Method, device, electronic device and medium for determining vehicle power driving mode

Abstract

The present application relates to the technical field of new energy vehicles, and provides a method, an apparatus, an electronic device and a medium for determining a vehicle's power driving mode. The method includes: obtaining a to-be-driven path of the vehicle and a section index corresponding to the to-be-driven path; segmenting the to-be-driven path according to the section index to obtain at least two driving sections, and determining a power allocation dependency parameter corresponding to each driving section, where the power allocation dependency parameter is used to characterize a parameter affecting the power allocation of the vehicle battery; determining a to-be-allocated target power corresponding to each driving section according to the power allocation dependency parameter corresponding to each driving section; and determining a power driving mode of the vehicle in each driving section according to the to-be-allocated target power corresponding to each driving section. The present application can improve the working efficiency of the drive system, thereby achieving the optimal energy utilization efficiency, and further improving the fuel economy and driving experience.

Description

Technical Field

[0001] The present application relates to the technical field of new energy vehicles, and in particular, to a method, device, electronic device and medium for determining a vehicle power driving mode. Background Art

[0002] In the configuration of the power system of a range-extended electric vehicle, generally a relatively large battery pack and a relatively small engine generator set are matched. The battery pack is used to support a pure electric driving range of dozens to hundreds of kilometers, and the engine generator set is used to extend the vehicle's driving range when the battery power is insufficient. The existing power control mode of range-extended electric vehicles is generally to first control the vehicle to drive completely in pure electric mode, and then switch to hybrid mode when the power is lower than a designed threshold. This control mode is simple and easy to implement. However, since the power of the engine generator set is relatively small, if there is a continuous high power demand in the low power stage, it will lead to insufficient power performance of the vehicle, and it is impossible to ensure that the power system has high working efficiency throughout the whole process, thus reducing fuel economy and affecting the driving experience. Summary of the Invention

[0003] In view of this, the embodiments of the present application provide a method, device, electronic device and medium for determining a vehicle power driving mode to solve the problems that the existing control mode of range-extended vehicles is simple, unable to ensure that the power system has high working efficiency throughout the whole process, resulting in low fuel economy, and affecting the driving experience.

[0004] In the first aspect of the embodiments of the present application, a method for determining a vehicle power driving mode is provided. The method includes: obtaining a to-be-driven path of the vehicle and a road condition index corresponding to the to-be-driven path; dividing the to-be-driven path according to the road condition index to obtain at least two driving segments, and determining a power allocation dependency parameter corresponding to each driving segment, where the power allocation dependency parameter is used to characterize a parameter affecting the battery power allocation of the vehicle; determining a to-be-allocated target power for each driving segment according to the power allocation dependency parameter corresponding to each driving segment; and determining a power driving mode of the vehicle in each driving segment according to the to-be-allocated target power corresponding to each driving segment, where the power driving mode includes a pure electric mode and a hybrid mode.

[0005] In the second aspect of the embodiments of the present application, a device for determining a vehicle power driving mode is provided. The device includes:

[0006] An acquisition module, configured to acquire a to-be-traveled path of a vehicle and a road condition index corresponding to the to-be-traveled path; a segmentation module, configured to segment the to-be-traveled path according to the road condition index to obtain at least two travel segments, and determine a power allocation dependency parameter corresponding to each travel segment, where the power allocation dependency parameter is used to characterize a parameter affecting the power allocation of the vehicle battery; an allocation module, configured to determine a to-be-allocated target power corresponding to each travel segment according to the power allocation dependency parameter corresponding to each travel segment; a determination module, configured to determine a power driving mode of the vehicle in each travel segment according to the to-be-allocated target power corresponding to each travel segment, where the power driving mode includes a pure electric mode and a hybrid power mode.

[0007] In a third aspect of the embodiments of the present application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above method are implemented.

[0008] In a fourth aspect of the embodiments of the present application, a computer storage medium is provided. The computer storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0009] The beneficial effects of the embodiments of the present application compared with the prior art are as follows:

[0010] According to the technical solution provided by the embodiments of the present application, the to-be-traveled path of the vehicle and the road condition index corresponding to the to-be-traveled path are acquired; the to-be-traveled path is segmented according to the road condition index to obtain at least two travel segments, and a power allocation dependency parameter corresponding to each travel segment is determined, where the power allocation dependency parameter is used to characterize a parameter affecting the power allocation of the vehicle battery; the to-be-allocated target power corresponding to each travel segment is determined according to the power allocation dependency parameter corresponding to each travel segment; the power driving mode of the vehicle in each travel segment is determined according to the to-be-allocated target power corresponding to each travel segment, where the power driving mode includes a pure electric mode and a hybrid power mode. By using the road condition index corresponding to the to-be-traveled path to segment the to-be-traveled path to obtain at least two travel segments, and then determining the power allocation dependency parameter corresponding to each travel segment, a reasonable to-be-allocated target power is determined for each travel segment, so as to reasonably allocate the power usage of the battery in each travel segment, optimize the battery usage efficiency, ensure that the vehicle power system can improve the working efficiency of the drive system by using the power output of the battery in each travel segment, thereby achieving the optimal energy utilization efficiency, improving fuel economy and driving experience. It avoids the problems that the existing driving mode of new energy vehicles cannot ensure that the power system has a high working efficiency throughout the whole process, resulting in low fuel economy and affecting the driving experience. Description of the Drawings

[0011] To more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the accompanying drawings required for use in the embodiments or the description of the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0012] Figure 1 FIG. is a schematic flowchart of a method for determining a vehicle power driving mode provided by an embodiment of the present application;

[0013] Figure 2 FIG. is a schematic structural diagram of a device for determining a vehicle power driving mode provided by an embodiment of the present application;

[0014] Figure 3 FIG. is a schematic structural diagram of an electronic device provided by an embodiment of the present application. Detailed Embodiments

[0015] In the following description, specific details such as specific system structures and technologies are proposed for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present application.

[0016] Next, a method, device, electronic device, and medium for determining a vehicle power driving mode according to an embodiment of the present application will be described in detail with reference to the accompanying drawings.

[0017] Figure 1 FIG. Figure 1 is a schematic flowchart of a method for determining a vehicle power driving mode provided by an embodiment of the present application. As Figure 1 shown, the method includes:

[0018] S101, obtaining a to-be-driven path of the vehicle and a road condition index corresponding to the to-be-driven path;

[0019] S102, dividing the to-be-driven path according to the road condition index to obtain at least two driving segments, and determining a power allocation dependency parameter corresponding to each driving segment, where the power allocation dependency parameter is used to characterize a parameter affecting the power allocation of the vehicle battery;

[0020] S103, determining a target power to be allocated corresponding to each driving segment according to the power allocation dependency parameter corresponding to each driving segment;

[0021] S104. Determine the power driving mode of the vehicle on each driving section according to the target power to be allocated corresponding to each driving section, where the power driving mode includes a pure electric mode and a hybrid power mode.

[0022] It can be understood that the above method for determining the vehicle power driving mode provided in this example is applied to the vehicle's vehicle control unit. The above vehicle includes new energy vehicles (including passenger-carrying vehicles (such as sedans, buses, coaches, minibuses, etc.), cargo-carrying vehicles (such as ordinary trucks, van trucks, trailer trucks, closed trucks, tank trucks, flatbed trucks, container trucks, dump trucks, special-structured trucks), special vehicles (such as logistics delivery vehicles, automatic guided vehicle AGVs, patrol vehicles, cranes, hoists, excavators, bulldozers, forklifts, road rollers, loaders, off-road engineering vehicles, armored engineering vehicles, sewage treatment vehicles, sanitation vehicles, vacuum cleaners, floor scrubbers, sprinkler trucks, floor sweeping robots, food delivery robots, shopping guide robots, lawn mowers, golf carts, etc.), vehicles with entertainment functions (such as entertainment vehicles, amusement park self-driving devices, segways, etc.), rescue vehicles (such as fire trucks, ambulances, power repair trucks, engineering rescue trucks, etc.)).

[0023] Specifically, after the vehicle starts, the navigation system configured in the vehicle or the navigation system of the user mobile terminal that communicates with the vehicle can obtain the path to be traveled by the vehicle. It can be understood that this example is applicable to new energy vehicles, especially applicable to the pure electric drive mode and the hybrid drive mode of the vehicle. Since the length of the sections of the path to be traveled is generally long, it is not conducive to fine-grained power prediction. Therefore, after obtaining the path to be traveled, the traffic condition index corresponding to the path to be traveled can be obtained to divide the path to be traveled, and at least two driving sections are obtained. The traffic condition index is used to characterize the smoothness of the path to be traveled, and the traffic parameters used to characterize the traffic condition index, such as vehicle speed, traffic flow, road conditions, etc., can be monitored and collected in real time by the vehicle's on-board navigation or on-board sensor system.

[0024] In some examples, the road can be divided into 4 levels: smooth, slow, congested, and severely congested according to different traffic condition indexes. Then, it is determined which level of smooth, slow, congested, and severely congested each driving section in at least two driving sections corresponds to. Based on this, the path to be traveled is divided to obtain at least two predicted sub-sections. In addition, when dividing the path to be traveled, in addition to dividing it using the traffic condition index, the path to be traveled can also be divided into at least two driving sections with the same distance. It should be noted that the specific number of driving sections can be determined according to the total length of the path to be traveled and the distance interval. This embodiment does not limit this.

[0025] It can be understood that when allocating power for each driving section, it is necessary to determine the power allocation dependence parameters corresponding to each driving section. Among them, the power allocation dependence parameters are used to characterize the parameters affecting the power allocation of the vehicle battery. The power allocation dependence parameters can be obtained through devices such as the vehicle's sensors, in-vehicle information system, and external environment monitoring system to ensure the accuracy of the power allocation dependence parameters for each driving section. After determining the power allocation dependence parameters, according to the power allocation dependence parameters corresponding to each driving section, determine the target power to be allocated for each driving section. According to the target power to be allocated for each driving section, determine the power driving mode of the vehicle in each driving section. Specifically, how to determine the target power to be allocated for each driving section according to the power allocation dependence parameters and how to determine the power driving mode of the vehicle in each driving section according to the target power to be allocated for each driving section will be described in detail in the subsequent embodiments and will not be elaborated here.

[0026] According to the technical solution provided by the embodiment of the present application, obtain the to-be-driven path of the vehicle and the road condition index corresponding to the to-be-driven path; segment the to-be-driven path according to the road condition index to obtain at least two driving sections, and determine the power allocation dependence parameters corresponding to each driving section, where the power allocation dependence parameters are used to characterize the parameters affecting the power allocation of the vehicle battery; according to the power allocation dependence parameters corresponding to each driving section, determine the target power to be allocated for each driving section; according to the target power to be allocated for each driving section, determine the power driving mode of the vehicle in each driving section, where the power driving mode includes a pure electric mode and a hybrid power mode. By using the road condition index corresponding to the to-be-driven path to segment the to-be-driven path, at least two driving sections are obtained, and then the power allocation dependence parameters corresponding to each driving section are determined, so as to determine a reasonable target power to be allocated for each driving section, so as to reasonably allocate the power usage of the battery in each driving section, optimize the battery usage efficiency, and ensure that the vehicle power system can improve the working efficiency of the drive system by utilizing the power output of the battery in each driving section, so as to achieve the optimal energy utilization efficiency, improve fuel economy and driving experience. It avoids the problems that the existing drive mode of new energy vehicles cannot ensure that the power system has a high working efficiency throughout the whole process, resulting in low fuel economy and affecting the driving experience.

[0027] In some embodiments, the power allocation dependence parameters include the average speed of each driving section, the length of the section, and the section environment parameters corresponding to each driving section; determining the target power to be allocated for each driving section according to the power allocation dependence parameters corresponding to each driving section includes:

[0028] According to the average speed and length of each driving section, calculate the first target value corresponding to each driving section, and based on the first target value corresponding to each driving section, obtain the total sum of the first target values corresponding to all driving sections; according to the corresponding relationship between the pre-set road section environmental parameters and the road section environmental coefficient, determine the road section environmental coefficient corresponding to each driving section; successively take each driving section as the target driving section, and based on the total sum of the first target values, the average speed and length of the target driving section, the road section environmental coefficient corresponding to the target driving section, and the available vehicle power, determine the target power to be allocated corresponding to the target driving section.

[0029] Specifically, the power allocation dependent parameters in this embodiment include the average speed of the road section, the length of the road section, and the road section environmental parameters. The average speed of the road section represents the average driving speed of the vehicle within a specific driving section, and this parameter can reflect the traffic conditions and driving conditions of the road section. The length of the road section refers to the distance that the vehicle needs to travel within a specific driving section. When determining the target power to be allocated corresponding to each driving section, calculate the first target value corresponding to each driving section according to the average speed and length of the road section corresponding to each driving section, and then add up the first target values of all driving sections to obtain the total sum of the first target values.

[0030] Furthermore, successively take each driving section as the target driving section, and based on the total sum of the first target values, the average speed and length of the target driving section, the road section environmental parameters corresponding to the target driving section, and the available vehicle power, determine the target power to be allocated corresponding to the target driving section, so as to obtain the target power to be allocated corresponding to each driving section, where the average speed of the road section is negatively correlated with the target power to be allocated. Exemplarily, the target power to be allocated corresponding to the target driving section can be calculated by the following formula (1):

[0031]

[0032] where, ΔSOC total is the available vehicle power, ΔSOC is the target power to be allocated corresponding to the target driving section, s k is the length of the target driving section, v k is the average speed of the target driving section, s i is the length of each driving section, v i is the average speed of each driving section, H is the road section environmental coefficient, and n is the number of driving sections; the value of in the formula is the first target value corresponding to each driving section, and is the total sum of the first target values corresponding to all driving sections.

[0033] In some examples, there is a pre-set corresponding relationship between the road section environmental parameters and the road section environmental coefficient. For example, the road section environmental parameters include, but are not limited to, weather conditions, road section types, etc. For example, the weather conditions can include sunny days, rainy days, snowy days, etc., and the road surface conditions can include urban road sections, highway road sections, etc. The above road section environmental parameters have a significant impact on the driving performance and energy consumption of the vehicle. The higher the road section environmental coefficient, the greater the impact of the road section environmental parameters on the vehicle speed. Therefore, considering the road section environmental parameters corresponding to each driving road section when determining the target power to be allocated for each driving road section can improve the accuracy and intelligence of power allocation. Exemplarily, the road section environmental coefficient corresponding to the road section environmental parameters can be determined with reference to the following table;

[0034] Table of Corresponding Relationship between Road Section Environmental Parameters and Road Section Environmental Coefficient

[0035] Road section environmental parameters Road section environmental coefficient H High-speed road section, sunny day 0.8 High-speed road section, rainy day 0.9 High-speed road section, snowy day 1.0 Urban road section, sunny day 1.1 Urban road section, rainy day 1.2 Urban road section, snowy day 1.3 Other road sections, sunny day 1.15 Other road sections, rainy day 1.25 Other road sections, snowy day 1.35

[0036] In addition, the available power of the vehicle is determined by the end value of the battery power and the current battery power value, that is, the difference between the current battery power value and the end value of the battery power is the available power of the vehicle. The end value of the battery power can be determined by the forced power conservation mode set by the user. For example, when the user sets (the battery power is lower than 15%) to enter the forced power conservation mode, the end value of the battery power at this time is the value corresponding to the battery power of 15%. At this time, the available power of the vehicle is the current battery power - the value corresponding to the battery power of 15%. In addition, if the user does not set the forced power conservation mode, the available power of the vehicle can be the current battery power - the value corresponding to the battery power of 0%. In addition, to ensure the accuracy of power allocation, the vehicle's vehicle control unit will monitor the vehicle's power consumption in real time and dynamically adjust the power allocation plan according to the actual situation of the vehicle's available power. For example, if the actual power consumption of a certain driving road section is lower than the allocated power, the controller will recalculate the power allocation for the remaining driving road sections based on the new current available power.

[0037] According to the technical solution provided by the embodiments of the present application, by calculating the target power to be allocated for each driving road section under the influence of the average road section speed, road section length, and road section environmental parameters of each driving road section, the existing power control mode of new energy vehicles with power first and then range extender is changed, so that the vehicle can reasonably allocate the power usage of the battery, optimize the battery usage efficiency, thereby ensuring the coordinated operation of the power hybrid mode and the pure electric mode, and further achieving the optimal energy utilization efficiency, improving fuel economy and driving experience.

[0038] In some embodiments, the power allocation dependent parameters include the road condition index, road section length, and road section environmental parameters corresponding to each driving road section; according to the power allocation dependent parameters corresponding to each driving road section, determining the target power to be allocated for each driving road section includes:

[0039] According to the road condition index and road length corresponding to each driving section, the second target value corresponding to each driving section is calculated, and based on the second target value corresponding to each driving section, the total sum of the second target values corresponding to all driving sections is obtained; according to the corresponding relationship between the preset road environment parameters and the road environment coefficient, the road environment coefficient corresponding to each driving section is determined; each driving section is sequentially used as the target driving section, and based on the total sum of the second target values, the road condition index and road length corresponding to the target driving section, the road environment coefficient corresponding to the target driving section, and the available vehicle power, the target power to be allocated corresponding to the target driving section is determined.

[0040] Specifically, the power allocation dependent parameters in this example include the road condition index, road length, and road environment parameters. The road condition index can reflect the congestion degree of the driving section, that is, the smoothness of each driving section is used to allocate power to each predicted sub-section. Exemplarily, the road condition index can be any one of smooth, slow, congested, and severely congested. During the calculation of the target power to be allocated using the road condition index, values can be assigned to the road index, and different numerical values are used to represent the smoothness degree of each driving section, such as smooth (1), slow (1.5), congested (2), severely congested (2.5), so as to facilitate calculation according to the road condition index. Then, according to the road condition index and road length corresponding to each driving section, the second target value corresponding to each driving section is calculated, and based on the second target value corresponding to each driving section, the total sum of the second target values corresponding to all driving sections is obtained.

[0041] According to the corresponding relationship between the preset road environment parameters and the road environment coefficient, the road environment coefficient corresponding to each driving section is determined, which can refer to the determination method in the previous embodiment and will not be elaborated here.

[0042] Furthermore, based on the total sum of the second target values, the road condition index and road length corresponding to the target driving section, the road environment coefficient corresponding to the target driving section, and the available vehicle power, the target power to be allocated corresponding to the target driving section is determined, so as to obtain the target power to be allocated corresponding to each driving section, where the road condition index is positively correlated with the target power to be allocated. Exemplarily, the target power to be allocated corresponding to the target driving section can be calculated by the following formula (2):

[0043]

[0044] where, ΔSOC total is the available vehicle power, ΔSOC is the target power to be allocated corresponding to the target driving section, s k is the road length of the target driving section, c k is the road condition index of the target driving section, si is the road length of each driving section, c i is the road condition index of each driving section, H is the road section environment coefficient, and n is the number of driving sections; s in the formula i c i The value of is the second target value corresponding to each driving section, The sum of the second target values corresponding to all driving sections.

[0045] According to the technical solution provided by the embodiments of the present application, by calculating the target power to be allocated for each driving section under the influence of the road condition index, road length, and road section environment parameters of each driving section, the existing power control mode of new energy vehicles with power first and then range extender is changed, so that the vehicle can reasonably allocate the power usage of the battery, optimize the battery usage efficiency, thereby ensuring the coordinated operation of the power hybrid mode and the pure electric mode, and further achieving the optimal energy utilization efficiency, improving fuel economy and driving experience.

[0046] In some embodiments, the power distribution dependent parameters include the average road speed, road condition index, road length, and road section environment parameters corresponding to each driving section. According to the power distribution dependent parameters corresponding to each driving section, determining the target power to be allocated for each driving section includes:

[0047] According to the average road speed and road length corresponding to each driving section, calculate the first target value corresponding to each driving section, and according to the first target value corresponding to each driving section, obtain the sum of the first target values corresponding to all driving sections; according to the preset corresponding relationship between the road section environment parameters and the road section environment coefficient, determine the road section environment coefficient corresponding to each driving section; sequentially use each driving section as the target driving section, and according to the sum of the first target values, the average road speed and road length corresponding to the target driving section, the road section environment coefficient corresponding to the target driving section, and the available vehicle power, determine the target power to be allocated for the target driving section corresponding to the average road speed; according to the road condition index and road length corresponding to each driving section, calculate the second target value corresponding to each driving section, and according to the second target value corresponding to each driving section, obtain the sum of the second target values corresponding to all driving sections; according to the sum of the second target values, the road condition index and road length corresponding to the target driving section, the road section environment coefficient corresponding to the target driving section, and the available vehicle power, determine the target power to be allocated for the target driving section corresponding to the road condition index; according to the target power to be allocated for the target driving section corresponding to the average road speed and the target power to be allocated for the target driving section corresponding to the road index, determine the target power to be allocated for the target driving section.

[0048] Specifically, in this example, the average speed of the road section, the road condition index of the road section, the road section length, and the road section environment parameters are jointly used as the power distribution dependent parameters to more comprehensively evaluate the driving conditions of the road section. The method for determining the target power to be distributed corresponding to the average speed of the road section for the target driving road section, and the method for determining the target power to be distributed corresponding to the road condition index of the road section for the target driving road section by using the average speed of the road section, the road section length, and the road section environment parameters can refer to the foregoing embodiments and will not be elaborated here.

[0049] Further, after determining the method for determining the target power to be distributed corresponding to the average speed of the road section for the target driving road section and the target power to be distributed corresponding to the road condition index of the road section for the target driving road section, the target power to be distributed corresponding to the target driving road section can be determined. Exemplarily, the two target powers to be distributed can be combined through weighted averaging, or the weight coefficients can be dynamically adjusted according to the vehicle's real-time state and historical data. For example, if the vehicle often encounters congestion in the driving history of a certain road section, the weight coefficient of the road condition index can be appropriately increased to ensure that the vehicle has sufficient power to cope with possible low-speed driving or parking waiting situations in this road section.

[0050] In some embodiments, determining the target power to be distributed corresponding to the target driving road section according to the target power to be distributed corresponding to the average speed of the road section for the target driving road section and the target power to be distributed corresponding to the road condition index of the road section for the target driving road section includes:

[0051] Performing weighted summation on the target power to be distributed corresponding to the average speed of the road section for the target driving road section and the target power to be distributed corresponding to the road condition index of the road section for the target driving road section according to each coefficient combination in at least two sets of pre-set coefficient combinations to obtain multiple intermediate distributed powers, where the coefficient combination includes a first coefficient value corresponding to the average speed of the road section and a second coefficient value corresponding to the road condition index, and the sum of the first coefficient value and the second coefficient value is 1; determining the energy consumption data corresponding to each intermediate distributed power, and determining the intermediate distributed power with the smallest energy consumption data among the energy consumption data as the target power to be distributed.

[0052] Specifically, at least two sets of coefficient combinations are pre-set, where the coefficient combination includes a first coefficient value corresponding to the average speed of the road section and a second coefficient value corresponding to the road condition index, and the sum of the first coefficient value and the second coefficient value is 1. Exemplarily, the first coefficient value and the second coefficient value can be (0.1, 0.9), (0.2, 0.8), (0.3, 0.7), (0.4, 0.6), etc. By traversing different combinations of the first coefficient value and the second coefficient value, that is, considering the influence of the average speed of the road section and the road condition index with different weights on power distribution, the precise calculation of the power distribution for each predicted sub-road section is realized to maintain the optimal fuel economy.

[0053] Further, based on the first coefficient values and the second coefficient values of different combinations, and the target power to be allocated corresponding to the average speed of the target driving section and the target power to be allocated corresponding to the road condition index of the target driving section, a weighted sum is performed to determine multiple intermediate allocated powers for each driving section. Exemplarily, the intermediate allocated power of the target driving section can be calculated by the following formula (3):

[0054]

[0055] where α1 is the first coefficient value corresponding to the average speed of the section, α2 is the second coefficient value corresponding to the road condition index of the section, and ΔSOC k3 is the intermediate allocated power of the target driving section, ΔSOC k1 is the target power to be allocated corresponding to the average speed of the target driving section, and ΔSOC k2 is the target power to be allocated corresponding to the road condition index of the target driving section;

[0056] Continuing with the above example, if the target power to be allocated corresponding to the average speed of the target driving section is 5 kWh, the target power to be allocated corresponding to the road condition index of the target driving section is 8 kWh, and the combination of the first coefficient value and the second coefficient value is (0.1, 0.9), the intermediate allocated power at this time is (0.1·5 + 0.9·8) = 7.7 kWh; when the combination of the first coefficient value and the second coefficient value is (0.2, 0.8), the intermediate allocated power at this time is (0.2·5 + 0.8·8) = 7.4 kWh; and so on, until all combinations of the first coefficient value and the second coefficient value are calculated to obtain the intermediate allocated powers corresponding to the number of combinations;

[0057] It can be understood that after obtaining the intermediate allocated powers, an intermediate allocated power needs to be selected as the target power to be allocated. At this time, the energy consumption data of each intermediate allocated power can be calculated to obtain multiple energy consumption results, and the intermediate allocated power with the smallest energy consumption data is selected from the energy consumption results as the target power to be allocated. Exemplarily, the Adaptive Energy Management Control Strategy Algorithm (A-ECMS) can be used to calculate the energy consumption data of each intermediate allocated power, and finally the intermediate allocated power with the smallest energy consumption data is selected as the target power to be allocated

[0058] According to the technical solution provided by the embodiments of the present application, the target power to be allocated for each driving section is calculated under the influence of the average speed of the section, the road condition index of the section, the length of the section, and the environmental parameters of the section, so as to change the existing power control mode of pure electric drive of new energy vehicles, enabling the vehicle to reasonably allocate the power usage of the battery, optimize the battery usage efficiency, ensure the coordinated operation of the power hybrid mode and the pure electric mode, and thus achieve the optimal energy utilization efficiency, improve fuel economy and driving experience.

[0059] In some embodiments, according to the target power to be allocated for each driving section, the power driving mode of the vehicle in each driving section is determined, including:

[0060] For each driving section, determine the vehicle speed corresponding to the vehicle when driving a preset distance in the driving section, and determine the driving distance corresponding to the target power to be allocated according to the vehicle speed; in the case where the driving distance is less than the length of the driving section corresponding to the driving section, determine that the power driving mode of the vehicle in the driving section is the hybrid power mode; in the case where the driving distance is greater than or equal to the length of the driving section corresponding to the driving section, determine that the power driving mode of the vehicle in the driving section is the pure electric mode.

[0061] Specifically, after determining the target power to be allocated for each driving section, it is necessary to use this as a basis to control the power output mode of the vehicle to keep the vehicle running under the optimal combined control mode. Furthermore, optimize the energy usage efficiency of the vehicle in different sections. For each driving section, determine the vehicle speed corresponding to the vehicle when driving a preset distance in the driving section, determine the driving distance corresponding to the target power to be allocated according to the vehicle speed and compare it with the allocated power, which can determine which power mode the vehicle should use in this section. If the allocated power is sufficient to cover the predicted driving distance, the vehicle can use the pure electric mode, which can reduce fuel consumption and emissions. If the power is not enough to cover the driving distance, the vehicle switches to the hybrid power mode to ensure that the vehicle can complete the driving of the entire section while making the best use of electric energy and improving the energy usage efficiency.

[0062] In one example, after determining the target power to be allocated for each driving section, the corresponding drivable distance of the allocated power can be determined. For example, if the allocated power for a certain driving section is determined to be 4 kWh, and based on the vehicle speed, the driving distance corresponding to the allocated power is determined to be 25 km. At this time, the section distance of this driving section is 35 km. Comparing the driving distance of 25 km corresponding to the allocated power with the section distance of 35 km corresponding to the predicted sub-section, it can be obtained that the driving distance corresponding to the allocated power is less than the section distance corresponding to the predicted sub-section. At this time, it is necessary to control the vehicle to drive in a hybrid power mode on this driving section so as to complete the driving of this driving section by combining the battery power and the engine power. If the driving section is 25 km at this time, comparing the driving distance of 25 km corresponding to the allocated power with the section distance of 25 km corresponding to the driving section, it can be obtained that the driving distance corresponding to the allocated power is equal to the section distance corresponding to the driving section. At this time, controlling the vehicle to drive in an all-electric mode on this driving section can complete the driving of this section. It should be noted that if the allocated power is greater than the driving distance corresponding to the driving section during actual driving, at this time, control the vehicle to drive in an all-electric mode on the driving section, and the remaining power can be used for the power allocation calculation of subsequent sections.

[0063] According to the technical solution provided by the embodiment of the present application, for each driving section, determine the vehicle speed corresponding to the vehicle when driving a preset distance in the driving section, and determine the driving distance corresponding to the target power to be allocated according to the vehicle speed; in the case where the driving distance is less than the section length corresponding to the driving section, determine that the power driving mode of the vehicle in the driving section is a hybrid power mode; in the case where the driving distance is greater than or equal to the section length corresponding to the driving section, determine that the power driving mode of the vehicle in the driving section is an all-electric mode. Through such a control strategy, the energy utilization efficiency of the vehicle under different road conditions can be significantly improved, thereby reducing the operating cost and environmental pollution.

[0064] In some embodiments, the method further includes: in the case where the to-be-driven path of the vehicle is not obtained, discretely divide the acceleration data and speed data in the preset data set into finite-length sequences, and obtain a transition probability matrix based on the finite-length sequences; obtain the current vehicle speed of the vehicle, and based on the transition probability matrix and the current vehicle speed, use the maximum likelihood method to determine the predicted vehicle speed of the vehicle within the prediction time domain, and determine the predicted power allocation trajectory of the vehicle according to the predicted vehicle speed, so as to allocate power for the vehicle by using the predicted power allocation trajectory.

[0065] It can be understood that in a network-free environment or a poor network environment, when the to-be-driven path of the vehicle is not obtained, in order to expand the usage scenario of the power allocation method in this example, this example uses a preset data set to predict the predicted vehicle speed of the vehicle within the prediction time domain, and determines the predicted power allocation trajectory of the vehicle according to the predicted vehicle speed, so as to allocate power for the vehicle by using the predicted power allocation trajectory.

[0066] Specifically, the preset data set is pre-stored in the vehicle's system. The preset data set includes acceleration data and speed data, and there are multiple pieces of acceleration data and speed data. In this example, the acceleration data and speed data in the preset data set are discretely divided into finite-length sequences to simplify the computational complexity of data processing and improve data processing efficiency. Then, based on the finite-length sequences, a transition probability matrix is constructed. The transition probability matrix can describe the conversion probability between different speed and acceleration states of the vehicle, thereby providing a probability model for the vehicle speed change within the prediction time domain.

[0067] It can be understood that after constructing the transition probability matrix, in this example, the maximum likelihood method is further used to determine the predicted vehicle speed of the vehicle within the prediction time domain (for example, within the next 10 s). The maximum likelihood method is used to estimate model parameters based on known observation data. In this example, the model parameter is the predicted vehicle speed of the vehicle. Through the current vehicle speed and the transition probability matrix, the probability distribution in different vehicle speed states can be calculated, thereby determining the most likely vehicle speed trajectory.

[0068] In one example, for instance, the current vehicle speed of the vehicle is 60 km / h, and there are multiple speed samples in the preset data set that are close to the current vehicle speed. Through discretization processing, these samples are divided into finite-length sequences. For example, each sequence contains 5 consecutive speed samples and corresponding acceleration samples. Based on the finite-length sequences, a transition probability matrix is constructed, and this matrix can reflect the conversion probability between different vehicle speed states.

[0069] After constructing the transition probability matrix, the maximum likelihood method is used to determine the predicted vehicle speed of the vehicle within the prediction time domain. Assume that the vehicle is currently in a speed state of 60 km / h. According to the transition probability matrix, it can be calculated that within the next 1 second, the probability that the vehicle speed may increase to 65 km / h is 0.3, the probability of remaining at 60 km / h is 0.5, and the probability of decreasing to 55 km / h is 0.2. According to the maximum likelihood method, the vehicle speed state with the highest probability is selected as the predicted vehicle speed, that is, remaining at 60 km / h. Repeat this operation to determine the predicted vehicle speed of the vehicle within the prediction time domain. Then, based on the predicted vehicle speed, the predicted power distribution trajectory of the vehicle is determined.

[0070] According to the technical solution provided by this embodiment, if the predicted driving route of the vehicle is not obtained during the vehicle driving process, the acceleration data and speed data in the preset data set are discretely divided into finite-length sequences, and a transition probability matrix is obtained based on the finite-length sequences; the current vehicle speed of the vehicle is obtained, and based on the transition probability matrix and the current vehicle speed, the predicted vehicle speed within the prediction time domain is determined by using the maximum likelihood method, and the predicted power distribution trajectory of the vehicle is determined according to the predicted vehicle speed, so as to allocate power to the vehicle by using the predicted power distribution trajectory. In this way, even in the case of no network or poor network conditions, the vehicle system can effectively predict the driving speed of the vehicle by using the preset data set and the maximum likelihood method, and allocate reasonable power to the vehicle accordingly, optimize the battery usage efficiency, ensure the coordinated operation of the power hybrid mode and the pure electric mode, so as to achieve the optimal energy utilization efficiency, improve the fuel economy and driving experience.

[0071] In some embodiments, determining the predicted power distribution trajectory of the vehicle according to the predicted vehicle speed includes: constructing a state transition equation, where the state transition equation uses the power battery power as the state variable, the fuel consumption as the state transition cost, and the output power of the range extender as the control parameter; obtaining the current power of the power battery, the range extender power, and the power battery power, and obtaining the initial battery power value according to the current power of the power battery, the range extender power, and the power battery power; calculating the power change trajectory and the final battery power value of the power battery through the state transition equation according to the initial battery power value and the predicted vehicle speed; determining the cost function within the prediction time domain, and using the final battery power value, the power change trajectory, and the cost function to inversely solve the target cost function and the engine control parameter at each preset time step within the prediction time domain; obtaining the predicted power distribution trajectory according to the target cost function and the engine control parameter.

[0072] It can be understood that when constructing the state transition equation, it is first necessary to define the initial state and the target state of the power battery power. The initial state of the power battery power is determined by the current power, while the target state is jointly determined by the driving demand within the prediction time domain and the battery capacity limit. The state transition equation regards the change of the power battery power as a dynamic process, where the fuel consumption is used as the state transition cost and the output power of the range extender is used as the control parameter.

[0073] Specifically, after obtaining the current power of the power battery, the power of the range extender, and the power of the power battery, the initial value of the battery power can be calculated. Combining with the predicted vehicle speed, the power change trajectory and the final value of the battery power of the power battery can be calculated through the state transition equation. Then, the cost function within the prediction time domain is determined. The cost function usually includes factors such as fuel consumption, battery life loss, emissions, etc., to comprehensively evaluate the costs under different control strategies. Using the final value of the battery power, the power change trajectory, and the cost function, the objective cost function and the engine control parameters at each preset time step (for example, within 1 s) within the prediction time domain are solved reversely. In order to find the optimal control strategy to minimize the total cost.

[0074] Finally, according to the objective cost function and the engine control parameters, the predicted power distribution trajectory is solved forward. The predicted power distribution trajectory will guide the energy management of the vehicle within the prediction time domain, ensuring the coordinated operation of the power hybrid mode and the pure electric mode to achieve the optimal energy utilization efficiency.

[0075] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution. The execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the process of the embodiments of the present application.

[0076] All the above optional technical solutions can be combined arbitrarily to form optional embodiments of the present application, which will not be elaborated herein one by one.

[0077] The following is an embodiment of the device of the present application, which can be used to execute the method embodiment of the present application. For the details not disclosed in the embodiment of the device of the present application, please refer to the method embodiment of the present application.

[0078] Figure 2 It is a schematic structural diagram of a device for determining a vehicle power driving mode provided by an embodiment of the present application. As Figure 2 shown, the device includes:

[0079] An acquisition module 201, configured to acquire the path to be traveled by the vehicle and the road condition index corresponding to the path to be traveled;

[0080] A segmentation module 202, configured to segment the path to be traveled according to the road condition index to obtain at least two driving segments, and determine the power distribution dependent parameters corresponding to each driving segment, where the power distribution dependent parameters are used to characterize the parameters affecting the power battery power distribution of the vehicle;

[0081] A distribution module 203, configured to determine the target power to be distributed corresponding to each driving segment according to the power distribution dependent parameters corresponding to each driving segment;

[0082] A determination module 204, configured to determine a power driving mode of the vehicle on each driving section according to a target power to be allocated corresponding to each driving section, where the power driving mode includes an all-electric mode and a hybrid power mode.

[0083] In some embodiments, the allocation module 203 is further configured to calculate a first target value corresponding to each driving section according to the average speed and length of each driving section, and obtain the total sum of the first target values corresponding to all driving sections according to the first target values corresponding to each driving section; determine the road section environment coefficient corresponding to each driving section according to the corresponding relationship between the preset road section environment parameters and the road section environment coefficient; sequentially use each driving section as a target driving section, and determine the target power to be allocated corresponding to the target driving section according to the total sum of the first target values, the average speed and length of the target driving section, the road section environment coefficient corresponding to the target driving section, and the available power of the vehicle.

[0084] In some embodiments, the allocation module 203 is further configured to calculate a second target value corresponding to each driving section according to the road condition index and length of each driving section, and obtain the total sum of the second target values corresponding to all driving sections according to the second target values corresponding to each driving section; determine the road section environment coefficient corresponding to each driving section according to the corresponding relationship between the preset road section environment parameters and the road section environment coefficient; sequentially use each driving section as a target driving section, and determine the target power to be allocated corresponding to the target driving section according to the total sum of the second target values, the road condition index and length of the target driving section, the road section environment coefficient corresponding to the target driving section, and the available power of the vehicle.

[0085] In some embodiments, the allocation module 203 is further configured to calculate a first target value corresponding to each driving section based on the average vehicle speed and the length of each driving section, and obtain the total sum of the first target values corresponding to all driving sections according to the first target values corresponding to each driving section; determine the road section environment coefficient corresponding to each driving section according to the correspondence between the pre-set road section environment parameters and the road section environment coefficients; sequentially use each driving section as the target driving section, and determine the target power to be allocated for the target driving section corresponding to the average vehicle speed of the road section according to the total sum of the first target values, the average vehicle speed and the length of the target driving section, the road section environment coefficient corresponding to the target driving section, and the available power of the vehicle; calculate a second target value corresponding to each driving section according to the road condition index and the length of each driving section, and obtain the total sum of the second target values corresponding to all driving sections according to the second target values corresponding to each driving section; determine the target power to be allocated for the target driving section corresponding to the road condition index of the road section according to the total sum of the second target values, the road condition index and the length of the target driving section, the road section environment coefficient corresponding to the target driving section, and the available power of the vehicle; and determine the target power to be allocated corresponding to the target driving section according to the target power to be allocated for the target driving section corresponding to the average vehicle speed of the road section and the target power to be allocated for the target driving section corresponding to the road condition index of the road section.

[0086] In some embodiments, the allocation module 203 is further configured to perform a weighted summation on the target power to be allocated for the target driving section corresponding to the average vehicle speed of the road section and the target power to be allocated for the target driving section corresponding to the road condition index of the road section according to each coefficient combination in at least two pre-set coefficient combinations, to obtain a plurality of intermediate allocated powers, where the coefficient combination includes a first coefficient value corresponding to the average vehicle speed of the road section and a second coefficient value corresponding to the road condition index of the road section, and the sum of the first coefficient value and the second coefficient value is 1; determine the energy consumption data corresponding to each intermediate allocated power, and determine the intermediate allocated power with the smallest energy consumption data among the energy consumption data as the target power to be allocated.

[0087] In some embodiments, the determination module 204 is further configured to, for each driving section, determine the vehicle speed corresponding to the vehicle when driving a preset distance in the driving section, and determine the driving distance corresponding to the target power to be allocated according to the vehicle speed; in the case where the driving distance is less than the length of the driving section corresponding to the driving section, determine that the power driving mode of the vehicle in the driving section is a hybrid power mode; and in the case where the driving distance is greater than or equal to the length of the driving section corresponding to the driving section, determine that the power driving mode of the vehicle in the driving section is an all-electric mode.

[0088] In some embodiments, the determination module 204 is further configured to, when the to-be-traveled path of the vehicle is not obtained, discretely divide the acceleration data and speed data in the preset data set into finite-length sequences, and obtain a transition probability matrix based on the finite-length sequences; obtain the current vehicle speed of the vehicle, and based on the transition probability matrix and the current vehicle speed, use the maximum likelihood method to determine the predicted vehicle speed of the vehicle within the prediction time domain, and determine the predicted power distribution trajectory of the vehicle according to the predicted vehicle speed, so as to allocate power to the vehicle by using the predicted power distribution trajectory.

[0089] In some embodiments, the determination module 204 is further configured to construct a state transition equation, where the state transition equation uses the power battery power as the state variable, the fuel consumption as the state transition cost, and the output power of the range extender as the control parameter; obtain the current power of the power battery, the range extender power, and the power battery power, and obtain the initial battery power value according to the current power of the power battery, the range extender power, and the power battery power; calculate the power change trajectory and the final battery power value of the power battery through the state transition equation according to the initial battery power value and the predicted vehicle speed; determine the cost function within the prediction time domain, and use the final battery power value, the power change trajectory, and the cost function to inversely solve the target cost function and the engine control parameter at each preset time step within the prediction time domain; and obtain the predicted power distribution trajectory according to the target cost function and the engine control parameter.

[0090] Figure 3 is a schematic diagram of the electronic device 3 provided by an embodiment of the present application. As Figure 3 shown, the electronic device 3 of this embodiment includes: a processor 301, a memory 302, and a computer program 303 stored in the memory 302 and executable on the processor 301. When the processor 301 executes the computer program 303, the steps in the above-mentioned various method embodiments are implemented. Alternatively, when the processor 301 executes the computer program 303, the functions of each module / unit in the above-mentioned various device embodiments are implemented.

[0091] The electronic device 3 may be a desktop computer, a notebook, a palm computer, a cloud server, and other electronic devices. The electronic device 3 may include, but is not limited to, the processor 301 and the memory 302. Those skilled in the art can understand that Figure 3 merely examples of the electronic device 3, which do not constitute a limitation to the electronic device 3, and may include more or fewer components than those shown in the figure, or different components.

[0092] The processor 301 may be a Central Processing Unit (CPU), or may be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0093] The memory 302 may be an internal storage unit of the electronic device 3, for example, the hard disk or memory of the electronic device 3. The memory 302 may also be an external storage device of the electronic device 3, for example, a plug-in hard disk equipped on the electronic device 3, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. The memory 302 may also include both an internal storage unit and an external storage device of the electronic device 3. The memory 302 is used to store computer programs and other programs and data required by the electronic device.

[0094] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the above division of each functional unit and module is used as an example for illustration. In practical applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiments can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0095] If the integrated module / 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 storage medium. Based on this understanding, to implement all or part of the processes in the above-described embodiment methods of this application, it can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in the storage medium. When the computer program is executed by a processor, the steps of the above-described various method embodiments can be implemented. The computer program can include computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The storage medium can include: any entity or device that can carry computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0096] The above embodiments are only used to illustrate the technical solutions of this application, rather than to limit them; although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for determining a vehicle's power driving mode, characterized in that, Including: Obtain the to-be-traveled path of the vehicle and the road condition index corresponding to the to-be-traveled path; Segment the to-be-traveled path according to the road condition index to obtain at least two driving segments, and determine the power distribution dependence parameters corresponding to each driving segment, where the power distribution dependence parameter is used to characterize the parameter affecting the power distribution of the vehicle battery; Determine the target power to be distributed corresponding to each driving segment according to the power distribution dependence parameters corresponding to each driving segment; Determine the power driving mode of the vehicle in each driving segment according to the target power to be distributed corresponding to each driving segment, where the power driving mode includes a pure electric mode and a hybrid power mode; Determine the power driving mode of the vehicle in each driving segment according to the target power to be distributed corresponding to each driving segment, including: For each driving segment, determine the vehicle speed corresponding to when the vehicle travels a preset distance in the driving segment, and determine the driving distance corresponding to the target power to be distributed according to the vehicle speed; In the case where the driving distance is less than the segment length corresponding to the driving segment, determine that the power driving mode of the vehicle in the driving segment is a hybrid power mode; In the case where the driving distance is greater than or equal to the segment length corresponding to the driving segment, determine that the power driving mode of the vehicle in the driving segment is a pure electric mode; The method further includes: In the case where the to-be-traveled path of the vehicle is not obtained, discretize the acceleration data and speed data in the preset data set into finite-length sequences, and obtain a transition probability matrix based on the finite-length sequences; Obtain the current vehicle speed of the vehicle, determine the predicted vehicle speed of the vehicle within the prediction time domain based on the transition probability matrix and the current vehicle speed using the maximum likelihood method, and determine the predicted power distribution trajectory of the vehicle according to the predicted vehicle speed, so as to allocate power for the vehicle using the predicted power distribution trajectory.

2. The method according to claim 1, wherein The power distribution dependence parameter includes the segment average vehicle speed, segment length, and segment environment parameter corresponding to each driving segment; The determining the target power to be distributed corresponding to each driving segment according to the power distribution dependence parameters corresponding to each driving segment includes: Calculate the first target value corresponding to each driving segment according to the segment average vehicle speed and segment length corresponding to each driving segment, and obtain the sum of the first target values corresponding to all driving segments according to the first target values corresponding to each driving segment; Determine the segment environment coefficient corresponding to each driving segment according to the corresponding relationship between the preset segment environment parameter and the segment environment coefficient; Successively use each driving segment as the target driving segment, and determine the target power to be distributed corresponding to the target driving segment according to the sum of the first target values, the segment average vehicle speed and segment length corresponding to the target driving segment, the segment environment coefficient corresponding to the target driving segment, and the available power of the vehicle.

3. The method according to claim 1, characterized in that, The power distribution dependence parameter includes the segment road condition index, segment length, and segment environment parameter corresponding to each driving segment; Determining the target power to be allocated for each driving section according to the power allocation dependence parameters corresponding to each driving section includes: Calculating a second target value for each driving section according to the road condition index and road length corresponding to each driving section, and obtaining the total sum of the second target values for all driving sections according to the second target values corresponding to each driving section; Determining the road environment coefficient corresponding to each driving section according to the corresponding relationship between the pre-set road environment parameters and the road environment coefficient; Sequentially taking each driving section as the target driving section, and determining the target power to be allocated for the target driving section according to the total sum of the second target values, the road condition index and road length corresponding to the target driving section, the road environment coefficient corresponding to the target driving section, and the available power of the vehicle.

4. The method according to claim 1, wherein The power allocation dependence parameters include the average road speed, road condition index, road length, and road environment parameters corresponding to each driving section. Determining the target power to be allocated for each driving section according to the power allocation dependence parameters corresponding to each driving section includes: Calculating a first target value for each driving section according to the average road speed and road length corresponding to each driving section, and obtaining the total sum of the first target values for all driving sections according to the first target values corresponding to each driving section; Determining the road environment coefficient corresponding to each driving section according to the corresponding relationship between the pre-set road environment parameters and the road environment coefficient; Sequentially taking each driving section as the target driving section, and determining the target power to be allocated for the target driving section corresponding to the average road speed according to the total sum of the first target values, the average road speed and road length corresponding to the target driving section, the road environment coefficient corresponding to the target driving section, and the available power of the vehicle; Calculating a second target value for each driving section according to the road condition index and road length corresponding to each driving section, and obtaining the total sum of the second target values for all driving sections according to the second target values corresponding to each driving section; Determining the target power to be allocated for the target driving section corresponding to the road condition index according to the total sum of the second target values, the road condition index and road length corresponding to the target driving section, the road environment coefficient corresponding to the target driving section, and the available power of the vehicle; Determining the target power to be allocated for the target driving section according to the target power to be allocated for the target driving section corresponding to the average road speed and the target power to be allocated for the target driving section corresponding to the road condition index.

5. The method according to claim 4, wherein Determining the target power to be allocated for the target driving section according to the target power to be allocated for the target driving section corresponding to the average road speed and the target power to be allocated for the target driving section corresponding to the road condition index includes: According to each coefficient combination in at least two pre-set coefficient combinations, perform weighted summation on the target power to be allocated corresponding to the average speed of the target driving section and the target power to be allocated corresponding to the road condition index of the target driving section, to obtain multiple intermediate allocated powers, where the coefficient combination includes a first coefficient value corresponding to the average speed of the section and a second coefficient value corresponding to the road condition index of the section, and the sum of the first coefficient value and the second coefficient value is 1; Determine the energy consumption data corresponding to each of the intermediate allocated powers, and determine the intermediate allocated power with the smallest energy consumption data among the energy consumption data as the target power to be allocated.

6. The method according to claim 1, characterized in that, According to the predicted vehicle speed, determine the predicted power allocation trajectory of the vehicle, including: Construct a state transition equation, where the state transition equation uses the battery power of the power battery as the state variable, the fuel consumption as the state transition cost, and the output power of the range extender as the control parameter; Obtain the current power of the power battery, the power of the range extender, and the power of the power battery, and obtain the initial battery power value according to the current power of the power battery, the power of the range extender, and the power of the power battery; According to the initial battery power value and the predicted vehicle speed, calculate the power change trajectory and the final battery power value of the power battery through the state transition equation; Determine the cost function within the prediction time domain, and use the final battery power value, the power change trajectory, and the cost function to inversely solve the target cost function and the engine control parameter at each preset time step within the prediction time domain; According to the target cost function and the engine control parameter, obtain the predicted power allocation trajectory.

7. An apparatus for determining a vehicle power driving mode, characterized in that Including: An acquisition module, configured to acquire the path to be traveled by the vehicle and the road condition index corresponding to the path to be traveled; A segmentation module, configured to segment the path to be traveled according to the road condition index to obtain at least two driving sections, and determine the power allocation dependent parameters corresponding to each of the driving sections, where the power allocation dependent parameters are used to characterize the parameters affecting the battery power allocation of the vehicle; An allocation module, configured to determine the target power to be allocated corresponding to each of the driving sections according to the power allocation dependent parameters corresponding to each of the driving sections; A determination module, configured to determine the power driving mode of the vehicle in each of the driving sections according to the target power to be allocated corresponding to each of the driving sections, where the power driving mode includes a pure electric mode and a hybrid mode; The determination module is configured to, for each of the driving sections, determine the vehicle speed corresponding to when the vehicle travels a preset distance in the driving section, and determine the driving distance corresponding to the target power to be allocated according to the vehicle speed; In the case where the driving distance is less than the section length corresponding to the driving section, determine that the power driving mode of the vehicle in the driving section is the hybrid mode; in the case where the driving distance is greater than or equal to the section length corresponding to the driving section, determine that the power driving mode of the vehicle in the driving section is the pure electric mode; The determination module is further configured to discretely divide the acceleration data and speed data in the preset data set into finite-length sequences when the to-be-traveled path of the vehicle is not obtained, and obtain a transition probability matrix based on the finite-length sequences; obtain the current vehicle speed of the vehicle, and based on the transition probability matrix and the current vehicle speed, determine the predicted vehicle speed of the vehicle within the prediction time domain by using the maximum likelihood method, and determine the predicted power distribution trajectory of the vehicle according to the predicted vehicle speed, so as to allocate power to the vehicle by using the predicted power distribution trajectory.

8. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

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