CN116756509A

CN116756509A - Energy management optimization method, device, equipment and storage medium

Info

Publication number: CN116756509A
Application number: CN202310785570.5A
Authority: CN
Inventors: 吴宁
Original assignee: Zhejiang Geely Holding Group Co Ltd; Geely Automobile Research Institute Ningbo Co Ltd
Current assignee: Zhejiang Geely Holding Group Co Ltd; Geely Automobile Research Institute Ningbo Co Ltd
Priority date: 2023-06-29
Filing date: 2023-06-29
Publication date: 2023-09-15

Abstract

The application provides an energy management optimization method, device, equipment and storage medium, and relates to the technical field of hybrid vehicles, wherein the method comprises the following steps: under the condition that the hybrid vehicle does not start navigation, determining that an owner is oil preference, triggering a cloud server to determine a target travel route from a historical repeated travel route according to the current travel position and the current travel time of the hybrid vehicle, wherein the oil saving amount corresponding to the historical repeated travel route is larger than the sum of the oil saving amounts of single routes for splitting the historical repeated travel route into independent travel; the cloud server predicts energy management planning for the hybrid vehicle according to the target travel route, obtains target battery power required by the target travel route, and sends the target battery power to the hybrid vehicle; the hybrid vehicle performs a predictive energy management function based on the target battery level. The application can better exert the energy-saving potential of the power assembly of the hybrid electric vehicle and improve the fuel efficiency.

Description

Energy management optimization method, device, equipment and storage medium

Technical Field

The present application relates to the field of hybrid vehicles, and in particular, to an energy management optimization method, apparatus, device, and storage medium.

Background

With the development of new energy vehicle technology, hybrid vehicles are increasingly commonly used. The hybrid electric vehicle is, for example, a Plug-in hybrid electric vehicle (PHEV), and the PHEV has a plurality of energy sources, and under different driving conditions, the energy supply and distribution conditions of the power sources need to be reasonably coordinated, so that the engine and the motor can keep running in a high-efficiency interval, and the fuel economy is improved.

Currently, energy of a hybrid vehicle is generally managed by a predictive energy management function mounted on the hybrid vehicle. The predictive energy management function is to plan the energy consumption of the hybrid electric vehicle based on navigation traffic information, generate power in a high-efficiency interval of the engine and achieve the aim of saving fuel by pure electricity in a low-efficiency interval of the engine. However, when the energy of the hybrid vehicle is managed by the predictive energy management function, there are cases where the energy saving potential of the hybrid vehicle powertrain cannot be exerted.

Disclosure of Invention

The application provides an energy management optimization method, an energy management optimization device, energy management equipment and a storage medium, which are used for solving the problem that the energy-saving potential of a power assembly of a hybrid vehicle cannot be exerted when the energy of the hybrid vehicle is managed through a predictive energy management function at present.

In a first aspect, the present application provides an energy management optimization method, applied to a cloud server, the energy management optimization method including:

receiving a trigger instruction, wherein the trigger instruction is sent when a vehicle owner is determined to be favored by oil under the condition that the hybrid vehicle does not start navigation;

responding to a trigger instruction, determining a target travel route from a historical repeated travel route according to the current travel position and the current travel time of the hybrid electric vehicle, wherein the historical repeated travel route is based on a preset rule, and performing similar processing on a route obtained by splicing the historical travel route in a preset time window end to end according to a travel sequence to obtain a similar travel route with a travel probability larger than a travel threshold, wherein the oil saving amount corresponding to the historical repeated travel route is larger than the sum of the oil saving amounts of single routes for splitting the historical repeated travel route into independent travel;

according to the target travel route, carrying out predictive energy management planning on the hybrid electric vehicle to obtain target battery power required by the target travel route;

the target battery level is sent to the hybrid vehicle to cause the hybrid vehicle to perform a predictive energy management function based on the target battery level.

Optionally, the historical repeatedly going routes are obtained by: acquiring historical travel route information of the vehicle within a preset duration; dividing the vehicle history travel route information by a preset time window to obtain vehicle history travel route information corresponding to a plurality of preset time windows respectively; splicing the historical travel routes of the vehicles corresponding to each preset time window end to end according to the travel sequence to obtain a plurality of spliced travel routes; performing similar processing on the spliced travel routes based on a preset rule to obtain similar travel routes with the similarity larger than a first threshold value, wherein the preset rule comprises the length of a road section of the spliced travel route, the time period in which the road section is positioned, the name of the road section and the average speed of the road section; determining the travel probability as a ratio of a first quantity to a second quantity, wherein the first quantity is used for representing the quantity of similar travel routes, and the second quantity is used for representing the quantity of spliced travel routes within a preset duration; if the travel probability is greater than or equal to the travel threshold, determining the similar travel route as an initial repeated travel route; inputting the initial repeated travel route into a fuel-saving quantity simulation model corresponding to the predictive energy management function to obtain a first fuel-saving quantity; splitting the initial repeated travel route into single routes for independent travel, and respectively inputting the single routes into an oil saving quantity simulation model to obtain second oil saving quantity corresponding to the single routes; and if the first oil saving amount is larger than the sum of the second oil saving amounts, determining the initial repeated travel route as a historical repeated travel route.

Optionally, based on a preset rule, performing similar processing on the plurality of spliced travel routes to obtain a similar travel route with similarity greater than a first threshold, including: based on a preset rule, comparing the spliced travel routes in pairs; for each road section respectively contained in the two spliced travel routes to be compared, if the difference value of the lengths of the corresponding road sections is smaller than a second threshold value, the difference value of the time periods of the corresponding road sections is smaller than a third threshold value, the difference value of the average speed of the corresponding road sections is smaller than a fourth threshold value, and the names of the corresponding road sections are the same, setting the similarity value of the corresponding road sections to be 1; determining a first addition, wherein the first addition is used for representing the sum of similar values corresponding to the spliced travel routes; determining a second addition, wherein the second addition is used for representing the sum of the number of road sections contained in the spliced travel route; determining the similarity as the ratio of the first addition to the second addition; and comparing the similarity with a first threshold value to obtain a similar travel route with the similarity larger than the first threshold value.

Optionally, the energy management optimization method further includes: if the travel probability is smaller than the travel threshold, determining that no historical repeated travel route exists; and replacing the length of the preset time window, and re-executing the steps of dividing the vehicle history travel route information by the preset time window to obtain vehicle history travel route information corresponding to the preset time windows respectively.

Optionally, the energy management optimization method further includes: and obtaining a historical repeated travel route according to a preset period.

Optionally, before dividing the vehicle history travel route information with the preset time window, the energy management optimization method further includes: route information of the historical travel route of the vehicle is aggregated into a piece of label information.

Optionally, determining the target travel route from the historical repeated travel routes according to the current travel position and the current travel time of the hybrid vehicle includes: and comparing the current travel position and the current travel time of the hybrid vehicle with route information of the historical repeated travel route, and determining the historical repeated travel route which contains the current travel position and has a time difference value of less than a fifth threshold value from the current travel time as a target travel route.

Optionally, the energy management optimization method further includes: if a plurality of historical repeated travel routes are obtained from the historical repeated travel routes according to the current travel position and the current travel time of the hybrid electric vehicle, determining the historical repeated travel route with the highest travel probability in the plurality of historical repeated travel routes as a target travel route; if the historical repeated travel route with the highest travel probability is a plurality of, determining the historical repeated travel route with the highest travel probability and the largest fuel saving amount as the target travel route.

Optionally, after sending the target battery power to the hybrid vehicle, the energy management optimization method further includes: and if the current travel position of the hybrid power vehicle deviates from the target travel route, sending a message for stopping executing the predictive energy management function to the hybrid power vehicle, and reducing the travel probability corresponding to the target travel route.

In a second aspect, the present application provides an energy management optimization method applied to a hybrid vehicle, the energy management optimization method including:

determining whether the vehicle owner is an oil preference under the condition that the hybrid vehicle does not start navigation;

if yes, a trigger instruction is sent to a cloud server to obtain a target battery power required by a travel route, the target battery power is obtained by the cloud server responding to the trigger instruction, a target travel route is determined from a historical repeated travel route according to the current travel position and the current travel time of the hybrid vehicle, the historical repeated travel route is obtained by carrying out predictive energy management planning on the hybrid vehicle according to the target travel route, the historical repeated travel route is a similar travel route which is obtained by carrying out similar processing on a route obtained by splicing the historical travel routes end to end according to the travel sequence in a preset time window, the travel probability is larger than a travel threshold value, and the oil saving amount corresponding to the historical repeated travel route is larger than the sum of the oil saving amounts of single routes for splitting the historical repeated travel route into independent travel;

And executing the predictive energy management function according to the target battery power.

Optionally, determining whether the vehicle owner is oil-preferred includes: determining that the vehicle owner is an oil preference in response to an oil preference setting operation of the vehicle owner on a vehicle machine of the hybrid vehicle; or if the ratio of the historical fueling times to the historical charging times of the hybrid vehicle obtained from the cloud server is greater than a ratio threshold, determining that the vehicle owner is the oil preference; or if the mileage of the hybrid vehicle in the pure electric mode operation is smaller than the mileage of the hybrid vehicle in the fuel oil mode operation in the history time, determining that the vehicle owner is the preference for using the fuel oil.

Optionally, after performing the predictive energy management function according to the target battery power, the energy management optimization method further includes: receiving a message sent by a cloud server and used for stopping executing the predictive energy management function; terminating execution of the predictive energy management function.

In a third aspect, the present application provides an energy management optimization device, applied to a cloud server, the energy management optimization device comprising:

the receiving module is used for receiving a triggering instruction, wherein the triggering instruction is sent when the vehicle owner is determined to be favored by oil under the condition that the hybrid vehicle does not start navigation;

The determining module is used for responding to the triggering instruction, determining a target travel route from the historical repeated travel route according to the current travel position and the current travel time of the hybrid electric vehicle, wherein the historical repeated travel route is based on a preset rule, and the travel probability of the route obtained by performing head-to-tail splicing on the historical travel route in a preset time window according to the travel sequence is larger than that of the similar travel route with the travel threshold, and the oil saving amount corresponding to the historical repeated travel route is larger than the sum of the oil saving amounts of the single routes for splitting the historical repeated travel route into independent travel;

the processing module is used for carrying out predictive energy management planning on the hybrid power vehicle according to the target travel route to obtain target battery power required by the target travel route;

and the sending module is used for sending the target battery electric quantity to the hybrid electric vehicle so that the hybrid electric vehicle can execute the predictive energy management function according to the target battery electric quantity.

Optionally, the energy management optimizing apparatus further includes an obtaining module, configured to obtain the historical repeated trip route by: acquiring historical travel route information of the vehicle within a preset duration; dividing the vehicle history travel route information by a preset time window to obtain vehicle history travel route information corresponding to a plurality of preset time windows respectively; splicing the historical travel routes of the vehicles corresponding to each preset time window end to end according to the travel sequence to obtain a plurality of spliced travel routes; performing similar processing on the spliced travel routes based on a preset rule to obtain similar travel routes with the similarity larger than a first threshold value, wherein the preset rule comprises the length of a road section of the spliced travel route, the time period in which the road section is positioned, the name of the road section and the average speed of the road section; determining the travel probability as a ratio of a first quantity to a second quantity, wherein the first quantity is used for representing the quantity of similar travel routes, and the second quantity is used for representing the quantity of spliced travel routes within a preset duration; if the travel probability is greater than or equal to the travel threshold, determining the similar travel route as an initial repeated travel route; inputting the initial repeated travel route into a fuel-saving quantity simulation model corresponding to the predictive energy management function to obtain a first fuel-saving quantity; splitting the initial repeated travel route into single routes for independent travel, and respectively inputting the single routes into an oil saving quantity simulation model to obtain second oil saving quantity corresponding to the single routes; and if the first oil saving amount is larger than the sum of the second oil saving amounts, determining the initial repeated travel route as a historical repeated travel route.

Optionally, the obtaining module is configured to, when performing similar processing on the plurality of spliced travel routes based on a preset rule to obtain a similar travel route with a similarity greater than a first threshold, specifically be configured to: based on a preset rule, comparing the spliced travel routes in pairs; for each road section respectively contained in the two spliced travel routes to be compared, if the difference value of the lengths of the corresponding road sections is smaller than a second threshold value, the difference value of the time periods of the corresponding road sections is smaller than a third threshold value, the difference value of the average speed of the corresponding road sections is smaller than a fourth threshold value, and the names of the corresponding road sections are the same, setting the similarity value of the corresponding road sections to be 1; determining a first addition, wherein the first addition is used for representing the sum of similar values corresponding to the spliced travel routes; determining a second addition, wherein the second addition is used for representing the sum of the number of road sections contained in the spliced travel route; determining the similarity as the ratio of the first addition to the second addition; and comparing the similarity with a first threshold value to obtain a similar travel route with the similarity larger than the first threshold value.

Optionally, the acquiring module is further configured to: if the travel probability is smaller than the travel threshold, determining that no historical repeated travel route exists; and replacing the length of the preset time window, and re-executing the steps of dividing the vehicle history travel route information by the preset time window to obtain vehicle history travel route information corresponding to the preset time windows respectively.

Optionally, the acquiring module is further configured to: and obtaining a historical repeated travel route according to a preset period.

Optionally, the acquiring module is further configured to: before dividing the historical travel route information of the vehicle in a preset time window, the route information of the historical travel route of the vehicle is aggregated into a piece of label information.

Optionally, the determining module is specifically configured to: and comparing the current travel position and the current travel time of the hybrid vehicle with route information of the historical repeated travel route, and determining the historical repeated travel route which contains the current travel position and has a time difference value of less than a fifth threshold value from the current travel time as a target travel route.

Optionally, the determining module is further configured to: if a plurality of historical repeated travel routes are obtained from the historical repeated travel routes according to the current travel position and the current travel time of the hybrid electric vehicle, determining the historical repeated travel route with the highest travel probability in the plurality of historical repeated travel routes as a target travel route; if the historical repeated travel route with the highest travel probability is a plurality of, determining the historical repeated travel route with the highest travel probability and the largest fuel saving amount as the target travel route.

Optionally, the sending module is further configured to: after the target battery power is sent to the hybrid vehicle, if the current travel position of the hybrid vehicle is determined to deviate from the target travel route, a message for stopping executing the predictive energy management function is sent to the hybrid vehicle, and the travel probability corresponding to the target travel route is reduced.

In a fourth aspect, the present application provides an energy management optimization method applied to a hybrid vehicle, the energy management optimization device including:

the determining module is used for determining whether the vehicle owner is oil preference under the condition that the hybrid vehicle does not start navigation;

the transmission module is used for transmitting a trigger instruction to the cloud server to obtain a target battery power required by a travel route, wherein the target battery power is a similar travel route with a travel probability larger than a travel threshold value, which is obtained by similar processing of a route obtained by splicing the historical travel route in a preset time window according to the travel sequence, from the beginning to the end, according to a preset rule, according to the target travel route and predictive energy management planning of the hybrid vehicle, and according to the current travel position and the current travel time of the hybrid vehicle, the fuel saving amount corresponding to the historical travel route is larger than the sum of fuel saving amounts of single routes for splitting the historical travel route into independent travel;

And the execution module is used for executing the predictive energy management function according to the target battery electric quantity.

Optionally, the determining module is specifically configured to: determining that the vehicle owner is an oil preference in response to an oil preference setting operation of the vehicle owner on a vehicle machine of the hybrid vehicle; or if the ratio of the historical fueling times to the historical charging times of the hybrid vehicle obtained from the cloud server is greater than a ratio threshold, determining that the vehicle owner is the oil preference; or if the mileage of the hybrid vehicle in the pure electric mode operation is smaller than the mileage of the hybrid vehicle in the fuel oil mode operation in the history time, determining that the vehicle owner is the preference for using the fuel oil.

Optionally, the execution module is further configured to: after the predicted energy management function is executed according to the target battery power, receiving a message sent by the cloud server for stopping executing the predicted energy management function; terminating execution of the predictive energy management function.

In a fifth aspect, the present application provides an electronic device, comprising: a processor, a memory communicatively coupled to the processor;

the memory stores computer-executable instructions;

the processor executes the computer-executable instructions stored in the memory to implement the energy management optimization method according to the first or second aspect of the present application.

In a sixth aspect, the present application provides a computer readable storage medium having stored therein computer program instructions which, when executed, implement the energy management optimization method according to the first or second aspect of the present application.

In a seventh aspect, the present application provides a computer program product comprising a computer program which when executed implements the energy management optimization method according to the first or second aspect of the application.

According to the energy management optimization method, the device, the equipment and the storage medium, whether the vehicle owner is the preference of oil consumption is determined through the hybrid vehicle under the condition that navigation is not started, if yes, the hybrid vehicle sends a trigger instruction to the cloud server so as to obtain the target battery power required by the travel route; the cloud server responds to the trigger instruction, a target travel route is determined from a historical repeated travel route according to the current travel position and the current travel time of the hybrid electric vehicle, the historical repeated travel route is based on a preset rule, the travel probability of the route obtained by splicing the historical travel route in a preset time window end to end according to the travel sequence is larger than that of the similar travel route of the travel threshold, and the oil saving amount corresponding to the historical repeated travel route is larger than the sum of the oil saving amounts of the single routes for splitting the historical repeated travel route into independent travel; the cloud server predicts energy management planning for the hybrid vehicle according to the target travel route to obtain target battery power required by the target travel route; the cloud server sends the target battery power to the hybrid electric vehicle, and the hybrid electric vehicle executes a predictive energy management function according to the target battery power after receiving the target battery power. According to the method and the device, the target travel route is determined from the historical repeated travel route according to the current travel position and the current travel time of the hybrid vehicle and is used for carrying out predictive energy management planning on the hybrid vehicle, the historical repeated travel route is a travel route which is spliced out for a plurality of times and is more energy-saving, the problem that the low-efficiency engine energy consumption cannot be converted into the high-efficiency engine energy consumption area due to the fact that the single travel route does not generate electricity in the engine high-efficiency area at first time can be avoided, the problem that predictive energy management is energy-saving is limited can be solved, the hybrid vehicle can be guaranteed to generate electricity in the engine high-efficiency area in advance on the basis of accurately predicting the target battery power required by the travel route of the hybrid vehicle, therefore pure electricity can be used in the engine low-efficiency area, the energy-saving potential of the power assembly of the hybrid vehicle can be better played, and the overall fuel efficiency of the whole travel can be improved.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions of the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to the drawings without inventive effort to a person skilled in the art.

Fig. 1 is a schematic view of an application scenario provided in an embodiment of the present application;

FIG. 2 is a schematic signaling interaction diagram of an energy management optimization method according to an embodiment of the present application;

FIG. 3 is a flowchart of a method for obtaining a historical repeated travel route according to an embodiment of the present application;

FIG. 4 is a schematic diagram of an energy management optimization device according to an embodiment of the present application;

FIG. 5 is a schematic view of an energy management optimization device according to another embodiment of the present application;

fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the application.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

It should be noted that, the user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are information and data authorized by the user or fully authorized by each party, and the collection, use and processing of the related data need to comply with the related laws and regulations and standards of the related country and region, and provide corresponding operation entries for the user to select authorization or rejection.

Hybrid vehicles are increasingly popular. The hybrid vehicle is a PHEV vehicle, and the PHEV vehicle can obtain the optimal target battery electric quantity consumption curve in the process of reaching a destination through the optimization of the predictive energy management function by collecting traffic information, vehicle speed information, ramp information, traffic light information, information of a vehicle distance in front of the PHEV vehicle and the like on a route, so that the engine is optimized to run in an oil-saving section more, and the effects of saving oil and reducing emission are achieved. The prediction energy management essence is to utilize the time when the low-efficiency interval of the engine of the whole stroke is predicted to occur to generate electricity in advance in the high-efficiency interval of the engine, and the low-efficiency interval of the engine uses the battery electric quantity prepared in advance for pure electric drive to improve the overall fuel efficiency of the whole stroke.

However, not all the travel routes of the hybrid vehicles have conditions to adjust the power generation area to save energy, and at least the hybrid vehicles need to be charged at a high speed before having a chance of discharging at a low speed. Although the predictive energy management function of the hybrid vehicle may achieve the maximum of a single energy saving in a single trip, as a whole, the driver periodically drives the hybrid vehicle, there are still many periods of time without a high-speed charging condition before congestion, which results in that the engine is still operated in an energy-saving-free area, and a part of thermal efficiency is lost. For example, when the hybrid vehicle starts, the battery level is low, the following mileage is a long low vehicle speed road section, and even if there is traffic information, there is no opportunity to generate electricity in advance. In practical application, a high-speed generator is needed, and pure electric driving can be used later, so that the full energy-saving potential of the power assembly cannot be exerted.

In the related art, a global optimization strategy, such as a driving equivalent factor learning method, a model predictive control (Model Predictive Control, MPC) method, various vehicle speed prediction algorithms, a random dynamic programming method, a reinforcement learning method and the like, may be used, but the above optimization strategy generally requires a controller or a cloud server of the hybrid vehicle to have extremely high calculation power and extremely long learning time at the vehicle end, so that the driving mileage which can be practically coped with is extremely limited, and the practical application is difficult. In addition, there is a scheme of counting the familiar routes of the single trip of the user and optimizing the predictive energy management function of the single trip, but if the single trip route does not have a high-efficiency operation section of the engine or the high-efficiency operation section is after a low-efficiency operation section, the purpose of saving oil cannot be achieved.

Based on the problems, the application provides an energy management optimization method, device, equipment and storage medium, which are used for searching travel routes with a high travel rule in different time spans based on the travel history rule of a driver, merging travel routes of multiple continuous travel of the driver into one travel, analyzing whether the fuel saving amount of the travel, which is supposed to be completed by the merged travel by using the predicted energy management, is larger than the total fuel saving amount of single travel, if so, the predicted energy management taking multiple travel as one travel can solve the working condition that the vehicle is not charged at a high speed before some congestion, a higher energy saving effect is obtained, judging whether the driver travels in the history repeated travel routes with the high rule and more energy saving each time, if so, matching with the predicted energy management of the road sections on the corresponding travel routes, ensuring that the hybrid vehicle generates electricity in the high-efficiency section of the engine in advance, thereby, playing the energy saving potential of the power assembly of the hybrid vehicle and improving the overall efficiency of the whole travel.

In the following, first, an application scenario of the solution provided by the present application is illustrated.

Fig. 1 is a schematic view of an application scenario provided in an embodiment of the present application. As shown in fig. 1, in the present application scenario, when the hybrid vehicle 101 does not start navigation, it is determined that the vehicle owner is the oil preference, and the target battery power corresponding to the travel route is obtained from the cloud server 102. The hybrid vehicle 101 performs a predictive energy management function based on the target battery level.

It should be noted that fig. 1 is only a schematic diagram of an application scenario provided by an embodiment of the present application, and the embodiment of the present application does not limit the devices included in fig. 1 or limit the positional relationship between the devices in fig. 1.

The technical scheme of the application is described in detail through specific embodiments. It should be noted that the following embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

Fig. 2 is a signaling interaction schematic diagram of an energy management optimization method according to an embodiment of the present application, where a hybrid vehicle is in communication connection with a cloud server. As shown in fig. 2, the method of the embodiment of the present application includes:

S201, the hybrid vehicle determines whether the vehicle owner is preferred to use oil under the condition that navigation is not started.

In the embodiment of the application, after the hybrid power vehicle is electrified, if the hybrid power vehicle starts navigation, normal predictive energy management function is executed according to the navigation, and after the hybrid power vehicle reaches a destination, execution of the predictive energy management function is exited. Under the condition that the hybrid power vehicle does not start navigation, the energy management optimization method provided by the embodiment of the application is adopted to optimize the predictive energy management function of the hybrid power vehicle. Firstly, whether a vehicle owner prefers to use oil or not is determined, and the vehicle owner prefers to use oil, so that the energy management optimization method provided by the embodiment of the application can improve the overall fuel efficiency of the whole travel.

Optionally, the determining whether the vehicle owner is oil-preferred by the hybrid vehicle may include: determining that the vehicle owner is an oil preference in response to an oil preference setting operation of the vehicle owner on a vehicle machine of the hybrid vehicle; or if the ratio of the historical fueling times to the historical charging times of the hybrid vehicle obtained from the cloud server is greater than a ratio threshold, determining that the vehicle owner is the oil preference; or if the mileage of the hybrid vehicle in the pure electric mode operation is smaller than the mileage of the hybrid vehicle in the fuel oil mode operation in the history time, determining that the vehicle owner is the preference for using the fuel oil.

For example, the vehicle owner may set an oil usage preference on the vehicle body of the hybrid vehicle, thereby determining that the vehicle owner is the oil usage preference. Or, the hybrid vehicle may determine that the vehicle owner is the preference for using oil according to the ratio of the historical fueling number to the historical charging number of the hybrid vehicle obtained from the cloud server, for example, the vehicle owner has fueling number of 20 times in the past month and charging number of 1 time, and then may determine that the vehicle owner is the preference for using oil. Alternatively, the hybrid vehicle may determine that the vehicle owner is the oil preference based on the mileage of the hybrid vehicle in the pure electric mode operation and the mileage of the fuel mode operation in the past month obtained from the cloud server.

S202, if the vehicle owner is determined to be preferred in oil consumption, the hybrid vehicle sends a trigger instruction to the cloud server so as to obtain target battery power required by the travel route.

Correspondingly, the cloud server receives the trigger instruction.

In the step, after determining that the vehicle owner is the oil preference, the hybrid vehicle sends a trigger instruction for obtaining the target battery power required by the travel route to the cloud server, and accordingly, the cloud server receives the trigger instruction.

S203, the cloud server responds to the trigger instruction, and determines a target travel route from the historical repeated travel route according to the current travel position and the current travel time of the hybrid electric vehicle, wherein the historical repeated travel route is based on a preset rule, and the travel probability of the route obtained by performing head-to-tail splicing on the historical travel route in a preset time window according to the travel sequence is larger than that of a similar travel route with a travel threshold value, and the oil saving amount corresponding to the historical repeated travel route is larger than the sum of the oil saving amounts of single routes for splitting the historical repeated travel route into independent travel.

In the step, the historical repeated travel route can be understood as a high-probability travel route, the historical repeated travel route is based on a preset rule, the travel probability obtained by similar processing on the route obtained by splicing the historical travel route in a preset time window from head to tail according to the travel sequence is larger than a similar travel route with a travel threshold, and the fuel saving amount of the driver once driving the historical repeated travel route is larger than the sum of fuel saving amounts of individual routes of individual travel contained in the driver respectively driving the historical repeated travel route. For example, the cloud server may obtain the historical repeated travel route according to a preset period, and for how to obtain the historical repeated travel route, reference may be made to the following embodiments, which are not described herein. After receiving the trigger instruction, the cloud server can determine a target travel route from the historical repeated travel routes according to the current travel position and the current travel time of the hybrid vehicle, wherein the target travel route is the most similar route to the travel route of the hybrid vehicle.

Optionally, the determining, by the cloud server, the target travel route from the historical repeated travel routes according to the current travel position and the current travel time of the hybrid vehicle may include: and comparing the current travel position and the current travel time of the hybrid vehicle with route information of the historical repeated travel route, and determining the historical repeated travel route which contains the current travel position and has a time difference value of less than a fifth threshold value from the current travel time as a target travel route.

The embodiment is used for searching a route which is most similar to the travel route of the hybrid vehicle in the historical repeated travel route, and the target travel route can be determined by comparing whether the historical repeated travel route contains the travel time with the time difference smaller than the fifth threshold value from the current travel time of the hybrid vehicle and whether the current travel position is contained.

Optionally, the energy management optimization method provided by the embodiment of the present application may further include: if a plurality of historical repeated travel routes are obtained from the historical repeated travel routes according to the current travel position and the current travel time of the hybrid electric vehicle, determining the historical repeated travel route with the highest travel probability in the plurality of historical repeated travel routes as a target travel route; if the historical repeated travel route with the highest travel probability is a plurality of, determining the historical repeated travel route with the highest travel probability and the largest fuel saving amount as the target travel route.

For example, if a plurality of historical repeated travel routes are obtained from the historical repeated travel routes according to the current travel position and the current travel time of the hybrid vehicle, the travel probabilities respectively corresponding to the plurality of historical repeated travel routes are compared first, and thus the historical repeated travel route with the highest travel probability is determined as the target travel route. If the historical repeated travel route with the highest travel probability is multiple, the oil saving amounts respectively corresponding to the historical repeated travel routes with the highest travel probability can be continuously compared, so that the historical repeated travel route with the highest oil saving amount is determined to be the target travel route.

Optionally, if the target travel route is not queried from the historical repeated travel route according to the current travel position and the current travel time of the hybrid vehicle, a message for stopping execution of the predictive energy management function is sent to the hybrid vehicle, so that the hybrid vehicle stops execution of the predictive energy management function.

S204, the cloud server performs predictive energy management planning on the hybrid vehicle according to the target travel route to obtain target battery power required by the target travel route.

In the step, after determining a target travel route, the cloud server can conduct predictive energy management planning on the hybrid vehicle according to the target travel route, select corresponding time and road sections in the target travel route to control predictive energy management parameters of the current travel of the hybrid vehicle, and obtain target battery power required by the target travel route, wherein the target battery power is the target battery power required by the travel route of the hybrid vehicle. It can be understood that the prediction energy management planning is performed on the hybrid electric vehicle based on the target travel route, so that the hybrid electric vehicle can be ensured to generate electricity in the high-efficiency interval of the engine in advance, thereby pure electricity is used in the low-efficiency interval of the engine, the energy-saving potential of the power assembly of the hybrid electric vehicle is exerted, and the purpose of saving fuel is achieved. By way of example, assuming that the target travel route includes two trips (i.e., two routes for separate travel) of the user, the second trip is a congested road section at the beginning, since the first trip hybrid vehicle performs power generation and energy storage in advance according to the target battery level required for the target travel route, the pure electric mode operation can be used in the congested road section, thereby saving fuel.

S205, the cloud server sends the target battery power to the hybrid vehicle.

Accordingly, the hybrid vehicle receives the target battery level.

In the step, after obtaining the target battery power required by the target travel route, the cloud server sends the target battery power to the hybrid vehicle, and accordingly, the hybrid vehicle receives the target battery power.

S206, the hybrid electric vehicle executes a predictive energy management function according to the target battery electric quantity.

In this step, the hybrid vehicle may perform the predictive energy management function according to the target battery level required for the travel route after receiving the target battery level. For example, during traveling of the hybrid vehicle, the actual battery power of the hybrid vehicle is compared with the target battery power to determine whether to generate electricity, so as to generate electricity in an engine high-efficiency interval in advance, and to facilitate pure electricity use in an engine low-efficiency interval. After the hybrid vehicle reaches the purpose of the current travel route, the predictive energy management function execution of the hybrid vehicle ends.

According to the energy management optimization method provided by the embodiment of the application, whether the vehicle owner is the preference of using oil is determined through the hybrid vehicle under the condition that the navigation is not started, if yes, the hybrid vehicle sends a trigger instruction to the cloud server so as to obtain the target battery power required by the travel route; the cloud server responds to the trigger instruction, a target travel route is determined from a historical repeated travel route according to the current travel position and the current travel time of the hybrid electric vehicle, the historical repeated travel route is based on a preset rule, the travel probability of the route obtained by splicing the historical travel route in a preset time window end to end according to the travel sequence is larger than that of the similar travel route of the travel threshold, and the oil saving amount corresponding to the historical repeated travel route is larger than the sum of the oil saving amounts of the single routes for splitting the historical repeated travel route into independent travel; the cloud server predicts energy management planning for the hybrid vehicle according to the target travel route to obtain target battery power required by the target travel route; the cloud server sends the target battery power to the hybrid electric vehicle, and the hybrid electric vehicle executes a predictive energy management function according to the target battery power after receiving the target battery power. According to the embodiment of the application, the target travel route is determined from the historical repeated travel route according to the current travel position and the current travel time of the hybrid vehicle and is used for carrying out predictive energy management planning on the hybrid vehicle, the historical repeated travel route is a travel route which is spliced out by a plurality of times of travel with a high rule and is more energy-saving, the problem that the energy consumption of a low-efficiency engine cannot be converted into an efficient engine energy area because the single travel route does not generate electricity in the engine efficient area at first can be avoided, the problem that the predictive energy management is energy-saving is limited can be solved, the hybrid vehicle can be ensured to generate electricity in the engine efficient area in advance on the basis of accurately predicting the target battery electric quantity required by the travel route of the hybrid vehicle, so that pure electricity can be used in the engine low-efficiency area, the energy-saving potential of the power assembly of the hybrid vehicle can be better exerted, and the overall fuel efficiency of the whole travel can be improved.

On the basis of the above embodiment, optionally, after the cloud server sends the target battery power to the hybrid vehicle, if it is determined that the current travel position of the hybrid vehicle deviates from the target travel route, a message for terminating execution of the predictive energy management function is sent to the hybrid vehicle, and the travel probability corresponding to the target travel route is reduced; accordingly, the hybrid vehicle receives a message sent by the cloud server for terminating execution of the predictive energy management function; terminating execution of the predictive energy management function.

For example, the cloud server may determine whether the current travel position of the hybrid vehicle deviates from the target travel route according to a global positioning system (Global Positioning System, GPS) position of the hybrid vehicle. If the current travel position of the hybrid vehicle deviates from the target travel route, the cloud server sends a message for stopping executing the predictive energy management function to the hybrid vehicle, so that the hybrid vehicle stops executing the predictive energy management function, and the travel probability corresponding to the target travel route is reduced. Illustratively, assume that the trip probability corresponding to the target trip route is The trip probability corresponding to the lowered target trip route is +.>Reference may be made to the following embodiments for how to obtain travel probabilities.

On the basis of the above embodiment, fig. 3 is a flowchart of a method for obtaining a historical repeated travel route according to an embodiment of the present application, which is applied to a cloud server. As shown in fig. 3, the method of the embodiment of the present application may include:

s301, acquiring historical travel route information of the vehicle within a preset duration.

For example, if the preset duration is, for example, the last three months, the historical travel route information of the vehicle in the last three months is acquired. It can be appreciated that for owners with oil preference, the embodiments of the present application are performed to obtain historical duplicate travel routes for the owners.

S302, route information of the historical travel route of the vehicle is aggregated into one item of label information.

In this step, the road segments included in each of the vehicle history travel routes may be determined according to the navigation of the hybrid vehicle, and the information of each of the road segments includes, for example, the start position information, the end position information, the length of the road segment, the average vehicle speed of the road segment, the time period in which the road segment is located, and the like. The information of the road sections included in each vehicle history travel route can be aggregated into one item of target information, namely, one corresponding data record, and the target information can be stored in the database.

And S303, dividing the vehicle history travel route information by a preset time window to obtain vehicle history travel route information corresponding to a plurality of preset time windows respectively.

The preset time window is, for example, half a day, one day, three days, one week, etc., and the vehicle history travel route information can be divided through different preset time windows to aggregate and find the aggregated history repeated travel route capable of realizing the maximum energy saving amount. Assuming that the preset duration is the last month and the preset time window is half a day, the vehicle history travel route information corresponding to 60 half days can be obtained.

And S304, splicing the historical travel routes of the vehicles corresponding to each preset time window end to end according to the travel sequence to obtain a plurality of spliced travel routes.

For example, assuming that a preset time window corresponds to 3 vehicle history travel routes, performing end-to-end splicing according to the travel sequence of the 3 vehicle history travel routes to obtain a spliced travel route. Because the spliced travel route is spliced by a plurality of travel routes, the last travel terminal point and the next travel departure place are one position, and the whole spliced travel route has a plurality of departure and arrival.

S305, carrying out similar processing on the spliced travel routes based on a preset rule to obtain similar travel routes with similarity larger than a first threshold, wherein the preset rule comprises the length of a road section of the spliced travel route, the time period of the road section, the name of the road section and the average speed of the road section.

In the step, after a plurality of spliced travel routes are obtained, similar processing can be performed on the plurality of spliced travel routes based on preset rules to obtain the similarity corresponding to the similar travel routes, and then the similar travel routes with the similarity larger than a first threshold value are obtained.

Further, optionally, based on a preset rule, performing a similar process on the plurality of spliced travel routes to obtain a similar travel route with a similarity greater than a first threshold value, which may include: based on a preset rule, comparing the spliced travel routes in pairs; for each road section respectively contained in the two spliced travel routes to be compared, if the difference value of the lengths of the corresponding road sections is smaller than a second threshold value, the difference value of the time periods of the corresponding road sections is smaller than a third threshold value, the difference value of the average speed of the corresponding road sections is smaller than a fourth threshold value, and the names of the corresponding road sections are the same, setting the similarity value of the corresponding road sections to be 1; determining a first addition, wherein the first addition is used for representing the sum of similar values corresponding to the spliced travel routes; determining a second addition, wherein the second addition is used for representing the sum of the number of road sections contained in the spliced travel route; determining the similarity as the ratio of the first addition to the second addition; and comparing the similarity with a first threshold value to obtain a similar travel route with the similarity larger than the first threshold value.

For example, when determining whether the two spliced travel routes are similar travel routes, for each road segment respectively included in the two spliced travel routes, the length of the corresponding road segment, the time period in which the corresponding road segment is located, the name of the corresponding road segment, and the average speed of the corresponding road segment may be sequentially compared, and when determining that the corresponding road segments are similar, the corresponding road segment is comparedThe similarity value of the segments is set to be 1, so that the sum of the similarity values corresponding to each spliced travel route can be obtained. Assuming that the two spliced travel routes all comprise 80 road sections, after each road section respectively comprising the two spliced travel routes is compared, if 79 road sections are similar, the sum of similarity values corresponding to the two spliced travel routes is 79, so that the similarity can be obtainedAnd comparing the similarity with a first threshold value to obtain a similar travel route with the similarity larger than the first threshold value.

S306, determining the travel probability as a ratio of a first quantity to a second quantity, wherein the first quantity is used for representing the quantity of similar travel routes, and the second quantity is used for representing the quantity of spliced travel routes in a preset duration.

For example, assuming that the preset time period is the last month and the preset time window is half a day, 60 spliced travel routes can be obtained, assuming that 40 similar travel routes exist in the 60 spliced travel routes, a first ratio of a first number of the similar travel routes to a second number of the spliced travel routes within the preset time period can be determined to be The travel probability is obtained.

S307, if the travel probability is greater than or equal to the travel threshold, determining that the similar travel route is an initial repeated travel route.

In the step, after the travel probability corresponding to the similar travel route is obtained, the travel probability is compared with the travel threshold value, and the similar travel route with the travel probability larger than or equal to the travel threshold value can be determined as an initial repeated travel route, wherein the initial repeated travel route indicates that the user has consistent height in a preset time window. It can be understood that by judging the travel probability, the similar travel route is determined as the initial repeated travel route, when the probability is extremely high, the predicted energy management global optimum of the spliced route can be realized by using the route splicing mode, and the habit of traveling of a driver in the future is judged by the travel probability mode, so that the problem that the global energy management optimization does not know how to travel in the future can be solved.

Optionally, if the trip probability is smaller than the trip threshold, determining that no historical repeated trip route exists; the preset time window length may be replaced and step S303 is re-performed.

By replacing the preset time window length, the embodiment can try to find all possible splicing modes of the global energy-saving route. If the length of the preset time window reaches the maximum, for example, the preset time window is maximum to be one week, the replacement of the preset time window is stopped.

S308, inputting the initial repeated travel route into a fuel-saving quantity simulation model corresponding to the predictive energy management function to obtain a first fuel-saving quantity; and splitting the initial repeated travel route into single routes for independent travel, and respectively inputting the single routes into the fuel-saving quantity simulation model to obtain a second fuel-saving quantity corresponding to the single routes.

In the step, the fuel-saving quantity simulation model corresponding to the predictive energy management function is pre-deployed on a speech segment server and is used for calculating the fuel-saving quantity of the travel route. Inputting an initial repeated travel route which is supposed to be completed by the driver in one run into a fuel-saving quantity simulation model corresponding to a predicted energy management function to obtain a first fuel-saving quantity; splitting the initial repeated travel route into single routes for the individual travel of the original driver, and respectively inputting the single routes into the fuel-saving quantity simulation model to obtain second fuel-saving quantities respectively corresponding to the single routes.

S309, if the first oil saving amount is larger than the sum of the second oil saving amounts, determining the initial repeated travel route as a historical repeated travel route.

In the step, after the first oil saving amount and the second oil saving amount are obtained, the sum of the second oil saving amount is obtained, the sum of the first oil saving amount and the second oil saving amount is compared, if the first oil saving amount is larger than the sum of the second oil saving amount, the initial repeated travel route is determined to be a historical repeated travel route, and the first oil saving amount is recorded. For example, the historical repeatedly-traveling route and the first fuel amount corresponding to the historical repeatedly-traveling route are stored in a database. The historic repeated travel route obtained through the step is a high-probability travel route, and fuel can be saved.

Optionally, if the first oil saving amount is less than or equal to the sum of the second oil saving amounts, determining that no historical repeated travel route exists; the preset time window length may be replaced and step S303 is re-performed.

Alternatively, the historical repeated travel route may be obtained according to a preset period.

For example, if the preset period is, for example, six, each Saturday cloud server processes the travel rule of the driver according to the history information of the remote service provider (Telemmatic Service Provider, TSP), that is, the steps S303 to S309 are executed according to the preset period, so as to obtain the historical repeated travel route.

According to the method for obtaining the historical repeated travel route, which is provided by the embodiment of the application, the route information of the historical travel route of the vehicle is aggregated into one item of label information by obtaining the historical travel route information of the vehicle within the preset duration; dividing the vehicle history travel route information by using preset time windows to obtain vehicle history travel route information corresponding to a plurality of preset time windows respectively, and splicing the vehicle history travel routes corresponding to each preset time window end to end according to a travel sequence to obtain a plurality of spliced travel routes; performing similar processing on the spliced travel routes based on a preset rule to obtain similar travel routes with the similarity larger than a first threshold value, wherein the preset rule comprises the length of a road section of the spliced travel route, the time period in which the road section is positioned, the name of the road section and the average speed of the road section; determining the travel probability as a ratio of a first quantity to a second quantity, wherein the first quantity is used for representing the quantity of similar travel routes, and the second quantity is used for representing the quantity of spliced travel routes within a preset duration; if the travel probability is greater than or equal to the travel threshold, determining the similar travel route as an initial repeated travel route; inputting the initial repeated travel route into a fuel-saving quantity simulation model corresponding to the predictive energy management function to obtain a first fuel-saving quantity; splitting the initial repeated travel route into single routes for independent travel, and respectively inputting the single routes into an oil saving quantity simulation model to obtain second oil saving quantity corresponding to the single routes; and if the first oil saving amount is larger than the sum of the second oil saving amounts, determining the initial repeated travel route as a historical repeated travel route. According to the embodiment of the application, based on the spliced travel route corresponding to the preset time window, the historical repeated travel route with the travel probability larger than or equal to the travel threshold and the fuel saving amount larger than the sum of the fuel saving amounts corresponding to the single route for splitting the spliced travel route into the independent travel is obtained, when the historical repeated travel route is used for the predictive energy management planning of the hybrid vehicle, the target battery electric quantity required by the travel route of the hybrid vehicle can be accurately predicted, so that the hybrid vehicle can better exert the energy saving potential of the power assembly of the hybrid vehicle when the predictive energy management function is executed according to the target battery electric quantity, and the overall fuel efficiency of the whole travel is improved.

In summary, the technical scheme provided by the application has at least the following advantages:

(1) Compared with the global optimization algorithm in the prior art, the calculation amount is very small, only the travel routes which are easiest to repeat are needed to be combined and found, the sum of the fuel saving amount of the combined travel routes and the fuel saving amount of the single route which is split into each single travel is compared, so that the length of a reasonable optimal global optimization time window and the aggregated historical travel routes are determined, the global optimal energy saving under the condition that the historical travel routes are approximately fixed is realized, and the benefit of a vehicle owner is maximum;

(2) The historical repeated travel route is used, and because the data quantity is large, the speed prediction of vehicle travel is more accurate than that of navigation single time, and the oil-saving effect of prediction energy management is better than that of single travel;

(3) The application can optimize the segmentation of different preset time window lengths and the data of different travel times to aggregate and find the aggregated historical repeated travel route which can realize the maximum fuel saving amount;

(4) The application does not require the travel routes of all sections to be completely consistent, only the travel routes of all sections are required to be extremely large in time sequence and recurrence probability, and the commute routes such as commute routes of business and the like can be easily judged and obtained;

(5) The oil-saving quantity simulation model used by the application is deployed on the cloud server, has higher precision than the traditional table look-up method, and can be iteratively updated at any time according to actual use conditions.

The following are examples of the apparatus of the present application that may be used to perform the method embodiments of the present application. For details not disclosed in the embodiments of the apparatus of the present application, please refer to the embodiments of the method of the present application.

Fig. 4 is a schematic structural diagram of an energy management optimization device according to an embodiment of the present application, which is applied to a cloud server. As shown in fig. 4, an energy management optimization device 400 of an embodiment of the present application includes: a receiving module 401, a determining module 402, a processing module 403 and a transmitting module 404. Wherein:

the receiving module 401 is configured to receive a trigger instruction, where the trigger instruction is sent when the hybrid vehicle determines that the vehicle owner is preferred to use oil when the hybrid vehicle does not start navigation.

The determining module 402 is configured to determine, according to the current travel position and the current travel time of the hybrid vehicle, a target travel route from the historical repeated travel routes, where the historical repeated travel routes are based on a preset rule, and similar travel routes obtained by performing similar processing on routes obtained by performing end-to-end splicing on the historical travel routes in a preset time window according to a travel sequence are similar travel routes with a travel probability greater than a travel threshold, and an oil saving amount corresponding to the historical repeated travel routes is greater than a sum of oil saving amounts of single routes obtained by splitting the historical repeated travel routes into separate trips.

And the processing module 403 is configured to perform predictive energy management planning on the hybrid vehicle according to the target travel route, so as to obtain a target battery power required by the target travel route.

The transmitting module 404 is configured to transmit the target battery power to the hybrid vehicle, so that the hybrid vehicle performs the predictive energy management function according to the target battery power.

In some embodiments, the energy management optimization device 400 may further include an acquisition module 405 for obtaining a historical recurring travel route by: acquiring historical travel route information of the vehicle within a preset duration; dividing the vehicle history travel route information by a preset time window to obtain vehicle history travel route information corresponding to a plurality of preset time windows respectively; splicing the historical travel routes of the vehicles corresponding to each preset time window end to end according to the travel sequence to obtain a plurality of spliced travel routes; performing similar processing on the spliced travel routes based on a preset rule to obtain similar travel routes with the similarity larger than a first threshold value, wherein the preset rule comprises the length of a road section of the spliced travel route, the time period in which the road section is positioned, the name of the road section and the average speed of the road section; determining the travel probability as a ratio of a first quantity to a second quantity, wherein the first quantity is used for representing the quantity of similar travel routes, and the second quantity is used for representing the quantity of spliced travel routes within a preset duration; if the travel probability is greater than or equal to the travel threshold, determining the similar travel route as an initial repeated travel route; inputting the initial repeated travel route into a fuel-saving quantity simulation model corresponding to the predictive energy management function to obtain a first fuel-saving quantity; splitting the initial repeated travel route into single routes for independent travel, and respectively inputting the single routes into an oil saving quantity simulation model to obtain second oil saving quantity corresponding to the single routes; and if the first oil saving amount is larger than the sum of the second oil saving amounts, determining the initial repeated travel route as a historical repeated travel route.

Optionally, when the obtaining module 405 is configured to perform similar processing on the plurality of spliced travel routes based on a preset rule to obtain a similar travel route with a similarity greater than a first threshold, the obtaining module may be specifically configured to: based on a preset rule, comparing the spliced travel routes in pairs; for each road section respectively contained in the two spliced travel routes to be compared, if the difference value of the lengths of the corresponding road sections is smaller than a second threshold value, the difference value of the time periods of the corresponding road sections is smaller than a third threshold value, the difference value of the average speed of the corresponding road sections is smaller than a fourth threshold value, and the names of the corresponding road sections are the same, setting the similarity value of the corresponding road sections to be 1; determining a first addition, wherein the first addition is used for representing the sum of similar values corresponding to the spliced travel routes; determining a second addition, wherein the second addition is used for representing the sum of the number of road sections contained in the spliced travel route; determining the similarity as the ratio of the first addition to the second addition; and comparing the similarity with a first threshold value to obtain a similar travel route with the similarity larger than the first threshold value.

Optionally, the obtaining module 405 may be further configured to: if the travel probability is smaller than the travel threshold, determining that no historical repeated travel route exists; and replacing the length of the preset time window, and re-executing the steps of dividing the vehicle history travel route information by the preset time window to obtain vehicle history travel route information corresponding to the preset time windows respectively.

Optionally, the obtaining module 405 may be further configured to: and obtaining a historical repeated travel route according to a preset period.

Optionally, the obtaining module 405 may be further configured to: before dividing the historical travel route information of the vehicle in a preset time window, the route information of the historical travel route of the vehicle is aggregated into a piece of label information.

In some embodiments, the determining module 402 may be specifically configured to: and comparing the current travel position and the current travel time of the hybrid vehicle with route information of the historical repeated travel route, and determining the historical repeated travel route which contains the current travel position and has a time difference value of less than a fifth threshold value from the current travel time as a target travel route.

Optionally, the determining module 402 may be further configured to: if a plurality of historical repeated travel routes are obtained from the historical repeated travel routes according to the current travel position and the current travel time of the hybrid electric vehicle, determining the historical repeated travel route with the highest travel probability in the plurality of historical repeated travel routes as a target travel route; if the historical repeated travel route with the highest travel probability is a plurality of, determining the historical repeated travel route with the highest travel probability and the largest fuel saving amount as the target travel route.

Optionally, the sending module 404 may be further configured to: after the target battery power is sent to the hybrid vehicle, if the current travel position of the hybrid vehicle is determined to deviate from the target travel route, a message for stopping executing the predictive energy management function is sent to the hybrid vehicle, and the travel probability corresponding to the target travel route is reduced.

The device of the embodiment of the application can be used for executing the scheme of the cloud end server in any method embodiment, and the implementation principle and the technical effect are similar, and are not repeated here.

Fig. 5 is a schematic structural diagram of an energy management optimizing apparatus according to another embodiment of the present application, which is applied to a hybrid vehicle. As shown in fig. 5, an energy management optimization device 500 of an embodiment of the present application includes: a determining module 501, a transmitting module 502 and an executing module 503. Wherein:

a determination module 501 is configured to determine whether the vehicle owner is an oil preference if the hybrid vehicle is not navigating on.

The sending module 502 is configured to send a trigger instruction to the cloud server if the request is received, so as to obtain a target battery power required by a travel route, where the target battery power is a similar travel route with a travel probability greater than a travel threshold value, which is obtained by performing similar processing on a route obtained by performing end-to-end splicing on the historical travel route in a preset time window according to a travel sequence, according to a current travel position and a current travel time of the hybrid vehicle, determining a target travel route from a historical repeated travel route, and performing predictive energy management planning on the hybrid vehicle according to the target travel route, where the fuel saving amount corresponding to the historical repeated travel route is greater than a sum of fuel saving amounts of individual routes obtained by splitting the historical repeated travel route into individual trips;

An execution module 503 is configured to execute a predictive energy management function according to the target battery level.

In some embodiments, the determining module 501 may be specifically configured to: determining that the vehicle owner is an oil preference in response to an oil preference setting operation of the vehicle owner on a vehicle machine of the hybrid vehicle; or if the ratio of the historical fueling times to the historical charging times of the hybrid vehicle obtained from the cloud server is greater than a ratio threshold, determining that the vehicle owner is the oil preference; or if the mileage of the hybrid vehicle in the pure electric mode operation is smaller than the mileage of the hybrid vehicle in the fuel oil mode operation in the history time, determining that the vehicle owner is the preference for using the fuel oil.

Optionally, the execution module 503 may be further configured to: after the predicted energy management function is executed according to the target battery power, receiving a message sent by the cloud server for stopping executing the predicted energy management function; terminating execution of the predictive energy management function.

The device of the embodiment of the application can be used for executing the scheme of the hybrid electric vehicle in any method embodiment, and the implementation principle and the technical effect are similar, and are not repeated here.

Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the application. As shown in fig. 6, the electronic device 600 may include: at least one processor 601 and a memory 602.

A memory 602 for storing programs. In particular, the program may include program code including computer-executable instructions.

The memory 602 may include high-speed random access memory (Random Access Memory, RAM) and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.

The processor 601 is configured to execute computer-executable instructions stored in the memory 602 to implement the energy management optimization method described in the foregoing method embodiments. The processor 601 may be a central processing unit (Central Processing Unit, CPU), or an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits configured to implement embodiments of the present application. Specifically, when the energy management optimization method described in the foregoing method embodiment is implemented, the electronic device may be, for example, an electronic device having a processing function, such as a server. In carrying out the energy management optimization method described in the foregoing method embodiments, the electronic device may be, for example, an electronic control unit on a vehicle.

Optionally, the electronic device 600 may also include a communication interface 603. In a specific implementation, if the communication interface 603, the memory 602, and the processor 601 are implemented independently, the communication interface 603, the memory 602, and the processor 601 may be connected to each other through buses and perform communication with each other. The bus may be an industry standard architecture (Industry Standard Architecture, ISA) bus, an external device interconnect (Peripheral Component, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. Buses may be divided into address buses, data buses, control buses, etc., but do not represent only one bus or one type of bus.

Alternatively, in a specific implementation, if the communication interface 603, the memory 602, and the processor 601 are integrated on a chip, the communication interface 603, the memory 602, and the processor 601 may complete communication through internal interfaces.

The present application also provides a computer readable storage medium having stored therein computer program instructions which, when executed by a processor, implement the scheme of the energy management optimization method as above.

The application also provides a computer program product comprising a computer program which, when executed by a processor, implements the solution of the energy management optimization method as above.

The computer readable storage medium described above may be implemented by any type or combination of volatile or non-volatile Memory devices, such as static random access Memory (Static Random Access Memory, SRAM), electrically erasable programmable Read-Only Memory (Electrically Erasable Programmable Read Only Memory, EEPROM), erasable programmable Read-Only Memory (Erasable Programmable Read Only Memory, EPROM), programmable Read-Only Memory (Programmable Read Only Memory, PROM), read Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk. A readable storage medium can be any available medium that can be accessed by a general purpose or special purpose computer.

An exemplary readable storage medium is coupled to the processor such the processor can read information from, and write information to, the readable storage medium. In the alternative, the readable storage medium may be integral to the processor. The processor and the readable storage medium may reside in an application specific integrated circuit. Of course, the processor and the readable storage medium may reside as discrete components in an energy management optimization device.

Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the method embodiments described above may be performed by hardware associated with program instructions. The foregoing program may be stored in a computer readable storage medium. The program, when executed, performs steps including the method embodiments described above; and the aforementioned storage medium includes: various media that can store program code, such as ROM, RAM, magnetic or optical disks.

Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the application.

Claims

1. An energy management optimization method, which is applied to a cloud server, comprises the following steps:

responding to the trigger instruction, and determining a target travel route from a historical repeated travel route according to the current travel position and the current travel time of the hybrid electric vehicle, wherein the historical repeated travel route is a similar travel route with the travel probability larger than a travel threshold value obtained by performing similar processing on a route obtained by performing head-to-tail splicing on the historical travel route in a preset time window according to a travel sequence based on a preset rule, and the oil saving amount corresponding to the historical repeated travel route is larger than the sum of the oil saving amounts of single routes for splitting the historical repeated travel route into independent travel;

and sending the target battery power to the hybrid vehicle so that the hybrid vehicle executes a predictive energy management function according to the target battery power.

2. The energy management optimization method of claim 1, wherein the historical recurring travel route is obtained by:

acquiring historical travel route information of the vehicle within a preset duration;

dividing the vehicle history travel route information by a preset time window to obtain vehicle history travel route information corresponding to a plurality of preset time windows respectively;

splicing the historical travel routes of the vehicles corresponding to each preset time window end to end according to the travel sequence to obtain a plurality of spliced travel routes;

performing similar processing on the spliced travel routes based on the preset rule to obtain similar travel routes with similarity larger than a first threshold, wherein the preset rule comprises the length of a road section of the spliced travel route, the time period in which the road section is located, the name of the road section and the average speed of the road section;

determining the travel probability as a ratio of a first quantity to a second quantity, wherein the first quantity is used for representing the quantity of the similar travel routes, and the second quantity is used for representing the quantity of the spliced travel routes in the preset duration;

if the travel probability is greater than or equal to the travel threshold, determining the similar travel route as an initial repeated travel route;

Inputting the initial repeated travel route into a fuel saving quantity simulation model corresponding to a predictive energy management function to obtain a first fuel saving quantity; splitting the initial repeated travel route into single routes for independent travel, and respectively inputting the fuel saving quantity simulation model to obtain a second fuel saving quantity corresponding to the single routes;

and if the first oil saving amount is larger than the sum of the second oil saving amounts, determining the initial repeated travel route as the historical repeated travel route.

3. The energy management optimization method according to claim 2, wherein the performing, based on the preset rule, similar processing on the plurality of spliced travel routes to obtain similar travel routes with similarity greater than a first threshold value includes:

based on the preset rule, comparing the spliced travel routes in pairs;

for each road section respectively contained in the two spliced travel routes to be compared, if the difference value of the lengths of the corresponding road sections is smaller than a second threshold value, the difference value of the time periods in which the corresponding road sections are positioned is smaller than a third threshold value, the difference value of the average speed of the corresponding road sections is smaller than a fourth threshold value, and the names of the corresponding road sections are the same, setting the similarity value of the corresponding road sections to be 1;

Determining a first addition, wherein the first addition is used for representing the sum of similar values corresponding to the spliced travel routes;

determining a second addition, wherein the second addition is used for representing the sum of the number of road sections contained in the spliced travel route;

determining the similarity as a ratio of the first sum to the second sum;

and comparing the similarity with the first threshold value to obtain a similar travel route with the similarity larger than the first threshold value.

4. The energy management optimization method of claim 2, further comprising:

if the travel probability is smaller than the travel threshold, determining that no historical repeated travel route exists;

and replacing the length of the preset time window, and re-executing the step of dividing the vehicle history travel route information by the preset time window to obtain vehicle history travel route information corresponding to a plurality of preset time windows respectively.

5. The energy management optimization method of claim 2, further comprising:

and obtaining the historical repeated travel route according to a preset period.

6. The energy management optimization method of claim 2, wherein prior to dividing the vehicle history travel route information by a preset time window, further comprising:

And aggregating the route information of the historical travel route of the vehicle into a piece of label information.

7. The energy management optimization method of claim 2, wherein the determining a target travel route from the historical repeated travel routes according to the current travel position and the current travel time of the hybrid vehicle comprises:

and comparing the current travel position and the current travel time of the hybrid electric vehicle with the route information of the historical repeated travel route, and determining the historical repeated travel route which contains the current travel position and has a travel time with a time difference less than a fifth threshold value from the current travel time as the target travel route.

8. The energy management optimization method of claim 7, further comprising:

if a plurality of historical repeated travel routes are obtained from the historical repeated travel routes according to the current travel position and the current travel time of the hybrid electric vehicle, determining the historical repeated travel route with the highest travel probability in the plurality of historical repeated travel routes as the target travel route;

and if the historical repeated travel route with the highest travel probability is a plurality of, determining the historical repeated travel route with the highest travel probability and the largest fuel saving amount as the target travel route.

9. The energy management optimization method of any one of claims 1-8, further comprising, after the transmitting the target battery level to the hybrid vehicle:

and if the current travel position of the hybrid power vehicle deviates from the target travel route, sending a message for stopping executing the predictive energy management function to the hybrid power vehicle, and reducing the travel probability corresponding to the target travel route.

10. An energy management optimization method, applied to a hybrid vehicle, comprising:

determining whether a vehicle owner is an oil preference under the condition that the hybrid vehicle does not start navigation;

if yes, a trigger instruction is sent to a cloud server to obtain a target battery power required by a travel route, wherein the target battery power is a similar travel route with a travel probability larger than a travel threshold value, which is obtained by similar processing of a route obtained by performing head-to-tail splicing on the historical travel route in a preset time window according to a preset rule, and the oil saving amount corresponding to the historical repeated travel route is larger than the sum of oil saving amounts of single routes for splitting the historical repeated travel route into single travel routes according to the target travel route;

And executing a predictive energy management function according to the target battery electric quantity.

11. The energy management optimization method of claim 10, wherein the determining whether the vehicle owner is an oil usage preference comprises:

determining that the vehicle owner is an oil usage preference in response to an oil usage preference setting operation of the vehicle owner on a vehicle machine of the hybrid vehicle;

or if the ratio of the historical fueling times to the historical charging times of the hybrid vehicle obtained from the cloud server is greater than a ratio threshold, determining that the vehicle owner is the oil preference;

or if the mileage of the hybrid vehicle in the pure electric mode operation is smaller than the mileage of the hybrid vehicle in the fuel oil mode operation in the history time period, the vehicle owner is determined to be the preference of using the fuel oil.

12. The energy management optimization method according to claim 10 or 11, characterized by further comprising, after the performing a predictive energy management function according to the target battery level:

receiving a message sent by the cloud server for terminating execution of the predictive energy management function;

terminating execution of the predictive energy management function.

13. An energy management optimization device, characterized by being applied to a cloud server, the energy management optimization device comprising:

the receiving module is used for receiving a trigger instruction, wherein the trigger instruction is sent when the vehicle owner is determined to be favored by oil under the condition that the hybrid vehicle does not start navigation;

the determining module is used for responding to the triggering instruction, determining a target travel route from a historical repeated travel route according to the current travel position and the current travel time of the hybrid electric vehicle, wherein the historical repeated travel route is a similar travel route with the travel probability larger than a travel threshold value obtained by performing similar processing on a route obtained by splicing the historical travel route in a preset time window end to end according to the travel sequence, and the oil saving amount corresponding to the historical repeated travel route is larger than the sum of the oil saving amounts of single routes for splitting the historical repeated travel route into independent travel;

and the sending module is used for sending the target battery electric quantity to the hybrid electric vehicle so that the hybrid electric vehicle can execute a predictive energy management function according to the target battery electric quantity.

14. An energy management optimization device, characterized by being applied to a hybrid vehicle, comprising:

the determining module is used for determining whether the vehicle owner is oil preference or not under the condition that the hybrid vehicle does not start navigation;

the cloud server responds to the trigger instruction, determines a target travel route from a historical repeated travel route according to the current travel position and the current travel time of the hybrid vehicle, and performs predictive energy management planning on the hybrid vehicle according to the target travel route, wherein the historical repeated travel route is obtained by performing similar processing on a route obtained by performing head-to-tail splicing on the historical travel route in a preset time window according to a preset rule, and the travel probability of the similar travel route is larger than a travel threshold value, and the oil saving amount corresponding to the historical repeated travel route is larger than the sum of the oil saving amounts of single routes for splitting the historical repeated travel route into independent travel;

15. An electronic device, comprising: a processor, and a memory communicatively coupled to the processor;

the memory stores computer-executable instructions;

the processor executes the computer-executable instructions stored by the memory to implement the energy management optimization method of any one of claims 1-12.

16. A computer readable storage medium, characterized in that the computer readable storage medium has stored therein computer program instructions which, when executed, implement the energy management optimization method of any one of claims 1 to 12.