Endurance mileage prediction method and device, vehicle, computer equipment and storage medium

Abstract

The invention relates to an endurance mileage prediction method and device, a vehicle, computer equipment and a storage medium, in the running process of a target vehicle, nominal physical parameters, mileage parameters, energy consumption parameters and charge state parameters of the target vehicle are obtained, secondary analysis and calculation are carried out in combination with the mileage parameters, the energy consumption parameters and the charge state parameters, and the endurance mileage prediction result is obtained. The current endurance mileage and the current unit mileage energy consumption of the target vehicle in the current state are obtained. And finally predicting the endurance mileage of the target vehicle at any driving node in a future preset time period in combination with the nominal physical parameters, the current endurance mileage, the current unit mileage energy consumption and the mileage parameters. Through the scheme, the current endurance mileage, the current unit mileage energy consumption and the mileage parameter corresponding to the actual running state of the target vehicle and the inherent nominal physical parameters of the target vehicle can be combined to realize the endurance mileage prediction at the future moment.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a range prediction method, device, vehicle, computer equipment, storage medium and computer program product. Background Art

[0002] With the development of new energy technologies, battery-powered vehicles are becoming more and more widely used in daily life, bringing great convenience to people. The battery storage capacity of a vehicle is limited, and it is usually necessary to go to a charging station to recharge in order to meet the endurance requirements. Therefore, estimating the vehicle's endurance is of great significance to user travel planning and driving safety.

[0003] However, in the related art, the vehicle is unable to predict the range of the vehicle at a future time. Summary of the invention

[0004] Based on this, it is necessary to propose a range prediction method, device, vehicle, computer equipment, storage medium and computer program product to realize the range prediction of the vehicle at any time within a certain period of time in the future.

[0005] The present application provides a method for predicting a cruising range, comprising: obtaining operating data and nominal physical parameters of a target vehicle; the operating data comprising mileage parameters, energy consumption parameters and state of charge parameters; determining a current cruising range and a current energy consumption per unit mileage according to the mileage parameters, the energy consumption parameters and the state of charge parameters; predicting a cruising range of the target vehicle at any driving node within a preset time period in the future according to the nominal physical parameters, the current cruising range, the current energy consumption per unit mileage and the mileage parameters.

[0006] The above-mentioned range prediction method obtains the nominal physical parameters of the target vehicle, as well as the mileage parameters, energy consumption parameters, and state of charge parameters during the operation of the target vehicle, and performs secondary analysis and calculation in combination with the mileage parameters, energy consumption parameters, and state of charge parameters to obtain the current range and current unit mileage energy consumption of the target vehicle in the current state. Finally, the range of the target vehicle at any driving node in the future preset time period is predicted by combining the nominal physical parameters, the current range, the current unit mileage energy consumption, and the mileage parameters. Through this solution, the current range, the current unit mileage energy consumption, the mileage parameters corresponding to the actual operating state of the target vehicle, and the inherent nominal physical parameters of the target vehicle itself can be combined to achieve the prediction of the range at a future moment.

[0007] In some embodiments, predicting the cruising range of the target vehicle at any driving node within a preset time period in the future based on the nominal physical parameters, the current cruising range, the current energy consumption per unit mileage and the mileage parameters includes: obtaining vehicle model information of the target vehicle; inputting the vehicle model information, the nominal physical parameters, the current cruising range, the current energy consumption per unit mileage and the mileage parameters into a preset cruising range attenuation model for prediction, so as to obtain the cruising range of the target vehicle at any driving node within the preset time period in the future.

[0008] The above solution, combined with a pre-trained range attenuation model for range prediction, has a high prediction efficiency.

[0009] In some embodiments, the predicting of the cruising range of the target vehicle at any driving node within a preset time period in the future includes: determining the cruising range of the target vehicle at the future time node or the future mileage node in combination with the future time node or future mileage node to be predicted.

[0010] The above scheme can predict the cruising range from two dimensions: mileage and time, and has a high prediction convenience.

[0011] In some embodiments, the method for determining the range attenuation model includes: obtaining the nominal physical parameters of the sample vehicle, and the historical operating data under each charging condition within a preset historical period; the historical operating data includes historical mileage parameters, historical energy consumption parameters and historical state of charge parameters, and the vehicle type of the sample vehicle is the same as the vehicle type of the target vehicle; analyzing and processing the historical mileage parameters, the historical energy consumption parameters and the historical state of charge parameters to determine the range data set and the unit mileage energy consumption data set; jointly determining the sample data corresponding to the sample vehicle by the historical mileage parameters, the nominal physical parameters, the range data set and the unit mileage energy consumption data set; obtaining a sample data set based on the sample data of multiple sample vehicles; and performing model training in combination with the sample data set and a preset training model to determine the range attenuation model.

[0012] The above scheme, for the same type of sample vehicles, combines the historical operation data to obtain the range data set and unit mileage energy consumption data set through secondary calculation, completes the construction of the range attenuation model, and improves the accuracy of the range attenuation model.

[0013] In some embodiments, the analyzing and processing based on the historical mileage parameters, the historical energy consumption parameters and the historical state of charge parameters to determine the endurance data set and the unit mileage energy consumption data set includes: calculating based on the historical mileage parameters and the historical state of charge parameters to determine the endurance data set; calculating based on the historical mileage parameters, the historical state of charge parameters and the historical energy consumption parameters to determine the unit mileage energy consumption data set.

[0014] In the above scheme, the cruising range data set and the unit mileage energy consumption data set are respectively established through different historical operation data, thereby improving the accuracy of the cruising range data set and the unit mileage energy consumption data set.

[0015] In some embodiments, the calculation based on the historical mileage parameters and the historical state of charge parameters to determine the endurance data set includes: calculating the running mileage difference between each charging condition and the previous charging condition based on the historical mileage parameters; calculating the state of charge consumption value between each charging condition and the previous charging condition based on the historical state of charge parameters; and analyzing and calculating based on the running mileage difference and the state of charge consumption value to determine the endurance data set.

[0016] The above scheme takes each charge as a working point, combines the mileage changes and battery charge state changes of each working point, and establishes a highly reliable endurance data set.

[0017] In some embodiments, the method further includes: determining the time difference between each charging condition and the previous charging condition; performing data jump and loss judgment on each charging condition based on the time difference and the state of charge consumption value; and when the data jump and loss judgment is passed, executing the step of analyzing and calculating based on the running mileage difference and the state of charge consumption value to determine the endurance data set.

[0018] The above scheme combines the time difference and the charge state consumption value to perform data jump and loss judgment correction processing on each operating point, that is, to perform noise reduction processing on the data obtained at each operating point to improve the data accuracy of each operating point.

[0019] In some embodiments, analyzing and calculating based on the running mileage difference and the state of charge consumption value to determine the endurance data set includes: analyzing and calculating based on the running mileage difference and the state of charge consumption value to determine the original endurance data set; and correcting the original endurance data set to obtain the endurance data set.

[0020] In the above scheme, after determining the original endurance data set by combining the running mileage difference and the state of charge consumption value, the original endurance data set will be corrected, and the corrected original endurance data set will be used as the endurance data set, thereby further improving the accuracy of the endurance data set.

[0021] In some embodiments, the correcting the original endurance data set to obtain the endurance data set includes: correcting the original endurance data set according to the nominal physical parameters to obtain the endurance data set.

[0022] The above scheme, combined with the nominal physical parameters of the sample vehicles, eliminates the data that does not meet the nominal physical parameters in the original endurance data set, and has a high correction reliability.

[0023] In some embodiments, the correcting the original battery life data set to obtain the battery life data set includes: performing filtering and convergence correction on the original battery life data set to obtain the battery life data set.

[0024] The above scheme corrects the original endurance data set in a filtering convergence manner, which has a high correction efficiency.

[0025] In some embodiments, the calculation based on the historical mileage parameters, the historical state of charge parameters and the historical energy consumption parameters to determine the unit mileage energy consumption data set includes: calculating the running mileage difference between each charging condition and the previous charging condition based on the historical mileage parameters; calculating the state of charge consumption value between each charging condition and the previous charging condition based on the historical state of charge parameters; analyzing and calculating the historical energy consumption parameters corresponding to each charging condition, as well as the running mileage difference and the state of charge consumption value to determine the unit mileage energy consumption data set.

[0026] The above scheme combines the mileage changes of each operating point, the battery state of charge changes, and the historical energy consumption parameters of each operating point to establish a unit mileage energy consumption data set, thereby improving the accuracy of the unit mileage energy consumption data set.

[0027] In some embodiments, the method further includes: when the running mileage difference is greater than or equal to a preset mileage threshold, and the state of charge consumption value is greater than or equal to the preset charge threshold, determining a cruising range data set or a unit mileage energy consumption data set using the running mileage difference and the state of charge consumption value; when the running mileage difference is less than the preset mileage threshold, and / or the state of charge consumption value is less than the preset charge threshold, splicing the running mileage difference and / or the state of charge consumption value to determine a cruising range data set or a unit mileage energy consumption data set using the splicing result.

[0028] The above scheme slices and fuses the consumed charge and / or mileage according to the actual situation, so that smaller mileage differences and / or smaller state of charge consumption values ​​have the opportunity to participate in the data set construction, further improving the accuracy of each data set.

[0029] In some embodiments, the historical operating data also includes historical ambient temperature parameters; the historical mileage parameters, the nominal physical parameters, the cruising range data set and the unit mileage energy consumption data set are combined to determine the sample data corresponding to the sample vehicle, including: respectively configuring the corresponding historical ambient temperature parameters for each operating point in the cruising range data set, and respectively configuring the corresponding historical ambient temperature parameters for each operating point in the unit mileage energy consumption data set; based on the historical mileage parameters, the nominal physical parameters, and the cruising range data set and the unit mileage energy consumption data set after configuring the historical ambient temperature parameters, the sample data corresponding to the sample vehicle are determined by combining.

[0030] The above solution introduces historical ambient temperature parameters to construct a sample data set, so that the endurance attenuation model better matches the actual operating environment and improves the accuracy of the endurance attenuation model.

[0031] In some embodiments, the historical operating data also includes behavioral operating condition parameters, and the historical mileage parameters, the nominal physical parameters, the endurance data set, and the unit mileage energy consumption data set are combined to determine the sample data corresponding to the sample vehicle, including: the historical mileage parameters, the nominal physical parameters, the endurance data set, the unit mileage energy consumption data set, and the behavioral operating condition parameters are combined to determine the sample data corresponding to the sample vehicle.

[0032] The above scheme introduces behavioral condition parameters to construct a sample data set, so that the sample data set better matches the user's behavioral habits, further improving the accuracy of the sample data set.

[0033] In some embodiments, the historical mileage parameters, the nominal physical parameters, the endurance data set and the unit mileage energy consumption data set are combined to determine the sample data corresponding to the sample vehicle, including: according to the historical mileage parameters and / or historical time points, the historical mileage parameters, the nominal physical parameters, the endurance data set and the unit mileage energy consumption data set are combined to obtain the sample data corresponding to the sample vehicle.

[0034] The above scheme combines historical mileage parameters and / or historical time points to jointly integrate the endurance data set and the unit mileage energy consumption data set, so that the sample data set changes with the mileage parameters or time points, thereby improving the accuracy of the endurance attenuation model.

[0035] The present application also provides a cruising range prediction device, including: a parameter acquisition module, used to obtain operating data and nominal physical parameters of a target vehicle; the operating data includes mileage parameters, energy consumption parameters and charge state parameters; a parameter calculation module, used to determine the current cruising range and the current unit mileage energy consumption according to the mileage parameters, the energy consumption parameters and the charge state parameters; a cruising range prediction module, used to predict the cruising range of the target vehicle at any driving node within a preset time period in the future according to the nominal physical parameters, the current cruising range, the current unit mileage energy consumption and the mileage parameters.

[0036] The present application also provides a vehicle, including a battery management system, a battery, a drive system and a vehicle controller, wherein the battery management system is arranged on the battery, and the battery management system and the drive system are respectively connected to the vehicle controller, and the vehicle controller is used to execute the steps of the above-mentioned cruising range prediction method.

[0037] The present application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned range prediction method when executing the computer program.

[0038] The present application also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned range prediction method are implemented.

[0039] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned range prediction method when executed by a processor. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Moreover, the same reference numerals are used throughout the drawings to represent the same components. In the drawings:

[0041] Figure 1 This is a flowchart of a method for predicting a cruising range in some embodiments of the present application;

[0042] Figure 2 A flowchart of a method for predicting a cruising range in some other embodiments of the present application;

[0043] Figure 3 This is a schematic diagram of the training process of the battery life attenuation model in some embodiments of the present application;

[0044] Figure 4 A schematic diagram of a flow chart for establishing a sample data set in some embodiments of the present application;

[0045] Figure 5 A schematic diagram of the process of establishing a battery life data set in some embodiments of the present application;

[0046] Figure 6 A schematic diagram of a process for establishing a battery life data set in some other embodiments of the present application;

[0047] Figure 7 A schematic diagram of a process for establishing a battery life data set in some other embodiments of the present application;

[0048] Figure 8 This is a schematic diagram of a battery life data set in some embodiments of the present application;

[0049] Fig. 9 A schematic diagram of a process for establishing a battery life data set in some embodiments of the present application;

[0050] Fig.10 A schematic diagram of a process for establishing a unit mileage energy consumption data set in some embodiments of the present application;

[0051] Fig.11 A schematic diagram of a sample data set establishment process in some other embodiments of the present application;

[0052] Fig.12 A schematic diagram of the training process of the battery life attenuation model in some other embodiments of the present application;

[0053] Fig.13 This is a schematic diagram of the training process of the battery life attenuation model in some other embodiments of the present application;

[0054] Fig.14 This is a schematic diagram of the structure of a cruising range prediction device in some embodiments of the present application;

[0055] Fig.15 This is a schematic diagram of the structure of a cruising range prediction device in some other embodiments of the present application;

[0056] Fig.16 This is a schematic diagram of the internal structure of a computer device in some embodiments of the present application. DETAILED DESCRIPTION

[0057] 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.

[0058] 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.

[0059] 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.

[0060] 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.

[0061] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0062] 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).

[0063] In the description of the embodiments of the present application, unless otherwise clearly specified and limited, technical terms such as "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the internal connection of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0064] At present, judging from the development of the market situation, battery-powered electric vehicles are being used more and more widely. The cruising range has always been one of the main concerns of electric vehicles. However, different energy consumption, power, driving environment, and user driving behavior will affect the vehicle's single available cruising range. It is particularly important to predict the vehicle's cruising range.

[0065] At present, common endurance standards include NEDC (New European Driving Cycle), CLTC (China Light-duty Vehicle Test Cycle), WLTC (World Light Vehicle Test Cycle) or (Environmental Protection Agency, endurance test standards developed by the U.S. Environmental Protection Agency), but the actual endurance of these endurance standards is often too different from the predicted results, and usually can only predict the endurance of the vehicle in its current state, and fail to reflect the vehicle's endurance in the future.

[0066] After in-depth research, it is found that the current range prediction methods mostly directly combine the collected vehicle operation data and vehicle models to make range predictions. They can often only predict the remaining range of the vehicle in the current state. It is easy to fail to truly reflect the current range of the vehicle due to data fluctuations and other reasons. At the same time, they also do not have the ability to predict future range.

[0067] To alleviate the above phenomenon, it is possible to consider performing a secondary calculation in combination with the vehicle's operating data to obtain parameters that can better characterize the vehicle's actual operating status. Using these parameters to predict the cruising range can not only improve the prediction accuracy of the cruising range, but also realize the prediction of the cruising range at any node within a certain period of time in the future.

[0068] Based on the above considerations, the present application provides a method for predicting the range of a target vehicle. During the operation of the target vehicle, the nominal physical parameters of the target vehicle, as well as the mileage parameters, energy consumption parameters, and state of charge parameters are obtained, and the mileage parameters, energy consumption parameters, and state of charge parameters are combined for secondary analysis and calculation to obtain the current range of the target vehicle in the current state and the current unit mileage energy consumption. Finally, the nominal physical parameters, current range of a target vehicle, current unit mileage energy consumption, and mileage parameters are combined to predict the range of the target vehicle at any node in a preset time period in the future.

[0069] Through the above scheme, the current cruising range, current unit mileage energy consumption, mileage parameters corresponding to the actual operating status of the target vehicle, and the vehicle's inherent nominal physical parameters can be combined to achieve the prediction of cruising range at future times.

[0070] The range prediction method provided in this application is applied to electric vehicles, which can be electric vehicles such as electric cars and electric motorcycles, without specific limitation. In more detail, in one embodiment, the range prediction method is applied to pure electric vehicles, that is, electric vehicles powered only by batteries, which can be passenger cars or commercial vehicles, without specific limitation.

[0071] See also Figure 1 The present application provides a method for predicting a cruising range, including step 102, step 104 and step 106.

[0072] Step 102, obtaining the operating data and nominal physical parameters of the target vehicle.

[0073] Specifically, the operating data includes mileage parameters, energy consumption parameters, and state of charge parameters. Nominal physical parameters refer to parameters calibrated when the vehicle leaves the factory and are used to characterize the physical properties of the vehicle, such as power, driving ability, and charging efficiency. Nominal physical parameters usually do not change with the use of the vehicle, and their types are not unique and are not specifically limited. For example, in one embodiment, the nominal physical parameters include at least one of nominal power, nominal endurance, and energy consumption per 100 kilometers.

[0074] The target vehicle is the vehicle that currently has a demand for range prediction; the operating data is the data related to the operating status generated during the actual operation of the target vehicle. The mileage parameter is the parameter related to the operating mileage of the target vehicle. Its type is not unique and can include at least one of the cumulative mileage, daily average mileage, monthly average mileage and annual average mileage, without specific limitation. The energy consumption parameter is the parameter related to the amount of electricity consumed when the target vehicle is running. Its specific type is not unique and can be the cumulative operating energy consumption, single operating energy consumption, etc., without specific limitation. The state of charge parameter is the battery state of charge parameter (SOC, State of Charge) of the target vehicle, which can also be understood as the remaining battery power.

[0075] During the operation of the target vehicle, the operation data can be collected in real time through the energy consumption data collector, battery management system, odometer or trip meter and other devices, and the collected operation data can be sent to the vehicle controller, and the vehicle controller can perform related operations of the range prediction. The operation of the target vehicle means that the target vehicle is in a discharging state, including but not limited to the target vehicle in a driving state, the target vehicle in a powered-on parking state (such as waiting for a traffic light), etc., without specific limitation.

[0076] Step 104, determining the current cruising range and the current energy consumption per unit mileage according to the mileage parameter, the energy consumption parameter and the state of charge parameter.

[0077] Specifically, the current cruising range refers to the actual cruising range of the target vehicle between the current charging condition and the previous charging condition. The current unit mileage energy consumption refers to the energy consumption per unit mileage of the vehicle between the current charging condition and the previous charging condition.

[0078] It should be noted that the specific size of the unit mileage is not unique and can be selected according to actual needs. In one embodiment, 100 kilometers can be used as a unit mileage; in another embodiment, 10 kilometers, 1 kilometer, etc. can also be used as a unit mileage, without specific limitation. For ease of understanding, the following embodiments can all use 100 kilometers as a unit mileage, and accordingly, the current unit mileage energy consumption refers to the energy consumption per 100 kilometers.

[0079] The solution of this embodiment takes each charging of the vehicle as a working point, and predicts the cruising range at each working point. After receiving the operating data, the vehicle controller will analyze and calculate it to obtain the current cruising range and the current energy consumption per unit mileage.

[0080] It can be understood that the calculation method of the current cruising range and the current energy consumption per unit mileage is not unique. In one embodiment, the current cruising range can be calculated based on the mileage parameter and the state of charge parameter, and the current energy consumption per unit mileage can be calculated by combining the mileage parameter, the state of charge parameter and the energy consumption parameter.

[0081] Furthermore, in one embodiment, for each operating point (not the first operating point), the current cruising range can be calculated by combining the running mileage difference between the current charging condition and the previous charging condition, and the state of charge consumption value between the current charging condition and the previous charging condition. In more detail, in one embodiment, the ratio of the running mileage difference to the state of charge consumption value can be used as the current cruising range.

[0082] For example, the operating points include adjacent operating points 1, 2, and 3, and their corresponding cumulative mileages are a, b, and c, respectively. The state-of-charge parameters at the start of charging are m0, n0, and p0, respectively, and the state-of-charge parameters at the end of charging are m1, n1, and p1, respectively. Then, the operating mileage difference of operating point 2 is ba, and the state-of-charge consumption value corresponding to operating point 2 is m1-n0. The cruising range of operating point 2 can be expressed as (ba) / (m1-n0); the operating mileage difference of operating point 3 is cb, and the state-of-charge consumption value corresponding to operating point 3 is n1-p0; the cruising range of operating point 3 can be expressed as (cb) / (n1-p0).

[0083] In one embodiment, for each operating point, the current unit mileage energy consumption can be calculated by combining the running mileage difference between the current charging condition and the previous charging condition, the state of charge consumption value between the current charging condition and the previous charging condition, and the energy consumption parameter corresponding to the current charging condition. The calculation method of the running mileage difference and the state of charge consumption value of each operating point is consistent with the calculation method in the current cruising range, and will not be repeated here.

[0084] In more detail, in one embodiment, the current cruising range can be calculated by combining the running mileage difference and the state of charge consumption value, and then the current unit mileage energy consumption can be calculated by the current cruising range and energy consumption parameter. Taking the energy consumption per 100 kilometers as an example, the energy consumption per 100 kilometers can be obtained by dividing the energy consumption parameter by the current cruising range and then multiplying by 100.

[0085] Step 106 , predicting the cruising range of the target vehicle at any driving node within a preset time period in the future based on the nominal physical parameters, the current cruising range, the current energy consumption per unit mileage and the mileage parameters.

[0086] Specifically, the driving node changes with the driving of the target vehicle, and is used to characterize the parameters of the cumulative driving state of the target vehicle. The cumulative driving state of the target vehicle is different at different driving nodes. The cumulative driving state is not unique, and can be the cumulative driving time or the cumulative mileage. Correspondingly, the driving node can include a time node or a mileage node. After calculating the current cruising range and the current energy consumption per unit mileage, the vehicle controller will combine the collected nominal physical parameters and mileage parameters, as well as the current cruising range and the current energy consumption per unit mileage obtained by the secondary calculation for prediction, and finally obtain the cruising range of any node within the preset time period in the future, that is, the remaining available cruising range of the target vehicle at any node.

[0087] It is understandable that the size of the preset time period is not unique, and can be a year, a quarter, a month, etc. There is no specific limitation, and it can be set according to the actual scenario.

[0088] The above-mentioned range prediction method obtains the nominal physical parameters of the target vehicle, as well as the mileage parameters, energy consumption parameters, and state of charge parameters during the operation of the target vehicle, and performs secondary analysis and calculation in combination with the mileage parameters, energy consumption parameters, and state of charge parameters to obtain the current range and current unit mileage energy consumption of the target vehicle in the current state. Finally, the range of the target vehicle at any node in the future preset time period is predicted by combining the nominal physical parameters, the current range, the current unit mileage energy consumption, and the mileage parameters. Through this solution, the current range, the current unit mileage energy consumption, the mileage parameters corresponding to the actual operating state of the target vehicle, and the inherent nominal physical parameters of the target vehicle itself can be combined to achieve the prediction of the range at a future moment.

[0089] See also Figure 2 In some embodiments, step 106 includes step 202 and step 204 .

[0090] Step 202, obtaining the model information of the target vehicle.

[0091] In step 204, the vehicle model information, nominal physical parameters, current cruising range, current energy consumption per unit mileage and mileage parameters are input into a preset cruising range attenuation model for prediction to obtain the cruising range of the target vehicle at any driving node within a preset time period in the future.

[0092] Specifically, the vehicle model information is information related to the type of the target vehicle, which may include the model of the target vehicle; in another embodiment, the vehicle model information may also include the capacity, manufacturer, battery type and other information of the target vehicle battery, without specific limitation. The range attenuation model is a prediction model used to predict the range of the target vehicle at any time node based on the nominal physical parameters, current range, current unit mileage energy consumption and mileage parameters of the target vehicle. It can be obtained by training the historical operating data of the target vehicle in the historical time period, without specific limitation. It can be understood that each type of vehicle can be trained separately according to its historical operating data, etc., and the training results can be obtained and stored in the range attenuation model, that is, the range attenuation model can realize the range prediction of a variety of different models.

[0093] In the solution of this embodiment, the vehicle controller pre-stores a range attenuation model. For each type of vehicle, the range attenuation model can be used to perform predictive analysis based on the vehicle model information, nominal physical parameters, current range, current energy consumption per unit mileage and mileage parameters to obtain the range of any node in the subsequent preset time period.

[0094] It should be noted that, in one embodiment, the future preset time period can be determined according to the time span of the historical operation data used when training the endurance attenuation model. For example, in one embodiment, the historical operation data is historical operation data within one year, and the future preset time period can be configured as the next year.

[0095] The above solution, combined with a pre-trained range attenuation model for range prediction, has a high prediction efficiency.

[0096] In some embodiments, predicting the range of a target vehicle at any driving node within a preset time period in the future includes: determining the range of the target vehicle at a future time node or a future mileage node in combination with the future time nodes or future mileage nodes to be predicted.

[0097] Specifically, in actual operation scenarios, the accumulated mileage of a vehicle will increase with time. Therefore, the mileage can be predicted based on the mileage node, or based on the time node, without specific limitation. The future time node is the time point within the future preset time period; the future mileage node is the accumulated mileage that reaches the time within the future preset time period.

[0098] The above scheme can predict the cruising range from two dimensions: mileage and time, and has a high prediction convenience.

[0099] See also Figure 3 In some embodiments, the method for determining the endurance attenuation model includes: step 302 , step 304 , step 306 , step 308 and step 310 .

[0100] Step 302, obtaining nominal physical parameters of the sample vehicle and historical operating data under various charging conditions within a preset historical period.

[0101] Step 304 , analyzing and processing the historical mileage parameters, the historical energy consumption parameters, and the historical state of charge parameters to determine a cruising range data set and a unit mileage energy consumption data set.

[0102] Step 306 , the historical mileage parameters, the nominal physical parameters, the endurance data set, and the unit mileage energy consumption data set are combined to determine the sample data corresponding to the sample vehicle.

[0103] Step 308 , obtaining a sample data set based on the sample data of the plurality of sample vehicles.

[0104] Step 310, perform model training in combination with the sample data set and the preset training model to determine the endurance attenuation model.

[0105] Specifically, the historical operation data includes historical mileage parameters, historical energy consumption parameters and historical state of charge parameters. The vehicle type of the sample vehicle is the same as the vehicle type of the target vehicle. The historical mileage parameters are a series of parameters related to the mileage of the sample vehicle within the historical time period, which may include the accumulated mileage at different operating points or different times, and may also include daily average mileage, monthly average mileage or annual average mileage, etc., without specific limitation. The historical energy consumption parameters are parameters related to the energy consumption of the sample vehicle at different operating points or different time points within the historical time period. The state of charge parameters are the battery state of charge parameters of the sample vehicle at different operating points or different time points within the historical time period. The endurance data set is the collection of endurance data corresponding to each charging condition of the sample vehicle within the preset historical period. The unit mileage energy consumption data set refers to the electric energy consumed by the sample vehicle when it runs a unit mileage (for example, 100 kilometers) under each charging condition within the preset historical period.

[0106] The time span of the preset historical period is not unique, and can be two years, one year, one quarter, etc., without specific limitation. In order to facilitate understanding of the technical solution of the present application, the preset historical period can be understood as the most recent year in the following embodiments.

[0107] The training of the range attenuation model can be performed on the vehicle controller of the sample vehicle. At this time, the vehicle controller of the sample vehicle can cluster the historical operating data and nominal physical parameters of other sample vehicles of the same type, thereby realizing the training of the range attenuation model corresponding to this type of vehicle. In another embodiment, the training of the range attenuation model can also be performed by a host computer or a background server. At this time, the historical operating data and nominal physical parameters of sample vehicles of the same type are clustered by the host computer or the background server, thereby realizing the training of the range attenuation model corresponding to this type of vehicle.

[0108] In order to facilitate understanding of the technical solution of the present application, the following embodiments can all be understood that the training of the range attenuation model is performed through a background server, and after the training is completed, the range attenuation model is sent to the target vehicle for application.

[0109] Similar to the prediction of the cruising range of the above-mentioned target vehicle in combination with the operating data and nominal physical parameters, after obtaining the historical operating data, the historical mileage parameters, historical energy consumption parameters and historical state of charge parameters in the historical operating data will be combined for secondary calculation to obtain the historical cruising range parameters and historical unit mileage energy consumption parameters corresponding to different operating points in the preset historical period. The set of historical cruising range parameters for all operating points is taken as the cruising range data set, and the set of historical unit mileage energy consumption parameters for all operating points is taken as the unit mileage energy consumption data set. Then, the nominal physical parameters, the historical mileage parameters of each operating point, and the cruising range data set and unit mileage energy consumption data set obtained by the secondary calculation are merged to obtain a sample data set. Finally, part of the data in the sample data set is used as a training set and part as a test set. The model is trained in combination with the preset training model to obtain the cruising range attenuation model.

[0110] It should be pointed out that in the actual training process, the historical endurance parameters corresponding to the endurance data set in the sample data set are used as labels to characterize the actual output or expected output endurance of the training sample. For different data groups (i.e., data corresponding to different operating points or time points), the data corresponding to the historical mileage parameters, nominal physical parameters, and unit mileage energy consumption data sets are input into the preset training model for model training, and the historical endurance parameters corresponding to the endurance data set in the data group are combined for supervised learning, and the various parameters in the preset training model are continuously corrected to finally complete the training.

[0111] It is understandable that the type of preset training model is not unique, as long as it is a model that uses multiple input independent variables to predict and output a dependent variable, it can be any model, and there is no specific limitation. For example, a regression model, a neural network model, etc. can be used. In order to facilitate understanding of the technical solution of the present application, the following embodiments are explained by taking a multiple regression model as an example.

[0112] The above scheme, for the same type of vehicles, combines the historical operation data to perform secondary calculations to obtain the range data set and unit mileage energy consumption data set, completes the construction of the range attenuation model, and improves the accuracy of the range attenuation model.

[0113] See also Figure 4 In some embodiments, step 304 includes step 402 and step 404 .

[0114] Step 402 , performing calculations based on historical mileage parameters and historical state of charge parameters to determine a cruising range data set.

[0115] Step 404 , performing calculations based on historical mileage parameters, historical state of charge parameters, and historical energy consumption parameters to determine a unit mileage energy consumption data set.

[0116] Specifically, in this embodiment, the endurance data set and the unit mileage energy consumption data set are calculated separately. The endurance data set can be analyzed and calculated in combination with the historical mileage parameters and the historical state of charge parameters; specifically, the cumulative mileage parameters corresponding to each historical charging condition in the historical mileage parameters and the corresponding state of charge parameters are analyzed, and the endurance data of each historical charging condition are calculated respectively, and finally these endurance data are stored according to time points, cumulative mileage nodes or operating points to obtain the endurance data set. The unit mileage energy consumption data set is analyzed and calculated in combination with the historical mileage parameters, the historical state of charge parameters and the historical energy consumption parameters; specifically, the cumulative mileage parameters corresponding to each historical charging condition in the historical mileage parameters and the corresponding state of charge parameters and energy consumption parameters are analyzed, and the unit mileage energy consumption data of each historical charging condition are calculated respectively, and finally these unit mileage energy consumption data are stored according to time points, cumulative mileage nodes or operating points to obtain the unit mileage energy consumption data set.

[0117] In the above scheme, the cruising range data set and the unit mileage energy consumption data set are respectively established through different historical operation data, thereby improving the accuracy of the cruising range data set and the unit mileage energy consumption data set.

[0118] See also Figure 5 In some embodiments, step 402 includes step 502 , step 504 , and step 506 .

[0119] Step 502: Calculate the running mileage difference between each charging condition and the last charging condition based on the historical mileage parameter.

[0120] Step 504: Calculate the state of charge consumption value between each charging condition and the last charging condition according to the historical state of charge parameters.

[0121] Step 506 , analyzing and calculating based on the running mileage difference and the state of charge consumption value to determine the endurance data set.

[0122] Specifically, the running mileage difference is the difference in the cumulative running mileage of the sample vehicle between the last charge and the current charge, which actually represents the actual running mileage of the sample vehicle. The state of charge consumption value is the actual state of charge consumed by the sample vehicle between the last charge and the current charge. In the process of establishing the actual data set, it can be combined with the collected historical operation data for analysis to calculate the running mileage difference of the sample vehicle between any two adjacent charging conditions, that is, the cumulative mileage of the current operating point is subtracted from the cumulative mileage of the previous charging condition (which can be the beginning or end of charging, and the mileage will not change during charging) to obtain the running mileage difference.

[0123] The historical state of charge parameters at the beginning of the current charging condition are subtracted from the historical state of charge parameters at the end of the previous charging condition to obtain the state of charge consumption value between the two charging conditions. This state of charge consumption value can be equivalent to the battery power consumed when the sample vehicle travels the difference in mileage. After that, the historical mileage corresponding to the current charging condition can be obtained by analyzing the difference in mileage and the state of charge consumption value. In a similar way, each operating point is analyzed and calculated separately, and finally the collection of historical mileages of each operating point is used as the endurance data set.

[0124] It is understandable that the method of determining the endurance data set according to the running mileage difference and the state of charge consumption value is not unique. In one embodiment, the ratio of the two may be used as the historical endurance mileage, that is: , Indicates the historical mileage. Indicates the running mileage difference. Indicates the state of charge consumption value. Correspondingly, if the historical mileage corresponding to the charging conditions in the preset historical period is placed in the same data set, we have:

[0125]

[0126] The subscripts 1, 2, 3, ... n represent the numbers of the charging conditions respectively.

[0127] The above scheme takes each charge as a working point, combines the mileage changes and battery charge state changes of each working point, and establishes a highly reliable endurance data set.

[0128] In some embodiments, the method also includes: determining the time difference between each charging condition and the previous charging condition; performing data jump and loss judgment for each charging condition based on the time difference and the state of charge consumption value; when the data jump and loss judgment is passed, performing analysis and calculation based on the running mileage difference and the state of charge consumption value to determine the endurance data set step.

[0129] Specifically, the time difference is the time interval between the last charge and the current charge. In the process of constructing the sample data set in combination with the historical mileage parameters, in order to improve the accuracy and authenticity of the sample data, it is necessary to determine the jump and loss of the data, filter out the data with loss or jump, and retain the data without jump and loss.

[0130] Specifically, if the time difference between two adjacent charging conditions is too large, it means that the sample vehicle may have data jumps and losses, that is, there may be charging conditions between the two charging conditions, but these data have not been recorded. If the power consumption value between two adjacent charging conditions is abnormal, for example, the state of charge parameter at the beginning of the current charging condition is greater than the state of charge parameter at the end of the previous charging condition, then the sample vehicle is also considered to have data jumps and losses.

[0131] Data jump and loss judgment passed means: the time difference between two adjacent charging conditions is within the set time range, and the state of charge consumption value between two adjacent charging conditions is within the normal difference range. If the time difference is not within the set time range, and / or the state of charge consumption value is not within the normal difference range, it is considered that the data jump and loss judgment failed. At this time, the data corresponding to the current condition will be discarded, and the sample data set will be established with the data under other conditions.

[0132] The above scheme combines the time difference and the charge state consumption value to perform data jump and loss judgment correction processing on each operating point, that is, to perform noise reduction processing on the data obtained at each operating point to improve the data accuracy of each operating point.

[0133] See also Figure 6 , in some embodiments, step 506 includes step 602 and step 604 .

[0134] Step 602 , analyzing and calculating based on the running mileage difference and the state of charge consumption value to determine the original endurance data set.

[0135] Step 604: modify the original endurance data set to obtain an endurance data set.

[0136] Specifically, when establishing the endurance data set, the original endurance data set is first established, and then the original endurance data set is corrected to obtain a high-precision endurance data set.

[0137] It should be pointed out that in other embodiments, in order to increase the sample size, the original endurance data set may be directly used as the endurance data set to participate in the subsequent establishment of the sample data set, without specific limitation.

[0138] In the above scheme, after determining the original endurance data set by combining the running mileage difference and the state of charge consumption value, the original endurance data set will be corrected, and the corrected original endurance data set will be used as the endurance data set, thereby further improving the accuracy of the endurance data set.

[0139] See also Figure 7 , in some embodiments, step 604 includes step 702.

[0140] Step 702: Correct the original endurance data set according to the nominal physical parameters to obtain an endurance data set.

[0141] Specifically, when the endurance data is reasonable, the parameters it represents should be within the allowable range of the nominal physical parameters. If it exceeds the allowable range of the nominal physical parameters, the data is considered to be erroneous. Therefore, the solution of this embodiment can use the nominal physical parameters to correct the original endurance data set, eliminate some unreasonable data, and improve the accuracy of the endurance data set.

[0142] It is understandable that the type of nominal data set is not unique. In one embodiment, it can be nominal endurance or nominal energy consumption. The data of each operating point in the original endurance data set can be compared and corrected by using the nominal endurance or nominal energy consumption. For ease of understanding, taking the nominal endurance as an example, each endurance data in the original endurance data set can be compared and analyzed with the nominal endurance in the nominal physical parameters, and the endurance data that is significantly greater than the nominal endurance is eliminated to obtain an accurate endurance data set.

[0143] For example, in one embodiment, after converting the original endurance data set into a 1-dimensional n-row array, as shown in FIG. Figure 8 As shown in the figure, the horizontal axis represents the cumulative mileage, and the vertical axis represents the cruising range, with the unit being kilometers or kilometers, with no specific limitation. For a certain model, its nominal cruising range is 600 kilometers. In the figure, the cruising range of some data is obviously greater than 600 kilometers, which does not meet the nominal cruising range requirements. At this time, this part of the data in the original cruising range data set can be removed.

[0144] The above scheme, combined with the nominal physical parameters of the sample vehicles, eliminates the data that does not meet the nominal physical parameters in the original endurance data set, and has a high correction reliability.

[0145] See also Fig. 9 , in some embodiments, step 604 includes step 902.

[0146] Step 902: filter and converge the original endurance data set to obtain an endurance data set.

[0147] Specifically, the solution of this embodiment uses a filtering algorithm to directly perform filtering and convergence correction on the original endurance data set, and convergence corrects the discrete data into highly convergent data.

[0148] It should be noted that when performing filter convergence correction, the filtering method used is not unique. In one embodiment, IQR (Interquartile Range) filtering can be used for correction. In other embodiments, other methods can be used for filter correction, such as median filtering, etc., which are not specifically limited.

[0149] The above scheme corrects the original endurance data set in a filtering convergence manner, which has a high correction efficiency.

[0150] See also Fig.10 In some embodiments, step 404 includes step 1002 , step 1004 , and step 1006 .

[0151] Step 1002, based on the historical mileage parameters, calculate the running mileage difference between each charging condition and the last charging condition.

[0152] Step 1004: Calculate the state of charge consumption value between each charging condition and the last charging condition based on the historical state of charge parameters.

[0153] Step 1006, determining a unit mileage energy consumption data set based on historical energy consumption parameters corresponding to each charging condition, as well as the running mileage difference and the state of charge consumption value.

[0154] Specifically, similar to the establishment of the endurance data set in the above embodiment, this embodiment also takes each charging condition as a node, calculates the unit mileage energy consumption corresponding to each charging condition, and then takes the set of each unit mileage energy consumption as the unit mileage energy consumption data set.

[0155] In order to facilitate understanding of the technical solution of the present application, the following embodiment is explained by taking the energy consumption data set per unit mileage as a 100-kilometer energy consumption data set as an example. For each charging condition point, the cruising range can be calculated in a similar manner as described above, combining the running mileage difference and the state of charge consumption value, and then the energy consumption per 100 kilometers is obtained by dividing the energy consumption parameter by the current cruising range and multiplying by 100. Finally, the set of energy consumption per 100 kilometers of each charging condition is used as the 100-kilometer energy consumption data set.

[0156] The above scheme combines the mileage changes of each operating point, the battery state of charge changes, and the historical energy consumption parameters of each operating point to establish a unit mileage energy consumption data set, thereby improving the accuracy of the unit mileage energy consumption data set.

[0157] In some embodiments, the method further includes: when the running mileage difference is greater than or equal to a preset mileage threshold, and the state of charge consumption value is greater than or equal to the preset charge threshold, determining the endurance data set or the unit mileage energy consumption data set using the running mileage difference and the state of charge consumption value; when the running mileage difference is less than the preset mileage threshold, and / or the state of charge consumption value is less than the preset charge threshold, splicing the running mileage difference and / or the state of charge consumption value, and determining the endurance data set or the unit mileage energy consumption data set using the splicing result.

[0158] Specifically, the scheme of this embodiment uses the state of charge consumption value greater than or equal to the preset charge threshold as the long SOC, and the state of charge consumption value less than the preset charge threshold as the short SOC; the running mileage difference greater than or equal to the preset mileage threshold is used as the long mileage, and the running mileage difference less than the preset mileage threshold is used as the short mileage. For long mileage and long SOC, they can directly participate in the calculation of the endurance data set or the unit mileage energy consumption data set; and for short mileage and / or short SOC, they can be spliced ​​and then participate in the calculation of the endurance data set or the unit mileage energy consumption data set.

[0159] Among them, splicing means taking the sum of multiple short mileages as a running mileage difference, and the sum of the SOCs corresponding to multiple short mileages (which can be long SOCs or short SOCs) as a state of charge consumption value, and calculating a range data. Or taking the sum of multiple short SOCs as a state of charge consumption value, and the sum of the mileages corresponding to multiple short SOCs (which can be short mileages or long mileages) as a running mileage difference, and calculating a range data.

[0160] The above scheme slices and fuses the consumed charge and / or mileage according to the actual situation, so that smaller mileage differences and / or smaller state of charge consumption values ​​have the opportunity to participate in the data set construction, further improving the accuracy of each data set.

[0161] See also Fig.11 In some embodiments, the historical operating data also includes historical ambient temperature parameters; step 306 includes step 112 and step 114 .

[0162] Step 112, respectively configure the corresponding historical ambient temperature parameters for each operating point in the endurance data set, and respectively configure the corresponding historical ambient temperature parameters for each operating point in the unit mileage energy consumption data set.

[0163] Step 114 , based on the historical mileage parameters, the nominal physical parameters, and the endurance data set and the unit mileage energy consumption data set after configuring the historical ambient temperature parameters, the sample data corresponding to the sample vehicle are determined.

[0164] Specifically, the historical ambient temperature parameter refers to the ambient temperature corresponding to each charging condition within a preset historical period. In actual scenarios, changes in ambient temperature will also have a certain impact on the operating conditions of the sample vehicles. Therefore, the solution of this embodiment, for each operating point, comprehensively considers the impact of the user's ambient temperature on the battery life, introduces the ambient condition as a scene label, and determines the sample data and sample data set. In this way, in the subsequent training process, the attenuation coefficient can be corrected in combination with the ambient temperature to reflect the proportion of the attenuation coefficient affected by the ambient temperature in different seasons or different regions, and dynamically adjusts the attenuation coefficient of the preset training model according to the actual season or usage area.

[0165] The above solution introduces historical ambient temperature parameters to construct a sample data set, so that the endurance attenuation model better matches the actual operating environment and improves the accuracy of the endurance attenuation model.

[0166] See also Fig.12 In some embodiments, the historical operating data further includes behavioral operating condition parameters, and step 306 includes step 122 .

[0167] Step 122 , historical mileage parameters, nominal physical parameters, endurance data set, unit mileage energy consumption data set, and behavior condition parameters are combined to determine sample data corresponding to the sample vehicle.

[0168] Specifically, the behavioral operating condition parameters are parameters that characterize different operating conditions of the sample vehicles caused by user behavior. It can be understood that the types of behavioral operating condition parameters are not unique. In one embodiment, the behavioral operating condition parameters may include the power-on and power-off intervals of the sample vehicles. In other embodiments, the behavioral operating condition parameters may also include the ambient temperature when the sample vehicles are in use (different users prefer to use the vehicles in different environments), the energy consumption of the sample vehicles (different users use other energy-consuming products in the car differently, which will result in different energy consumption, for example, some users like to turn on the air conditioner, car player, etc.), without specific limitation.

[0169] Therefore, the solution of this embodiment introduces some behavioral operating parameters caused by user behavior habits, combines historical mileage parameters, nominal physical parameters, endurance data set, and unit mileage energy consumption data set to obtain a sample data set for model training.

[0170] The above scheme introduces behavioral condition parameters to construct a sample data set, so that the sample data set better matches the user's behavioral habits, further improving the accuracy of the sample data set.

[0171] See also Fig.13 , in some embodiments, step 306 includes step 132 .

[0172] Step 132 , based on the historical mileage parameters and / or historical time points, the historical mileage parameters, the nominal physical parameters, the endurance data set, and the unit mileage energy consumption data set are combined to determine the sample data corresponding to the sample vehicle.

[0173] Specifically, within a preset historical period, the mileage of the sample vehicle will increase with the increase in usage time. In order to facilitate the distinction between different groups of data and to facilitate subsequent mileage prediction from the mileage dimension and / or time dimension, mileage labels or time labels can be assigned to different groups of data to obtain sample data and sample data sets containing time labels and / or mileage labels.

[0174] It is understandable that whether to assign mileage labels or time labels can be selected according to actual needs. If predictions need to be made from the mileage dimension in the future, mileage labels can be assigned to the sample data in the sample data set, that is, the historical mileage parameters, nominal physical parameters, endurance data sets and unit mileage energy consumption data sets under the same historical mileage parameters can be combined as a set of sample data.

[0175] If you need to make predictions from the time dimension later, you can give time labels to the sample data in the sample data set, that is, combine the historical mileage parameters, nominal physical parameters, endurance data set, and unit mileage energy consumption data set at the same historical time as a set of sample data. If you need to make predictions in both mileage and time dimensions, you can give both time and mileage labels to the same set of sample data.

[0176] The above scheme combines historical mileage parameters and / or historical time points to jointly integrate the endurance data set and the unit mileage energy consumption data set, so that the sample data set changes with the mileage parameters or time points, thereby improving the accuracy of the endurance attenuation model.

[0177] In order to facilitate understanding of the technical solution of the present application, the present application is explained below in conjunction with detailed embodiments.

[0178] Model training phase:

[0179] The vehicle controller or backend server can be used to cluster the historical mileage parameters (including cumulative mileage, average daily mileage and average annual mileage), historical energy consumption parameters and historical state of charge parameters of sample vehicles of the same type (including vehicle model, battery manufacturer, battery capacity, etc.) in the past year, as well as the nominal physical parameters of the sample vehicles of this type (including nominal power, nominal cruising range, and energy consumption per 100 kilometers).

[0180] Then, for each charging condition point, the running mileage difference between the previous charging condition is calculated according to the historical mileage parameters, and the state of charge consumption value between the previous charging condition is calculated according to the historical state of charge parameters. Combined with the time difference and the state of charge consumption value between two adjacent charging conditions, data jump and loss judgment is performed. After the data jump and loss judgment is passed, for each operating point, the ratio of the running mileage difference and the state of charge consumption value can be used to calculate the historical cruising range, and the set of historical cruising ranges of each operating point is used as the original cruising range data set. Then, the physical nominal parameters are introduced to correct the original data set, and the original cruising range data set is converged and corrected by combining IQR filtering to finally obtain the cruising range data set.

[0181] Similarly, for each operating point, the energy consumption per 100 kilometers is calculated by combining the mileage difference, charge state consumption value and energy consumption parameters, and the collection of energy consumption per 100 kilometers at each operating point is used as the energy consumption data set per 100 kilometers.

[0182] After that, according to the time point or cumulative mileage parameters, the historical mileage parameters, nominal physical parameters, endurance data set and unit mileage energy consumption data set are combined to obtain a set of data at each time point or cumulative mileage parameter. For each set of sample data, the behavioral operating parameters are introduced, namely the ambient temperature, power-on and power-off intervals, energy consumption parameters, etc., and finally the collection of each set of data is used as the sample data. The sample data is substituted into the multivariate regression prediction model and divided into training sets and test sets for model training, and finally the endurance attenuation model corresponding to the sample vehicle is obtained.

[0183] Finally, a similar method to the above is used to cluster the historical mileage parameters, historical energy consumption parameters, historical state of charge parameters and nominal physical parameters of different vehicle models for training and analysis, and a range attenuation model covering a variety of different vehicle models is obtained.

[0184] Model application phase:

[0185] The target vehicle analyzes and calculates the current cruising range and current energy consumption per 100 kilometers (i.e., current energy consumption per unit mileage) based on mileage parameters, energy consumption parameters, state of charge parameters, and nominal physical parameters. Then, the vehicle model, nominal physical parameters (including nominal cruising range, energy consumption per 100 kilometers, and nominal power), mileage parameters (including cumulative mileage, daily average mileage, and annual average mileage), current cruising range, current energy consumption per unit mileage, and behavioral operating parameters (including ambient temperature, power-on and power-off intervals, etc.) are substituted into the trained cruising range attenuation model for prediction, and the cruising range of the target vehicle at any node in the next year can be obtained. Specifically, the corresponding cruising range can be output based on the future time node or future mileage node that needs to be predicted.

[0186] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0187] Based on the same inventive concept, the embodiment of the present application also provides a range prediction device for implementing the range prediction method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more range prediction device embodiments provided below can refer to the limitations of the range prediction method above, and will not be repeated here.

[0188] See also Fig.14 The present application also provides a cruising range prediction device, including a parameter collection module 142 , a parameter calculation module 144 and a cruising range prediction module 146 .

[0189] The parameter acquisition module 142 is used to obtain the operating data and nominal physical parameters of the target vehicle; the parameter calculation module 144 is used to determine the current cruising range and the current unit mileage energy consumption based on the mileage parameters, energy consumption parameters and charge state parameters; the cruising range prediction module 146 is used to predict the cruising range of the target vehicle at any driving node within a preset time period in the future based on the nominal physical parameters, the current cruising range, the current unit mileage energy consumption and mileage parameters.

[0190] In some embodiments, the range prediction module 146 is also used to obtain the vehicle model information of the target vehicle; the vehicle model information, nominal physical parameters, current range, current unit mileage energy consumption and mileage parameters are input into a preset range attenuation model for prediction to obtain the range of the target vehicle at any driving node within a preset time period in the future.

[0191] In some embodiments, the cruising range prediction module 146 is also used to determine the cruising range of the target vehicle at a future time node or a future mileage node in combination with the future time node or future mileage node to be predicted.

[0192] See also Fig.15In some embodiments, the cruising range prediction device also includes a model training module 152 .

[0193] The model training module 152 is used to obtain the nominal physical parameters of the sample vehicle, as well as the historical operating data under each charging condition within a preset historical period; to analyze and process the historical mileage parameters, historical energy consumption parameters and historical state of charge parameters to determine the endurance data set and the unit mileage energy consumption data set; to combine the historical mileage parameters, the nominal physical parameters, the endurance data set and the unit mileage energy consumption data set to determine the sample data corresponding to the sample vehicle; to obtain a sample data set based on the sample data of multiple sample vehicles; to perform model training in combination with the sample data set and the preset training model to determine the endurance attenuation model.

[0194] In some embodiments, the model training module 152 is also used to calculate based on historical mileage parameters and historical state of charge parameters to determine the endurance data set; calculate based on historical mileage parameters, historical state of charge parameters and historical energy consumption parameters to determine the unit mileage energy consumption data set.

[0195] In some embodiments, the model training module 152 is also used to calculate the mileage difference between each charging condition and the previous charging condition based on historical mileage parameters; calculate the state of charge consumption value between each charging condition and the previous charging condition based on historical state of charge parameters; and perform analysis and calculation based on the mileage difference and the state of charge consumption value to determine the endurance data set.

[0196] In some embodiments, the model training module 152 is also used to determine the time difference between each charging condition and the previous charging condition; based on the time difference and the state of charge consumption value, data jump and loss judgment is performed on each charging condition; when the data jump and loss judgment is passed, analysis and calculation are performed based on the running mileage difference and the state of charge consumption value to determine the operation of the endurance data set.

[0197] In some embodiments, the model training module 152 is further used to perform analysis and calculation based on the running mileage difference and the state of charge consumption value to determine an original endurance data set; and to correct the original endurance data set to obtain an endurance data set.

[0198] In some embodiments, the model training module 152 is further used to modify the original endurance data set according to the nominal physical parameters to obtain the endurance data set.

[0199] In some embodiments, the model training module 152 is further used to perform filtering and convergence correction on the original endurance data set to obtain the endurance data set.

[0200] In some embodiments, the model training module 152 is also used to calculate the running mileage difference between each charging condition and the previous charging condition based on historical mileage parameters; calculate the charge state consumption value between each charging condition and the previous charging condition based on historical charge state parameters; and determine the unit mileage energy consumption data set based on the historical energy consumption parameters corresponding to each charging condition, as well as the running mileage difference and the charge state consumption value.

[0201] In some embodiments, the model training module 152 is also used to configure corresponding historical ambient temperature parameters for each operating point in the endurance data set, and to configure corresponding historical ambient temperature parameters for each operating point in the unit mileage energy consumption data set; based on the historical mileage parameters, nominal physical parameters, and the endurance data set and the unit mileage energy consumption data set after the historical ambient temperature parameters are configured, the sample data corresponding to the sample vehicle is determined.

[0202] In some embodiments, the model training module 152 is also used to combine historical mileage parameters, nominal physical parameters, endurance data sets, unit mileage energy consumption data sets, and behavioral operating condition parameters to determine sample data corresponding to the sample vehicle.

[0203] In some embodiments, the range prediction module 146 is also used to combine historical mileage parameters, nominal physical parameters, range data sets and unit mileage energy consumption data sets according to historical mileage parameters and / or historical time points to determine sample data corresponding to the sample vehicle.

[0204] Each module in the above-mentioned cruising range prediction device can be implemented in whole or in part by software, hardware or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each module.

[0205] The above-mentioned cruising range prediction device obtains the nominal physical parameters of the target vehicle, as well as the mileage parameters, energy consumption parameters, and state of charge parameters during the operation of the target vehicle, and performs secondary analysis and calculation in combination with the mileage parameters, energy consumption parameters, and state of charge parameters to obtain the current cruising range and current unit mileage energy consumption of the target vehicle in the current state. Finally, the nominal physical parameters, current cruising range, current unit mileage energy consumption, and mileage parameters are combined to predict the cruising range of the target vehicle at any driving node in the future preset time period. Through this solution, the current cruising range, current unit mileage energy consumption, mileage parameters corresponding to the actual operating state of the target vehicle, and the nominal physical parameters inherent to the target vehicle itself can be combined to achieve the prediction of the cruising range at a future moment.

[0206] The present application also provides a vehicle, including a battery management system, a battery, a drive system and a vehicle controller. The battery management system is arranged on the battery. The battery management system and the drive system are respectively connected to the vehicle controller. The vehicle controller is used to execute the steps of the above-mentioned cruising range prediction method.

[0207] Specifically, the method for predicting the cruising range is as shown in the above-mentioned embodiments and the accompanying drawings, and will not be repeated here. During the operation of the target vehicle, the nominal physical parameters of the target vehicle, as well as the mileage parameters, energy consumption parameters, and state of charge parameters are obtained, and the mileage parameters, energy consumption parameters, and state of charge parameters are combined for secondary analysis and calculation to obtain the current cruising range and current unit mileage energy consumption of the target vehicle in the current state. Finally, the nominal physical parameters, current cruising range, current unit mileage energy consumption, and mileage parameters are combined to predict the cruising range of the target vehicle at any driving node in the future preset time period. Through this solution, the current cruising range, current unit mileage energy consumption, mileage parameters corresponding to the actual operating state of the target vehicle, and the nominal physical parameters inherent in the target vehicle itself can be combined to achieve the prediction of the cruising range at a future moment.

[0208] In some embodiments, the present application provides a computer device, which may be a server, and its internal structure diagram may be as follows: Fig.16 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store vehicle operation data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for predicting a cruising range is implemented.

[0209] Those skilled in the art will understand that Fig.16 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0210] The present application also provides a computer device, including a memory and a processor, the memory storing a computer program, and the processor implementing the following steps of a range prediction method when executing the computer program: obtaining the operating data and nominal physical parameters of a target vehicle; determining the current range and the current energy consumption per unit mileage based on the mileage parameters, energy consumption parameters, and state of charge parameters; predicting the range of the target vehicle at any driving node within a preset time period in the future based on the nominal physical parameters, the current range, the current energy consumption per unit mileage, and the mileage parameters.

[0211] The present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the following steps of a method for predicting a range: obtaining operating data and nominal physical parameters of a target vehicle; determining a current range and current energy consumption per unit mileage based on mileage parameters, energy consumption parameters, and state of charge parameters; and predicting a range of the target vehicle at any driving node within a preset time period in the future based on the nominal physical parameters, current range, current energy consumption per unit mileage, and mileage parameters.

[0212] The present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the following steps of a range prediction method: obtaining operating data and nominal physical parameters of a target vehicle; determining a current range and current energy consumption per unit mileage based on mileage parameters, energy consumption parameters, and state of charge parameters; and predicting the range of the target vehicle at any driving node within a preset time period in the future based on the nominal physical parameters, current range, current energy consumption per unit mileage, and mileage parameters.

[0213] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0214] The above-mentioned computer equipment, storage medium and computer program product obtain the nominal physical parameters of the target vehicle, as well as the mileage parameters, energy consumption parameters and state of charge parameters during the operation of the target vehicle, and perform secondary analysis and calculation in combination with the mileage parameters, energy consumption parameters and state of charge parameters to obtain the current cruising range and current unit mileage energy consumption of the target vehicle in the current state. Finally, the nominal physical parameters, current cruising range, current unit mileage energy consumption and mileage parameters are combined to predict the cruising range of the target vehicle at any driving node in the future preset time period. Through this solution, the current cruising range, current unit mileage energy consumption, mileage parameters corresponding to the actual operating state of the target vehicle, and the nominal physical parameters inherent to the target vehicle itself can be combined to achieve the prediction of the cruising range at a future moment.

[0215] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and they should all be included in the scope of the claims and specification of the present application. In particular, as long as there is no structural conflict, the various technical features mentioned in the various embodiments can be combined in any way. The present application is not limited to the specific embodiments disclosed herein, but includes all technical solutions that fall within the scope of the claims.

Claims

1. A method for predicting a cruising range, characterized in that: include: When the target vehicle is running, obtaining vehicle model information, operating data, and nominal physical parameters of the target vehicle; The operating data includes mileage parameters, energy consumption parameters and state of charge parameters; Determine the current cruising range and the current energy consumption per unit mileage according to the mileage parameter, the energy consumption parameter and the state of charge parameter; The vehicle model information, the nominal physical parameters, the current cruising range, the current energy consumption per unit mileage and the mileage parameters are input into a preset cruising range attenuation model for prediction to obtain the cruising range of the target vehicle at any driving node within a preset time period in the future.

2. The method for predicting the cruising range according to claim 1, characterized in that: The predicting of the cruising range of the target vehicle at any driving node within a preset time period in the future includes: In combination with the future time node or future mileage node to be predicted, the cruising range of the target vehicle at the future time node or the cruising range at the future mileage node is determined.

3. The method for predicting the cruising range according to claim 1, characterized in that: The method for determining the endurance attenuation model includes: Obtaining nominal physical parameters of the sample vehicle and historical operating data under various charging conditions within a preset historical period; the historical operating data includes historical mileage parameters, historical energy consumption parameters, and historical state of charge parameters, and the vehicle type of the sample vehicle is the same as the vehicle type of the target vehicle; Analyze and process the historical mileage parameter, the historical energy consumption parameter, and the historical state of charge parameter to determine a cruising range data set and a unit mileage energy consumption data set; The historical mileage parameter, the nominal physical parameter, the endurance data set and the unit mileage energy consumption data set are combined to determine sample data corresponding to the sample vehicle; Obtaining a sample data set according to the sample data of the plurality of sample vehicles; Model training is performed in combination with the sample data set and a preset training model to determine a range attenuation model.

4. The method for predicting the cruising range according to claim 3, characterized in that: The analyzing and processing according to the historical mileage parameter, the historical energy consumption parameter and the historical state of charge parameter to determine the endurance data set and the unit mileage energy consumption data set includes: Calculate according to the historical mileage parameter and the historical state of charge parameter to determine the endurance data set; A unit mileage energy consumption data set is determined by performing calculations based on the historical mileage parameter, the historical state of charge parameter, and the historical energy consumption parameter.

5. The method for predicting the cruising range according to claim 4, characterized in that: The calculating according to the historical mileage parameter and the historical state of charge parameter to determine the endurance data set includes: Calculate the running mileage difference between each charging condition and the last charging condition according to the historical mileage parameter; Calculating the state of charge consumption value between each charging condition and the previous charging condition according to the historical state of charge parameters; An analysis and calculation is performed based on the running mileage difference and the state of charge consumption value to determine a cruising range data set.

6. The method for predicting the cruising range according to claim 5, characterized in that: The method further comprises: Determine the time difference between each charging condition and the previous charging condition; According to the time difference and the state of charge consumption value, data jump and loss determination is performed for each charging condition; In the case where the data jump and loss determination is passed, the step of analyzing and calculating according to the running mileage difference and the state of charge consumption value to determine the endurance data set is executed.

7. The method for predicting the cruising range according to claim 5, characterized in that: The analyzing and calculating according to the running mileage difference and the state of charge consumption value to determine the endurance data set includes: Analyze and calculate the running mileage difference and the state of charge consumption value to determine an original endurance data set; The original endurance data set is corrected to obtain an endurance data set.

8. The method for predicting the cruising range according to claim 7, characterized in that: The correcting the original endurance data set to obtain the endurance data set includes: The original endurance data set is corrected according to the nominal physical parameters to obtain an endurance data set.

9. The method for predicting the cruising range according to claim 7, characterized in that: The correcting the original endurance data set to obtain the endurance data set includes: The original endurance data set is filtered and converged to obtain an endurance data set.

10. The method for predicting the cruising range according to claim 4, characterized in that: The calculating according to the historical mileage parameter, the historical state of charge parameter and the historical energy consumption parameter to determine the unit mileage energy consumption data set includes: Calculate the running mileage difference between each charging condition and the last charging condition according to the historical mileage parameter; Calculating the state of charge consumption value between each charging condition and the previous charging condition according to the historical state of charge parameters; The unit mileage energy consumption data set is determined by analyzing and calculating the historical energy consumption parameters corresponding to each charging condition, the running mileage difference, and the state of charge consumption value.

11. The method for predicting the cruising range according to claim 5 or 10, characterized in that: The method further comprises: When the running mileage difference is greater than or equal to the preset mileage threshold, and the state of charge consumption value is greater than or equal to the preset charge threshold, determining the endurance data set or the unit mileage energy consumption data set by using the running mileage difference and the state of charge consumption value; When the running mileage difference is less than the preset mileage threshold, and / or the state of charge consumption value is less than the preset charge threshold, the running mileage difference and / or the state of charge consumption value are spliced ​​to determine the endurance data set or the unit mileage energy consumption data set based on the splicing result.

12. The method for predicting the range of a vehicle according to any one of claims 3 to 10, characterized in that: The historical operation data also includes historical ambient temperature parameters; the historical mileage parameters, the nominal physical parameters, the endurance data set, and the unit mileage energy consumption data set are combined to determine the sample data corresponding to the sample vehicle, including: respectively configuring the corresponding historical ambient temperature parameters for each operating point in the endurance data set, and respectively configuring the corresponding historical ambient temperature parameters for each operating point in the unit mileage energy consumption data set; The sample data corresponding to the sample vehicle is determined by combining the historical mileage parameters, the nominal physical parameters, and the endurance data set and the unit mileage energy consumption data set after configuring the historical ambient temperature parameters.

13. The method for predicting the range of a vehicle according to any one of claims 3 to 10, characterized in that: The historical operation data also includes behavior condition parameters, and the historical mileage parameters, the nominal physical parameters, the endurance data set, and the unit mileage energy consumption data set are combined to determine the sample data corresponding to the sample vehicle, including: The historical mileage parameters, the nominal physical parameters, the endurance data set, the unit mileage energy consumption data set, and the behavior condition parameters are combined to determine sample data corresponding to the sample vehicle.

14. The method for predicting the range of a vehicle according to any one of claims 3 to 10, characterized in that: The combining of the historical mileage parameter, the nominal physical parameter, the endurance data set, and the unit mileage energy consumption data set to determine the sample data corresponding to the sample vehicle includes: According to the historical mileage parameters and / or historical time points, the historical mileage parameters, the nominal physical parameters, the endurance data set and the unit mileage energy consumption data set are combined to obtain sample data corresponding to the sample vehicle.

15. A cruising range prediction device, characterized in that: include: A parameter acquisition module, used to obtain the model information, operation data and nominal physical parameters of the target vehicle when the target vehicle is running; The operating data includes mileage parameters, energy consumption parameters and state of charge parameters; A parameter calculation module, used to determine the current cruising range and the current energy consumption per unit mileage according to the mileage parameter, the energy consumption parameter and the state of charge parameter; The cruising range prediction module is used to input the vehicle model information, the nominal physical parameters, the current cruising range, the current unit mileage energy consumption and the mileage parameters into a preset cruising range attenuation model for prediction, so as to obtain the cruising range of the target vehicle at any driving node within a preset time period in the future.

16. A vehicle, characterized in that: It includes a battery management system, a battery, a drive system and a vehicle controller, wherein the battery management system is arranged on the battery, the battery management system and the drive system are respectively connected to the vehicle controller, and the vehicle controller is used to execute the steps of the cruising range prediction method according to any one of claims 1 to 14.

17. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the cruising range prediction method described in any one of claims 1 to 14 are implemented.

18. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the cruising range prediction method described in any one of claims 1 to 14 are implemented.

19. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the range prediction method described in any one of claims 1 to 14 are implemented.

Images