Method, device, electronic device and storage medium for estimating cruising range

Abstract

The present application provides a method, device, electronic device and storage medium for estimating a cruising range, and relates to the field of vehicle technology. The method comprises: determining first working condition data that meets a preset condition and second working condition data that does not meet the preset condition based on the working condition data of the vehicle within a preset time period, wherein the preset condition includes that the working condition duration is greater than or equal to a preset time; performing data splicing on the second working condition data according to the preset time to obtain spliced ​​data including at least one spliced ​​working condition, and the duration of the spliced ​​working condition meets the preset condition; constructing a cruising range data set based on the first working condition data and the spliced ​​data, wherein the cruising range data set characterizes the correlation between the vehicle running mileage, the consumed charge amount and the cruising range corresponding to each working condition; obtaining the vehicle's cruising range based on the cruising range data set. The present application can more accurately reflect the performance of the vehicle in actual operation and improve the accuracy and reliability of the cruising range estimation.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a method, device, electronic device and storage medium for estimating a cruising range. Background Art

[0002] The cruising range has always been an important indicator of new energy electric vehicles. The actual cruising range of electric vehicles is affected by many factors such as battery charge status, vehicle weight, speed, power, traffic conditions, etc. The actual cruising range used by users changes dynamically in real time.

[0003] The common method of estimating the cruising range is to calculate the cruising range by the total remaining energy of the battery and the average power consumption. This does not take into account the impact of various parameters such as operating conditions and temperature on the calculation results, resulting in inaccurate calculation results of the actual cruising range.

[0004] It should be noted that the above statements are only used to provide background technical information related to the present application and do not necessarily constitute prior art. Summary of the invention

[0005] In view of this, the purpose of the present application is to propose a method, device, electronic device and storage medium for estimating cruising range. The present application can specifically solve the problem of low accuracy of existing cruising range calculation results.

[0006] Based on the above purpose, in a first aspect, the present application proposes a method for estimating a cruising range, the method comprising: determining first operating condition data that meets a preset condition and second operating condition data that does not meet the preset condition based on operating condition data of a vehicle within a preset time period, the preset condition comprising that the operating condition duration is greater than or equal to a preset time; performing data splicing on the second operating condition data according to the preset time to obtain spliced ​​data including at least one spliced ​​operating condition, the duration of the spliced ​​operating condition meeting the preset condition; constructing a cruising range data set based on the first operating condition data and the spliced ​​data, the cruising range data set characterizing the correlation between the vehicle running mileage, the consumed charge, and the cruising range corresponding to each operating condition; correcting the cruising range data set according to preset parameters to obtain a target data set, and obtaining the vehicle's cruising range based on the target data set; the preset parameters include at least one factor affecting the vehicle's cruising range; the preset parameters include physical parameters and operating condition parameters.

[0007] The above-mentioned embodiment processes the operating condition data by classification and splicing, so that all the operating condition data have a sufficient time length to facilitate accurate cruising range prediction, and can improve the utilization rate of the operating condition data whose operating condition duration is less than a preset time, thereby increasing the accuracy of the cruising range prediction. By correcting the cruising range data set through preset parameters, the accuracy and reliability of the data in the data set can be improved, and the vehicle cruising range under actual driving conditions can be better reflected.

[0008] In some embodiments, the operating condition data includes the start time of each operating condition, and the second operating condition data is spliced ​​according to the preset time to obtain spliced ​​data, including: sorting each operating condition in the second operating condition data according to the start time sequence of each operating condition; according to the sorting, sequentially obtaining the total duration of a continuous target number of operating conditions, and the target number is greater than or equal to 2; when the total duration is greater than or equal to the preset time, the target number of operating conditions is taken as a spliced ​​operating condition to obtain spliced ​​data including at least one of the spliced ​​operating conditions; wherein the operating condition data of the spliced ​​operating condition is obtained by obtaining the average value of the operating condition data of the target number of operating conditions.

[0009] The above embodiment can achieve the sorting of the second working conditions according to the start time of each working condition, and splice the second working condition data with similar time as much as possible to improve the continuity of the spliced ​​data. The duration of the working condition is obtained by the start time and the end time, and then the spliced ​​working condition is generated by using the relationship between the duration of the continuous target number of second working conditions and the preset time, thereby improving the data reliability of the spliced ​​working condition.

[0010] In some embodiments, the operating condition data includes vehicle mileage and charge consumption. A cruising range data set is constructed based on the first operating condition data and the spliced ​​data, including: taking the average value of the vehicle mileage of the target number of operating conditions as the vehicle mileage of the spliced ​​operating condition, and taking the average value of the charge consumption of the target number of operating conditions as the charge consumption of the spliced ​​operating condition; constructing the cruising range data set based on the vehicle mileage and charge consumption of each operating condition in the first operating condition data, and the vehicle mileage and charge consumption of each spliced ​​operating condition in the spliced ​​data.

[0011] In the above embodiment, by taking the average value of the charge consumption and vehicle mileage of each second working condition data in the spliced ​​working condition as the overall charge consumption and vehicle mileage of the spliced ​​working condition, the overall performance of the spliced ​​working condition can be characterized, the data accuracy of the spliced ​​working condition can be improved, and the data accuracy of the cruising range data set can be further improved.

[0012] In some embodiments, obtaining the vehicle's cruising range based on the cruising range data set includes: correcting the cruising range data set according to preset parameters to obtain a target data set, the preset parameters including at least one factor affecting the vehicle's cruising range; and obtaining the vehicle's cruising range based on the target data set.

[0013] In some embodiments, the correcting the cruising range data set according to preset parameters to obtain the target data set includes: correcting the cruising range data set according to the physical parameters, the physical parameters including at least one of the vehicle's nominal cruising range, rated power per 100 kilometers, and vehicle model; and correcting the corrected cruising range data set again according to the operating condition parameters to obtain the target data set, the operating condition parameters including at least one of the battery temperature and energy consumption per 100 kilometers corresponding to each operating condition.

[0014] The above embodiment first corrects the cruising range data set by physical parameters, and then corrects the corrected cruising range data set again by operating condition parameters to obtain a target data set. Multi-stage correction can be implemented to filter out unrealistic or inaccurate data, and finally obtain more accurate vehicle cruising range data.

[0015] In some embodiments, the range data set is corrected using the physical parameters, including: using the nominal range corresponding to each vehicle model, removing the operating conditions in the range data set where the range is greater than the nominal range; obtaining the actual energy consumption per 100 kilometers of the vehicle based on the charge consumed and the vehicle mileage of each operating condition in the range data set; and removing the operating conditions in the range data set where the actual energy consumption per 100 kilometers is greater than the rated power per 100 kilometers, using the rated power per 100 kilometers corresponding to the vehicle.

[0016] The above-mentioned embodiment can filter out data that is inconsistent with reality or inaccurate, and can make the operating condition data in the revised cruising range data set conform to the nominal cruising range and the rated power per 100 kilometers.

[0017] In some embodiments, the operating condition parameters include the battery temperature and the energy consumption per 100 kilometers corresponding to each operating condition. The corrected cruising range data set is corrected again by using the operating condition parameters to obtain the target data set, including: determining the operating condition data within a preset temperature range from the cruising range data set based on the battery temperature; determining the operating condition data within a preset energy consumption range from the cruising range data set based on the energy consumption per 100 kilometers; obtaining the target data set based on the operating condition data within the preset temperature range and the operating condition data within the preset energy consumption range.

[0018] The above embodiment further corrects the cruising range data set according to the battery temperature and the energy consumption per 100 kilometers, which helps to predict the cruising range of the vehicle under mild working conditions and can help to more accurately predict the vehicle's cruising range performance under similar conditions.

[0019] In some embodiments, the cruising range of the vehicle is obtained based on the cruising range data set, including: dividing the preset temperature range into multiple temperature sub-ranges, and dividing the target data set into multiple data subsets according to the multiple temperature sub-ranges; according to the weight corresponding to each operating condition, weighted averaging the cruising range of each operating condition in the multiple data subsets, and outputting the cruising range value corresponding to each temperature sub-range as the cruising range of the vehicle in the corresponding temperature sub-range; the weight corresponding to each operating condition is pre-set according to the speed, road condition or driving behavior corresponding to each operating condition.

[0020] The above embodiment divides the preset temperature range into multiple temperature sub-ranges to obtain multiple data subsets, and performs weighted averaging on the cruising range of each operating condition in the multiple data subsets according to the weight corresponding to each operating condition, and outputs the cruising range value corresponding to each temperature sub-range. A smaller temperature range that conforms to the vehicle's operating habits can be obtained to reduce the impact of temperature on the calculated cruising range value, thereby achieving a more precise prediction of the vehicle's cruising range under different temperature conditions.

[0021] In a second aspect, a cruising range estimation device is also provided, including: a data acquisition module, used to determine first operating condition data that meets preset conditions and second operating condition data that does not meet preset conditions based on the operating condition data of the vehicle within a preset time period, the preset condition including that the operating condition duration is greater than or equal to the preset time; a data splicing module, used to perform data splicing on the second operating condition data according to the preset time, and obtain spliced ​​data including at least one spliced ​​operating condition, and the duration of the spliced ​​operating condition meets the preset condition; a data set creation module, used to construct a cruising range data set based on the first operating condition data and the spliced ​​data, the cruising range data set characterizing the correlation between the vehicle running mileage, the consumed charge, and the cruising range corresponding to each operating condition; an estimation module, used to correct the cruising range data set according to preset parameters to obtain a target data set, and obtain the vehicle's cruising range based on the target data set; the preset parameters include at least one factor affecting the vehicle's cruising range; the preset parameters include physical parameters and operating condition parameters.

[0022] According to a third aspect, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method described in the first aspect.

[0023] According to a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and the program is executed by a processor to implement any method described in the first aspect.

[0024] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the multiple drawings represent the same or similar parts or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings only depict some embodiments disclosed in the present application and should not be regarded as limiting the scope of the present application. In addition, the same reference numerals are used to represent the same parts in all the drawings.

[0026] Figure 1 A flowchart showing the steps of the method for estimating the cruising range according to an embodiment of the present application;

[0027] Figure 2 A flowchart showing the steps of splicing the working conditions of an embodiment of the present application;

[0028] Figure 3 A flowchart showing the steps of obtaining a target data set according to an embodiment of the present application is shown;

[0029] Figure 4 Another flowchart showing the method for estimating the cruising range according to an embodiment of the present application;

[0030] Figure 5 A schematic diagram of the structure of a cruising range estimation device provided by an embodiment of the present application is shown;

[0031] Figure 6 A schematic diagram of the structure of an electronic device provided by an embodiment of the present application is shown;

[0032] Figure 7 A schematic diagram of a storage medium provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0033] The following embodiments of the technical solution of the present application are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present application, and are therefore only used as examples, and cannot be used to limit the scope of protection of the present application.

[0034] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[0035] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0036] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0037] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0038] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0039] With the development of new energy vehicles, the vehicle's endurance has become a performance that has received much attention. In practical applications, the actual endurance of a vehicle is different from the theoretical endurance (i.e., the endurance under ideal test conditions). The actual endurance of an electric vehicle is affected by multiple factors such as the battery charge state, vehicle weight, speed, power, and traffic conditions. The actual endurance used by users changes dynamically in real time. Calculating the actual endurance of a vehicle can help users plan their trips in advance and understand vehicle performance. The common way to estimate endurance is to calculate the endurance by using the remaining total energy of the battery and the average power consumption. This does not take into account the impact of various parameters such as working conditions and temperature on the calculation results, resulting in low accuracy in the calculation results of the actual endurance.

[0040] Based on the above problems, this embodiment proposes a method for estimating the cruising range, which uses the working condition data that meets the preset conditions as the first working condition data, and uses the working condition data that does not meet the preset conditions as the second working condition data, the preset conditions include that the working condition duration is greater than or equal to the preset time, and then splices the second working condition data according to the preset time to obtain spliced ​​data including at least one spliced ​​working condition, and the duration of the spliced ​​working condition meets the preset conditions; all working condition data can have a sufficient time length to accurately predict the cruising range, and can improve the utilization rate of short working condition (working condition with a working condition duration less than the preset time) data, and increase the cruising range prediction accuracy. Then, according to the first working condition data and the spliced ​​data, a cruising range data set is constructed, and the cruising range data set characterizes the correlation between the vehicle running mileage, the consumed charge, and the cruising range corresponding to each working condition; the cruising range data set is corrected according to the preset parameters to obtain the target data set, and the cruising range of the vehicle is obtained based on the target data set. By classifying and splicing the working condition data, the present application can more accurately reflect the performance of the vehicle in actual operation and improve the accuracy and reliability of the cruising range estimation.

[0041] Among them, the vehicle can be a pure electric vehicle, a hybrid vehicle and other vehicles. For the convenience of explanation in the following embodiments, the embodiments of this application take a pure electric vehicle as an example. In one example, this application can realize online estimation of vehicle range.

[0042] Figure 1 The flowchart of the method for estimating the cruising range of the present application is shown in FIG. Figure 1 In the embodiment of the present application, the cruising range estimation method includes the following steps S101 to S104:

[0043] S101. Determine first operating condition data that meets preset conditions and second operating condition data that does not meet preset conditions based on operating condition data of a vehicle within a preset time period. The preset conditions include that the operating condition duration is greater than or equal to a preset time.

[0044] S102. Splicing the second working condition data according to a preset time to obtain spliced ​​data including at least one spliced ​​working condition, wherein the duration of the spliced ​​working condition meets a preset condition.

[0045] S103: construct a cruising range data set based on the first operating condition data and the spliced ​​data, where the cruising range data set represents the correlation between the vehicle running mileage, the consumed charge, and the cruising range corresponding to each operating condition.

[0046] S104. Correct the cruising range data set according to preset parameters to obtain a target data set, and obtain the cruising range of the vehicle based on the target data set.

[0047] In one example, the vehicle operating data can be obtained through devices that record the vehicle operating conditions, such as vehicle sensors, vehicle recorders, and vehicle networking platforms. The operating data includes but is not limited to the vehicle's operating status, speed information, acceleration information, battery information, throttle and brake data, driving mode, environmental data, energy consumption data, etc. The operating status includes driving direction, road type, etc., battery information includes battery power (SOC, State of Charge), battery voltage, battery temperature, etc., driving modes include economic mode, sports mode, etc., and energy consumption data includes power consumption per unit time or unit distance.

[0048] The preset time period can be in units of years, quarters, months, or weeks, and can be customized based on the total usage time of the vehicle or user needs.

[0049] The preset time in the preset conditions of this embodiment can be 5 minutes, 6 minutes, 8 minutes, 10 minutes, etc. The specific time can be set by technical personnel based on experience and will not be listed here one by one.

[0050] In one example, according to the working condition data of the vehicle within a preset time period, the first working condition data that meets the preset conditions and the second working condition data that does not meet the preset conditions are determined. The start time and the end time of a working condition can be obtained by extracting the fields in the working condition data, and then the duration of a working condition is obtained. When the duration of the working condition is greater than or equal to the preset time, it is classified as the first working condition data. It can be seen that the working condition duration of the first working condition data is longer, and its data integrity is stronger, which can improve the data accuracy. When the duration of the working condition is less than the preset time, it is classified as the second working condition data. It can be seen that the working condition duration of the second working condition data is shorter, and its data is more discrete, which may affect the data accuracy. Therefore, this embodiment splices the second working condition data. On the one hand, it can process the discrete working condition data and improve the utilization rate of vehicle data. On the other hand, it can increase the data volume of the working condition data and reuse the discrete working condition data, so that the cruising range prediction result is more in line with the actual cruising range, more accurately reflects the performance of the vehicle in actual operation, and improves the accuracy and reliability of the cruising range estimation.

[0051] Specifically, data splicing of the second operating condition data according to the preset time refers to splicing multiple second operating condition data to obtain at least one spliced ​​operating condition, and the duration of the spliced ​​operating condition meets the preset conditions, that is, the duration of the spliced ​​operating condition is greater than or equal to the preset time. In this way, the basic data used for cruising range prediction can have a sufficient time length to improve the accuracy of the cruising range prediction.

[0052] In one example, the duration of the spliced ​​working condition is the sum of the times of multiple second working condition data that constitute the spliced ​​working condition. For example, the preset time is 10 minutes. When the total time of three second working condition data is greater than or equal to 10 minutes, the three second working condition data are spliced, and the durations of the three second working condition data are added together, and the total time obtained is used as the duration of the spliced ​​working condition.

[0053] In one example, the relationship between vehicle mileage, charge consumption, and cruising range can be expressed by the following formula 1:

[0054] (Formula 1)

[0055] in, Indicates the starting mileage difference of the working condition, It indicates the amount of charge consumed corresponding to the difference in mileage between the start and end of operation. The K value indicates the actual cruising range of the vehicle under this operating point.

[0056] According to the first working condition data and the spliced ​​data, a cruising range data set is constructed. The cruising range data set represents the correlation between the vehicle running mileage, the consumed charge, and the cruising range corresponding to each working condition, and the matrix shown in Formula 2 can be obtained:

[0057] (Formula 2)

[0058] In formula 2, ~ Corresponding to n The difference in the starting mileage of each working condition is ~ Corresponding to n The actual cruising range of the vehicle under each working condition, ~ Corresponding to n The amount of charge consumed corresponding to the difference in the starting mileage of each operating condition.

[0059] From the matrix shown in formula 2, we can see that ( ~ )and Dividing, we can get 1-dimensional n The K value dataset of the row can then be used to predict the actual cruising range of the vehicle based on the K value dataset using data analysis methods or prediction models.

[0060] In this embodiment, at least one factor affecting the vehicle's cruising range may include battery capacity, driving speed, driving habits, road conditions, climate conditions, vehicle load, tire pressure, etc. The type of preset parameters can be set according to actual conditions, and then various parameters such as the actual working conditions and temperature of the vehicle can be taken into account, thereby reducing the impact of complex working conditions on the vehicle's cruising range prediction results and improving the accuracy of the actual cruising range calculation results.

[0061] This embodiment corrects the cruising range data set by presetting parameters, which can improve the accuracy and reliability of the data in the data set and better reflect the vehicle cruising range under actual driving conditions.

[0062] In an embodiment of the present application, the preset parameters include physical parameters and operating condition parameters. The cruising range data set is corrected according to the preset parameters to obtain a target data set, and then the vehicle's cruising range is obtained based on the target data set. This can further more accurately reflect the vehicle's performance in actual operation and improve the accuracy and reliability of the cruising range estimation.

[0063] The above embodiment provides a method for estimating the range of cruising, wherein the working condition data that meets the preset conditions is used as the first working condition data, and the working condition data that does not meet the preset conditions is used as the second working condition data, wherein the preset conditions include that the working condition duration is greater than or equal to the preset time, and then the second working condition data is spliced ​​according to the preset time to obtain spliced ​​data including at least one spliced ​​working condition, and the duration of the spliced ​​working condition meets the preset conditions; all working condition data can have a sufficient time length to accurately predict the range of cruising, and the utilization rate of short working condition (working condition with a working condition duration less than the preset time) data can be improved, thereby increasing the accuracy of the prediction of the range of cruising. Then, according to the first working condition data and the spliced ​​data, a cruising range data set is constructed, and the cruising range data set characterizes the correlation between the vehicle running mileage, the consumed charge, and the cruising range corresponding to each working condition; the cruising range data set is corrected according to the preset parameters to obtain the target data set, and the cruising range of the vehicle is obtained based on the target data set. By classifying and splicing the working condition data, the present application can more accurately reflect the performance of the vehicle in actual operation and improve the accuracy and reliability of the cruising range estimation.

[0064] In the embodiment of the present application, the working condition data includes the start time and end time of each working condition, and the working condition data of the second working condition are spliced ​​according to the preset time to obtain spliced ​​data. Figure 2 The flowchart of the working condition splicing step of the embodiment of the present application is shown, refer to Figure 2 , including the following steps S201~S203:

[0065] S201. Sort each operating condition in the second operating condition data according to the start time sequence of each operating condition.

[0066] S202. Obtain the total duration of a target number of consecutive operating conditions in sequence according to the order.

[0067] S203: When the total duration is greater than or equal to the preset time, the target number of working conditions are taken as a splicing working condition to obtain splicing data including at least one splicing working condition.

[0068] In one example, the start time and end time corresponding to each operating condition can be obtained by extracting the field information of the operating condition data. When splicing the second operating condition data, this embodiment uses the operating condition start time to splice the second operating condition data with similar time as much as possible to improve the reliability of the spliced ​​data.

[0069] In this embodiment, the start time refers to the moment when the current working condition begins to occur, and the end time refers to the moment when the current working condition ends. The duration of the current working condition can be obtained by the difference between the end time and the start time. After sorting each working condition in the second working condition data according to the start time, the durations corresponding to multiple consecutive second working conditions can be added together to sequentially obtain the total duration of the consecutive target number of working conditions. For example, the durations corresponding to 4 consecutive second working condition data are 2 minutes, 3 minutes, 5 minutes, and 4 minutes, respectively. The preset interval in the preset condition is, for example, 10 minutes. The sum of the durations of the first 3 second working condition data is 10 minutes, which means that these 3 second working conditions can be spliced ​​into a spliced ​​working condition, and the total duration of the spliced ​​working condition is 10 minutes. Then, a number of second working condition data are sequentially obtained according to the above sorting for splicing. In this way, at least one spliced ​​working condition can be obtained, and the working condition data corresponding to at least one spliced ​​working condition are combined into spliced ​​data.

[0070] Among them, the target number is greater than or equal to 2. From the above embodiments, it can be seen that the total duration corresponding to the splicing working condition is the sum of the target number of second working condition data.

[0071] The above embodiment can achieve the sorting of the second working conditions according to the start time of each working condition, and splice the second working condition data with similar time as much as possible to improve the continuity of the spliced ​​data. The duration of the working condition is obtained by the start time and the end time, and then the spliced ​​working condition is generated by using the relationship between the duration of the continuous target number of second working conditions and the preset time, thereby improving the data reliability of the spliced ​​working condition.

[0072] In an embodiment of the present application, the operating condition data includes vehicle mileage and charge consumption. A cruising range data set is constructed based on the first operating condition data and the spliced ​​data, including: taking the average value of the vehicle mileage of a target number of operating conditions as the vehicle mileage of the spliced ​​operating condition, and taking the average value of the charge consumption of the target number of operating conditions as the charge consumption of the spliced ​​operating condition; constructing a cruising range data set based on the vehicle mileage and charge consumption of each operating condition in the first operating condition data, and the vehicle mileage and charge consumption of each spliced ​​operating condition in the spliced ​​data.

[0073] It can be seen from the above embodiments that since the spliced ​​working condition is obtained by splicing multiple working condition data, multiple second working condition data in the spliced ​​working condition may be discontinuous. Therefore, in this embodiment, the average value of the vehicle mileage of the target number of working conditions is used as the vehicle mileage of the spliced ​​working condition, and the average value of the consumed charge of the target number of working conditions is used as the consumed charge of the spliced ​​working condition. In other words, the numerical parameters such as the consumed charge and vehicle mileage corresponding to the spliced ​​working condition can be obtained by calculating the average value. In this way, the overall performance of the spliced ​​working condition can be characterized.

[0074] As for the first operating condition data, since its operating condition duration is greater than the preset time, its operating condition data can be considered as complete operating condition data, so the vehicle mileage and charge consumption of each operating condition in the first operating condition data can be directly used. That is, the cruising range data set is constructed based on the vehicle mileage and charge consumption of each operating condition in the first operating condition data and the vehicle mileage and charge consumption of each spliced ​​operating condition in the spliced ​​data.

[0075] In the above embodiment, by taking the average value of the charge consumption and vehicle mileage of each second working condition data in the spliced ​​working condition as the overall charge consumption and vehicle mileage of the spliced ​​working condition, the overall performance of the spliced ​​working condition can be characterized, the data accuracy of the spliced ​​working condition can be improved, and the data accuracy of the cruising range data set can be further improved.

[0076] In an embodiment of the present application, step S104 obtains the vehicle's cruising range based on the cruising range data set, including: correcting the cruising range data set according to preset parameters to obtain a target data set, the preset parameters including at least one factor affecting the vehicle's cruising range; based on the target data set, obtaining the vehicle's cruising range.

[0077] In the embodiment of the present application, the preset parameters include physical parameters and operating condition parameters. The cruising range data set is corrected according to the preset parameters to obtain a target data set. Figure 3 A flowchart showing the steps of obtaining a target data set according to an embodiment of the present application is shown. Figure 3 , including the following steps S301~S302:

[0078] S301. Correct the cruising range data set using physical parameters.

[0079] S302 , further correcting the corrected cruising range data set using operating condition parameters to obtain a target data set.

[0080] In this embodiment, the physical parameters include at least one of the vehicle's nominal cruising range, rated power per 100 kilometers, and vehicle model, wherein the vehicle's nominal cruising range refers to the theoretical maximum driving distance of the vehicle after a single charge or full fuel under standard test conditions. Rated power per 100 kilometers refers to the theoretical power consumed by an electric vehicle when it travels 100 kilometers, usually expressed in kilowatt-hours (kWh), which is used to measure the vehicle's energy efficiency. The rated power per 100 kilometers helps to evaluate the vehicle's energy consumption level under different conditions and provides a standard for understanding the vehicle's cruising performance and efficiency.

[0081] In one example, different models of vehicles have different corresponding nominal cruising ranges and rated power per 100 kilometers. The physical parameters set in this embodiment include the vehicle model, which can verify the rationality of vehicle performance of different models. The cruising range data set is corrected by at least one of the vehicle's nominal cruising range, rated power per 100 kilometers, and vehicle model, so that the real-time cruising range estimation value obtained according to the corrected data set can be consistent with the actual vehicle performance and within a reasonable range.

[0082] In this embodiment, the operating condition parameters include at least one of the battery temperature and the energy consumption per 100 kilometers corresponding to each operating condition. The energy consumption per 100 kilometers refers to the amount of electricity consumed by the electric vehicle when it travels 100 kilometers, indicating the amount of electricity consumed by the vehicle when it travels a certain distance.

[0083] The battery temperature can be the battery surface temperature at the start of the operating condition, which can be collected by a temperature sensor, or the ambient temperature at the start of the operating condition. By using the battery temperature as an operating condition parameter, the operating condition data can be divided according to different temperature ranges, and the vehicle's range under mild operating conditions can be predicted according to the preset temperature range. By using the energy consumption per 100 kilometers as an operating condition parameter, the operating condition data with actual energy consumption values ​​greater than the rated power can be removed, making the data in the corrected data set more accurate.

[0084] The above embodiment first corrects the cruising range data set by physical parameters, and then corrects the corrected cruising range data set again by operating condition parameters to obtain a target data set. Multi-stage correction can be implemented to filter out unrealistic or inaccurate data, and finally obtain more accurate vehicle cruising range data.

[0085] In an embodiment of the present application, the physical parameters include the vehicle's nominal cruising range, rated power per 100 kilometers and vehicle model. The cruising range data set is corrected using the physical parameters, including: removing the operating conditions in the cruising range data set where the cruising range is greater than the nominal cruising range through the nominal cruising range corresponding to each vehicle model; obtaining the vehicle's actual energy consumption per 100 kilometers based on the charge consumed in each operating condition in the cruising range data set and the vehicle's mileage; and removing the operating conditions in the cruising range data set where the actual energy consumption per 100 kilometers is greater than the rated power per 100 kilometers through the vehicle's corresponding rated power per 100 kilometers.

[0086] That is to say, the nominal cruising range corresponding to each car model is used as the judgment boundary of the abnormal value of the data set. When the cruising range in the cruising range data set is greater than the nominal cruising range, the operating condition data corresponding to the cruising range is deleted from the data set.

[0087] In one example, the actual energy consumption per 100 kilometers of a vehicle is obtained by dividing the consumed charge by the vehicle mileage, then multiplying by 100, and converting the units. Then, by comparing the actual energy consumption per 100 kilometers of the vehicle with the rated charge per 100 kilometers of the vehicle, the operating conditions in the range data set where the actual energy consumption per 100 kilometers is greater than the rated charge per 100 kilometers can be removed.

[0088] In one example, the range data set can be corrected using the following formula 3:

[0089] (Formula 3)

[0090] in, Indicates the corrected K Dataset (Cruise Range Dataset), stand Indicates the vehicle's nominal range. express K Datasets and stand The slope of the value can represent the vehicle's power consumption rate per unit mileage. power Indicates rated capacity. vehicle Indicates the model.

[0091] Through the above method, it is possible to filter out unrealistic or inaccurate data, so that the operating condition data in the revised cruising range data set is consistent with the nominal cruising range and the rated power per 100 kilometers.

[0092] In an embodiment of the present application, the operating condition parameters include the battery temperature and the energy consumption per 100 kilometers corresponding to each operating condition. The corrected cruising range data set is corrected again by the operating condition parameters to obtain a target data set, including: determining the operating condition data within a preset temperature range from the cruising range data set based on the battery temperature; determining the operating condition data within a preset energy consumption range from the cruising range data set based on the energy consumption per 100 kilometers; obtaining the target data set based on the operating condition data within the preset temperature range and the operating condition data within the preset energy consumption range.

[0093] In this embodiment, the specific temperature values ​​and energy consumption range values ​​of the preset temperature range and the preset energy consumption range can be obtained based on the data under the temperature conditions of the vehicle (vehicle driving habits), wherein the mild conditions of the vehicle refer to the state in which the vehicle is running under relatively stable, low-load and suitable environmental conditions. Setting the preset temperature range and the preset energy consumption range under mild conditions helps to predict the vehicle's range when running under optimal conditions, and can help to more accurately predict the vehicle's range performance under similar conditions.

[0094] In addition, the temperature range can also be set according to the temperature changes throughout the year, so that the vehicle range corresponding to different temperature ranges in different seasons can be obtained.

[0095] In one example, the above corrected cruising range data set can be corrected again by the following formula 4:

[0096] (Formula 4)

[0097] in, ~ It means n The actual energy consumption per 100 kilometers corresponding to each operating point.

[0098] This embodiment further corrects the cruising range data set according to the battery temperature and the energy consumption per 100 kilometers, which helps to predict the cruising range of the vehicle under mild operating conditions and can help to more accurately predict the vehicle's cruising range performance under similar conditions.

[0099] In an embodiment of the present application, the cruising range of the vehicle is obtained according to the cruising range data set, including: dividing a preset temperature range into multiple temperature sub-ranges, and dividing the target data set into multiple data subsets according to the multiple temperature sub-ranges; according to the weight corresponding to each operating condition, weighted averaging the cruising range of each operating condition in the multiple data subsets, and outputting the cruising range value corresponding to each temperature sub-range as the cruising range of the vehicle in the corresponding temperature sub-range; the weight corresponding to each operating condition is pre-set according to the speed, road condition or driving behavior corresponding to each operating condition.

[0100] Specifically, the weight corresponding to each working condition is pre-set according to the speed, road condition or driving behavior corresponding to each working condition. For example, the driving environment corresponding to the working condition is urban driving, high-speed driving, mountain driving, etc., and different driving environments correspond to different weights, which can be set according to actual experience.

[0101] In one example, battery performance and energy consumption may vary significantly at different temperatures. This embodiment divides the preset temperature range into multiple temperature sub-ranges. For example, the preset temperature range is -20°C to 40°C. This embodiment divides the preset temperature range into -20°C to 0°C, 0°C to 20°C, and 20°C to 40°C. This can achieve a more detailed prediction of the vehicle's cruising range under different temperature conditions.

[0102] According to the weight corresponding to each working condition, the cruising range of each working condition in multiple data subsets is weighted averaged, and the cruising range value corresponding to each temperature sub-range is output as the cruising range of the vehicle in the corresponding temperature sub-range. The cruising range in each temperature sub-range can be fitted by the following formula 5:

[0103] (Formula 5)

[0104] in, represents the corrected cruising range dataset, Indicates temperature range, Indicated in The estimated mileage under the temperature range, which represents the corresponding temperature range The actual driving range that meets the user's driving habits.

[0105] This embodiment divides the preset temperature range into multiple temperature sub-ranges to obtain multiple data subsets, and performs weighted averaging on the cruising range of each operating condition in the multiple data subsets according to the weight corresponding to each operating condition, and outputs the cruising range value corresponding to each temperature sub-range. A smaller temperature range that conforms to the vehicle's operating habits can be obtained to reduce the impact of temperature on the calculated cruising range value, thereby achieving a more precise prediction of the vehicle's cruising range under different temperature conditions.

[0106] The method for estimating the range of the vehicle provided in this embodiment uses the operating condition data that meets the preset conditions as the first operating condition data, and uses the operating condition data that does not meet the preset conditions as the second operating condition data. The preset conditions include that the duration of the operating condition is greater than or equal to the preset time. Then, the second operating condition data is spliced ​​according to the preset time to obtain spliced ​​data including at least one spliced ​​operating condition, and the duration of the spliced ​​operating condition meets the preset conditions; all operating condition data can have a sufficient time length to make accurate range predictions, and the utilization rate of the operating condition data whose duration is less than the preset time can be improved, thereby increasing the accuracy of the prediction of the range of the vehicle. Then, based on the first operating condition data and the spliced ​​data, a range of the vehicle data set is constructed, and the range of the vehicle data set characterizes the correlation between the vehicle running mileage, the consumed charge, and the range of the vehicle corresponding to each operating condition; based on the range of the vehicle data set, the range of the vehicle is obtained. By classifying and splicing the operating condition data, the present application can more accurately reflect the performance of the vehicle in actual operation and improve the accuracy and reliability of the range of the vehicle estimation.

[0107] The following is a specific example to illustrate the method for estimating the cruising range in some embodiments of the present application, in which the method is illustrated by taking the cruising range estimation of a pure electric vehicle as an example. Figure 4 Another flowchart showing the method for estimating the cruising range according to an embodiment of the present application is shown in FIG. Figure 4 , including the following steps S401~S405:

[0108] S401, extracting vehicle operating condition data and performing preprocessing.

[0109] In this embodiment, the vehicle operating condition data can be either a charging condition or a discharging condition. Taking the charging condition as an example, the vehicle's charging condition data is extracted to obtain field information of the charging condition data, which includes the start and end time of continuous charging, the start and end mileage of continuous operation, the start and end display SOC, and the start and end ambient temperature. Then, the time difference, mileage difference, SOC difference, and ambient temperature of each condition are obtained, and noise reduction processing such as data jump and loss judgment can be performed on the above time difference, mileage difference, SOC difference, and ambient temperature.

[0110] At the same time, the first operating condition data and the second operating condition data are distinguished according to a preset condition, and the second operating condition data are spliced ​​according to a preset time to obtain spliced ​​data including at least one spliced ​​operating condition.

[0111] S402: Construct a cruising range data set.

[0112] According to the first working condition data and the second working condition data and formula 1, the mileage difference of each working condition is divided by the SOC difference, that is, ( ~ )and Divide them to get the K value data set, which is used as the cruising range data set.

[0113] S403: Correct the cruising range data set using physical parameters.

[0114] The nominal cruising range corresponding to each vehicle model is used as the judgment boundary of the data set outliers. When the cruising range in the cruising range data set is greater than the nominal cruising range, the operating condition data corresponding to the cruising range is deleted from the data set. By comparing the actual energy consumption per 100 kilometers of the vehicle with the rated power per 100 kilometers corresponding to the vehicle, the operating conditions corresponding to the actual energy consumption per 100 kilometers in the cruising range data set that are greater than the rated power per 100 kilometers are removed. Unrealistic or inaccurate data can be filtered out, and the operating condition data in the corrected cruising range data set can be consistent with the nominal cruising range and the rated power per 100 kilometers.

[0115] S404: Use the operating condition parameters to correct the cruising range data set again to obtain a target data set.

[0116] According to the data under the temperature condition of the vehicle (vehicle driving habits), the specific temperature value of the temperature range and the energy consumption range value of the energy consumption range are set. According to the battery temperature, the working condition data within the preset temperature range is determined from the cruising range data set; according to the energy consumption value per 100 kilometers, the working condition data within the preset energy consumption range is determined from the cruising range data set; according to the working condition data within the preset temperature range and the working condition data within the preset energy consumption range, the target data set is obtained. Setting the preset temperature range and the preset energy consumption range through mild working conditions helps to predict the cruising range of the vehicle under optimal conditions, and can help to more accurately predict the vehicle's cruising performance under similar conditions.

[0117] S405 . Based on the target data set, output the cruising range values ​​corresponding to the different temperature sub-intervals.

[0118] The preset temperature range is divided into multiple temperature sub-ranges to obtain multiple data subsets. According to the weight corresponding to each operating condition, the cruising range of each operating condition in the multiple data subsets is weighted averaged, and the cruising range value corresponding to each temperature sub-range is output. A smaller temperature range that conforms to the vehicle's operating habits can be obtained to reduce the impact of temperature on the calculated cruising range value, thereby achieving a more precise prediction of the vehicle's cruising range under different temperature conditions.

[0119] The above description of various embodiments tends to emphasize the differences between the various embodiments. The same or similar aspects can be referenced to each other, and for the sake of brevity, they will not be repeated herein.

[0120] The present application embodiment provides a cruising range estimation device, which is used to execute the cruising range estimation method described in the above embodiment. Figure 5 FIG. 1 shows a schematic diagram of a device for estimating a cruising range provided by an embodiment of the present application. Figure 5 As shown, the cruising range estimation device 500 includes:

[0121] The data acquisition module 501 is used to determine first operating condition data that meets preset conditions and second operating condition data that does not meet preset conditions based on the operating condition data of the vehicle within a preset time period, wherein the preset conditions include that the operating condition duration is greater than or equal to a preset time.

[0122] The data splicing module 502 is used to perform data splicing on the second working condition data according to the preset time to obtain splicing data including at least one splicing working condition, and the duration of the splicing working condition meets the preset condition.

[0123] The data set creation module 503 is used to construct a cruising range data set based on the first operating condition data and the spliced ​​data, and the cruising range data set represents the correlation between the vehicle running mileage, the consumed charge, and the cruising range corresponding to each operating condition.

[0124] The estimation module 504 is used to obtain the cruising range of the vehicle according to the cruising range data set.

[0125] In one example, the data splicing module 502 is used to sort each operating condition in the second operating condition data according to the start time sequence of each operating condition; according to the sorting, the total duration of a continuous target number of operating conditions is obtained in sequence, and the target number is greater than or equal to 2; when the total duration is greater than or equal to the preset time, the target number of operating conditions is taken as a spliced ​​operating condition to obtain spliced ​​data including at least one of the spliced ​​operating conditions; wherein the operating condition data of the spliced ​​operating condition is obtained by obtaining the average value of the operating condition data of the target number of operating conditions.

[0126] In one example, the operating condition data includes vehicle mileage and charge consumption. The data set creation module 503 is used to take the average value of the vehicle mileage of the target number of operating conditions as the vehicle mileage of the spliced ​​operating condition, and take the average value of the charge consumption of the target number of operating conditions as the charge consumption of the spliced ​​operating condition; and construct the cruising range data set based on the vehicle mileage and charge consumption of each operating condition in the first operating condition data, and the vehicle mileage and charge consumption of each spliced ​​operating condition in the spliced ​​data.

[0127] In one example, the estimation module 504 is used to modify the cruising range data set according to preset parameters to obtain a target data set, wherein the preset parameters include at least one factor affecting the vehicle's cruising range; and obtain the vehicle's cruising range based on the target data set.

[0128] In one example, the estimation module 504 is used to correct the cruising range data set using the physical parameters, wherein the physical parameters include at least one of the vehicle's nominal cruising range, rated power per 100 kilometers, and vehicle model; and to further correct the corrected cruising range data set using the operating condition parameters to obtain the target data set, wherein the operating condition parameters include at least one of the battery temperature and energy consumption per 100 kilometers corresponding to each operating condition.

[0129] In one example, the physical parameters include the vehicle's nominal cruising range, rated power per 100 kilometers, and vehicle model. The estimation module 504 is used to remove the operating conditions in the cruising range data set where the cruising range is greater than the nominal cruising range, based on the nominal cruising range corresponding to each vehicle model; obtain the actual energy consumption per 100 kilometers of the vehicle based on the consumed charge and vehicle mileage of each operating condition in the cruising range data set; and remove the operating conditions in the cruising range data set where the actual energy consumption per 100 kilometers is greater than the rated power per 100 kilometers, based on the rated power per 100 kilometers corresponding to the vehicle.

[0130] In one example, the operating condition parameters include the battery temperature and the energy consumption per 100 kilometers corresponding to each operating condition. The estimation module 504 is used to determine the operating condition data within a preset temperature range from the cruising range data set based on the battery temperature; determine the operating condition data within a preset energy consumption range from the cruising range data set based on the energy consumption per 100 kilometers; and obtain the target data set based on the operating condition data within the preset temperature range and the operating condition data within the preset energy consumption range.

[0131] In one example, the estimation module 504 is used to divide a preset temperature range into multiple temperature sub-ranges, and divide the target data set into multiple data subsets according to the multiple temperature sub-ranges; according to the weight corresponding to each operating condition, the cruising range of each operating condition in the multiple data subsets is weighted averaged, and the cruising range value corresponding to each temperature sub-range is output as the cruising range of the vehicle in the corresponding temperature sub-range; the weight corresponding to each operating condition is pre-set according to the speed, road condition or driving behavior corresponding to each operating condition.

[0132] The cruising range estimation device provided in the above-mentioned embodiments of the present application and the cruising range estimation method provided in the embodiments of the present application are based on the same application concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0133] The embodiment of the present application also provides an electronic device corresponding to the range estimation method provided in the above embodiment, so as to execute the above range estimation method. The embodiment of the present application is not limited.

[0134] Please refer to Figure 6 , which shows a schematic diagram of an electronic device provided by some embodiments of the present application. Figure 6 As shown, the electronic device 20 includes: a processor 200, a memory 201, a bus 202 and a communication interface 203, and the processor 200, the communication interface 203 and the memory 201 are connected via the bus 202; the memory 201 stores a computer program that can be run on the processor 200, and when the processor 200 runs the computer program, the cruising range estimation method provided in any of the aforementioned embodiments of the present application is executed.

[0135] The memory 201 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 203 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0136] The bus 202 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. Among them, the memory 201 is used to store programs, and the processor 200 executes the program after receiving an execution instruction. The cruising range estimation method disclosed in any implementation of the aforementioned embodiment of the present application may be applied to the processor 200, or implemented by the processor 200.

[0137] The processor 200 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 200. The above processor 200 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a readily available programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as a hardware decoding processor to be executed, or the hardware and software modules in the decoding processor can be executed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 201, and the processor 200 reads the information in the memory 201 and completes the steps of the above method in combination with its hardware.

[0138] The electronic device provided in the embodiment of the present application and the cruising range estimation method provided in the embodiment of the present application are based on the same application concept and have the same beneficial effects as the methods adopted, operated or implemented therein.

[0139] The present application also provides a computer-readable storage medium corresponding to the range estimation method provided in the above embodiment. Figure 7 , which shows a computer-readable storage medium 30, which may be a CD, on which a program product is stored, which may be an operating system, application software, game, tool software, etc. The program product includes a computer program, which usually exists in source code or compiled binary form. When the computer program is run by the processor, it will execute the range estimation method provided by any of the aforementioned embodiments.

[0140] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical or magnetic storage media, which are not listed here one by one.

[0141] The computer-readable storage medium provided in the above-mentioned embodiments of the present application and the range estimation method provided in the embodiments of the present application are based on the same application concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0142] It should be noted that:

[0143] In the above text, the term "comprises", "includes" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "including one..." do not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be noted that the scope of the method and device in the embodiment of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0144] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0145] The embodiments of the present application are described above in conjunction with the accompanying drawings, which are only specific implementation modes of the present application. However, the present application is not limited to the above-mentioned specific implementation modes. The above-mentioned specific implementation modes are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

Claims

1. A method for estimating a cruising range, characterized in that: The method comprises: Determining first operating condition data that meets a preset condition and second operating condition data that does not meet a preset condition based on operating condition data of the vehicle within a preset time period, wherein the preset condition includes that the operating condition duration is greater than or equal to a preset time; The second operating condition data is spliced ​​according to the preset time to obtain spliced ​​data including at least one spliced ​​operating condition, and the duration of the spliced ​​operating condition meets the preset condition; wherein the duration of the second operating condition is obtained by the start time and the end time, and the spliced ​​operating condition is generated by using the relationship between the duration of a continuous target number of second operating conditions and the preset time; Constructing a cruising range data set according to the first operating condition data and the spliced ​​data, wherein the cruising range data set represents a correlation between the vehicle running mileage, the consumed charge, and the cruising range corresponding to each operating condition; The cruising range data set is corrected according to preset parameters to obtain a target data set, and the cruising range of the vehicle is obtained based on the target data set; the preset parameters include at least one factor affecting the cruising range of the vehicle; the preset parameters include physical parameters and operating condition parameters.

2. The method according to claim 1, characterized in that The working condition data includes the start time of each working condition, and the second working condition data is spliced ​​according to the preset time to obtain spliced ​​data, including: Sorting each operating condition in the second operating condition data according to the start time sequence of each operating condition; According to the order, sequentially obtain the total duration of a target number of consecutive operating conditions, where the target number is greater than or equal to 2; When the total duration is greater than or equal to the preset time, the target number of operating conditions are taken as a spliced ​​operating condition to obtain spliced ​​data including at least one of the spliced ​​operating conditions; wherein the operating condition data of the spliced ​​operating condition is obtained by obtaining the average value of the operating condition data of the target number of operating conditions.

3. The method according to claim 2, characterized in that The operating condition data includes vehicle running mileage and consumed charge. A cruising range data set is constructed based on the first operating condition data and the spliced ​​data, including: The average value of the vehicle running mileage of the target number of working conditions is used as the vehicle running mileage of the spliced ​​working condition, and the average value of the consumed charge of the target number of working conditions is used as the consumed charge of the spliced ​​working condition; The cruising range data set is constructed based on the vehicle running mileage and the amount of charge consumed in each operating condition in the first operating condition data, and the vehicle running mileage and the amount of charge consumed in each spliced ​​operating condition in the spliced ​​data.

4. The method according to claim 1, characterized in that: The step of correcting the cruising range data set according to preset parameters to obtain a target data set includes: Correcting the cruising range data set by using the physical parameters, wherein the physical parameters include at least one of the vehicle's nominal cruising range, rated power per 100 kilometers, and vehicle model; The corrected cruising range data set is corrected again by using the operating condition parameters to obtain the target data set, wherein the operating condition parameters include at least one of a battery temperature and an energy consumption value per 100 kilometers corresponding to each operating condition.

5. The method according to claim 4, characterized in that The step of correcting the cruising range data set by using the physical parameter includes: According to the nominal cruising range corresponding to each vehicle model, removing the operating conditions in the cruising range data set where the cruising range is greater than the nominal cruising range; Obtaining the actual energy consumption per 100 kilometers of the vehicle according to the consumed charge amount and the vehicle running mileage of each working condition in the cruising range data set; The operating conditions in which the actual energy consumption per 100 kilometers in the cruising range data set is greater than the rated power per 100 kilometers corresponding to the vehicle are removed.

6. The method according to claim 4, characterized in that The operating condition parameters include the battery temperature and the energy consumption per 100 kilometers corresponding to each operating condition. The corrected cruising range data set is corrected again by using the operating condition parameters to obtain the target data set, including: According to the battery temperature, determining operating condition data within a preset temperature range from the cruising range data set; According to the energy consumption value per 100 kilometers, determining the operating condition data within a preset energy consumption range from the cruising range data set; The target data set is obtained according to the operating condition data within a preset temperature range and the operating condition data within a preset energy consumption range.

7. The method according to claim 6, characterized in that Acquiring the cruising range of the vehicle according to the cruising range data set includes: Dividing the preset temperature interval into a plurality of temperature sub-intervals, and dividing the target data set into a plurality of data subsets according to the plurality of temperature sub-intervals; According to the weight corresponding to each operating condition, the cruising range of each operating condition in the multiple data subsets is weighted averaged, and the cruising range value corresponding to each temperature sub-range is output as the cruising range of the vehicle in the corresponding temperature sub-range; the weight corresponding to each operating condition is pre-set according to the speed, road condition or driving behavior corresponding to each operating condition.

8. A cruising range estimation device, characterized in that: include: A data acquisition module, used to determine first operating condition data that meets a preset condition and second operating condition data that does not meet the preset condition based on the operating condition data of the vehicle within a preset time period, wherein the preset condition includes that the operating condition duration is greater than or equal to a preset time; A data splicing module, used for splicing the second working condition data according to the preset time to obtain splicing data including at least one splicing working condition, the duration of the splicing working condition meets the preset condition; wherein, the duration of the second working condition is obtained by the start time and the end time, and the splicing working condition is generated by using the relationship between the duration of a continuous target number of second working conditions and the preset time; A data set creation module, used to construct a cruising range data set according to the first operating condition data and the spliced ​​data, wherein the cruising range data set represents the correlation between the vehicle running mileage, the consumed charge, and the cruising range corresponding to each operating condition; An estimation module is used to correct the cruising range data set according to preset parameters to obtain a target data set, and obtain the cruising range of the vehicle based on the target data set; the preset parameters include at least one factor affecting the cruising range of the vehicle; the preset parameters include physical parameters and operating condition parameters.

9. An electronic device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the method according to any one of claims 1 to 7.

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