average vehicle speed, and environmental information during the test;

[0007] Obtain vehicle parameters of the low-speed unmanned vehicle to be tested, including vehicle weight, rolling resistance coefficient, drive system information, motor information, DC-DC information, battery information, and low-voltage load;

[0008] Obtaining a preset energy consumption prediction model, wherein the energy consumption prediction model is a model that performs digital calculation of energy flow based on the law of conservation of energy;

[0009] The energy consumption prediction model is used to process the vehicle parameters and the operating condition characteristics to obtain the predicted energy consumption of the low-speed unmanned vehicle.

[0010] In one possible implementation, the using the energy consumption prediction model to process the vehicle parameters and the operating condition characteristics to obtain the predicted energy consumption of the low-speed unmanned vehicle includes:

[0011] When the energy consumption prediction model includes multiple integral algorithms and multiple differential algorithms, the integral algorithms and the differential algorithms are used to calculate the vehicle parameters and the operating condition characteristics in sequence to obtain the predicted energy consumption of the low-speed unmanned vehicle.

[0012] In one possible implementation, the using the integral algorithm and the differential algorithm to sequentially calculate the vehicle parameters and the operating condition characteristics to obtain the predicted energy consumption of the low-speed unmanned vehicle includes:

[0013] Calculating the vehicle weight and the rolling resistance coefficient using a first integral algorithm to obtain a first calculation result;

[0014] Calculating the first calculation result and the drive system information using a first differential algorithm to obtain a second calculation result;

[0015] Calculating the second calculation result and the operating condition characteristic using the second integration algorithm to obtain a third calculation result;

[0016] Calculating the third calculation result and the motor information using a second differential algorithm to obtain a fourth calculation result;

[0017] Processing the fourth calculation result and the DC-DC information according to a preset control strategy to obtain a fifth calculation result;

[0018] The third differential algorithm is used to calculate the fifth calculation result, the battery information, and the low-voltage load to obtain the predicted energy consumption of the low-speed unmanned vehicle.

[0019] In a possible implementation, the method further includes:

[0020] Obtaining actual test results of the low-speed unmanned vehicle under various operating conditions;

[0021] Calculating the actual energy consumption of the low-speed unmanned vehicle according to the actual test results;

[0022] The predicted energy consumption is corrected according to the actual energy consumption.

[0023] In a possible implementation, the method further includes:

[0024] An energy consumption and endurance report is generated according to the predicted energy consumption and the actual energy consumption.

[0025] According to a second aspect of the present application, a device for predicting energy consumption of a low-speed unmanned vehicle is provided, the device comprising:

[0026] A feature acquisition module is used to obtain the operating characteristics of the low-speed unmanned vehicle through big data. The operating characteristics include test time, test mileage, average vehicle speed, and environmental information during the test;

[0027] A parameter acquisition module is used to obtain vehicle parameters of the low-speed unmanned vehicle to be tested, including vehicle weight, rolling resistance coefficient, drive system information, motor information, DC-DC information, battery information, and low-voltage load;

[0028] A model acquisition module is used to acquire a preset energy consumption prediction model, wherein the energy consumption prediction model is a model that performs digital calculation of energy flow based on the law of conservation of energy;

[0029] An energy consumption prediction module is used to process the vehicle parameters and the operating condition characteristics using the energy consumption prediction model to obtain the predicted energy consumption of the low-speed unmanned vehicle.

[0030] In a possible implementation, the energy consumption prediction module is further configured to:

[0031] When the energy consumption prediction model includes multiple integral algorithms and multiple differential algorithms, the integral algorithms and the differential algorithms are used to calculate the vehicle parameters and the operating condition characteristics in sequence to obtain the predicted energy consumption of the low-speed unmanned vehicle.

[0032] In a possible implementation, the energy consumption prediction module is further configured to:

[0033] Calculating the vehicle weight and the rolling resistance coefficient using a first integral algorithm to obtain a first calculation result;

[0034] Calculating the first calculation result and the drive system information using a first differential algorithm to obtain a second calculation result;

[0035] Calculating the second calculation result and the operating condition characteristic using the second integration algorithm to obtain a third calculation result;

[0036] Calculating the third calculation result and the motor information using a second differential algorithm to obtain a fourth calculation result;

[0037] Processing the fourth calculation result and the DC-DC information according to a preset control strategy to obtain a fifth calculation result;

[0038] The third differential algorithm is used to calculate the fifth calculation result, the battery information, and the low-voltage load to obtain the predicted energy consumption of the low-speed unmanned vehicle.

[0039] According to a third aspect of the present application, a computer-readable storage medium is provided, in which at least one instruction is stored. The at least one instruction is loaded and executed by a processor to implement the energy consumption prediction method for a low-speed unmanned vehicle as described above.

[0040] According to a fourth aspect of the present application, a computer device is provided, which includes the above-mentioned energy consumption prediction device for the low-speed unmanned vehicle.

[0041] The beneficial effects of the technical solution provided by this application include at least:

[0042] Since the operating characteristics of low-speed unmanned vehicles are extracted from the driving characteristics of a large number of low-speed unmanned vehicles and can reflect the operating characteristics of low-speed unmanned vehicles, the energy consumption prediction model can be used to process the vehicle parameters and operating characteristics to obtain the predicted energy consumption of the low-speed unmanned vehicle. The energy consumption prediction model is a model that digitally calculates the energy flow based on the law of conservation of energy. In this way, the energy consumption of the low-speed unmanned vehicle can be predicted based on the actual operating conditions, thereby improving the accuracy of the energy consumption prediction.

[0043] Obtain the actual test results of the low-speed unmanned vehicle under various working conditions; calculate the actual energy consumption of the low-speed unmanned vehicle based on the actual test results; correct the predicted energy consumption based on the actual energy consumption. Energy consumption prediction can be performed from two dimensions: simulation prediction and actual testing, which improves the accuracy of energy consumption prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0045] Figure 1 This is a flow chart of a method for predicting energy consumption of a low-speed unmanned vehicle provided by one embodiment of the present application;

[0046] Figure 2 This is a flow chart of a method for predicting energy consumption of a low-speed unmanned vehicle provided by one embodiment of the present application;

[0047] Figure 3 This is a structural block diagram of an energy consumption prediction device for a low-speed unmanned vehicle provided in one embodiment of the present application. DETAILED DESCRIPTION

[0048] In order to make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0049] like Figure 1 As shown, it shows a method flow chart of an energy consumption prediction method for a low-speed unmanned vehicle provided by an embodiment of the present application. The energy consumption prediction method for a low-speed unmanned vehicle can be applied to a computer device. The energy consumption prediction method for a low-speed unmanned vehicle can include:

[0050] Step 101: Obtain the operating characteristics of the low-speed unmanned vehicle through big data. The operating characteristics include test time, test mileage, average vehicle speed, and environmental information during the test.

[0051] A low-speed unmanned vehicle can be an unmanned vehicle whose speed is lower than a speed threshold. The speed threshold mentioned here can be set according to the actual needs of each city. In this embodiment, the speed of the low-speed unmanned vehicle is not limited.

[0052] Low-speed unmanned vehicles can be applied to multiple scenarios, such as using low-speed unmanned vehicles for logistics transportation and delivery, using low-speed unmanned vehicles for food delivery, etc. The application scenarios of low-speed unmanned vehicles are not limited in this embodiment.

[0053] In order to analyze the driving characteristics of low-speed unmanned vehicles under different working conditions, we can collect the driving big data of low-speed unmanned vehicles in different cities, analyze the big data of each city, obtain the average working condition characteristics of low-speed unmanned vehicles in different cities, and then standardize the average working condition characteristics in each city to obtain the final working condition characteristics.

[0054] Step 102: Obtain vehicle parameters of the low-speed unmanned vehicle to be tested, including vehicle weight, rolling resistance coefficient, drive system information, motor information, DC-DC information, battery information, and low-voltage load.

[0055] In this embodiment, the model of the low-speed unmanned vehicle to be tested can be first obtained, and then the corresponding vehicle parameters can be obtained based on the model. Among them, some vehicle parameters are preset parameters corresponding to the model and can be directly read; some parameters are measured parameters and can be measured by circuits or sensors on the low-speed unmanned vehicle.

[0056] Step 103: Obtain a preset energy consumption prediction model, where the energy consumption prediction model is a model that performs digital calculations on energy flow based on the law of conservation of energy.

[0057] In this embodiment, an energy consumption prediction model can be developed based on the driving characteristics of the low-speed unmanned vehicle, and the energy consumption of the low-speed unmanned vehicle can be simulated through the energy consumption prediction model.

[0058] Step 104 : Process the vehicle parameters and operating condition characteristics using the energy consumption prediction model to obtain the predicted energy consumption of the low-speed unmanned vehicle.

[0059] In this embodiment, vehicle parameters and operating condition characteristics can be input into the energy consumption prediction model in a predetermined order, and the energy consumption prediction model can be used to predict the energy consumption of the low-speed unmanned vehicle. The specific prediction method is described in detail below.

[0060] To sum up, the energy consumption prediction method for low-speed unmanned vehicles provided in the embodiment of the present application, since the operating condition characteristics of low-speed unmanned vehicles are characteristics extracted from the driving characteristics of a large number of low-speed unmanned vehicles, can reflect the operating condition characteristics of low-speed unmanned vehicles. Therefore, the energy consumption prediction model can be used to process vehicle parameters and operating condition characteristics to obtain the predicted energy consumption of the low-speed unmanned vehicle. The energy consumption prediction model is a model that digitally calculates energy flow based on the law of conservation of energy. In this way, the energy consumption of the low-speed unmanned vehicle can be predicted based on the actual operating conditions, thereby improving the accuracy of the energy consumption prediction.

[0061] like Figure 2 FIG2 shows a flow chart of a method for predicting energy consumption of a low-speed unmanned vehicle provided by an embodiment of the present application. The method for predicting energy consumption of a low-speed unmanned vehicle can be applied to a computer device. The method for predicting energy consumption of a low-speed unmanned vehicle can include:

[0062] Step 201: Obtain the operating characteristics of the low-speed unmanned vehicle through big data. The operating characteristics include test time, test mileage, average vehicle speed, and environmental information during the test.

[0063] A low-speed unmanned vehicle can be an unmanned vehicle whose speed is lower than a speed threshold. The speed threshold mentioned here can be set according to the actual needs of each city. In this embodiment, the speed of the low-speed unmanned vehicle is not limited.

[0064] Low-speed unmanned vehicles can be applied to multiple scenarios, such as using low-speed unmanned vehicles for logistics transportation and delivery, using low-speed unmanned vehicles for food delivery, etc. The application scenarios of low-speed unmanned vehicles are not limited in this embodiment.

[0065] In order to analyze the driving characteristics of low-speed unmanned vehicles under different working conditions, we can collect the driving big data of low-speed unmanned vehicles in different cities, analyze the big data of each city, obtain the average working condition characteristics of low-speed unmanned vehicles in different cities, and then standardize the average working condition characteristics in each city to obtain the final working condition characteristics.

[0066] Step 202: Obtain vehicle parameters of the low-speed unmanned vehicle to be tested, including vehicle weight, rolling resistance coefficient, drive system information, motor information, DC-DC information, battery information, and low-voltage load.

[0067] In this embodiment, the model of the low-speed unmanned vehicle to be tested can be first obtained, and then the corresponding vehicle parameters can be obtained based on the model. Among them, some vehicle parameters are preset parameters corresponding to the model and can be directly read; some parameters are measured parameters and can be measured by circuits or sensors on the low-speed unmanned vehicle.

[0068] Step 203: Obtain a preset energy consumption prediction model, where the energy consumption prediction model is a model that performs digital calculations on energy flow based on the law of conservation of energy.

[0069] In this embodiment, an energy consumption prediction model can be developed based on the driving characteristics of the low-speed unmanned vehicle, and the energy consumption of the low-speed unmanned vehicle can be simulated through the energy consumption prediction model.

[0070] Step 204: When the energy consumption prediction model includes multiple integral algorithms and multiple differential algorithms, the integral algorithm and the differential algorithm are used to calculate the vehicle parameters and operating condition characteristics in sequence to obtain the predicted energy consumption of the low-speed unmanned vehicle.

[0071] In this embodiment, vehicle parameters and operating condition characteristics may be input into an energy consumption prediction model in a predetermined order, and the energy consumption prediction model may be used to predict the energy consumption of the low-speed unmanned vehicle.

[0072] Specifically, the vehicle parameters and operating condition characteristics are calculated in sequence using the integral algorithm and the differential algorithm to obtain the predicted energy consumption of the low-speed unmanned vehicle, which may include:

[0073] (1) The vehicle weight and rolling resistance coefficient are calculated using a first integral algorithm to obtain a first calculation result.

[0074] (2) Calculate the first calculation result and the drive system information using the first differential algorithm to obtain a second calculation result.

[0075] (3) Calculate the second calculation result and the operating condition characteristics using the second integral algorithm to obtain a third calculation result.

[0076] (4) The third calculation result and the motor information are calculated using the second differential algorithm to obtain a fourth calculation result.

[0077] (5) Processing the fourth calculation result and the DC-DC information according to a preset control strategy to obtain a fifth calculation result.

[0078] (6) The third differential algorithm is used to calculate the fifth calculation result, battery information, and low-voltage load to obtain the predicted energy consumption of the low-speed unmanned vehicle.

[0079] After obtaining the predicted energy consumption, energy consumption tests can also be conducted on low-speed unmanned vehicles so that the predicted results can be corrected through actual test results to improve the accuracy of energy consumption prediction.

[0080] Specifically, actual test results of the low-speed unmanned vehicle under various working conditions are obtained; the actual energy consumption of the low-speed unmanned vehicle is calculated based on the actual test results; and the predicted energy consumption is corrected based on the actual energy consumption.

[0081] In this embodiment, in addition to using the actual energy consumption to correct the predicted energy consumption, the energy consumption prediction model may also be corrected to make the prediction result of the energy consumption prediction model closer to the actual energy consumption.

[0082] In this embodiment, an energy consumption and endurance report can also be generated based on the predicted energy consumption and actual energy consumption to facilitate subsequent big data analysis of the energy consumption of the low-speed unmanned vehicle.

[0083] To sum up, the energy consumption prediction method for low-speed unmanned vehicles provided in the embodiment of the present application, since the operating condition characteristics of low-speed unmanned vehicles are characteristics extracted from the driving characteristics of a large number of low-speed unmanned vehicles, can reflect the operating condition characteristics of low-speed unmanned vehicles. Therefore, the energy consumption prediction model can be used to process vehicle parameters and operating condition characteristics to obtain the predicted energy consumption of the low-speed unmanned vehicle. The energy consumption prediction model is a model that digitally calculates energy flow based on the law of conservation of energy. In this way, the energy consumption of the low-speed unmanned vehicle can be predicted based on the actual operating conditions, thereby improving the accuracy of the energy consumption prediction.

[0084] Obtain the actual test results of the low-speed unmanned vehicle under various working conditions; calculate the actual energy consumption of the low-speed unmanned vehicle based on the actual test results; correct the predicted energy consumption based on the actual energy consumption. Energy consumption prediction can be performed from two dimensions: simulation prediction and actual testing, which improves the accuracy of energy consumption prediction.

[0085] like Figure 3 FIG2 shows a block diagram of an energy consumption prediction device for a low-speed unmanned vehicle provided by an embodiment of the present application. The energy consumption prediction device for a low-speed unmanned vehicle can be applied to a computer device. The energy consumption prediction device for a low-speed unmanned vehicle can include:

[0086] A feature acquisition module 310 is used to acquire the operating characteristics of the low-speed unmanned vehicle through big data. The operating characteristics include test time, test mileage, average vehicle speed, and environmental information during the test;

[0087] Parameter acquisition module 320, for acquiring vehicle parameters of the low-speed unmanned vehicle to be tested, including vehicle weight, rolling resistance coefficient, drive system information, motor information, DC-DC information, battery information, and low-voltage load;

[0088] The model acquisition module 330 is used to acquire a preset energy consumption prediction model, which is a model that digitally calculates energy flow based on the law of conservation of energy;

[0089] The energy consumption prediction module 340 is used to process vehicle parameters and operating condition characteristics using an energy consumption prediction model to obtain the predicted energy consumption of the low-speed unmanned vehicle.

[0090] In an optional embodiment, the energy consumption prediction module 340 is further configured to:

[0091] When the energy consumption prediction model includes multiple integral algorithms and multiple differential algorithms, the integral algorithm and the differential algorithm are used to calculate the vehicle parameters and operating condition characteristics in turn to obtain the predicted energy consumption of the low-speed unmanned vehicle.

[0092] In an optional embodiment, the energy consumption prediction module 340 is further configured to:

[0093] Calculating the vehicle weight and rolling resistance coefficient using a first integral algorithm to obtain a first calculation result;

[0094] Calculating the first calculation result and the drive system information using a first differential algorithm to obtain a second calculation result;

[0095] Calculating the second calculation result and the operating condition characteristic using a second integral algorithm to obtain a third calculation result;

[0096] Calculating the third calculation result and the motor information using the second differential algorithm to obtain a fourth calculation result;

[0097] Processing the fourth calculation result and the DC-DC information according to a preset control strategy to obtain a fifth calculation result;

[0098] The third differential algorithm is used to calculate the fifth calculation result, battery information and low-voltage load to obtain the predicted energy consumption of the low-speed unmanned vehicle.

[0099] In an optional embodiment, the device further includes an energy consumption correction module, configured to:

[0100] Obtain actual test results of low-speed unmanned vehicles under various working conditions;

[0101] Calculate the actual energy consumption of low-speed unmanned vehicles based on actual test results;

[0102] Correct the predicted energy consumption based on the actual energy consumption.

[0103] In an optional embodiment, the device further includes a report generating module for generating an energy consumption and endurance report based on the predicted energy consumption and the actual energy consumption.

[0104] To sum up, the energy consumption prediction device for low-speed unmanned vehicles provided in the embodiment of the present application, since the operating condition characteristics of the low-speed unmanned vehicle are characteristics extracted from the driving characteristics of a large number of low-speed unmanned vehicles, can reflect the operating condition characteristics of the low-speed unmanned vehicle. Therefore, the energy consumption prediction model can be used to process the vehicle parameters and operating condition characteristics to obtain the predicted energy consumption of the low-speed unmanned vehicle. The energy consumption prediction model is a model that digitally calculates the energy flow based on the law of conservation of energy. In this way, the energy consumption of the low-speed unmanned vehicle can be predicted based on the actual operating conditions, thereby improving the accuracy of the energy consumption prediction.

[0105] Obtain the actual test results of the low-speed unmanned vehicle under various working conditions; calculate the actual energy consumption of the low-speed unmanned vehicle based on the actual test results; correct the predicted energy consumption based on the actual energy consumption. Energy consumption prediction can be performed from two dimensions: simulation prediction and actual testing, which improves the accuracy of energy consumption prediction.

[0106] One embodiment of the present application provides a computer-readable storage medium, wherein the storage medium stores at least one instruction, and the at least one instruction is loaded and executed by a processor to implement the energy consumption prediction method for a low-speed unmanned vehicle as described above.

[0107] One embodiment of the present application provides a computer device, which includes the energy consumption prediction device for any low-speed unmanned vehicle described above.

[0108] It should be noted that: the energy consumption prediction device for a low-speed unmanned vehicle provided in the above embodiment only uses the division of the above-mentioned functional modules as an example when performing energy consumption prediction of a low-speed unmanned vehicle. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the energy consumption prediction device for a low-speed unmanned vehicle is divided into different functional modules to complete all or part of the functions described above. In addition, the energy consumption prediction device for a low-speed unmanned vehicle provided in the above embodiment and the energy consumption prediction method embodiment for a low-speed unmanned vehicle belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0109] Those skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware, or by a program to instruct the relevant hardware, and the program may be stored in a computer-readable storage medium, which may be a read-only memory, a disk, or an optical disk, etc.

[0110] The above description is not intended to limit the embodiments of the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the embodiments of the present application should be included in the scope of protection of the embodiments of the present application.

Claims

1. A method for predicting energy consumption of a low-speed unmanned vehicle, characterized in that: The method comprises: Obtaining operating characteristics of low-speed autonomous vehicles through big data, including test time, test mileage, average vehicle speed, and environmental information during the test; Obtain vehicle parameters of the low-speed unmanned vehicle to be tested, including vehicle weight, rolling resistance coefficient, drive system information, motor information, DC-DC information, battery information, and low-voltage load; Obtaining a preset energy consumption prediction model, wherein the energy consumption prediction model is a model that performs digital calculation of energy flow based on the law of conservation of energy; The energy consumption prediction model is used to process the vehicle parameters and the operating condition characteristics to obtain the predicted energy consumption of the low-speed unmanned vehicle.

2. The energy consumption prediction method for a low-speed unmanned vehicle according to claim 1, characterized in that: The step of processing the vehicle parameters and the operating condition characteristics using the energy consumption prediction model to obtain the predicted energy consumption of the low-speed unmanned vehicle includes: When the energy consumption prediction model includes multiple integral algorithms and multiple differential algorithms, the integral algorithms and the differential algorithms are used to calculate the vehicle parameters and the operating condition characteristics in sequence to obtain the predicted energy consumption of the low-speed unmanned vehicle.

3. The energy consumption prediction method for a low-speed unmanned vehicle according to claim 2, characterized in that: The method of calculating the vehicle parameters and the operating condition characteristics in sequence using the integral algorithm and the differential algorithm to obtain the predicted energy consumption of the low-speed unmanned vehicle includes: Calculating the vehicle weight and the rolling resistance coefficient using a first integral algorithm to obtain a first calculation result; Calculating the first calculation result and the drive system information using a first differential algorithm to obtain a second calculation result; Calculating the second calculation result and the operating condition characteristic using the second integration algorithm to obtain a third calculation result; Calculating the third calculation result and the motor information using a second differential algorithm to obtain a fourth calculation result; Processing the fourth calculation result and the DC-DC information according to a preset control strategy to obtain a fifth calculation result; The third differential algorithm is used to calculate the fifth calculation result, the battery information, and the low-voltage load to obtain the predicted energy consumption of the low-speed unmanned vehicle.

4. The energy consumption prediction method for a low-speed unmanned vehicle according to claim 1, characterized in that: The method further comprises: Obtaining actual test results of the low-speed unmanned vehicle under various operating conditions; Calculating the actual energy consumption of the low-speed unmanned vehicle according to the actual test results; The predicted energy consumption is corrected according to the actual energy consumption.

5. The energy consumption prediction method for a low-speed unmanned vehicle according to claim 4, characterized in that: The method further comprises: An energy consumption and endurance report is generated according to the predicted energy consumption and the actual energy consumption.

6. An energy consumption prediction device for a low-speed unmanned vehicle, characterized in that: The device comprises: A feature acquisition module is used to obtain the operating characteristics of the low-speed unmanned vehicle through big data. The operating characteristics include test time, test mileage, average vehicle speed, and environmental information during the test; A parameter acquisition module is used to obtain vehicle parameters of the low-speed unmanned vehicle to be tested, including vehicle weight, rolling resistance coefficient, drive system information, motor information, DC-DC information, battery information, and low-voltage load; A model acquisition module is used to acquire a preset energy consumption prediction model, wherein the energy consumption prediction model is a model that performs digital calculation of energy flow based on the law of conservation of energy; An energy consumption prediction module is used to process the vehicle parameters and the operating condition characteristics using the energy consumption prediction model to obtain the predicted energy consumption of the low-speed unmanned vehicle.

7. The energy consumption prediction device for a low-speed unmanned vehicle according to claim 6, characterized in that: The energy consumption prediction module is further used to: When the energy consumption prediction model includes multiple integral algorithms and multiple differential algorithms, the integral algorithms and the differential algorithms are used to calculate the vehicle parameters and the operating condition characteristics in sequence to obtain the predicted energy consumption of the low-speed unmanned vehicle.

8. The energy consumption prediction device for a low-speed unmanned vehicle according to claim 7, characterized in that: The energy consumption prediction module is further used to: Calculating the vehicle weight and the rolling resistance coefficient using a first integral algorithm to obtain a first calculation result; Calculating the first calculation result and the drive system information using a first differential algorithm to obtain a second calculation result; Calculating the second calculation result and the operating condition characteristic using the second integration algorithm to obtain a third calculation result; Calculating the third calculation result and the motor information using a second differential algorithm to obtain a fourth calculation result; Processing the fourth calculation result and the DC-DC information according to a preset control strategy to obtain a fifth calculation result; The third differential algorithm is used to calculate the fifth calculation result, the battery information, and the low-voltage load to obtain the predicted energy consumption of the low-speed unmanned vehicle.

9. A computer-readable storage medium, characterized in that The storage medium stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement the energy consumption prediction method for a low-speed unmanned vehicle as described in any one of claims 1 to 5.

10. A computer device, characterized in that: The computer device includes: the energy consumption prediction device for a low-speed unmanned vehicle as described in any one of claims 6-8.

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