Power distribution system, method and apparatus, computer device, and storage medium

Abstract

A power distribution system, method and apparatus, a computer device, and a storage medium. The system comprises: a cloud platform (10) configured to receive vehicle operation data sent by a target vehicle, recognize common path features in the vehicle operation data within a fixed time period, and send the common path features to a vehicle end controller (20) on the basis of a subscription request of a vehicle; and the vehicle end controller (20) configured to receive the latest common path features after sending the subscription request to the cloud platform (10) on the basis of an update rule, and update the common path features to common path parameters of the vehicle, so that the vehicle guides, on the basis of the common path features in the driving process, an ECMS strategy of the vehicle to perform real-time power distribution. According to the solution, the common path features can be extracted on the cloud platform without relying on complex computing power and external input, the common path features are matched on the vehicle, and then the ECMS strategy is guided to perform power distribution.

Description

Power distribution system, method, device, computer equipment and storage medium

[0001] This application claims priority to Chinese patent application number 202311261309.1, filed on September 27, 2023, entitled “Power distribution system, method, apparatus, computer equipment and storage medium,” the entire contents of which are incorporated by reference into this application. Technical Field

[0002] This application relates to the technical field of plug-in hybrid electric vehicles, and in particular to a power distribution system, method, device, computer equipment and storage medium. Background Art

[0003] Plug-in hybrid vehicles balance economy and range. The power allocation in their energy management strategies is key to determining their ultimate energy consumption. However, existing energy management strategies rely heavily on external input of planned route information, limiting their application scenarios. If the user does not enable navigation, the corresponding strategies will not take effect. Furthermore, they place high demands on the controller's computing power, making them difficult to apply on a large scale directly on existing on-board controllers.

[0004] Based on this, it is necessary to optimize the energy management strategy to address the above problems.

[0005] Summary of the Invention

[0006] Based on this, in order to solve the above technical problems, a power distribution system, method, device, computer equipment and storage medium are provided, which can be deployed on the existing vehicle-side controller based on low computing power and low memory without relying on complex calculations or external navigation input, so as to solve the power distribution problem in the existing energy management strategy.

[0007] In a first aspect, the present application provides a power distribution system, the system including a cloud platform and a vehicle-side controller, wherein the vehicle-side controller is communicatively connected to the cloud platform;

[0008] The cloud platform is configured to receive vehicle operation data sent by the target vehicle, identify common path features in the vehicle operation data within a fixed time period, and send the common path features to the vehicle-side controller based on the vehicle's subscription request;

[0009] The vehicle-side controller is configured to send a subscription request to the cloud platform based on the update rule, receive the latest common path characteristics, and update the common path characteristics to the common path parameters of the vehicle, so that the vehicle can guide the vehicle ECMS strategy to perform real-time power distribution according to the common path characteristics during driving.

[0010] In one embodiment, the cloud platform is configured with an encrypted data form, which stores common path features. The common path features are bound to the vehicle VIN code. When receiving a vehicle subscription request, the corresponding common path features are matched according to the vehicle VIN code in the subscription request.

[0011] In one embodiment, the update rule of the vehicle-side controller is configured such that when the common route feature is updated to the common route parameter of the vehicle, the accumulated mileage and interval days in the common route parameter are cleared, and new mileage and interval days are accumulated again;

[0012] If the re-accumulated mileage exceeds the preset mileage threshold, or the re-accumulation interval exceeds the preset day threshold, a subscription request is sent to the cloud platform to obtain new common path features.

[0013] In a second aspect, the present application provides a power distribution method, which is applied to a vehicle-side controller. The method includes:

[0014] Receive real-time vehicle operation data and battery status data;

[0015] Extract common path features that are consistent with the vehicle operation data route, where common path features include energy consumption characteristics, end point charging identifiers, and round-trip identifiers;

[0016] The real-time equivalent fuel factor is calculated using energy consumption characteristics, end-point charging identifiers, round-trip identifiers, and battery status data, so that the optimal equivalent fuel factor is selected based on the real-time equivalent fuel factor and the cost function of the vehicle's ECMC strategy configuration;

[0017] According to the optimal equivalent fuel factor and real-time drive motor power request, the vehicle ECMS strategy is guided to perform real-time power distribution.

[0018] In one embodiment, the common path features further include: a first departure time, a first initial longitude and latitude, and a first longitude and latitude along the way. The step of extracting the common path features consistent with the vehicle operation data route includes:

[0019] Extracting the second departure time, the second initial longitude and latitude, and the second longitude and latitude along the route from the real-time operation data;

[0020] Matching the first departure time with the second departure time, matching the first initial longitude and latitude with the second initial longitude and latitude, and matching the first longitude and latitude along the route with the second longitude and latitude along the route;

[0021] If all are matched successfully, it is determined that the current real-time operation data is consistent with the route of the common path characteristics.

[0022] In one embodiment, the common path feature further includes a common path distance. After the step of extracting the common path feature consistent with the vehicle operation data route, the following steps are included:

[0023] Extract the cumulative driving distance of the current trip;

[0024] If the difference between the accumulated travel distance and the commonly used route distance is less than the preset distance threshold, the vehicle is determined to have reached the destination and the route matching ends.

[0025] In one embodiment, the step of calculating the real-time equivalent fuel factor using the energy consumption characteristics, the charging destination identifier, the round trip identifier, and the battery status data includes:

[0026] Determine the target SOC of the current vehicle using the round trip mark and the destination charging mark;

[0027] Calculate the remaining available energy of the battery based on the current vehicle's target SOC and battery status data;

[0028] determining an estimated driving energy consumption and an estimated accessory energy consumption using the energy consumption characteristics, and calculating an estimated energy consumption for the remaining trip based on the estimated driving energy consumption and the estimated accessory energy consumption;

[0029] Calculates a real-time equivalent fuel factor based on the remaining available energy in the battery and the estimated energy consumption for the remaining range.

[0030] In one embodiment, the step of selecting the optimal equivalent fuel factor based on the real-time equivalent fuel factor and the cost function of the vehicle ECMC strategy configuration includes:

[0031] Based on the real-time equivalent fuel factor, fuel consumption rate, and battery status data, the cost function configured in the vehicle ECMS strategy is used to calculate the real-time equivalent fuel consumption total cost;

[0032] The optimal equivalent fuel factor is selected using the change rate of the real-time equivalent fuel consumption total cost.

[0033] In one embodiment, the target SOC is preset with a first balance point and a second balance point, and the second balance point is greater than the first balance point;

[0034] The steps of determining the target SOC of the current vehicle using the round-trip identifier and the terminal charging identifier include:

[0035] Adaptively and dynamically adjust the target SOC of the current vehicle to the first balance point, the second balance point, or the dynamic real-time balance point based on the round-trip identifier and the destination charging identifier;

[0036] The dynamic real-time balance point = current SOC - (current SOC - first balance point) * remaining distance of the trip / (remaining distance of the trip + round-trip distance).

[0037] In one embodiment, the step of calculating the remaining available energy of the battery based on the current SOC balance point of the vehicle and the battery status data includes:

[0038] The remaining available energy of the battery is calculated using formula (1), where formula (1) is: E BR =f(SOC, SOC trgt ,SOH,E batt_capc ,T batt ) (1)

[0039] Among them, E BR The remaining available energy of the battery, SOC is the current state of charge of the battery of the vehicle, SOC trgt is the current SOC balance point of the vehicle, SOH is the current battery health of the vehicle, E batt_capc is the rated total capacity of the battery pack, T batt is the battery pack temperature.

[0040] In one embodiment, the step of calculating the estimated energy consumption for the remaining range based on the estimated driving energy consumption and the estimated accessory energy consumption includes:

[0041] The estimated energy consumption for the remaining journey is calculated using formula (2), where formula (2) is: E c =E p +α*E a (2)

[0042] Among them, E p To estimate the driving energy consumption and E a To estimate the energy consumption of the accessory, α is the correction factor of the accessory energy consumption, which is calculated using the ratio of the current accessory power to the average accessory power in the past calculation period.

[0043] In one embodiment, the step of calculating the real-time equivalent fuel factor based on the remaining available energy of the battery and the estimated energy consumption of the remaining range includes:

[0044] The real-time equivalent fuel factor is calculated using formula (3), where formula (3) is:

[0045] Among them, EqFac(t) is the real-time equivalent fuel factor, EqFac LB is the lower limit of the equivalent fuel factor, EqFac HB is the upper limit of the equivalent fuel factor, E BR is the remaining available energy of the battery, E c Estimated energy consumption for the remaining journey.

[0046] In one embodiment, the step of calculating the total cost of the real-time equivalent fuel consumption based on the real-time equivalent fuel factor, fuel consumption rate, and battery status data using a cost function configured in the vehicle ECMS strategy includes:

[0047] The total cost of real-time equivalent fuel consumption is calculated using formula (4), where formula (4) is:

[0048] Among them, J is the total cost of real-time equivalent fuel consumption, is the real-time fuel consumption rate, is the rate of change of battery state of charge.

[0049] In one embodiment, the step of guiding the vehicle ECMS strategy to perform real-time power allocation based on the optimal equivalent fuel factor and the real-time drive motor power request includes:

[0050] Formula (5) is used to guide the vehicle ECMS strategy to perform real-time power allocation, where formula (5) is: P m (t) = P ICE (t)+P batt (t) (5)

[0051] Among them, EqFac(t) is the optimal equivalent fuel factor, P m (t) is the current real-time drive motor power request, P ICE (t) is the power requirement of the engine / range extender, P batt (t) is the power request of the battery pack.

[0052] In a third aspect, the present application provides a power distribution method, which is applied to a cloud platform, wherein the cloud platform is in communication with a vehicle-side controller, and the method includes:

[0053] Receive vehicle operation data;

[0054] Perform route recognition on vehicle operation data within a fixed time period, screen common driving routes, and extract common path features in common driving routes;

[0055] In response to the vehicle's subscription request, after it is determined to be the target vehicle, the common path characteristics are sent to the vehicle-side controller.

[0056] In one embodiment, the step of receiving vehicle operation data includes:

[0057] Receive vehicle operation data of the target vehicle,

[0058] Normalize and map the longitude and latitude information of the route points in the vehicle operation data to ensure the uniform format of the route information;

[0059] The processed vehicle operation data is stored in the corresponding vehicle history operation database.

[0060] In one embodiment, the steps of identifying routes and screening common driving routes based on vehicle operation data within a fixed time period include:

[0061] Extracting the longitude and latitude information of the points along the way from the vehicle operation data within a fixed time period to form a travel route;

[0062] Clustering the itineraries using a clustering algorithm to obtain at least one group of itineraries of the same type;

[0063] The travel routes classified into the same category are checked to verify whether the clustering results are reasonable, and the travel routes corresponding to the reasonable clustering results are determined as common driving routes.

[0064] In one embodiment, the steps of verifying the travel routes classified into the same category to verify whether the clustering result is reasonable, and determining the travel routes corresponding to the reasonable clustering result as the common driving routes include:

[0065] Formula (6) is used to calculate the coefficient of variation of the parameter set for the sample parameter sets belonging to the same clustering result; where formula (6) is:

[0066] The coefficient of variation of the parameter set is compared with the preset threshold. If it does not exceed the preset threshold, the clustering result is reasonable, otherwise the clustering result is discarded.

[0067] In a fourth aspect, the present application provides a power distribution device, comprising:

[0068] A first receiving module is used to receive real-time vehicle operation data and battery status data;

[0069] A first extraction module is used to extract common path features consistent with the vehicle operation data route, wherein the common path features include energy consumption characteristics, terminal charging identifiers, and round-trip identifiers;

[0070] The factor acquisition module is used to calculate the real-time equivalent fuel factor using energy consumption characteristics, end-point charging identifier, round-trip identifier, and battery status data, so as to select the optimal equivalent fuel factor based on the real-time equivalent fuel factor and the cost function of the vehicle ECMC strategy configuration;

[0071] The power allocation module is used to guide the vehicle ECMS strategy to perform real-time power allocation based on the optimal equivalent fuel factor and real-time drive motor power request.

[0072] In a fifth aspect, the present application provides a power distribution device, comprising:

[0073] A second receiving module is used to receive vehicle operation data;

[0074] The second extraction module is used to identify the routes of vehicle operation data within a fixed time period, screen common driving routes, and extract common path features in the common driving routes;

[0075] The request response module is used to respond to the vehicle's subscription request, and after determining it as the target vehicle, send the common path characteristics to the vehicle-side controller.

[0076] In a sixth aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the second aspect or the third aspect when executing the computer program.

[0077] In a seventh aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the second aspect or the third aspect when the computer program is executed by a processor.

[0078] The aforementioned power allocation system, method, apparatus, computer device, and storage medium utilize stored vehicle operation data on a cloud platform to identify commonly used driving routes and determine the common path characteristics corresponding to these routes. In the vehicle-side controller, the charging behavior and energy consumption characteristics of these common path characteristics are used to guide the optimization of power allocation within the vehicle's ECMC strategy. Compared to existing intelligent, predictive energy management strategies, this solution does not rely on external navigation input and has a wider range of applications, enabling users to reduce energy consumption in a wider range of scenarios. Furthermore, with lower computing power and memory requirements, it can be widely deployed within existing vehicle-side controllers to implement equivalent fuel factor optimization strategies and ECMS algorithms, achieving the power allocation goals within the vehicle's ECMC strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0080] FIG1 is a schematic diagram of the framework of the power distribution system in Example 1.

[0081] FIG2 is a schematic diagram of the framework principle of the power distribution system in Example 1.

[0082] FIG3 is a schematic diagram of common path characteristics of vehicles in Example 1.

[0083] FIG4 is a flow chart of the power distribution method on the vehicle-side controller in Example 2.

[0084] FIG5 is a flow chart of the route matching process and the ending steps in Example 2.

[0085] FIG6 is a flow chart of the real-time equivalent fuel factor calculation method in Example 2.

[0086] FIG7 is a flow chart of the target SOC calculation method in Example 2.

[0087] FIG8 is a flow chart of the power allocation method on the cloud platform in Example 3.

[0088] FIG9 is a structural block diagram of the power distribution device in Example 4.

[0089] FIG10 is a structural block diagram of the power distribution device in Example 5.

[0090] FIG11 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0091] Specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the described embodiments are merely some, and not all, of the embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the description of the present invention without inventive effort are intended to fall within the scope of protection of the present invention.

[0092] In the description of the present invention, unless otherwise specified or limited, the terms "disposed," "installed," and "connected" should be understood broadly. For example, they may refer to fixed, detachable, or integral connections; mechanical or electrical connections; and direct or indirect connections through an intermediary. Those skilled in the art will understand the specific meanings of these terms based on the specific circumstances.

[0093] The directions or positional relationships indicated by terms such as "upper", "lower", "left", "right", "front", "back", "top", "bottom", "inside" and "outside" are based on the directions or positional relationships shown in the accompanying drawings, or are the directions or positional relationships in which the inventive product is usually placed when in use. They are only for the convenience and simplification of description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operate in a specific direction. Therefore, they should not be understood as limiting the present invention.

[0094] The terms "first," "second," "third," etc. are merely used to distinguish elements of similar nature and do not indicate or imply relative importance or a particular order.

[0095] The terms "comprises," "comprising," or any other variations thereof, are intended to cover a non-exclusive inclusion of elements other than the listed elements and may also include additional elements not specifically listed.

[0096] Existing plug-in hybrid electric vehicles / extended-range electric vehicles are increasingly accepted by consumers because they take into account both economy and driving range, and have the advantages of low fuel consumption and low emissions. The application and promotion of plug-in / extended-range hybrid electric vehicles are of great significance to achieving the strategic goals of carbon peak and carbon neutrality, and energy management strategies are the key to determining their ultimate energy consumption level. Different from the traditional power consumption-power retention strategy, various existing intelligent energy management strategies are based on external input path planning information (such as navigation data, etc.), using various optimization control theories (such as dynamic programming), equivalent minimum consumption strategies, multi-objective optimization, deep reinforcement learning, etc. to achieve energy management optimization during the journey. It can be seen that the existing intelligent energy management strategies are heavily dependent on external input planning path information, so the application scenarios are limited. For example, when navigation is not turned on, this method will not take effect. In addition, the intelligent design requires high computing power of the controller, which makes it difficult to apply it on a large scale on the vehicle-side controller.

[0097] Based on this, this embodiment provides a power distribution system, method, device, computer equipment and storage medium that can be designed to be compatible with existing energy management strategies, does not rely on planning path information input from the outside world, has simple computing power, and can be deployed on a large scale on the vehicle-side controller.

[0098] Further explanation: The power distribution system, method, apparatus, computer device, and storage medium provided in this embodiment are applied to new energy vehicles (pure electric vehicles, plug-in hybrid electric vehicles, and extended-range electric vehicles). New energy vehicles need to report vehicle operation data to the national unified supervision platform in real time.

[0099] New energy vehicles (pure electric vehicles, plug-in hybrid electric vehicles, and extended-range electric vehicles) need to report vehicle operating data to the national unified supervision platform in real time, and each new energy host supplier usually has a big data platform to store users' vehicle operating data, thereby creating the possibility of analyzing and summarizing users' driving habits and travel habits. It is further explained that the calculation of users' driving and travel habits needs to be based on a large amount of vehicle operating data and combined with AI algorithms, statistics and other methods. Moreover, such features are not time-sensitive information and are not easy to change in a short period of time. Therefore, to a certain extent, a longer update calculation cycle is allowed, and it is suitable to be deployed on a cloud platform for calculation. Based on this, the power distribution system, method, device, computer equipment and storage medium provided in this embodiment are based on the stored vehicle operating data of a large number of users to identify commonly used routes and their commonly used path characteristics including energy consumption characteristics, charging behavior, etc., and then automatically provide input for the vehicle's energy management strategy to optimize the vehicle's power distribution strategy on commonly used routes.

[0100] Example 1

[0101] In one embodiment, as shown in FIG1 , a power distribution system is provided. The system includes a cloud platform 10 and a vehicle-side controller 20 . The cloud platform 10 is communicatively connected to the vehicle-side controller 20 .

[0102] The cloud platform 10 is configured to receive vehicle operation data sent by the target vehicle, identify common path features in the vehicle operation data within a fixed time period, and send the common path features to the vehicle-side controller based on the vehicle's subscription request.

[0103] Among them, the vehicle operation data includes latitude and longitude, energy consumption, SOC (full name: State of Charge, current battery state of charge), vehicle status, operating time and other information. The cloud platform 10 is used to identify and extract common path features in the vehicle operation data. The cloud platform transmits the common path features to the vehicle-side controller through the cloud API communication interface. Common path features include charging behavior features, wherein the charging behavior features are determined based on changes in the vehicle SOC. Exemplarily, when the SOC of the vehicle at the start / end point of the common path feature increases, it is determined that the vehicle is performing charging behavior, and therefore it is determined that the common path feature corresponding to the start / end point has charging conditions.

[0104] The vehicle-side controller 20 is configured to send a subscription request to the cloud platform 10 based on an update schedule, receive the latest common route characteristics, and update the common route characteristics into the vehicle's common route parameters, so that the vehicle's ECMS strategy can be guided by the common route characteristics to implement real-time power allocation during driving. Common route characteristics include the longitude and latitude along the common driving route, energy consumption characteristics, departure time, driving duration, destination charging indicator, and round-trip indicator.

[0105] As shown in Figure 2, the vehicle-side controller 20 includes an HCU 21, a T-BOX 22, and a head unit 23. The HCU 21 is the hybrid system's hybrid control unit (HCU), the main controller responsible for managing the entire powertrain. The T-BOX 22 is a telematics box, providing a remote communication interface for the vehicle. The head unit 23 allows users to set charging preferences. The HCU 21 is connected to both the T-BOX 22 and the head unit 23.

[0106] HCU21 is used to send the triggered subscription request and vehicle operation data to the cloud platform 10 through T-BOX22, perform online matching of common route characteristics based on real-time vehicle operation data, obtain energy consumption characteristics and terminal charging identifiers in the common route characteristics, dynamically and adaptively calculate the SOC balance point using the terminal charging identifier, calculate the vehicle's available power based on the SOC balance point, calculate the real-time equivalent fuel factor using the vehicle's available power and energy consumption characteristics, and after screening the optimal equivalent fuel factor among the real-time equivalent fuel factors, guide the ECMS strategy to perform real-time power allocation.

[0107] In one embodiment, the cloud platform 10 is configured with an encrypted data table that stores common path features. The common path features are associated with the vehicle VIN code. When receiving a vehicle subscription request, the cloud platform 10 matches the corresponding common path features based on the vehicle VIN code in the subscription request. Further, the common path features calculated by the cloud platform 10 are stored in the encrypted data table. Only when receiving a subscription request from the vehicle-side controller 20 is the common path features of the corresponding vehicle matched in the encrypted data table based on the vehicle VIN code parsed in the subscription request. After the common path features are packaged into corresponding data packets, they are sent to the vehicle-side controller via the T-BOX based on the subscription request. After the data is decrypted in the vehicle-side controller 20, they can be used in the vehicle HCU 21, thereby protecting the user's personal data privacy to the greatest extent possible.

[0108] As an example, as shown in FIG3 , a schematic table of commonly used path characteristics of a vehicle is shown. The schematic table of commonly used path characteristics in this example is stored in an encrypted data form. Common path characteristics may include: path number, longitude and latitude coordinates of path points 1 to N, average departure time, trip distance, trip duration, total trip energy consumption, trip drive energy consumption, trip accessory energy consumption, round-trip identification (round-trip path number), terminal charging identification, and cumulative number of path runs. For example, the table uniformly allocates 20 path storage points to each path (path point 1 is the starting point of the trip, and path point 20 is the end point of the trip), and the entire trip is divided into 19 equal parts by distance. The longitude and latitude coordinates of path points 2 to 19 are the coordinates of the equally divided points along the way.

[0109] In one embodiment, the update rule of the vehicle-side controller 20 is configured such that when the commonly used path characteristics are updated to the commonly used path parameters of the vehicle, the accumulated mileage and interval days in the original commonly used path parameters are cleared, and new mileage and interval days are re-accumulated; if the re-accumulated mileage exceeds the preset mileage threshold, or the re-accumulated interval days exceeds the preset day threshold, a subscription request is sent to the cloud platform 10 to obtain the new commonly used path characteristics.

[0110] To further explain, HCU21 automatically accumulates the mileage and interval days corresponding to the commonly used driving routes matched since the common route features were updated. When the accumulated mileage reaches a certain threshold (for example, 500 km) or the accumulated interval days exceed a threshold (for example, 30 days), it sends a subscription request to the cloud platform 10 through T-BOX22. Subsequently, based on the common route features issued by the cloud platform 10, the common route parameters of the vehicle stored in the vehicle are updated.

[0111] Example 2

[0112] In one embodiment, as shown in FIG4 , a power distribution method is provided. The method is described by taking the vehicle-side controller in FIG1 as an example, including the following steps:

[0113] Step S110: receiving real-time vehicle operation data and battery status data;

[0114] Step S120: extracting common path features consistent with the vehicle operation data route, wherein the common path features include energy consumption characteristics, end point charging identifiers, and round-trip identifiers;

[0115] Step S130: Calculate the real-time equivalent fuel factor using the energy consumption characteristics, the destination charging identifier, the round-trip identifier, and the battery status data, so as to select the optimal equivalent fuel factor based on the real-time equivalent fuel factor and the cost function configured by the vehicle ECMC strategy;

[0116] Step S140: Instruct the vehicle ECMS strategy to perform real-time power distribution based on the optimal equivalent fuel factor and the real-time drive motor power request.

[0117] In one embodiment, the common route features further include: a first departure time, a first initial longitude and latitude, and a first longitude and latitude along the route. As shown in FIG5 , a flow chart of the route matching process and the end step, step S120, the step of extracting common route features consistent with the vehicle operation data route, includes:

[0118] Step S121: extracting the second departure time, the second initial longitude and latitude, and the second longitude and latitude along the route from the real-time operation data;

[0119] Step S122: Match the first departure time with the second departure time, match the first initial longitude and latitude with the second initial longitude and latitude, and match the first longitude and latitude along the route with the second longitude and latitude along the route;

[0120] Step S123: If all matches are successful, it is determined that the current real-time operation data is consistent with the route of the common path feature.

[0121] To further illustrate, if there is more than one common path feature issued by the cloud platform, each common path feature needs to be matched. If there is only one path that is successfully matched, the corresponding common path feature is extracted and used as the power allocation strategy in this embodiment. If there is more than one common path feature that is successfully matched, the one with the largest cumulative number of runs is selected and output based on the cumulative number of runs of the successfully matched paths.

[0122] In one embodiment, the common path feature also includes the common path distance. After step S120, extracting the common path feature consistent with the vehicle operation data route, the following steps are included:

[0123] Step S124: extract the cumulative travel distance of the current trip;

[0124] Step S125: If the difference between the accumulated travel distance and the common route distance is less than the preset distance threshold, it is determined that the vehicle has reached the destination, and the route matching ends.

[0125] In one embodiment, as shown in FIG6 , a flow chart of a method for calculating a real-time equivalent fuel factor is provided. Step S130, calculating the real-time equivalent fuel factor using energy consumption characteristics, a charging destination identifier, a round-trip identifier, and battery status data, includes:

[0126] Step S131: Determine the current target SOC of the vehicle using the round trip identifier and the destination charging identifier;

[0127] Step S132: Calculate the remaining available energy of the battery based on the current target SOC of the vehicle and the battery status data;

[0128] Step S133: Determine the estimated driving energy consumption and the estimated accessory energy consumption using the energy consumption characteristics, and calculate the estimated energy consumption for the remaining trip based on the estimated driving energy consumption and the estimated accessory energy consumption;

[0129] Step S134: Calculate the real-time equivalent fuel factor based on the remaining available energy of the battery and the estimated energy consumption of the remaining range.

[0130] In one embodiment, the target SOC is preset with a first balance point and a second balance point, and the second balance point is greater than the first balance point. Step S131, using the round-trip identifier and the destination charging identifier to determine the current target SOC of the vehicle, includes:

[0131] Adaptively and dynamically adjust the target SOC of the current vehicle to the first balance point, the second balance point, or the dynamic real-time balance point based on the round-trip identifier and the destination charging identifier;

[0132] The dynamic real-time balance point = current SOC - (current SOC - first balance point) * remaining distance of the trip / (remaining distance of the trip + round-trip distance).

[0133] Among them, the current travel route is determined to be a one-way route or a round-trip route according to the round-trip mark; and the starting point / end point is determined to have charging conditions or not according to the end point charging mark.

[0134] Further explanation, as shown in Figure 7, is a flow chart of the target SOC calculation method. Determine whether the current travel route is the outbound leg of a round-trip route, and output a first judgment result; if in the first judgment result, the current travel route is the outbound leg of a round-trip route, then determine whether the vehicle's remaining power can support pure electric round trip under the premise that the target SOC is not lower than the first balance point threshold, and output a second judgment result; if in the first judgment result, the current travel route is not the outbound leg of a round-trip route, then determine whether the end point of the current travel route (one-way route) has charging conditions, if so, determine the target SOC to be the first balance point, if not, determine the target SOC to be the second balance point; if in the second judgment result, the vehicle's remaining power supports pure electric round trip, then determine the current vehicle's target SOC to be a dynamic real-time balance. Balance point; if in the second judgment result, the remaining power of the vehicle does not support pure electric round trip, then determine whether the final point of the round trip route (the return end point) has fixed charging conditions, and output the third judgment result; if in the third judgment result, the final point of the round trip route has fixed charging conditions, then determine the current target SOC of the vehicle as the dynamic real-time balance point; if in the third judgment result, the final point of the round trip route does not have charging conditions, then determine whether the one-way end point in the round trip route has fixed charging conditions, and output the fourth judgment result; if in the fourth judgment result, the one-way end point in the round trip route has fixed charging conditions, then determine the target SOC as the first balance point, otherwise determine the target SOC as the second balance point.

[0135] To further illustrate, when the travel route is a one-way route and the charging conditions are met, or when the travel route is a round-trip route and the remaining power does not support round trip and the single-thread end point meets the charging conditions, the target SOC is determined to be the first balance point; when the travel route is a one-way route and the charging conditions are not met, or when the travel route is a round-trip route and the remaining power does not support round trip and the single-thread end point does not meet the charging conditions, the target SOC is determined to be the second balance point; when the remaining power of the vehicle supports round trip, or when the remaining power of the vehicle does not support round trip and the round trip end point meets the charging conditions, the SOC balance point is determined to be the dynamic real-time balance point, where the dynamic real-time balance point = current SOC-(current SOC-first balance point)*remaining distance of the trip / (remaining distance of the trip + round-trip distance).

[0136] In one embodiment, step S132, the step of calculating the remaining available energy of the battery based on the current target SOC of the vehicle and the battery status data, includes:

[0137] The remaining available energy of the battery is calculated using formula (1), where formula (1) is: E BR =f(SOC, SOC trgt ,SOH,E batt_capc ,T batt ) (1)

[0138] Among them, E BR The remaining available energy of the battery, SOC is the current state of charge of the battery of the vehicle, SOC trgt is the current SOC balance point of the vehicle (target SOC), SOH is the current battery health of the vehicle (State of Health), E batt_capc is the rated total capacity of the battery pack, in kwh, T batt is the battery pack temperature, in °C.

[0139] Further explanation: SOH is related to battery charge and discharge times, battery internal resistance and other parameters; E batt_capc Indicates the factory parameters of the battery pack, which are fixed values ​​in this embodiment; T batt Represents the battery pack temperature, which can be obtained by measuring the temperature sensor. Therefore, the key information that needs to be confirmed in formula (1) is only SOC trgt , and this embodiment proposes to dynamically calculate the SOC based on the common driving routes and charging preferences in the common path characteristics when determining the target SOC of the current target vehicle. trgt Numerical methods.

[0140] In one embodiment, the estimated energy consumption E for the remaining trip c Depends on the estimated drive energy consumption E p and estimated accessory energy consumption Ea The result is derived from the statistical results of energy consumption characteristics in common route characteristics. Step S133, the step of calculating the estimated energy consumption of the remaining journey based on the estimated driving energy consumption and the estimated accessory energy consumption, includes:

[0141] The estimated energy consumption for the remaining journey is calculated using formula (2), where formula (2) is: E c =E p +α*E a (2)

[0142] Among them, E p To estimate the driving energy consumption and E a To estimate accessory energy consumption, α is the accessory energy consumption correction factor, calculated by taking the ratio of the current accessory power to the average accessory power over the past calculation period. Accessories can be electrical devices such as air conditioners and PTCs.

[0143] In one embodiment, step S134, calculating the real-time equivalent fuel factor based on the remaining available energy of the battery and the estimated energy consumption for the remaining range, includes:

[0144] The real-time equivalent fuel factor is calculated using formula (3), where formula (3) is:

[0145] Among them, EqFac(t) is the real-time equivalent fuel factor, EqFac LB is the lower limit of the equivalent fuel factor, EqFac HB is the upper limit of the equivalent fuel factor, E BR is the remaining available energy of the battery, E c Estimated energy consumption for the remaining journey.

[0146] Further explanation: EqFac(t) is the real-time calculation result of the equivalent fuel factor, in g / kwh; EqFac LB The lower limit of the equivalent fuel factor, which means the lowest cost of electricity, needs to be set according to the fuel consumption characteristic curve of the engine / range extender used, and the unit is g / kwh; EqFac HB E is the upper limit of the equivalent fuel factor. This value means the cost of electricity is the highest. It needs to be set according to the fuel consumption characteristic curve of the engine / range extender used. The unit is g / kwh. BR E is the remaining available energy of the battery, in kWh; c The estimated energy consumption for the remaining journey, in kWh.

[0147] To further illustrate, step S130, the step of selecting the optimal equivalent fuel factor based on the real-time equivalent fuel factor and the cost function of the vehicle ECMC strategy configuration, includes:

[0148] Step S135: Calculate the total cost of the real-time equivalent fuel consumption based on the real-time equivalent fuel factor, fuel consumption rate, and battery status data using the cost function configured in the vehicle ECMS strategy;

[0149] Step S136: Select the optimal equivalent fuel factor using the change rate of the real-time equivalent fuel consumption total cost.

[0150] Among them, step S135, the step of calculating the real-time equivalent fuel consumption total cost based on the real-time equivalent fuel factor, fuel consumption rate and battery status data using the cost function configured in the vehicle ECMS strategy, includes:

[0151] The total cost of real-time equivalent fuel consumption is calculated using formula (4), where formula (4) is:

[0152] Among them, J is the total cost of real-time equivalent fuel consumption, is the real-time fuel consumption rate, is the rate of change of battery state of charge.

[0153] To further illustrate, the real-time fuel consumption rate represents the cost of the engine using fuel as an energy source, and the specific value can be obtained from the engine's fuel consumption characteristic curve or by looking up a table based on the engine power. represents the rate of change of the battery's state of charge (SOC), which can be equivalently converted to electric power based on the ampere-hour integration principle, while EqFac(t) is the equivalent fuel factor that converts electric consumption into fuel consumption. Therefore, the total cost J obtained in formula (4) is the result of uniformly converting the energy consumption of the engine and battery into fuel consumption.

[0154] In one embodiment, step S140, the step of guiding the vehicle ECMS strategy to perform real-time power allocation based on the optimal equivalent fuel factor and the real-time drive motor power request, includes:

[0155] Formula (5) is used to guide the vehicle ECMS strategy to perform real-time power allocation, where formula (5) is: P m (t) = P ICE (t)+P batt (t) (5)

[0156] Among them, EqFac(t) is the optimal equivalent fuel factor, P m (t) is the current real-time drive motor power request, P ICE(t) is the power requirement of the engine / range extender, P batt (t) is the power request of the battery pack.

[0157] In this embodiment, after the commonly used path characteristics are successfully matched, the energy consumption characteristics of the corresponding path are output for use in calculating the equivalent fuel factor. The equivalent fuel factor is a core parameter in the equivalent consumption minimization strategy (ECMS). By optimizing the equivalent fuel factor values ​​under different operating conditions and different vehicle states, a power allocation strategy with the lowest cost (equivalent fuel consumption) is obtained under each operating condition.

[0158] Example 3

[0159] In one embodiment, as shown in FIG8 , a schematic flow chart of a power allocation method on a cloud platform is provided. This embodiment provides a power allocation method, which is described by taking the method applied to the cloud platform in FIG1 as an example, and includes the following steps:

[0160] Step S210: receiving vehicle operation data;

[0161] Step S220: performing route identification on vehicle operation data within a fixed time period, screening common driving routes, and extracting common path features in the common driving routes;

[0162] Step S230: In response to the vehicle's subscription request, after determining it as the target vehicle, the common path characteristics are sent to the vehicle-side controller.

[0163] In one embodiment, step S210, the step of receiving vehicle operation data, includes:

[0164] Step S211: Receive vehicle operation data of the target vehicle.

[0165] Step S212: normalizing and mapping the longitude and latitude information of the route points in the vehicle operation data to ensure uniform format information in the route;

[0166] Step S213: storing the processed vehicle operation data in a corresponding vehicle history operation database.

[0167] Further explanation: since the mileage length of each path may be different, when storing the longitude and latitude information of the path points, the travel path is normalized and mapped to ensure the uniform format of each path information.

[0168] In one embodiment, step S220, identifying routes of vehicle operation data within a fixed time period and screening common driving routes, includes:

[0169] Step S221: extracting the travel route consisting of the latitude and longitude information of the path points along the way from the vehicle operation data within a fixed time period;

[0170] Step S222: clustering the travel routes using a clustering algorithm to obtain at least one group of travel routes of the same category;

[0171] Step S223: Verify the travel routes classified into the same category to verify whether the clustering result is reasonable, and determine the travel routes corresponding to the reasonable clustering results as the commonly used driving routes.

[0172] For example, the clustering algorithm may be, but is not limited to, K-means, GMM Gaussian mixture model, etc.).

[0173] Among them, step S223, verifying the travel routes classified into the same category to verify whether the clustering result is reasonable, and determining the travel routes corresponding to the reasonable clustering results as the common driving routes, includes:

[0174] Formula (6) is used to calculate the coefficient of variation of the parameter set for the sample parameter sets belonging to the same clustering result; where formula (6) is:

[0175] The coefficient of variation of the parameter set is compared with a preset threshold. If it does not exceed the preset threshold (for example, the preset threshold is 20%), the clustering result is reasonable; otherwise, the clustering result is discarded.

[0176] In this embodiment, vehicle operation data for a target vehicle is accumulated and stored in a corresponding vehicle historical operation database. Based on the target vehicle's vehicle operation data in the historical operation database, the vehicle is identified over a relatively long, fixed period to determine whether it has frequently traveled routes within a past operating period (e.g., updated on a weekly / monthly basis). If a frequently traveled route exists, the latitude and longitude and energy consumption characteristics of the associated route points are calculated. Energy consumption characteristics include driving energy consumption, accessory energy consumption, and total energy consumption, and the number of times each frequently traveled route has been traveled is accumulated. Based on the frequently traveled route identification results, the existence of a round-trip route is determined based on the start / end locations and departure times associated with the frequently traveled routes. If two routes belong to the same round-trip route, they are assigned the same round-trip route number. If the frequently traveled route does not belong to any of the round-trip routes, the result is assigned a null value or an outlier. Based on changes in the vehicle's SOC in the vehicle operation data, the charging behavior characteristics of the vehicle driver are determined. Combined with the frequently traveled route identification results, it is determined whether the start / end points of the frequently traveled routes meet fixed charging conditions. After the frequently traveled routes are identified, the frequently traveled route characteristics associated with the frequently traveled routes are obtained through statistical methods.

[0177] In the above power allocation method, the cloud platform uses stored vehicle operation data to identify commonly used driving routes and determine the common path characteristics corresponding to these routes. In the vehicle-side controller, the charging behavior and energy consumption characteristics of these common path characteristics are used to guide the optimization of power allocation within the vehicle's ECMC strategy. Compared to existing intelligent predictive energy management strategies, this solution does not rely on external navigation input and has a wider range of application scenarios. It can reduce energy consumption for users in more scenarios, and has lower requirements for computing power and memory. It can be widely deployed in existing vehicle-side controllers to implement equivalent fuel factor optimization strategies and ECMS algorithms, thereby achieving the power allocation goals of the vehicle's ECMC strategy.

[0178] It should be understood that, although the various steps in the flow charts of Fig. 4-Fig. 8 are shown in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless clearly stated herein, the execution of these steps does not have strict order restrictions, and these steps can be performed in other orders. Moreover, at least a portion of the steps in Fig. 4-Fig. 8 may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0179] Example 4

[0180] In one embodiment, as shown in FIG9 , a structural block diagram of a power distribution device is provided. This embodiment provides a power distribution device, comprising: a first receiving module 110 , a first extraction module 120 , a factor calculation module 130 , and a power distribution module 140 , wherein:

[0181] The first receiving module 110 is used to receive real-time vehicle operation data and battery status data;

[0182] The first extraction module 120 is used to extract common path features consistent with the vehicle operation data route, wherein the common path features include energy consumption characteristics, terminal charging identifiers, and round-trip identifiers;

[0183] The factor acquisition module 130 is used to calculate the real-time equivalent fuel factor using the energy consumption characteristics, the end point charging identifier, the round trip identifier, and the battery status data, so as to select the optimal equivalent fuel factor based on the real-time equivalent fuel factor and the cost function configured by the vehicle ECMC strategy;

[0184] The power allocation module 140 is used to guide the vehicle ECMS strategy to perform real-time power allocation based on the optimal equivalent fuel factor and the real-time drive motor power request.

[0185] Example 5

[0186] In one embodiment, as shown in FIG10 , a structural block diagram of a power distribution device is provided. This embodiment provides a power distribution device, comprising: a second receiving module 210, a second extraction module 220, and a request response module 230, wherein:

[0187] The second receiving module 210 is used to receive vehicle operation data;

[0188] The second extraction module 220 is used to perform route recognition on the vehicle operation data within a fixed time period, screen common driving routes, and extract common path features in the common driving routes;

[0189] The request response module 230 is used to respond to the vehicle's subscription request, and after determining it as the target vehicle, send the common path characteristics to the vehicle-side controller.

[0190] For the specific definition of the power distribution device in Example 4, please refer to the definition of the power distribution method in Example 2 above. For the specific definition of the power distribution device in Example 5, please refer to the definition of the power distribution method in Example 6 above, which will not be repeated here. Each module in the above-mentioned power distribution device can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0191] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in Figure 11. The computer device includes a processor, a memory, a network interface, and a database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store commonly used path feature data. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a power distribution method is implemented.

[0192] Those skilled in the art will understand that the structure shown in FIG11 is merely a block diagram of a portion of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0193] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0194] Receive real-time vehicle operation data and battery status data; extract common path features consistent with the vehicle operation data route, where common path features include energy consumption characteristics, end-point charging identifiers, and round-trip identifiers; calculate a real-time equivalent fuel factor using the energy consumption characteristics, end-point charging identifiers, round-trip identifiers, and battery status data, so as to select an optimal equivalent fuel factor based on the real-time equivalent fuel factor and a cost function configured for the vehicle's ECMC strategy; and guide the vehicle's ECMS strategy to perform real-time power allocation based on the optimal equivalent fuel factor and the real-time drive motor power request.

[0195] Alternatively, when the processor executes the computer program, the following steps are implemented:

[0196] Receive vehicle operation data; perform route identification on the vehicle operation data within a fixed time period, screen common driving routes, and extract common path features from common driving routes; respond to the vehicle's subscription request, determine it as the target vehicle, and send the common path features to the vehicle-side controller.

[0197] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0198] Receive real-time vehicle operation data and battery status data; extract common path features consistent with the vehicle operation data route, where common path features include energy consumption characteristics, end-point charging identifiers, and round-trip identifiers; calculate a real-time equivalent fuel factor using the energy consumption characteristics, end-point charging identifiers, round-trip identifiers, and battery status data, so as to select an optimal equivalent fuel factor based on the real-time equivalent fuel factor and a cost function configured for the vehicle's ECMC strategy; and guide the vehicle's ECMS strategy to perform real-time power allocation based on the optimal equivalent fuel factor and the real-time drive motor power request.

[0199] Alternatively, when the computer program is executed by the processor, the following steps are implemented: receiving vehicle operation data; performing route identification on the vehicle operation data within a fixed time period, screening commonly used driving routes, and extracting commonly used path features in commonly used driving routes; responding to the vehicle's subscription request, after determining it as a target vehicle, sending the commonly used path features to the vehicle-side controller.

[0200] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0201] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0202] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A power distribution system, characterized in that: The system includes a cloud platform and a vehicle-side controller, wherein the vehicle-side controller is communicatively connected with the cloud platform; The cloud platform is configured to receive vehicle operation data sent by a target vehicle, identify common path features in the vehicle operation data within a fixed time period, and send the common path features to the vehicle-side controller based on a subscription request of the vehicle; The vehicle-side controller is configured to send a subscription request to the cloud platform based on an update rule, receive the latest common path characteristics, and update the common path characteristics to the common path parameters of the vehicle, so that the vehicle can guide the vehicle ECMS strategy to perform real-time power distribution according to the common path characteristics during driving.

2. The power distribution system according to claim 1, characterized in that: The cloud platform is configured with an encrypted data form, through which the commonly used path features are stored, and the commonly used path features are bound and associated with the vehicle VIN code. When receiving a vehicle subscription request, the corresponding commonly used path features are matched according to the vehicle VIN code in the subscription request.

3. The power distribution system according to claim 1, characterized in that: The update rule of the vehicle-side controller is configured such that when the common route feature is updated to the common route parameter of the vehicle, the accumulated mileage and interval days in the original common route parameter are cleared, and new mileage and interval days are accumulated again; If the re-accumulated driving mileage exceeds the preset mileage threshold, or the re-accumulated interval days exceeds the preset day threshold, a subscription request is sent to the cloud platform to obtain new common path features.

4. A power distribution method, applied to a vehicle-side controller, characterized in that: The method comprises: Receive real-time vehicle operation data and battery status data; Extracting common path features consistent with the vehicle operation data route, wherein the common path features include energy consumption features, terminal charging identifiers, and round-trip identifiers; Calculating a real-time equivalent fuel factor using the energy consumption characteristics, the charging destination identifier, the round-trip identifier, and the battery status data, so as to select an optimal equivalent fuel factor according to the real-time equivalent fuel factor and a cost function configured by the vehicle ECMC strategy; According to the optimal equivalent fuel factor and the real-time drive motor power request, the vehicle ECMS strategy is guided to perform real-time power distribution.

5. The power distribution method according to claim 4, characterized in that: The common path features further include: a first departure time, a first initial longitude and latitude, and a first longitude and latitude along the way. The step of extracting the common path features consistent with the vehicle operation data route includes: Extracting the second departure time, the second initial longitude and latitude, and the second longitude and latitude along the route from the real-time operation data; Matching the first departure time with the second departure time, matching the first initial longitude and latitude with the second initial longitude and latitude, and matching the first longitude and latitude along the way with the second longitude and latitude along the way; If both are matched successfully, it is determined that the current real-time operation data is consistent with the route of the common path feature.

6. The power distribution method according to claim 5, characterized in that: The common path feature also includes a common path distance. After the step of extracting the common path feature consistent with the vehicle operation data route, the method includes: Extract the cumulative driving distance of the current trip; If the difference between the accumulated travel distance and the commonly used path distance is less than a preset distance threshold, it is determined that the vehicle has reached the destination and the route matching ends.

7. The power distribution method according to claim 4, characterized in that: The step of calculating the real-time equivalent fuel factor by using the energy consumption characteristics, the charging end point identifier, the round trip identifier, and the battery status data includes: Determine the target SOC of the current vehicle using the round trip mark and the destination charging mark; Calculating the remaining available energy of the battery based on the target SOC of the current vehicle and the battery status data; Determine an estimated driving energy consumption and an estimated accessory energy consumption using the energy consumption characteristics, and calculate an estimated energy consumption for a remaining trip based on the estimated driving energy consumption and the estimated accessory energy consumption; A real-time equivalent fuel factor is calculated according to the remaining available energy of the battery and the estimated energy consumption of the remaining trip.

8. The power distribution method according to claim 4, characterized in that: The step of selecting the optimal equivalent fuel factor according to the real-time equivalent fuel factor and the cost function of the vehicle ECMC strategy configuration includes: Based on the real-time equivalent fuel factor, the fuel consumption rate and the battery status data, the real-time equivalent fuel consumption total cost is calculated using the cost function configured in the vehicle ECMS strategy; The optimal equivalent fuel factor is selected using the change rate of the real-time equivalent fuel consumption total cost.

9. The power distribution method according to claim 7, characterized in that: The target SOC is preset with a first balance point and a second balance point, and the second balance point is greater than the first balance point; The step of determining the target SOC of the current vehicle by using the round-trip mark and the terminal charging mark comprises: Adaptively and dynamically adjust the target SOC of the current vehicle to the first balance point, the second balance point, or the dynamic real-time balance point based on the round-trip mark and the terminal charging mark; The dynamic real-time balance point = current SOC - (current SOC - first balance point) * remaining distance of the trip / (remaining distance of the trip + round trip distance).

10. The power distribution method according to claim 7, characterized in that: The step of calculating the remaining available energy of the battery based on the SOC balance point of the current vehicle and the battery status data comprises: The remaining available energy of the battery is calculated using formula (1), where formula (1) is: IN BR =f(SOC,SOC trgt ,SOH,E batt_capc ,T batt ) (1) Among them, E BR is the remaining available energy of the battery, SOC is the current battery charge state of the vehicle, SOC trgt is the current SOC balance point of the vehicle, SOH is the current battery health of the vehicle, E batt_capc is the total rated capacity of the battery pack, T batt is the battery pack temperature.

11. The power distribution method according to claim 7, characterized in that: The step of calculating the estimated energy consumption for the remaining trip based on the estimated driving energy consumption and the estimated accessory energy consumption comprises: The estimated energy consumption of the remaining journey is calculated using formula (2), where formula (2) is: AND c =And p +α*E a (2) Among them, E p To estimate the driving energy consumption and E a To estimate the energy consumption of the accessory, α is the correction factor of the energy consumption of the accessory, and the correction factor is calculated using the ratio of the current accessory power to the average accessory power in the past calculation period.

12. The power distribution method according to claim 7, characterized in that: The step of calculating the real-time equivalent fuel factor according to the remaining available energy of the battery and the estimated energy consumption of the remaining journey comprises: The real-time equivalent fuel factor is calculated using formula (3), where formula (3) is: Where EqFac(t) is the real-time equivalent fuel factor, EqFac LB is the lower limit of the equivalent fuel factor, EqFac HB is the upper limit of the equivalent fuel factor, E BR is the remaining available energy of the battery, E c Estimated energy consumption for the remaining journey.

13. The power distribution method according to claim 8, characterized in that: The step of calculating the total cost of the real-time equivalent fuel consumption based on the real-time equivalent fuel factor, the fuel consumption rate and the battery status data using the cost function configured in the vehicle ECMS strategy includes: The real-time equivalent fuel consumption total cost is calculated using formula (4), where formula (4) is: Among them, J is the total cost of real-time equivalent fuel consumption, is the real-time fuel consumption rate, is the rate of change of battery state of charge.

14. The power distribution method according to claim 4, characterized in that: The step of guiding the vehicle ECMS strategy to perform real-time power distribution according to the optimal equivalent fuel factor and the real-time drive motor power request includes: Formula (5) is used to guide the vehicle ECMS strategy to perform real-time power allocation, where formula (5) is: P m (t)=P ICE (t)+P batt (t) (5) Among them, EqFac(t) is the optimal equivalent fuel factor, P m (t) is the current real-time drive motor power request, P ICE (t) is the power requirement of the engine / range extender, P batt (t) is the power request of the battery pack.

15. A power distribution method, applied to a cloud platform, wherein the cloud platform is communicatively connected to a vehicle-side controller, characterized in that: The method comprises: Receive vehicle operation data; Perform route recognition on the vehicle operation data within a fixed time period, screen common driving routes, and extract common path features in the common driving routes; In response to the subscription request of the vehicle, after it is determined as the target vehicle, the common path characteristics are sent to the vehicle-side controller.

16. The power distribution method according to claim 15, characterized in that: The step of receiving vehicle operation data comprises: Receive vehicle operation data of the target vehicle, Normalizing and mapping the latitude and longitude information of the path points along the way in the vehicle operation data to ensure that the format information in the itinerary is unified; The processed vehicle operation data is stored in a corresponding vehicle history operation database.

17. The power distribution method according to claim 15, characterized in that: The step of identifying routes for the vehicle operation data within a fixed time period and screening common driving routes includes: Extracting the longitude and latitude information of the points along the way from the vehicle operation data within a fixed time period to form a travel route; Clustering the itineraries using a clustering algorithm to obtain at least one group of itineraries of the same type; The travel routes classified into the same category are checked to verify whether the clustering results are reasonable, and the travel routes corresponding to the reasonable clustering results are determined as the commonly used travel routes.

18. The power distribution method according to claim 17, characterized in that: The step of verifying the travel routes classified into the same category to verify whether the clustering result is reasonable, and determining the travel route corresponding to the reasonable clustering result as the common driving route includes: Formula (6) is used to calculate the coefficient of variation of the parameter set for the sample parameter set belonging to the same clustering result; where formula (6) is: The coefficient of variation of the parameter set is compared with the preset threshold. If it does not exceed the preset threshold, the clustering result is reasonable, otherwise the clustering result is discarded.

19. A power distribution device, characterized in that: The device comprises: A first receiving module, used for receiving real-time vehicle operation data and battery status data; A first extraction module is used to extract common path features consistent with the vehicle operation data route, wherein the common path features include energy consumption features, terminal charging marks, and round-trip marks; A factor acquisition module, used to calculate a real-time equivalent fuel factor using the energy consumption characteristics, the terminal charging identifier, the round-trip identifier, and the battery status data, so as to select an optimal equivalent fuel factor according to the real-time equivalent fuel factor and a cost function configured by the vehicle ECMC strategy; The power allocation module is used to guide the vehicle ECMS strategy to perform real-time power allocation according to the optimal equivalent fuel factor and the real-time drive motor power request.

20. A power distribution device, characterized in that: The device comprises: A second receiving module, used for receiving vehicle operation data; A second extraction module is used to perform route recognition on the vehicle operation data within a fixed time period, screen common driving routes, and extract common path features in the common driving routes; The request response module is used to respond to the subscription request of the vehicle, and after determining it as the target vehicle, send the common path characteristics to the vehicle-side controller.

21. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 4 to 18 are implemented.

22. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 4 to 18 are implemented.

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