Energy consumption determination method, device, electronic device and storage medium

Abstract

The present application relates to an energy consumption determination method, device, electronic device and storage medium, and relates to the field of automobiles. The method includes: determining the battery net energy consumption corresponding to each sub-time period included in the target time period according to the driving data of the vehicle in the target time period; the battery net energy consumption is determined based on the energy consumed by the vehicle's battery during discharge and the energy recovered through the energy recovery system, and by correcting the battery net energy consumption corresponding to each sub-time period according to the battery net energy consumption corresponding to multiple sub-time periods and the change in battery SOC, the accuracy of energy consumption estimation can be improved.

Description

Technical Field

[0001] The present application relates to the field of automobiles, and particularly relates to a method, device, electronic device and storage medium for determining energy consumption. Background Art

[0002] With the increasing shortage of global fossil energy and the intensification of greenhouse gas emissions problems, the automotive industry is accelerating its transformation towards electrification. In this context, accurately obtaining vehicle driving energy consumption data has become a core requirement in research fields such as the formulation of energy-saving control strategies, accurate estimation of driving range, and in-depth analysis of vehicle economy.

[0003] Currently, the estimation method of electric vehicle driving energy consumption mainly estimates energy consumption by calculating the product of the change in battery state of charge (SOC) and the rated total electrical energy. This method has a simple principle and is easy to implement in engineering, but its estimation accuracy highly depends on the accuracy of SOC data. Since there are estimation errors in the battery SOC itself, and rounding errors will also occur during the storage of SOC data, which will affect the accuracy of energy consumption estimation. Especially for short driving segments with a small change in SOC, the rounding error of SOC will be significantly amplified, resulting in a deviation between the energy consumption estimation result and the actual energy consumption value. Therefore, how to improve the accuracy of electric vehicle driving energy consumption estimation has become an urgent technical problem to be solved. Summary of the Invention

[0004] One of the purposes of the present invention is to provide a method, device, electronic device and storage medium for determining energy consumption, which can improve the accuracy of energy consumption estimation.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0006] According to the first aspect provided by the present invention, there is provided a method for determining energy consumption, the method including: determining the battery net energy consumption corresponding to each sub-time period included in the target time period according to the driving data of the vehicle in the target time period; the battery net energy consumption is determined based on the energy consumed by the vehicle battery during discharge and the energy recovered through the energy recovery system. Correct the battery net energy consumption corresponding to each sub-time period according to the battery net energy consumption corresponding to multiple sub-time periods and the change in battery SOC.

[0007] According to the above technical means, the present application can determine the net battery energy consumption corresponding to each sub-time period based on the vehicle driving data related to the net battery energy consumption, so as to reduce the error introduced when directly calculating the energy consumption using the change in battery SOC, and can also understand the energy consumption of the vehicle at different times. Then, by combining the change in battery SOC corresponding to multiple sub-time periods, the net battery energy consumption corresponding to each sub-time period is corrected. The corrected net battery energy consumption can more accurately reflect the actual battery consumption, thereby improving the accuracy and reliability of the energy consumption data.

[0008] In a possible way, according to the net battery energy consumption corresponding to multiple sub-time periods and the change in battery SOC, correcting the net battery energy consumption corresponding to each sub-time period includes: when the sum of the changes in battery SOC corresponding to multiple sub-time periods is greater than or equal to the SOC change sum threshold, correcting the net battery energy consumption corresponding to each sub-time period according to the net battery energy consumption corresponding to multiple sub-time periods and the change in battery SOC.

[0009] According to the above technical means, the present application can correct the net battery energy consumption of each sub-time period when the change in battery SOC corresponding to multiple sub-time periods meets a predetermined condition, so as to improve the energy consumption correction efficiency.

[0010] In a possible way, the driving data includes the nominal energy of the battery. On this basis, for any target sub-time period among multiple sub-time periods, correcting the net battery energy consumption corresponding to each sub-time period according to the net battery energy consumption corresponding to multiple sub-time periods and the change in battery SOC includes: determining an energy consumption correction coefficient according to the nominal energy, the change in battery SOC corresponding to multiple sub-time periods, and the net battery energy consumption. Based on the energy consumption correction coefficient, the net battery energy consumption corresponding to the target sub-time period is corrected.

[0011] According to the above technical means, the present application determines the energy consumption correction coefficient by combining multi-dimensional information such as the nominal energy of the vehicle battery, the change in battery SOC corresponding to multiple sub-time periods, and the net battery energy consumption, which can comprehensively consider the actual performance of the battery under different working conditions. Using the energy consumption correction coefficient to correct the net battery energy consumption can make the corrected net battery energy consumption more in line with the actual situation. In addition, by analyzing the corrected net battery energy consumption data, the corresponding power control strategy can be optimized, thereby improving the energy utilization efficiency and reducing the overall energy consumption.

[0012] In a possible way, according to the nominal energy, the change in battery SOC corresponding to multiple sub - time periods, and the net battery energy consumption, determining an energy consumption correction coefficient includes: determining the product of the nominal energy and the sum of the change in battery SOC corresponding to each sub - time period as the target product. Determining the ratio of the target product to the sum of the net battery energy consumption corresponding to each sub - time period as the energy consumption correction coefficient.

[0013] In a possible way, for any target sub - time period among multiple sub - time periods, the above - mentioned method further includes: screening out at least one first sub - time period from the multiple sub - time periods; the first sub - time period is adjacent to the target sub - time period, and the net battery energy consumption corresponding to the first sub - time period is not zero. On this basis, the above - mentioned correction of the net battery energy consumption corresponding to each sub - time period according to the net battery energy consumption and the change in battery SOC corresponding to multiple sub - time periods includes: correcting the net battery energy consumption corresponding to the target sub - time period according to the net battery energy consumption and the change in battery SOC corresponding to at least one first sub - time period and the target sub - time period.

[0014] According to the above - mentioned technical means, the present application can screen out the first sub - time periods that are adjacent to the target sub - time period and have non - zero net battery energy consumption from multiple sub - time periods, so as to select effective and relevant sub - time periods to the target sub - time period. After that, by analyzing adjacent sub - time periods with actual energy consumption changes, the change trend of the net battery energy consumption in the target sub - time period can be understood more accurately, so as to correct the net battery energy consumption of the target sub - time period more precisely.

[0015] In a possible way, screening out at least one first sub - time period from multiple sub - time periods includes: taking the target sub - time period as the starting time period, and sequentially adding sub - time periods with non - zero net battery energy consumption to the first set and sub - time periods with zero net battery energy consumption to the second set in a preset time order until a preset screening condition is met.

[0016] According to the above - mentioned technical means, the present application can traverse each sub - time period in a preset time order, which can ensure the coherence and integrity of the data. At the same time, it can also distinguish the net battery energy consumption status of the vehicle in different sub - time periods, and quickly screen out sub - time periods that are adjacent to the target sub - time period and have non - zero net battery energy consumption.

[0017] In a possible way, the preset screening condition satisfies any one of the following: the sum of the change in battery SOC corresponding to the first set is greater than or equal to the SOC change sum threshold; the sum of the net battery output energy consumption corresponding to the second set is greater than or equal to the battery output net energy consumption sum threshold, and the battery output net energy consumption is determined based on the battery state data and the cumulative mileage increase amount corresponding to each sub - time period included in the second set.

[0018] According to the above technical means, the present application can stop traversing and screening according to preset screening conditions, thereby reducing resource waste.

[0019] In a possible way, the driving data includes the change amount of the battery SOC corresponding to each sub-time period. On this basis, according to the driving data of the vehicle in the target time period, determining the net battery energy consumption corresponding to each sub-time period in the target time period includes: for each sub-time period in the target time period, based on the change amount of the battery SOC corresponding to the sub-time period, determining the net battery energy consumption corresponding to the sub-time period.

[0020] According to the above technical means, the present application can determine the net battery energy consumption corresponding to each sub-time period based on the change amount of the battery SOC corresponding to each sub-time period, providing support for subsequent correction of the net battery energy consumption.

[0021] In a possible way, determining the net battery energy consumption corresponding to the sub-time period based on the change amount of the battery SOC corresponding to the sub-time period includes: when the change amount of the battery SOC corresponding to the sub-time period is less than the SOC change amount threshold, using a preset energy consumption estimation model to determine the net battery energy consumption corresponding to the sub-time period.

[0022] According to the above technical means, the present application can use a preset energy consumption estimation model to effectively handle the problem of estimating the net battery energy consumption when the change amount of the battery SOC is small, avoiding the problem that the net battery energy consumption cannot be accurately determined due to the insignificant change of the battery SOC. In addition, by determining the net battery energy consumption through the preset energy consumption estimation model, it is also possible to reduce the SOC estimation error and rounding error introduced when directly using the SOC change amount to calculate the energy consumption.

[0023] In a possible way, the driving data further includes the motor state data, battery state data, cumulative mileage increase amount, and driving duration corresponding to each sub-time period. On this basis, when the change amount of the battery SOC corresponding to the sub-time period is less than the SOC change amount threshold, using a preset energy consumption estimation model to determine the net battery energy consumption corresponding to the sub-time period includes: when the change amount of the battery SOC corresponding to the sub-time period is less than the SOC change amount threshold, based on the change amount of the battery SOC corresponding to the sub-time period, cumulative mileage increase amount, driving duration, motor state data, and battery state data corresponding to the sub-time period, determining the target energy consumption corresponding to the sub-time period; the target energy consumption is other energy consumption except the net battery energy consumption. Inputting the target energy consumption and the driving data corresponding to the sub-time period into the preset energy consumption estimation model to obtain the net battery energy consumption corresponding to the sub-time period.

[0024] According to the above technical means, the present application can comprehensively analyze and calculate the net battery energy consumption of the sub-time period by combining other energy consumption data related to the net battery energy consumption and multi-dimensional data information such as driving data, and can obtain a net battery energy consumption value closer to the actual situation, improving the accuracy of energy consumption calculation.

[0025] In one possible way, the driving data further includes the cumulative mileage increase, battery state data, and the nominal energy of the battery. On this basis, based on the change in battery SOC corresponding to the sub-time period, the net battery energy consumption corresponding to the sub-time period is determined, including: when the change in battery SOC corresponding to the sub-time period is greater than or equal to the SOC change threshold, based on the change in battery SOC corresponding to the sub-time period, the cumulative mileage increase, and the nominal energy of the power battery, the net battery energy consumption corresponding to the sub-time period is determined.

[0026] In one possible way, the net battery energy consumption corresponding to the sub-time period satisfies the following formula:

[0027] ;

[0028] Wherein, is the net battery energy consumption, is the change in battery SOC, is the nominal energy, is the cumulative mileage increase.

[0029] According to the above technical means, the present application can quickly determine the net battery energy consumption corresponding to each sub-time period based on the net battery energy consumption calculation formula.

[0030] In one possible way, each sub-time period includes multiple data frames, each data frame includes the driving data of the vehicle at a certain moment, and each sub-time period satisfies one or more of the following conditions: the time interval between any two adjacent data frames in the sub-time period is less than the interval threshold; the roads where the vehicle is located corresponding to any two adjacent data frames in the sub-time period are the same.

[0031] According to the above technical means, the present application can determine the driving segment to which the sub-time period belongs according to the time interval between any two adjacent data frames in the sub-time period, and / or the roads where the vehicle is located corresponding to any two adjacent data frames, so as to realize the segment division of the driving data.

[0032] According to the second aspect provided by the present invention, an energy consumption determination device is provided, and the device includes: a determination unit and a processing unit.

[0033] The determination unit is configured to determine the net battery energy consumption corresponding to each sub-time period in a plurality of sub-time periods included in the target time period according to the driving data of the vehicle in the target time period; the net battery energy consumption is determined based on the energy consumed by the vehicle's battery during discharge and the energy recovered through the energy recovery system.

[0034] The processing unit is configured to correct the net battery energy consumption corresponding to each sub-time period according to the net battery energy consumption corresponding to a plurality of sub-time periods and the change in battery SOC.

[0035] In a possible way, the processing unit is further configured to, when the sum of the battery SOC change amounts corresponding to multiple sub - time periods is greater than or equal to the SOC change amount sum threshold, correct the battery net energy consumption corresponding to each sub - time period according to the battery net energy consumption and the battery SOC change amount corresponding to the multiple sub - time periods.

[0036] In a possible way, the driving data includes the nominal energy of the battery. On this basis, for any target sub - time period among the multiple sub - time periods, the determining unit is further configured to determine an energy consumption correction coefficient according to the nominal energy, the battery SOC change amounts corresponding to the multiple sub - time periods, and the battery net energy consumption.

[0037] In a possible way, the processing unit is further configured to correct the battery net energy consumption corresponding to the target sub - time period based on the energy consumption correction coefficient.

[0038] In a possible way, the determining unit is further configured to determine the product of the nominal energy and the sum of the battery SOC change amounts corresponding to each sub - time period as the target product, and determine the ratio of the target product to the sum of the battery net energy consumptions corresponding to each sub - time period as the energy consumption correction coefficient.

[0039] In a possible way, for any target sub - time period among the multiple sub - time periods, the processing unit is further configured to correct the battery net energy consumption corresponding to the target sub - time period according to the battery net energy consumption and the battery SOC change amount corresponding to at least one first sub - time period and the target sub - time period.

[0040] In a possible way, the driving data includes the battery SOC change amount corresponding to each sub - time period. On this basis, the determining unit is further configured to, for each sub - time period within the target time period, determine the battery net energy consumption corresponding to the sub - time period based on the battery SOC change amount corresponding to the sub - time period.

[0041] In a possible way, the determining unit is further configured to, when the battery SOC change amount corresponding to the sub - time period is less than the SOC change amount threshold, use a preset energy consumption estimation model to determine the battery net energy consumption corresponding to the sub - time period.

[0042] In a possible way, the driving data further includes the motor state data, the battery state data, the cumulative mileage increase amount, and the driving duration corresponding to each sub - time period. On this basis, the determining unit is further configured to, when the battery SOC change amount corresponding to the sub - time period is less than the SOC change amount threshold, determine the target energy consumption corresponding to the sub - time period based on the battery SOC change amount, the cumulative mileage increase amount, the driving duration, the motor state data, and the battery state data corresponding to the sub - time period.

[0043] In one possible way, the driving data further includes the cumulative mileage increase, battery state data, and the nominal energy of the battery. On this basis, the determination unit is further configured to, when the change in battery SOC corresponding to the sub-time period is greater than or equal to the SOC change threshold, determine the net battery energy consumption corresponding to the sub-time period based on the change in battery SOC, the cumulative mileage increase, and the nominal energy of the power battery corresponding to the sub-time period.

[0044] According to a third aspect of the present invention, there is provided an electronic device, including: a processor; a memory for storing instructions executable by the processor; wherein the processor is configured to execute the instructions to implement the method according to the first aspect and any possible implementation manner thereof.

[0045] According to a fourth aspect of the present invention, there is provided a computer-readable storage medium, when the instructions in the computer-readable storage medium are executed by the processor of the processing device, enabling the processing device to execute the method according to the first aspect and any possible implementation manner thereof.

[0046] According to a fifth aspect of the present invention, there is provided a computer program product, the computer program product includes computer instructions, when the computer instructions run on the processing device, enabling the processing device to execute the method according to the first aspect and any possible implementation manner thereof.

[0047] Thus, the above technical features of the present invention have the following beneficial effects:

[0048] (1) It is possible to determine the net battery energy consumption corresponding to each sub-time period based on the vehicle driving data related to the net battery energy consumption, so as to reduce the error introduced when directly calculating the energy consumption using the change in battery SOC, and it is also possible to understand the energy consumption situation of the vehicle at different times. Then, the net battery energy consumption corresponding to each sub-time period is corrected by combining the changes in battery SOC corresponding to multiple sub-time periods. The corrected net battery energy consumption can more accurately reflect the actual battery consumption, thereby improving the accuracy and reliability of the energy consumption data.

[0049] (2) It is possible to correct the net battery energy consumption of each sub-time period when the changes in battery SOC corresponding to multiple sub-time periods meet a predetermined condition, so as to improve the energy consumption correction efficiency.

[0050] (3) By combining multi-dimensional information such as the nominal energy of the vehicle battery, the change in battery SOC corresponding to multiple sub-time periods, and the net battery energy consumption, the energy consumption correction coefficient can be determined. This can comprehensively consider the actual performance of the battery under different working conditions. Using the energy consumption correction coefficient to correct the net battery energy consumption can make the corrected net battery energy consumption more in line with the actual situation. In addition, by analyzing the corrected net battery energy consumption data, the corresponding power control strategy can be optimized, thereby improving the energy utilization efficiency and reducing the overall energy consumption.

[0051] (4) The first sub-time period adjacent to the target sub-time period and with non-zero net battery energy consumption can be selected from multiple sub-time periods, so that effective and relevant sub-time periods related to the target sub-time period can be selected. After that, by analyzing adjacent sub-time periods with actual energy consumption changes, the change trend of the net battery energy consumption in the target sub-time period can be understood more accurately, so as to correct the net battery energy consumption in the target sub-time period more precisely.

[0052] (5) Each sub-time period can be traversed in sequence according to the preset time order, which can ensure the coherence and integrity of the data. At the same time, it can also distinguish the net battery energy consumption status of the vehicle in different sub-time periods, and quickly screen out the sub-time period adjacent to the target sub-time period and with non-zero net battery energy consumption.

[0053] (6) The traversal and screening can be stopped according to the preset screening conditions, thereby reducing resource waste.

[0054] (7) Based on the change in battery SOC corresponding to each sub-time period, the net battery energy consumption corresponding to each sub-time period can be determined, providing support for subsequent correction of the net battery energy consumption.

[0055] (8) The preset energy consumption estimation model can be used to effectively handle the problem of estimating the net battery energy consumption when the change in battery SOC is small, avoiding the problem that the net battery energy consumption cannot be accurately determined due to the insignificant change in battery SOC. In addition, by determining the net battery energy consumption through the preset energy consumption estimation model, the SOC estimation error and rounding error introduced when directly using the change in SOC to calculate the energy consumption can also be reduced.

[0056] (9) By combining other energy consumption data related to the net battery energy consumption and multi-dimensional data information such as driving data, the net battery energy consumption of the sub-time period can be comprehensively analyzed and calculated, and a net battery energy consumption value closer to the actual situation can be obtained, improving the accuracy of energy consumption calculation.

[0057] (10) Based on the net battery energy consumption calculation formula, the net battery energy consumption corresponding to each sub-time period can be quickly determined.

[0058] (11) The driving segment to which the sub - time period belongs can be determined according to the time interval between any two adjacent data frames in the sub - time period and / or the roads where the vehicles corresponding to any two adjacent data frames are located, so as to realize the segmentation of driving data. Description of the Drawings

[0059] Figure 1 It is an architecture diagram of an energy consumption determination system provided by an embodiment of the present application;

[0060] Figure 2 It is a schematic flowchart of a method for determining energy consumption provided by an embodiment of the present application;

[0061] Figure 3 It is a schematic flowchart of another method for determining energy consumption provided by an embodiment of the present application;

[0062] Figure 4 It is a schematic flowchart of determining a preset energy consumption estimation model provided by an embodiment of the present application;

[0063] Figure 5 It is a schematic flowchart of determining another preset energy consumption estimation model provided by an embodiment of the present application;

[0064] Figure 6 It is a schematic structural diagram of an energy consumption determination device provided by an embodiment of the present application;

[0065] Figure 7 It is a block diagram of an electronic device provided by an embodiment of the present application. Detailed Embodiments

[0066] In order to enable those of ordinary skill in the art to better understand the technical solutions of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0067] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above - mentioned drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such used data can be interchanged under appropriate circumstances so that the embodiments of the present application described here can be implemented in an order different from those illustrated or described here. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are only examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0068] In the embodiments of the present application, words such as "exemplary", "such as", or "for example" are used to represent examples, illustrations, or explanations. Any embodiment or design solution described as "exemplary", "such as", or "for example" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "exemplary", "such as", or "for example" is intended to present relevant concepts in a specific manner.

[0069] Next, the technical solutions in the embodiments of the present application will be described with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments.

[0070] Figure 1 The architecture diagram of an energy consumption determination system provided for the embodiments of the present application is shown as Figure 1 shown, and the system architecture includes: a server 101.

[0071] Among them, the server 101 can be a high-performance server that provides various services on the Internet, can be an independent physical server, can also be a server cluster composed of multiple physical servers, or can be at least one of cloud servers that provide basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks, and big data or artificial intelligence platforms. The embodiments of the present application do not limit this. Of course, the server can also include other functions to provide more comprehensive and diverse services.

[0072] The server 101 in the embodiments of the present application can be a single server, can also be a server cluster, or can be a cloud server. The embodiments of the present application do not limit this.

[0073] In the embodiments of the present application, the server 101 can determine the battery net energy consumption corresponding to each sub-time period included in the target time period according to the driving data of the vehicle in the target time period. Then, the server can correct the battery net energy consumption corresponding to each sub-time period according to the battery net energy consumption corresponding to multiple sub-time periods and the change in battery SOC to obtain a battery net energy consumption that more conforms to the actual situation.

[0074] The energy consumption determination system provided by the embodiments of this application can be configured in a vehicle. A vehicle can also be referred to as a means of transportation (vehicle), a mobile carrier, an electric vehicle (EV), a hybrid electric vehicle (HEV), a plug-in hybrid electric vehicle (PHEV), a fuel cell vehicle (FCV), an autonomous vehicle, an intelligent and connected vehicle (ICV), a driverless vehicle, etc.

[0075] In the embodiments of this application, the vehicle can be a sedan, a sport utility vehicle (SUV), a truck, an electric vehicle, a motorcycle, a tricycle, a special vehicle (such as an ambulance, a fire truck, a police car, etc.), a driverless taxi, an intelligent and connected bus, an autonomous logistics vehicle, an electric truck, etc. In addition, this method is also applicable to various special vehicles, such as agricultural vehicles, mining vehicles, forestry vehicles, airport vehicles, port vehicles, etc. This application does not make specific limitations in this regard.

[0076] For the sake of easy understanding, the energy consumption determination method provided by this application will be specifically introduced below in conjunction with the accompanying drawings.

[0077] Figure 2 It is a schematic flowchart of an energy consumption determination method provided by the embodiments of this application. As Figure 2 shown, this method is executed by the Figure 1 shown server, and this method includes:

[0078] S201. According to the driving data of the vehicle in the target time period, determine the net battery energy consumption corresponding to each sub-time period included in the target time period.

[0079] Among them, the net battery energy consumption is determined based on the energy consumed by the vehicle's battery during discharge and the energy recovered by the energy recovery system. The energy consumed by the battery during discharge refers to the energy released by the battery during vehicle driving to drive the vehicle and maintain the operation of various vehicle systems. The energy recovered by the energy recovery system refers to the energy that the drive motor can switch to the generator mode during vehicle braking, deceleration, or downhill, etc., convert the vehicle's kinetic energy or gravitational potential energy into electrical energy, and feedback it back to the battery.

[0080] In the embodiments of the present application, the net energy consumption of the battery may include the internal energy consumption of the battery and the net output energy consumption of the battery. The internal energy consumption of the battery refers to the energy loss caused by the physical and chemical characteristics inside the battery during the charging and discharging process, mainly including internal resistance loss, chemical reaction loss, self-discharge loss, temperature-related loss, charging and discharging efficiency loss, and battery aging loss, etc. The net output energy consumption of the battery is the battery energy consumption other than the internal energy consumption of the battery in the net energy consumption of the battery.

[0081] In the embodiments of the present application, as shown in Table 1, the driving data of the vehicle may include but are not limited to data acquisition time, driving motor speed, driving motor torque, input voltage of the motor controller, DC bus current of the motor controller, vehicle position (such as longitude, latitude), vehicle status, charging status, vehicle speed, cumulative mileage, total voltage, total current, and SOC. For example, Table 1 shows the vehicle driving data collected based on the GB / T32960 standard, and each row may represent a data frame.

[0082] Table 1 Vehicle driving data

[0083]

[0084] In a possible implementation manner, the historical driving data of the vehicle is stored in the server. The server can screen out the vehicle driving data within the target time period from the historical driving data. Then, the server can determine the battery net energy consumption corresponding to each sub-time period based on the vehicle driving data included in each sub-time period within the target time period.

[0085] Exemplarily, assume that the three data frames in Table 1 belong to a sub-time period. The server can determine the battery net energy consumption corresponding to the sub-time period according to the multiple driving data included in the above three data frames.

[0086] It should be noted that the battery net energy consumption corresponding to each sub-time period is the battery net energy consumption per unit mileage.

[0087] S202. Modify the battery net energy consumption corresponding to each sub-time period according to the battery net energy consumption corresponding to multiple sub-time periods and the change amount of the battery SOC.

[0088] In a possible implementation manner, the server can determine the total sum of the change amounts of the battery SOC corresponding to multiple sub-time periods within the target time period. When the total sum of the change amounts of the battery SOC corresponding to multiple sub-time periods is greater than or equal to the SOC change amount sum threshold, the server can modify the battery net energy consumption corresponding to each sub-time period according to the battery net energy consumption corresponding to multiple sub-time periods and the change amount of the battery SOC to obtain the modified battery net energy consumption.

[0089] In another possible implementation, when the sum of the battery SOC change amounts corresponding to multiple sub-time periods is less than the SOC change amount sum threshold, the server does not correct the battery net energy consumption corresponding to the sub-time periods in the multiple sub-time periods. That is, the server can determine the battery net energy consumption corresponding to each sub-time period obtained through the above S201 as the final battery net energy consumption.

[0090] Based on the above technical means, the server can determine the battery net energy consumption corresponding to each sub-time period based on multiple vehicle driving data related to the battery net energy consumption, so as to effectively reduce the error introduced when calculating energy consumption only using the battery SOC change amount. In addition, when the sum of the battery SOC change amounts corresponding to multiple sub-time periods is greater than or equal to the SOC change amount sum threshold, the server can combine the battery SOC change amounts and the battery net energy consumption corresponding to multiple sub-time periods to correct the battery net energy consumption corresponding to each sub-time period. In this way, the corrected battery net energy consumption can more accurately reflect the actual battery energy consumption, thereby improving the accuracy and credibility of the energy consumption data, and can also screen out the sub-time periods that need to correct the battery net energy consumption to reduce resource waste. At the same time, the battery net energy consumption determined by the above method includes the internal energy consumption of the battery, so that the battery energy consumption data can still maintain a high degree of accuracy under conditions such as low temperature and battery aging.

[0091] In some embodiments, the driving data may further include the nominal energy of the battery. On this basis, for any target sub-time period in the multiple sub-time periods, in the above S202, according to the battery net energy consumption and the battery SOC change amount corresponding to the multiple sub-time periods, correcting the battery net energy consumption corresponding to each sub-time period may specifically include: the server can determine an energy consumption correction coefficient according to the nominal energy of the battery, the battery SOC change amount corresponding to the multiple sub-time periods, and the battery net energy consumption. After that, the server can correct the battery net energy consumption corresponding to the target sub-time period based on the energy consumption correction coefficient.

[0092] In the embodiments of the present application, the nominal energy of the battery refers to the total electric energy that the vehicle's battery can store or release under standard conditions, usually in kilowatt-hours (kWh).

[0093] In one possible implementation, the server can determine the product of the nominal energy and the sum of the battery SOC change amounts corresponding to each sub-time period as the target product. After that, the server can determine the ratio of the target product to the sum of the battery net energy consumptions corresponding to each sub-time period as the energy consumption correction coefficient. Finally, the server can use the product of the energy consumption correction coefficient and the battery net energy consumption corresponding to the target sub-time period as the corrected battery net energy consumption (hereinafter referred to as the target battery net energy consumption) corresponding to the target sub-time period.

[0094] Exemplarily, the target sub-time period The calculation formula for the corresponding net energy consumption of the target battery can refer to the following Formula 1.

[0095] (Formula 1).

[0096] Wherein, is the net energy consumption of the target battery corresponding to the target sub - time period is the net energy consumption of the battery corresponding to the target sub - time period (net energy consumption of the battery per unit mileage), is the nominal energy, is the change in battery SOC corresponding to the th sub - time period within the target time period, is the th net energy consumption per unit mileage corresponding to the sub - time period, is the th cumulative mileage increase corresponding to the sub - time period, is the th total net energy consumption of the battery corresponding to the sub - time period. , is an integer.

[0097] Based on the above - mentioned technical means, the present application determines the energy consumption correction coefficient by combining multi - dimensional information such as the nominal energy of the battery, the change in battery SOC corresponding to multiple sub - time periods, and the net energy consumption of the battery. In this way, the actual performance of the battery under different working conditions can be comprehensively considered. Then, using the energy consumption correction coefficient to correct the net energy consumption of the battery can make the corrected net energy consumption of the battery more in line with the actual situation. In addition, by analyzing the corrected net energy consumption data of the battery, it can provide support for optimizing the power control strategy, thereby improving the energy utilization efficiency and reducing the overall energy consumption.

[0098] In some embodiments, for any target sub - time period among multiple sub - time periods, in the above S202, according to the net energy consumption of the battery corresponding to multiple sub - time periods and the change in battery SOC, correcting the net energy consumption of the battery corresponding to each sub - time period may specifically further include: The server can screen out at least one first sub - time period from multiple sub - time periods. Then, the server can correct the net energy consumption of the target sub - time period according to the net energy consumption and the change in battery SOC corresponding to at least one first sub - time period and the target sub - time period.

[0099] Among them, the first sub-time period is adjacent to the target sub-time period, and the net battery energy consumption corresponding to the first sub-time period is not zero. The first sub-time period being adjacent to the target sub-time period means that the start time of the first sub-time period can be adjacent to or close to the end time of the target sub-time period, or the end time of the first sub-time period can be adjacent to or close to the start time of the target sub-time period. The net battery energy consumption corresponding to the first sub-time period not being zero means that the net battery energy consumption corresponding to the first sub-time period can be positive or negative.

[0100] In a possible implementation manner, with the target sub-time period as the starting time period, the server can sequentially add, in the preset time order, each sub-time period with non-zero net battery energy consumption in each sub-time period as the first sub-time period to the first set, and add the sub-time periods with zero net battery energy consumption to the second set until the preset screening condition is met. Then, the server can correct the net battery energy consumption corresponding to the target sub-time period based on the net battery energy consumption and the change in battery SOC corresponding to each first sub-time period in the first set and the target sub-time period, to obtain the target net battery energy consumption corresponding to the target sub-time period.

[0101] Among them, the preset screening condition satisfies any one of the following:

[0102] 1-1. The total change in battery SOC corresponding to the first set is greater than or equal to the SOC change total threshold.

[0103] Among them, the SOC change total threshold can be determined according to actual needs. For example, the SOC change total threshold can be 6%, 10%, etc., and there is no limitation on this.

[0104] 1-2. The total net battery output energy consumption corresponding to the second set is greater than or equal to the net battery output energy consumption total threshold.

[0105] Among them, the net battery output energy consumption is determined based on the battery state data and the cumulative mileage increase amount corresponding to each sub-time period included in the second set. In the embodiments of the present application, the net battery output energy consumption total threshold can be determined according to actual needs. For example, the net battery output energy consumption total threshold can be 5%, 10% or 20%, and can also be 0.5 times, 0.6 times, etc. of the nominal energy, and there is no limitation on this.

[0106] Optionally, the embodiments of the present application do not limit the preset time sequence. For example, taking the target sub-time period as the starting time period, the server can first search for the sub-time periods before the target sub-time period, and then search for the sub-time periods after the target sub-time period, and so on, traversing each sub-time period one by one until the preset screening condition is met. Or, the server can first search for the sub-time periods after the target sub-time period, and then search for the sub-time periods before the target sub-time period. That is, the server can screen out the first sub-time period from each sub-time period by means of forward and backward alternating search.

[0107] The following will, in combination with the above embodiments, take the preset time sequence as first searching forward and then backward as an example to introduce in detail the method for correcting the battery net energy consumption corresponding to the target sub-time period. As Figure 3 shown, the method includes: S301 - S322.

[0108] S301. Parameter initialization.

[0109] Exemplarily, assume that the target time period includes sub - time periods, that is, {f1, f2, f3,..., f n}, where f n represents the th sub - time period. Taking the target sub - time period f i as the starting time period, let start = i, end = i, the forward search flag backward = true, the backward search flag forward = true, the first set and the second set are empty sets, and the initial battery output net energy consumption corresponding to the second set .

[0110] S302. Determine whether the forward search flag backward is true. If so, execute S303; if not, execute S314.

[0111] S303. Determine whether the sub - time period f start-1 exists. If so, execute S304; if not, execute S314.

[0112] S304. Determine whether the credible battery net energy consumption corresponding to the sub - time period f start-1 is all zero. If so, execute S305; if not, execute S309.

[0113] In the embodiments of the present application, the credible battery net energy consumption corresponding to each sub - time period may include the battery net energy consumption obtained through a preset energy consumption estimation model (hereinafter represented by ), the battery net energy consumption obtained through a preset energy consumption calculation formula (hereinafter represented by ), and the target battery net energy consumption obtained through the above S202 (i.e., ).

[0114] It can be understood that taking the sub - time period f start-1 as an example. When the net battery energy consumption corresponding to the sub - time period f start-1 is corrected, the corresponding credible net battery energy consumption may include and / or , and , when the net battery energy consumption corresponding to the sub - time period f start-1 is not corrected, the corresponding credible net battery energy consumption may include and / or .

[0115] Among them, the net battery energy consumption obtained through the preset energy consumption estimation model can refer to the introduction of the following embodiments, and the net battery energy consumption obtained through the preset energy consumption calculation formula can refer to Formula 3 below, which will not be elaborated here.

[0116] S305. Add the sub - time period f start-1 to the second set.

[0117] S306. Determine whether the net battery output energy consumption corresponding to the second set is less than the total threshold of the net battery output energy consumption. If so, execute S307; if not, execute S308.

[0118] Among them, the net battery output energy consumption corresponding to the second set is the sum of the net battery output energy consumptions corresponding to each sub - time period included in the set.

[0119] For example, assume that the total net battery output energy consumption corresponding to the sub - time period f start-1 is . After adding the sub - time period f start-1 , the net battery output energy consumption corresponding to the second set. Among them, The calculation formula of can refer to Formula 2 below.

[0120] (Formula 2).

[0121] Among them, is the net battery output energy consumption per unit mileage corresponding to the sub - time period f start-1 , is the cumulative mileage increase corresponding to the sub - time period f start-1 .

[0122] S307. Let start = start - 1.

[0123] S308. Let the forward search flag backward = false.

[0124] S309. Segment the sub - time periods f start-1 Add them to the first set and let start = start - 1.

[0125] S310. Determine whether the sum of the battery SOC change amounts corresponding to the first set is greater than or equal to the SOC change amount sum threshold, or (backward | forward) = false. If so, execute S311; if not, execute S314.

[0126] Here, | represents the OR operation. For example, in backward and forward, as long as one of them is true, (backward | forward) = true; if both are false, (backward | forward) = false.

[0127] S311. Determine whether the sum of the battery SOC change amounts corresponding to the first set is greater than or equal to the SOC change amount sum threshold. If so, execute S312; if not, execute S313.

[0128] S312. Based on the first set, the battery net energy consumption corresponding to the target sub - time period, and the battery SOC change amount, correct the battery net energy consumption corresponding to the target sub - time period.

[0129] In the embodiments of this application, take the first set including the sub - time period f start-1 as an example. The battery net energy consumption corresponding to the sub - time period f start-1 can be selected from its corresponding trusted battery net energy consumption 、 . The selection method can be random selection or select the one with a higher priority according to the priority order. For example, has a higher priority than , has a higher priority than .

[0130] Among them, the server can correct the battery net energy consumption corresponding to the target sub - time period with reference to the above method, which will not be elaborated here.

[0131] S313. Take the battery net energy consumption corresponding to the target sub - time period as the final battery net energy consumption.

[0132] S314. Determine whether the backward search flag forward is true. If so, execute S315; if not, execute S302.

[0133] S315. Determine whether the sub - time period f end+1 exists. If so, execute S316; if not, execute S302.

[0134] S316. Determine sub - time period f end+1 Check if the net energy consumption of the corresponding credible battery is all zero. If so, execute S317; if not, execute S321.

[0135] Among them, for sub - time period f end+1 The corresponding net energy consumption of the credible battery can refer to the introduction in S304 above and will not be elaborated here.

[0136] S317. Add sub - time period f end+1 to the second set.

[0137] S318. Determine if the net battery output energy consumption corresponding to the second set is less than the total threshold of the net battery output energy consumption. If so, execute S319; if not, execute S320.

[0138] S319. Let end = end + 1.

[0139] S320. Let the forward search flag forward = false.

[0140] S321. Add sub - time period f end+1 to the first set and let end = end + 1.

[0141] S322. Determine if the sum of the battery SOC change amounts corresponding to the first set is greater than or equal to the total threshold of the SOC change amounts, or (backward | forward) = false. If so, execute S311; if not, execute S302.

[0142] Based on the above technical means, the present application can use the forward - backward alternating search method to sequentially screen out the first sub - time periods adjacent and valid to the target sub - time period from each sub - time period. At the same time, a skipping mechanism for sub - time periods is added, allowing a series of searched sub - time periods not to be strictly adjacent. Since the total net battery output energy consumption corresponding to the skipped sub - time periods (i.e., the sub - time periods in the second set) is extremely small, the increased error will also be extremely small. In this way, the accuracy of battery net energy consumption correction can be improved under the condition of slightly ignoring part of the error, and thus the accuracy of the final battery energy consumption can be improved.

[0143] The above - mentioned various embodiments introduce the method for correcting the net energy consumption of the battery corresponding to the target sub - time period. The following will introduce how to determine the net energy consumption of the battery corresponding to each sub - time period within the target time period in the embodiments of the present application.

[0144] In some embodiments, in the above S201, according to the driving data of the vehicle in the target time period, determining the net battery energy consumption corresponding to each sub-time period in the target time period may include: for each sub-time period in the target time period, the server may determine the net battery energy consumption corresponding to the sub-time period based on the change in battery SOC corresponding to the sub-time period.

[0145] In a possible implementation manner, for each sub-time period in the target time period, when the change in battery SOC corresponding to the sub-time period is greater than or equal to the SOC change threshold, the server may determine the net battery energy consumption corresponding to the sub-time period based on the change in battery SOC corresponding to the sub-time period, the increase in cumulative mileage, and the nominal energy of the power battery.

[0146] In the embodiments of the present application, the SOC change threshold can be set according to actual needs. For example, the SOC change threshold can be 4%, 5%, etc., and there is no limitation thereto.

[0147] Specifically, the server may take the product of the change in battery SOC corresponding to the sub-time period and the nominal energy as the target parameter. After that, the server may determine the ratio of the target parameter to the increase in cumulative mileage corresponding to the sub-time period as the net battery energy consumption corresponding to the sub-time period (i.e., in the above S304 )

[0148] Exemplarily, the calculation formula for the net battery energy consumption corresponding to the sub-time period can refer to the following formula 3.

[0149] (Formula 3)

[0150] Wherein, is the net battery energy consumption corresponding to the sub-time period (i.e., the net battery energy consumption per unit mileage), is the change in battery SOC corresponding to the sub-time period, is the nominal energy, is the increase in cumulative mileage corresponding to the sub-time period.

[0151] In another possible implementation manner, a preset energy consumption estimation model is deployed in the server. On this basis, for each sub-time period in the target time period, when the change in battery SOC corresponding to the sub-time period is less than the SOC change threshold, the server may use the preset energy consumption estimation model to determine the net battery energy consumption corresponding to the sub-time period.

[0152] Specifically, the driving data within the target time period may include motor status data, battery status data, cumulative mileage increase, and driving duration corresponding to each sub-time period. On this basis, for each sub-time period within the target time period, the server may determine the target energy consumption corresponding to the sub-time period based on the change in battery SOC, cumulative mileage increase, driving duration, motor status data, and battery status data corresponding to the sub-time period. Subsequently, the server may input the target energy consumption and the driving data corresponding to the sub-time period into a preset energy consumption estimation model to obtain the net battery energy consumption corresponding to the sub-time period (i.e., in the above S304).

[0153] Among them, the target energy consumption is other energy consumption except for the net battery energy consumption. In the embodiments of the present application, the target energy consumption may include, but is not limited to, net battery output energy consumption, internal battery energy consumption, net motor energy consumption, other accessory energy consumption, and regenerative braking recovery energy. The calculation formula for the net battery output energy consumption may refer to the following formula 4, and the calculation formula for the internal battery energy consumption may refer to the following formula 5, and the calculation formula for the net motor energy consumption may refer to the following formula 6, and the calculation formula for other accessory energy consumption may refer to the following formula 7, and the calculation formula for the regenerative braking recovery energy may refer to the following formula 8.

[0154] (Formula 4).

[0155] (Formula 5).

[0156] (Formula 6).

[0157] (Formula 7).

[0158] (Formula 8).

[0159] Among them, is the total battery voltage corresponding to time t in the sub-time period, is the total battery current corresponding to time t in the sub-time period, is the input voltage of the motor controller corresponding to time t in the sub-time period, is the DC bus current of the motor controller corresponding to time t in the sub-time period, is the cumulative mileage increase corresponding to the sub-time period, is the driving duration corresponding to the sub-time period.

[0160] Based on the above technical means, when the change in the battery SOC is small, using the preset energy consumption estimation model can reduce the error caused by the change in the battery SOC. When the change in the battery SOC is large, the accuracy of the net battery energy consumption obtained using the preset energy consumption calculation formula can meet the actual requirements. Thus, by selecting different strategies for determining the net battery energy consumption based on the change in the battery SOC, it is possible to adapt to the characteristics of the battery under different working conditions and flexibly select an appropriate method to determine the net battery energy consumption to ensure the accuracy of the battery energy consumption estimation. This improves the adaptability of the entire energy consumption estimation system.

[0161] In some embodiments, as Figure 4 shown, the above preset energy consumption estimation model can be obtained through the following S401 - S407.

[0162] S401. Preprocess the driving data of the vehicle.

[0163] Specifically, the driving data of the vehicle is stored in the server. The server can integrate the driving data into structured data with the acquisition time series as rows and each operating parameter as columns. Among them, each row in the structured data is a data frame, and each data frame contains the driving data of the vehicle at a certain moment, and the specific format refers to Table 1 above. Then, the server can process the outliers and missing values in the driving data to obtain high-quality driving data.

[0164] For example, each operating parameter corresponds to a threshold range, and each threshold range is determined according to the specifications of the vehicle and its components. Taking the driving motor speed as an example, if the driving motor speed in a certain data frame exceeds the threshold range of the driving motor speed (-5000 rpm to 13000 rpm), the driving motor speed of this data frame is replaced with a missing value. Then, the server can use a preset interpolation algorithm to process the missing value to obtain an interpolation value and fill the interpolation value at the corresponding missing value.

[0165] In the embodiments of the present application, the interpolation algorithm may include a regression interpolation algorithm, a multiple imputation algorithm, a linear interpolation algorithm, or a nearest neighbor interpolation algorithm, etc., which is not limited herein. For example, for the case where parameters such as vehicle speed and total current are missing values, a regression interpolation algorithm or a multiple imputation algorithm can be used for interpolation processing. For parameters such as cumulative mileage and SOC, a linear interpolation algorithm or a nearest neighbor interpolation algorithm can be used for interpolation processing.

[0166] S402. Obtain the driving data of the vehicle in multiple sub - time periods.

[0167] Among them, each sub - time period can meet one or more of the following conditions:

[0168] 2 - 1. The time interval between any two adjacent data frames in the sub - time period is less than the interval threshold.

[0169] In the embodiments of the present application, the interval threshold can be determined according to actual requirements. For example, the interval threshold can be 500 seconds, 600 seconds, etc., and no limitation is made thereto.

[0170] 2-2. The roads where the vehicles corresponding to any two adjacent data frames in the sub-time period are the same.

[0171] Specifically, the server can divide the data frames in the driving data according to a preset division rule to obtain the driving data in each sub-time period.

[0172] Among them, the preset division rule can include division according to time interval, division according to road, etc., and no limitation is made thereto.

[0173] Taking division according to time interval as an example. The server can traverse each data frame in the driving data. If the time interval between the first data frame and the second data frame is less than or equal to the interval threshold, the first data frame and the second data frame belong to the same sub-time period (that is, belong to the same driving segment). If the time interval between the first data frame and the second data frame is greater than the interval threshold, the first data frame is divided into the first sub-time period, and the second data frame is divided into the second sub-time period.

[0174] Taking division according to road as an example. If the road where the vehicle corresponding to the second data frame is located is the same as the road where the vehicle corresponding to the first data frame is located, the first data frame and the second data frame belong to the same sub-time period. If the road where the vehicle corresponding to the second data frame is located is different from the road where the vehicle corresponding to the first data frame is located, the first data frame is divided into the last data frame of the first sub-time period and the starting frame of the second sub-time period, and the second data frame is divided into the starting frame of the second sub-time period. That is, the first sub-time period and the second sub-time period both contain the first data frame. In the embodiments of the present application, the sub-time periods containing common data frames are determined as adjacent sub-time periods.

[0175] S403. Based on the driving data corresponding to each sub-time period, determine the battery net energy consumption, target energy consumption, and energy consumption influence parameter corresponding to each sub-time period.

[0176] Specifically, the calculation formula of the battery net energy consumption can refer to the above formula 3, and the calculation formula of the target energy consumption can refer to the above formulas 4-8, which will not be elaborated here. For the battery net energy consumption, as well as the battery output net energy consumption, motor net energy consumption, and regenerative braking recovery energy in the target energy consumption, the following description is made:

[0177] (1)The calculation of the net energy consumption of the battery output, the net energy consumption of the motor, and the energy recovered by regenerative braking is implemented based on composite numerical integration. There may be a large time interval between data frames within a sub-time period, which is very likely to cause a significant decrease in the integration accuracy and lead to inaccurate calculation results. In addition, when the number of data frames included in a sub-time period is less than the quantity threshold, the integration accuracy is relatively low. Therefore, for a sub-time period where the maximum time interval of the data frames is higher than a predetermined threshold (such as 30 seconds), or the number of data frames is less than the quantity threshold (such as 21), it is considered that numerical integration is not feasible. Therefore, the net energy consumption of the motor and the energy recovered by regenerative braking corresponding to this sub-time period are set as missing values, but the net energy consumption of the battery output corresponding to it is still calculated.

[0178] (2)If the change in the battery SOC corresponding to a certain sub-time period is less than the SOC change threshold, the net energy consumption of the battery corresponding to this sub-time period is set as a missing value.

[0179] In the embodiments of the present application, the energy consumption impact parameter refers to a parameter directly or indirectly related to the net energy consumption of the battery. As shown in Table 2, it is an energy consumption impact parameter determined based on the driving data and ground meteorological data corresponding to each sub-time period, mainly including five categories: kinematic parameters, time parameters, battery state data, motor state data, and meteorological data. The sub-parameters included in each category of data can refer to the introduction in Table 2.

[0180] Table 2 Energy consumption impact parameters

[0181]

[0182] It should be noted that after preprocessing the driving data in S401 above, there may still be hidden outliers in the driving data. Specifically, it is manifested that a certain operating parameter in the driving data may be within the threshold range, but the change direction or rate of this operating parameter along the time axis is contrary to common sense. For example, the cumulative mileage corresponding to a sub-time period should be a monotonically increasing sequence, but the cumulative mileage corresponding to some sub-time periods shows a decreasing situation. Another example is that the change in the battery SOC corresponding to a sub-time period during driving should decrease, that is, in an energy-consuming state, but the change in the battery SOC corresponding to some sub-time periods is an increment. Another example is that the internal energy consumption of the battery corresponding to a sub-time period is greater than the energy consumption threshold, and this sub-time period is regarded as an abnormal sub-time period.

[0183] Based on the above content, after the server determines the net energy consumption of the battery, the target energy consumption, and the energy consumption impact parameter corresponding to each sub-time period, it can delete the sub-time periods where there are abnormal situations in the driving data, the net energy consumption of the battery, and the target energy consumption, so as to obtain higher-quality training data to train the energy consumption estimation model and ensure the estimation accuracy of the energy consumption estimation model.

[0184] For example, the server may delete sub - time periods where the change in battery SOC is less than - 1%. For another example, the server may delete sub - time periods where the average speed is less than 0. For yet another example, for each target energy consumption, the server may calculate the high predetermined percentile (e.g., 99.7%) and the low predetermined percentile (e.g., 0.3%) in the target energy consumption, and then the server may delete sub - time periods where the target energy consumption is not between the high predetermined percentile and the low predetermined percentile.

[0185] S404. Determine a training set from multiple sub - time periods.

[0186] Specifically, for each sub - time period among the multiple sub - time periods, the server may filter out data frames with non - empty net battery energy consumption from the sub - time period. Then, the server may randomly shuffle the multiple sub - time periods and divide them into a training data set and a test data set based on a preset ratio.

[0187] Among them, the training set is used to train the energy consumption estimation model, and the test set is used to evaluate the generalization ability of the trained energy consumption estimation model, that is, the true performance of the model on unknown data (i.e., the test set).

[0188] The embodiments of the present application do not limit the preset ratio. For example, the data frames may be divided into a training set and a test set according to a ratio of 5:1, or the data frames may be divided into a training set and a test set according to a ratio of 3:1.

[0189] S405. Select optimal features from the driving data, target energy consumption, and energy consumption impact parameters corresponding to each sub - time period included in the training set as the input features of the energy consumption estimation model.

[0190] In the embodiments of the present application, methods such as manual feature selection and backward sequential feature elimination may be used to select optimal features.

[0191] In one example, taking the net battery energy consumption (which can also be referred to as the battery SOC energy consumption) as the variable predicted by the energy consumption estimation model, and the target energy consumption and the energy consumption impact parameters shown in Table 2 above as the input features of the energy consumption estimation model, any two of the start time of the segment (i.e., the start time of the sub - time segment), the end time of the segment (i.e., the end time of the sub - time segment), and the change in battery SOC of the segment can be deleted first by the manual feature selection method. Because the tendency of these three features on the net battery energy consumption is indirectly achieved by changing traffic and meteorological conditions, and they are redundant in the case of having kinematic parameter features and meteorological data features, that is, only one feature needs to be retained. After that, to avoid errors in the output net battery energy consumption of the model during the training process due to estimation errors and rounding errors in the training data, or the inability to output the predicted net battery energy consumption due to the lack of internal battery energy consumption, the internal battery energy consumption and the segment duration (i.e., the duration of the sub - time segment) can also be deleted. After deleting some features based on the above - mentioned empirical knowledge using manual feature selection, the backward sequential feature elimination method can also be used to obtain the optimal feature set. The specific selection method can refer to the embodiments such as Figure 5 shown below, which will not be elaborated here.

[0192] S406. Optimize the hyperparameters of the energy consumption estimation model.

[0193] Among them, hyperparameters refer to the parameters set before training the model, which are used to control the behavior and performance of the model. The selection of hyperparameters can affect the training speed, convergence, capacity, and generalization ability of the model, etc.

[0194] Specifically, the server can first determine the target hyperparameters and the search range corresponding to each target hyperparameter. Then, the server uses a preset hyperparameter optimization algorithm to determine the optimal value of each hyperparameter from the search range corresponding to each target hyperparameter.

[0195] For example, Table 3 shows the search range of each hyperparameter. The target hyperparameters can include the number of iterations (n_estimators), the number of leaf nodes per tree (num_leaves), the maximum depth of the tree (max_depth), the learning rate (learning_rate), the minimum number of samples required for a leaf node (min_child_samples), the proportion of samples used when training each tree (subsample), the subsample frequency, that is, how many rounds of iterations to perform a subsampling (subsample_freq), the feature subsampling ratio (colsample_bytree), the feature subsampling ratio (colsample_bytree), etc., which are not limited here.

[0196] Table 3 Target hyperparameters and the search range of each hyperparameter

[0197]

[0198] In the embodiments of the present application, the preset hyperparameter optimization algorithm may include a random search algorithm, a Bayesian optimization algorithm, a grid search algorithm, etc., and no limitation is made thereto.

[0199] In the embodiments of the present application, the energy consumption estimation model may include a LightGBM model, an XGBoost model, a random forest model, etc., and no limitation is made thereto.

[0200] Exemplarily, taking the energy consumption estimation model as the LightGBM model and the preset hyperparameter optimization algorithm as the Bayesian optimization algorithm as an example. The server can use the Python language to call the lightgbm package to implement the LightGBM model, that is, the energy consumption estimation model. Then the server can call the Bayesian optimization algorithm (tree-structured parzen estimator, TPE) provided by the optuna package to search for the optimal hyperparameters of the energy consumption estimation model. Among them, in the process of searching for hyperparameters, the accuracy index of each group of hyperparameters can be obtained by using the K-fold cross-validation method, and the accuracy index can be the root mean square error RMSE.

[0201] S407. Train the energy consumption estimation model based on the optimized hyperparameters and the training set containing the optimal features to obtain the trained energy consumption estimation model.

[0202] Specifically, the server can pre-set the hyperparameters of the energy consumption estimation model as the optimized hyperparameters. Then, the server can input the training set containing the optimal features into the energy consumption estimation model to obtain the trained energy consumption estimation model.

[0203] Based on the above technical means, the artificial feature selection method and the backward sequential feature elimination method can be used to determine the optimal input features and delete redundant features to ensure the accuracy of the energy consumption estimation model. Then, using the optimal features and the optimized hyperparameters to train the energy consumption estimation model can provide support for the calculation of the battery net energy consumption. At the same time, the battery net energy consumption output by the energy consumption estimation model trained based on a large amount of data is more accurate and can better reflect the actual energy consumption situation.

[0204] In some embodiments, as Figure 5 shown, to determine the optimal features using the backward sequential feature elimination method, the following steps 1-step 9 can be specifically referred to.

[0205] Step 1. Obtain a sub-training set for optimal feature selection from the training set.

[0206] It should be noted that the feature selection method based on backward sequence feature elimination has a huge computational cost. Therefore, the server can randomly sample a sub-training set from the training set for optimal feature selection. Among them, the sample size of the sub-training set can be determined according to the actual computing resources, and no limitation is imposed on this.

[0207] Step 2: Use the sub-training set to train the energy consumption estimation model and determine the initial accuracy index of the energy consumption estimation model.

[0208] In the embodiments of the present application, the accuracy index may include root mean squared error (RMSE), mean squared error (MSE), etc., and no limitation is imposed on this.

[0209] Specifically, the server can input other features in the sub-training set except the battery net energy consumption feature into the energy consumption estimation model to obtain the battery net energy consumption output by the model. Then, the server can calculate the accuracy index of the energy consumption estimation model based on the battery net energy consumption corresponding to each sub-time period in the sub-training set and the battery net energy consumption output by the model.

[0210] Step 3: Initialize the feature sequence in the sub-training set, i = 1.

[0211] Step 4: Determine whether i is greater than the remaining number of features i _ max in the sub-training set. If so, execute Step 7; if not, execute Step 5.

[0212] Step 5: Use other features in the sub-training set except the i-th feature to train the energy consumption estimation model and determine the corresponding accuracy index of the energy consumption estimation model.

[0213] Step 6: Let i = i + 1.

[0214] Step 7: Determine the target feature so that the energy consumption estimation model trained based on other features except the target feature has the highest accuracy index, and delete the target feature from the sub-training set.

[0215] Step 8: Determine whether the remaining number of features i _ max = 1 or whether the consecutive decrease times of the accuracy index of the energy consumption estimation model are greater than the number threshold. If so, execute Step 9; if not, execute Step 3.

[0216] Among them, the number threshold can be 5 times, 6 times, etc., and no limitation is imposed on this in the present application.

[0217] Step 9: Use the input features corresponding to the energy consumption estimation model with the highest accuracy index as the optimal features of the sub-training set.

[0218] Combining the above steps 1-9, it can be understood that backward sequence feature elimination can be iterated multiple times. By deleting one feature in the sub-training in each iteration, the original features in the sub-training set are refined into optimal features to improve the accuracy of the energy consumption estimation model. The finally obtained optimal features are the input features corresponding to the model with the highest accuracy index in all iterations.

[0219] In the embodiments of the present application, the optimal features may vary depending on the training set. For example, the optimal features may include the starting SOC of the segment, the starting cumulative mileage of the segment, the net energy consumption of the battery output, the energy consumption of other accessories, the average torque of the generating motor, the standard deviation of the torque of the power-consuming motor, the standard deviation of the torque of the generating motor, the proportion of the torque of the power-consuming motor, the proportion of the torque of the generating motor, the average temperature of the motor, the regenerative braking recovery energy, the average speed, the standard deviation of non-zero speed, the 5% quantile of non-zero speed, the kurtosis of non-zero speed, the skewness of non-zero speed, the absolute cumulative change in speed, the average temperature, the average relative humidity, etc.

[0220] Figure 6 As shown in the structural schematic diagram of an energy consumption determination device provided in the embodiments of the present application, Figure 6 as shown, the device includes: a determination unit 601 and a processing unit 602.

[0221] The determination unit 601 is configured to determine the battery net energy consumption corresponding to each sub-time period included in the target time period according to the driving data of the vehicle in the target time period.

[0222] The processing unit 602 is configured to correct the battery net energy consumption corresponding to each sub-time period according to the battery net energy consumption corresponding to multiple sub-time periods and the change in battery SOC.

[0223] In a possible way, the processing unit 602 is further configured to, when the sum of the changes in battery SOC corresponding to multiple sub-time periods is greater than or equal to the SOC change sum threshold, correct the battery net energy consumption corresponding to each sub-time period according to the battery net energy consumption corresponding to multiple sub-time periods and the change in battery SOC.

[0224] In a possible way, the driving data includes the nominal energy of the battery. On this basis, for any target sub-time period among multiple sub-time periods, the determination unit 601 is further configured to determine an energy consumption correction coefficient according to the nominal energy, the changes in battery SOC corresponding to multiple sub-time periods, and the battery net energy consumption.

[0225] In a possible way, the processing unit 602 is further configured to correct the battery net energy consumption corresponding to the target sub-time period based on the energy consumption correction coefficient.

[0226] In a possible way, the determination unit 601 is further configured to determine the product of the nominal energy and the sum of the battery SOC change amounts corresponding to each sub-time period as the target product, and determine the ratio of the target product to the sum of the battery net energy consumptions corresponding to each sub-time period as the energy consumption correction coefficient.

[0227] In a possible way, for any target sub-time period among multiple sub-time periods, the processing unit 602 is further configured to correct the battery net energy consumption corresponding to the target sub-time period according to the battery net energy consumption and the battery SOC change amount corresponding to at least one first sub-time period and the target sub-time period.

[0228] In a possible way, the driving data includes the battery SOC change amount corresponding to each sub-time period. On this basis, the determination unit 601 is further configured to, for each sub-time period within the target time period, determine the battery net energy consumption corresponding to the sub-time period based on the battery SOC change amount corresponding to the sub-time period.

[0229] In a possible way, the determination unit 601 is further configured to, when the battery SOC change amount corresponding to the sub-time period is less than the SOC change amount threshold, use a preset energy consumption estimation model to determine the battery net energy consumption corresponding to the sub-time period.

[0230] In a possible way, the driving data further includes the motor state data, the battery state data, the cumulative mileage increase amount, and the driving duration corresponding to each sub-time period. On this basis, the determination unit 601 is further configured to, when the battery SOC change amount corresponding to the sub-time period is less than the SOC change amount threshold, determine the target energy consumption corresponding to the sub-time period based on the battery SOC change amount, the cumulative mileage increase amount, the driving duration, the motor state data, and the battery state data corresponding to the sub-time period.

[0231] In a possible way, the driving data further includes the cumulative mileage increase amount, the battery state data, and the nominal energy of the battery. On this basis, the determination unit 601 is further configured to, when the battery SOC change amount corresponding to the sub-time period is greater than or equal to the SOC change amount threshold, determine the battery net energy consumption corresponding to the sub-time period based on the battery SOC change amount, the cumulative mileage increase amount, and the nominal energy of the power battery corresponding to the sub-time period.

[0232] Figure 7 This is a block diagram of an electronic device provided by an embodiment of the present application. As Figure 7 shown, the electronic device includes but is not limited to: a processor 701 and a memory 702.

[0233] Among them, the above-mentioned memory 702 is used to store the executable instructions of the above-mentioned processor 701. It can be understood that the above-mentioned processor 701 is configured to execute instructions to implement the vehicle control method in the above-mentioned embodiment.

[0234] It should be noted that those skilled in the art can understand that Figure 7 the structure of the electronic device shown in does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than Figure 7 shown, or combine certain components, or have a different component arrangement.

[0235] The processor 701 is the control center of the electronic device, connecting various parts of the entire electronic device through various interfaces and lines. By running or executing software programs and / or modules stored in the memory 702, and calling the data stored in the memory 702, it executes various functions of the electronic device and processes data, thereby monitoring the electronic device as a whole. The processor 701 may include one or more processing units. Optionally, the processor 701 may integrate an application processor and a modem processor. Among them, the application processor mainly processes the operating system, user interface, application programs, etc., and the modem processor mainly processes wireless communication. It can be understood that the above modem processor may not be integrated into the processor 701 either.

[0236] The memory 702 can be used to store software programs and various data. The memory 702 may mainly include a program storage area and a data storage area. Among them, the program storage area can store the operating system, application programs required by at least one functional module (such as a determination unit, a processing unit, etc.), etc. In addition, the memory 702 may include high-speed random access memory, and may also include non-volatile memory. For example, the non-volatile memory may include at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices.

[0237] In an exemplary embodiment, there is also provided a computer-readable storage medium including instructions, such as the memory 702 including instructions. The above instructions can be executed by the processor 701 of the electronic device to implement the method in the above embodiment.

[0238] In actual implementation, Figure 6 the functions of the determination unit 601 and the processing unit 602 in can both be implemented by Figure 7 the processor 701 in calling the computer program stored in the memory 702. The specific execution process can refer to the description of the method part in the above embodiment and will not be elaborated here.

[0239] Optionally, the computer-readable storage medium may be a non-transitory computer-readable storage medium. For example, the non-transitory computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0240] In an exemplary embodiment, the embodiment of the present application further provides a computer program product including one or more instructions, and the one or more instructions can be executed by a processor 701 of an electronic device to complete the method in the above embodiment.

[0241] It should be noted that when the instructions in the above computer-readable storage medium or the one or more instructions in the computer program product are executed by the processor of the electronic device, each process of the above method embodiment is implemented, and the same technical effects as the above method can be achieved. To avoid repetition, it will not be elaborated here.

[0242] Through the description of the above embodiments, those skilled in the art can clearly understand that for the convenience and brevity of description, only the division of the above functional modules is used as an example. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above.

[0243] In several embodiments provided by the present application, it should be understood that the disclosed device and method can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection can be through some interfaces, and the indirect coupling or communication connection of the device or unit can be in electrical, mechanical or other forms.

[0244] The unit described as a separated component may or may not be physically separated, and the component displayed as a unit may be a physical unit or multiple physical units, that is, it may be located in one place, or may be distributed to multiple different places. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0245] In addition, each functional unit in various embodiments of the present application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0246] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on such understanding, the technical solution of the embodiments of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The software product is stored in a storage medium and includes several instructions for causing a device (which may be a single-chip microcomputer, a chip, etc.) or a processor to execute all or part of the steps of the methods of the embodiments of the present application. The foregoing storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical discs.

[0247] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present application should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for determining energy consumption, characterized in that, The method includes: Determining the battery net energy consumption corresponding to each sub-time period included in the target time period according to the driving data of the vehicle during the target time period; the battery net energy consumption is determined based on the energy consumed by the vehicle's battery during discharge and the energy recovered by the energy recovery system; the driving data includes the nominal energy of the battery. When the sum of the battery SOC change amounts corresponding to the multiple sub-time periods is greater than or equal to the SOC change amount sum threshold, for any target sub-time period among the multiple sub-time periods: Determining the product of the nominal energy and the sum of the battery SOC change amounts corresponding to each sub-time period as the target product. Determining the ratio of the target product to the sum of the battery net energy consumptions corresponding to each sub-time period as the energy consumption correction coefficient. Correcting the battery net energy consumption corresponding to the target sub-time period based on the energy consumption correction coefficient.

2. The method according to claim 1, characterized in that For any target sub-time period among the multiple sub-time periods, the method further includes: Screening out at least one first sub-time period from the multiple sub-time periods; the first sub-time period is adjacent to the target sub-time period, and the battery net energy consumption corresponding to the first sub-time period is not zero. The correcting the battery net energy consumption corresponding to each sub-time period according to the battery net energy consumptions and battery SOC change amounts corresponding to the multiple sub-time periods includes: Correcting the battery net energy consumption corresponding to the target sub-time period according to the battery net energy consumptions and battery SOC change amounts corresponding to the at least one first sub-time period and the target sub-time period.

3. The method according to claim 2, wherein The screening out at least one first sub-time period from the multiple sub-time periods includes: Taking the target sub-time period as the starting time period, adding the sub-time periods with non-zero battery net energy consumption to the first set one by one in the preset time order, and adding the sub-time periods with zero battery net energy consumption to the second set until the preset screening condition is met.

4. The method according to claim 3, wherein The preset screening condition satisfies any one of the following: The sum of the battery SOC change amounts corresponding to the first set is greater than or equal to the SOC change amount sum threshold. The sum of the battery output net energy consumptions corresponding to the second set is greater than or equal to the battery output net energy consumption sum threshold. The battery output net energy consumption is determined based on the battery state data and the cumulative mileage increase amount corresponding to each sub-time period included in the second set.

5. The method according to claim 1, wherein The driving data includes the battery SOC change amount corresponding to each sub-time period. The determining the battery net energy consumption corresponding to each sub-time period within the target time period according to the driving data of the vehicle during the target time period includes: For each sub-time period within the target time period, determining the battery net energy consumption corresponding to the sub-time period based on the battery SOC change amount corresponding to the sub-time period.

6. The method according to claim 5, characterized in that The determining the battery net energy consumption corresponding to the sub-time period based on the battery SOC change amount corresponding to the sub-time period includes: When the change in the battery SOC corresponding to the sub - time period is less than the SOC change threshold, use a preset energy consumption estimation model to determine the net energy consumption of the battery corresponding to the sub - time period.

7. The method according to claim 6, characterized in that, The driving data also includes the motor state data, battery state data, cumulative mileage increase, and driving duration corresponding to each sub - time period; The step of using a preset energy consumption estimation model to determine the net energy consumption of the battery corresponding to the sub - time period when the change in the battery SOC corresponding to the sub - time period is less than the SOC change threshold includes: When the change in the battery SOC corresponding to the sub - time period is less than the SOC change threshold, based on the change in the battery SOC, cumulative mileage increase, driving duration, motor state data, and battery state data corresponding to the sub - time period, determine the target energy consumption corresponding to the sub - time period; the target energy consumption is other energy consumption except for the net energy consumption of the battery; Input the target energy consumption and the driving data corresponding to the sub - time period into the preset energy consumption estimation model to obtain the net energy consumption of the battery corresponding to the sub - time period.

8. The method according to claim 5, wherein The driving data also includes the cumulative mileage increase, battery state data, and the nominal energy of the battery; The step of determining the net energy consumption of the battery corresponding to the sub - time period based on the change in the battery SOC corresponding to the sub - time period includes: When the change in the battery SOC corresponding to the sub - time period is greater than or equal to the SOC change threshold, based on the change in the battery SOC, cumulative mileage increase, and the nominal energy of the power battery corresponding to the sub - time period, determine the net energy consumption of the battery corresponding to the sub - time period.

9. The method according to claim 8, wherein The net energy consumption of the battery corresponding to the sub - time period satisfies the following formula: ； Among them, the is the net energy consumption of the battery, is the change in the battery's SOC, is the nominal energy, is the increase in the cumulative mileage.

10. The method according to claim 1, wherein Each sub - time period contains multiple data frames, each data frame contains the driving data of the vehicle at a certain moment, and each sub - time period satisfies one or more of the following conditions: The time interval between any two adjacent data frames in the sub - time period is less than the interval threshold; The roads where the vehicle is located corresponding to any two adjacent data frames in the sub - time period are the same.

11. An energy consumption determination device, characterized in that, The device includes: a determination unit and a processing unit; The determination unit is used to determine the net energy consumption of the battery corresponding to each sub - time period in the multiple sub - time periods included in the target time period according to the driving data of the vehicle during the target time period; the net energy consumption of the battery is determined based on the energy consumed by the vehicle's battery during discharge and the energy recovered by the energy recovery system; the driving data includes the nominal energy of the battery; The processing unit is used to: when the sum of the changes in the battery SOC corresponding to the multiple sub - time periods is greater than or equal to the SOC change sum threshold, for any target sub - time period in the multiple sub - time periods: Determine the product of the nominal energy and the sum of the changes in the battery SOC corresponding to each sub - time period as the target product; Determine the ratio of the target product to the sum of the net energy consumptions of the battery corresponding to each sub - time period as the energy consumption correction coefficient; Based on the energy consumption correction coefficient, correct the net energy consumption of the battery corresponding to the target sub - time period.

12. An electronic device, characterized in that, including a memory and a processor; the memory and the processor are coupled; the memory is used for storing computer program code, and the computer program code includes computer instructions; when the processor executes the computer instructions, the electronic device executes the energy consumption determination method according to any one of claims 1-10.

13. A computer-readable storage medium, characterized in that, when the computer-executable instructions stored in the computer-readable storage medium are executed by the processor of the processing device, the processing device can execute the energy consumption determination method according to any one of claims 1-10.

14. A computer program product, characterized in that, the computer program product includes the computer program, and the computer program is adapted to be loaded and executed by the processor to execute the energy consumption determination method according to any one of claims 1-10.

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