CN117818625A - Method and related device for predicting energy consumption of vehicle power system - Google Patents
Method and related device for predicting energy consumption of vehicle power system Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
- B60W40/06—Road conditions
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W2050/0001—Details of the control system
- B60W2050/0002—Automatic control, details of type of controller or control system architecture
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Abstract
The application provides a vehicle power system energy consumption prediction method and a related device, wherein the method comprises the following steps: acquiring navigation information from the current position of the vehicle to the target position; performing data preprocessing on the acquired navigation information; according to the preprocessed navigation information, estimating the initial energy consumption of a vehicle driving list section based on a longitudinal dynamics model constructed in advance; according to the current road condition information and the vehicle parameters, driving characteristic factors are self-learned, and initial energy consumption of corresponding characteristic road sections is corrected; and estimating the total energy consumption of the road section to be driven in front according to the initial energy consumption of the corrected single road section, and carrying out dynamic correction according to self-learning of driving characteristic factors. The method and the related device for predicting the energy consumption of the vehicle power system can be used for more accurately predicting the energy consumption of the power system of the vehicle running path by utilizing the navigation front road sensing capability and the known current vehicle power parameters.
Description
Technical Field
The application belongs to the technical field of new energy automobiles, and particularly relates to an energy consumption prediction method and a related device for a vehicle power system.
Background
In the early development stage of new energy automobiles, the serious concerns of the endurance mileage, the battery energy density, the charging speed, the driving comfort and the like of the automobiles are gradually optimized or solved under the improvement of the industry technology in recent years and the technology level. Such as diversified automobile power architecture, safer and greatly improved capacity power batteries, richer comfort configurations, super fast charge solutions, and the like. The new energy automobile industry has been rapidly developed in recent years, and has come into a brand new stage, but invariably, the development of the new energy automobile still occupies a vital position.
In recent years, with industry development and market demand change, new energy automobiles gradually progress toward safer, more energy-saving and more intelligent fields. How to design a new energy automobile which has lower energy consumption, can release people from the roles of drivers as much as possible, and is absolutely safe and reliable at the same time becomes the target of a plurality of automobile enterprises. Therefore, energy consumption calculation and prediction are increasingly applied to the energy saving field of power systems and the intelligent driving field.
In view of the increasing abundance of current vehicle configurations, most of the vehicle configurations are equipped with vehicle navigation systems, and the driving assistance function is gradually lowered from the original high-configuration vehicle configuration to the medium-configuration or even the full-scale configuration. The vehicle navigation system not only can provide the functions of route guidance and the like for a driver, but also can acquire rich road condition information of a front road, and if the vehicle navigation system is only applied to the function of route guidance, resource waste is caused. In addition, the auxiliary driving controller has a margin in the platform operation capability besides the basic auxiliary driving function operation. The calculation of the remaining endurance mileage of the current new energy automobile is out of alignment, so that the possibility of misleading a driver exists, and the situation of halfway anchoring is easy to cause.
Disclosure of Invention
In view of the foregoing, the present application is directed to a vehicle power system energy consumption prediction method, so as to solve at least one of the above problems.
In order to achieve the above purpose, the technical scheme of the application is realized as follows:
in a first aspect, the present application provides a method for predicting energy consumption of a vehicle powertrain, the method comprising:
obtaining navigation information from the current position of the vehicle to the target position, wherein the navigation information at least comprises the position of the vehicle, the remaining distance, the total number of road sections and the road section information;
performing data preprocessing on the acquired navigation information;
according to the preprocessed navigation information, estimating the initial energy consumption of a vehicle driving list section based on a longitudinal dynamics model constructed in advance;
according to the current road condition information and the vehicle parameters, driving characteristic factors are self-learned, and initial energy consumption of corresponding characteristic road sections is corrected;
and estimating the total energy consumption of the road section to be driven in front according to the initial energy consumption of the corrected single road section, and carrying out dynamic correction according to self-learning of driving characteristic factors.
In a second aspect, based on the same inventive concept, the present application further provides a vehicle power system energy consumption prediction apparatus, including:
the information acquisition module is configured to acquire navigation information from the current position of the vehicle to the target position, wherein the navigation information at least comprises the vehicle position, the remaining distance, the total number of road segments and the road segment information;
the preprocessing module is configured to perform data preprocessing on the acquired navigation information;
the initial energy consumption calculation module is configured to estimate the initial energy consumption of the vehicle driving list section based on a pre-constructed longitudinal dynamics model according to the preprocessed navigation information;
the correction module is configured to perform driving characteristic factor self-learning according to the current road condition information and the vehicle parameters and correct the initial energy consumption of the corresponding characteristic road section;
and the energy consumption prediction module is configured to estimate the total energy consumption of the road section to be driven in front according to the initial energy consumption of the corrected single road section and dynamically correct the road section according to the self-learning of the driving characteristic factor.
In a third aspect, based on the same inventive concept, the present application further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the vehicle power system energy consumption prediction method according to the first aspect when executing the program.
In a fourth aspect, based on the same inventive concept, the present application also provides a non-transitory computer readable storage medium, wherein the non-transitory computer readable storage medium stores computer instructions for causing the computer to perform the vehicle power system energy consumption prediction method according to the first aspect.
Compared with the prior art, the vehicle power system energy consumption prediction method and the related device have the following beneficial effects:
according to the vehicle power system energy consumption prediction method and the related device, the power system energy consumption of the vehicle driving path is predicted more accurately by utilizing the navigation front road sensing capability and the known current vehicle power parameters. The method can be used for correcting the dynamic driving mileage of the vehicle; and secondly, auxiliary functions such as energy supply reminding, intelligent recommendation of energy supply places, intelligent energy planning and the like can be derived.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application, illustrate and explain the application and are not to be construed as limiting the application. In the drawings:
FIG. 1 is a flow chart of a method for predicting energy consumption of a vehicle powertrain according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a vehicle system architecture according to an embodiment of the present application;
FIG. 3 is a schematic diagram illustrating preprocessing of navigation information data according to an embodiment of the present application;
FIG. 4 is a schematic diagram of equivalent slope calculation according to an embodiment of the present application;
FIG. 5 is a schematic diagram of a vehicle powertrain energy consumption prediction apparatus according to an embodiment of the present application;
fig. 6 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present application.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail below with reference to the accompanying drawings.
It should be noted that unless otherwise defined, technical or scientific terms used in the embodiments of the present application should be given the ordinary meaning as understood by one of ordinary skill in the art to which the present application belongs. The terms "first," "second," and the like, as used in embodiments of the present application, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", etc. are used merely to indicate relative positional relationships, which may also be changed when the absolute position of the object to be described is changed.
Embodiments of the present application are described in detail below with reference to the accompanying drawings.
Referring to fig. 1, the present embodiment provides a method for predicting energy consumption of a vehicle power system, which specifically includes the following steps:
step S101, navigation information between the current position of the vehicle and the target position is acquired, wherein the navigation information at least comprises the vehicle position, the remaining distance, the total number of road segments and the road segment information.
Specifically, in this embodiment, as shown in fig. 2, the energy consumption prediction system of the vehicle includes a vehicle navigation system, an intelligent driving controller (intelligent driving controller in the figure) and a whole vehicle controller, where a terminal where the vehicle navigation system is located and the intelligent driving controller are located in the same local area network of the vehicle, and network communication test is smooth, and the intelligent driving controller keeps information interaction with the whole vehicle controller of the vehicle to control the whole vehicle to run;
the driver sets a destination to conduct route planning based on the current vehicle-mounted navigation system, a recommended route is given, the driver selects the route to start navigation according to the requirement, and the vehicle-mounted navigation system enters a navigation mode;
after navigation is started, the vehicle-mounted navigation system accesses the intelligent auxiliary driving controller through the set local area network port A, periodically transmits the whole-course navigation information between the current position of the vehicle and the destination, and transmits new route information after the navigation re-plans a route if the driver yaw in the whole driving process;
the route information includes, but is not limited to, coordinates of a current location of the vehicle, remaining mileage to the destination, total number of road segments, road segment ID, road segment length, road segment type, road segment speed limit, road segment congestion status, road segment congestion recommended speed, road segment inner sampling point gradient, and road segment inner sampling point distance.
Step S102, as shown in FIG. 3, data preprocessing is performed on the acquired navigation information.
In some embodiments, the navigation information of the remaining routes is set as a data packet by taking the route sections as units, and the navigation information of each n route sections is set as a data packet;
extracting information of the same field from each road section information to form a single array signal so as to divide the information into a plurality of data packets, and periodically transmitting the data packets in turn until all route planning information is transmitted;
after preprocessing is completed, on the basis of original navigation information, the total number of segmentation of navigation information data packets and the number information of the current transmission data packet are newly added and issued along with each segmentation data packet to characterize the current data packet transmission process and state.
Specifically, in this embodiment, the intelligent driving assistance controller receives and analyzes the original navigation information issued by the vehicle navigation system through the local area network port a, and in this embodiment, in order to reduce the internal storage space resource and the operating resource occupation condition of the controller, the original navigation data after the preliminary analysis is preprocessed, and the processing manner is as follows:
setting the route information of every n road sections as a data packet by taking the navigation information of the rest road sections as a unit of road sections;
extracting information of the same field from each road section information to form a single array signal, for example, extracting and recombining the road section ID of the road section between the 0 th and the nth into a road section ID array signal of the first data packet, extracting and recombining the rest road section information according to the method, and outputting the extracted and recombined road section information as information in the second and the third data packets until the data packets finish extracting and transmitting all the route planning information;
the divided data packets are sequentially released and transmitted, and when the data packets are issued, the total number of the divided packets and the ID of the data packet which is currently issued are increased in addition to the original information of the map, so that the transmission process and the state of the current data packet are represented. The information in each data packet only contains a single value signal and an array signal for subsequent energy consumption calculations.
And step S103, estimating the initial energy consumption of the vehicle driving list road section based on a pre-constructed longitudinal dynamics model according to the preprocessed navigation information.
In some embodiments, the equivalent slope and distance of a single road segment is calculated based on sample point information within the road segment;
estimating the virtual vehicle speed of the vehicle on the road section according to the road section type, the road section speed limit, the road section congestion condition, the road section gradient and the power parameters of the vehicle;
estimating initial energy consumption of a power system for a vehicle to travel on the road section based on a longitudinal dynamics model;
and calculating the initial energy consumption of all road sections, and superposing the initial energy consumption to obtain the total initial energy consumption of the vehicle running to the destination.
Specifically, in the present embodiment, the equivalent gradient and distance calculation of a single road section is performed based on the sampling point information in the road section, and the equivalent gradient calculation schematic diagram is shown in fig. 4:
a. the product of the slope sine value of the sampling point and the sampling road section is the height of the section;
b. and accumulating the heights of all the sampling points in the road section to obtain the height of the road section, and knowing the distance of the road section to obtain the average gradient of the road section.
Estimating the running speed of the vehicle based on parameters such as road segment speed limit information, road segment congestion information, road class and the like, and obtaining the virtual speed of the vehicle running on the road segment:
a. if the current road section has limited speed limit information, the maximum running speed does not exceed a speed limit value;
b. if the current road section has infinite speed information, different virtual running speeds (high speed: 75km/h; express way: 60km/h; national road: 50km/h; provincial road: 45km/h; common road: 40km/h; other: 30 km/h) are set according to different road types.
It should be noted that, specific vehicle speed setting needs to be set according to the type of the vehicle, such as passenger vehicles, light trucks, and heavy trucks; the virtual vehicle speeds of different road types are different due to regulations, vehicle characteristics and the like, and also need to be determined according to the actual working condition;
c. and if the current road section is congested, adopting the navigation suggested speed as the virtual speed of the congested road section.
Estimating initial energy consumption of a power system of the vehicle in the road section according to a longitudinal dynamics model of the vehicle:
driving force = delta·m·a+m·g·sin α+mu·m·g·cos α+c d AV 2 /21.15
Wherein delta is a rotational mass inertia coefficient, alpha is the running acceleration of the vehicle, alpha is the road gradient, mu is the rolling resistance coefficient, A is the windward area of the vehicle, cd is the windage coefficient, and V is the vehicle speed.
In the calculation method, due to the uncertainty of the running condition of the vehicle, the acceleration resistance is ignored, the deviation correction is carried out by the correction coefficient, and the rotation mass inertia coefficient, the rolling resistance coefficient, the windward area and the wind resistance coefficient are all set according to the actual test parameters of the vehicle.
And analyzing and calculating according to the longitudinal dynamics model to obtain the equivalent driving force of the vehicle, further obtaining the driving torque T of the motor and the motor rotating speed n of the corresponding vehicle speed based on the wheel radius and the transmission ratio parameters, and further calculating according to P=T x n/9550 to obtain the power of the power system.
And obtaining estimated time t of the vehicle running in the road section based on the road section distance and the virtual vehicle speed, and obtaining the initial energy consumption of the power system of the vehicle running in the road section by means of the estimation of W=P.
According to the method, the estimated initial power consumption of all road sections is calculated and overlapped by the method, and the total estimated initial power consumption Wraw when the vehicle runs to the destination is obtained.
And step S104, performing driving characteristic factor self-learning according to the current road condition information and the vehicle parameters, and correcting the initial energy consumption of the corresponding characteristic road section.
In some embodiments, calculating an energy consumption value of a vehicle power system in real time based on motor data, obtaining an actual energy consumption value corresponding to a road section after each road section is driven, and comparing the actual energy consumption value with initial energy consumption of the road section to obtain an energy consumption correction coefficient under road condition characteristics of the road section;
and carrying out real-time correction on the initial energy consumption matched with all road section characteristics of the rest route based on the energy consumption correction coefficient to obtain the corrected predicted energy consumption data of all road sections.
Specifically, in the present embodiment, the first and second embodiments,
(1) Setting a correction threshold value:
setting a road section characteristic gradient threshold range and a road section characteristic vehicle speed threshold range, and forming a cross correction coefficient table based on the gradient and the vehicle speed threshold range;
(2) Real-time energy consumption calculation and average vehicle speed calculation for vehicle
In the running process of the vehicle, according to the rotating speed and the torque of the motor, the energy consumption information of the power system is obtained through real-time integration, and when one road section is run, the real energy consumption corresponding to the road section is obtained;
in the running process of the vehicle, the running time of the vehicle is accumulated and calculated in real time, and the average vehicle speed corresponding to one road section is obtained when the road section is run;
(3) Road feature matching and correction coefficient calculation
When the vehicle runs on one road section, comparing and matching the gradient and average vehicle speed of the road section with the gradient threshold range and the vehicle speed threshold range of the correction table, and if the gradient and the average vehicle speed are both in the matching range, updating the correction coefficient in the gradient and the vehicle speed threshold range;
and filling the ratio of the estimated energy consumption to the actual energy consumption of the road section into a corresponding position as a correction coefficient, and storing the correction coefficient.
Based on the mechanism, updating and storing the correction coefficient under the characteristics of one road section when the vehicle runs out of the road section;
step S105, estimating the total energy consumption of the road section to be driven in front according to the initial energy consumption of the corrected single road section, and carrying out dynamic correction according to self-learning of driving characteristic factors.
In some embodiments, the predicted total energy consumption of the power system from the current position of the vehicle to the destination is obtained by superposition based on the predicted energy consumption of the single-path segment completed by real-time correction;
and the vehicle runs in a navigation state, and the correction coefficient of the road corresponding to the characteristic road section is updated in real time in a rolling way according to the running process and is stored.
Specifically, in the present embodiment, the first and second embodiments,
based on the correction coefficient calculated in the step S104, multiplying the predicted energy consumption of the road section by the correction coefficient of the corresponding characteristic road section to obtain new predicted energy consumption of the road section;
the predicted energy consumption of the single-path segment based on the real-time correction is overlapped to obtain the predicted total energy consumption of the power system from the current position of the vehicle to the destination;
based on the above description of all steps, the vehicle runs in a navigation state, and the correction coefficient of the road corresponding to the characteristic road section is updated in real time in a rolling way along with the running process and stored, so that the estimated total energy consumption of the vehicle is updated along with the update of the correction coefficient.
The method for predicting the energy consumption of the vehicle power system in the embodiment utilizes the navigation front road sensing capability and the known current vehicle power parameters to more accurately predict the energy consumption of the power system of the vehicle running path. The method can be used for correcting the dynamic driving mileage of the vehicle; and secondly, auxiliary functions such as energy supply reminding, intelligent recommendation of energy supply places, intelligent energy planning and the like can be derived.
It should be noted that some embodiments of the present application are described above. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments described above and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
Based on the same inventive concept, the embodiment of the application also provides a vehicle power system energy consumption prediction device corresponding to the method of any embodiment.
As shown in fig. 5, the vehicle power system energy consumption prediction apparatus includes:
an information acquisition module 11 configured to acquire navigation information between a current position of a vehicle and a target position, wherein the navigation information includes at least the vehicle position, a remaining distance, a total number of road segments, and road segment information;
a preprocessing module 12 configured to perform data preprocessing on the acquired navigation information;
an initial energy consumption calculation module 13, configured to estimate initial energy consumption of a vehicle driving list section based on a pre-constructed longitudinal dynamics model according to the preprocessed navigation information;
the correction module 14 is configured to perform driving characteristic factor self-learning according to the current road condition information and the vehicle parameters and correct the initial energy consumption of the corresponding characteristic road section;
the energy consumption prediction module 15 is configured to estimate total energy consumption of the road section to be driven ahead according to the initial energy consumption of the corrected single road section, and dynamically correct according to self-learning of driving characteristic factors.
For convenience of description, the above devices are described as being functionally divided into various modules, respectively. Of course, the functions of each module may be implemented in the same piece or pieces of software and/or hardware when implementing the embodiments of the present application.
The device of the foregoing embodiment is used to implement the corresponding method for predicting energy consumption of the vehicle power system in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiment, which is not described herein again.
Based on the same inventive concept, corresponding to the method of any embodiment, the embodiment of the application further provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to implement the method for predicting the energy consumption of the vehicle power system according to any embodiment.
Fig. 6 shows a more specific hardware architecture of an electronic device according to this embodiment, where the device may include: a processor 1010, a memory 1020, an input/output interface 1030, a communication interface 1040, and a bus 1050. Wherein processor 1010, memory 1020, input/output interface 1030, and communication interface 1040 implement communication connections therebetween within the device via a bus 1050.
The processor 1010 may be implemented by a general-purpose CPU (Central Processing Unit ), microprocessor, application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, etc. for executing relevant programs to implement the technical solutions provided in the embodiments of the present disclosure.
The Memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory ), static storage device, dynamic storage device, or the like. Memory 1020 may store an operating system and other application programs, and when the embodiments of the present specification are implemented in software or firmware, the associated program code is stored in memory 1020 and executed by processor 1010.
The input/output interface 1030 is used to connect with an input/output module for inputting and outputting information. The input/output module may be configured as a component in a device (not shown in the figure) or may be external to the device to provide corresponding functionality. Wherein the input devices may include a keyboard, mouse, touch screen, microphone, various types of sensors, etc., and the output devices may include a display, speaker, vibrator, indicator lights, etc.
Communication interface 1040 is used to connect communication modules (not shown) to enable communication interactions of the present device with other devices. The communication module may implement communication through a wired manner (such as USB, network cable, etc.), or may implement communication through a wireless manner (such as mobile network, WIFI, bluetooth, etc.).
Bus 1050 includes a path for transferring information between components of the device (e.g., processor 1010, memory 1020, input/output interface 1030, and communication interface 1040).
It should be noted that although the above-described device only shows processor 1010, memory 1020, input/output interface 1030, communication interface 1040, and bus 1050, in an implementation, the device may include other components necessary to achieve proper operation. Furthermore, it will be understood by those skilled in the art that the above-described apparatus may include only the components necessary to implement the embodiments of the present description, and not all the components shown in the drawings.
The electronic device of the foregoing embodiment is configured to implement the method for predicting energy consumption of a vehicle power system according to any one of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiment, which is not described herein.
Based on the same inventive concept, corresponding to any of the above-described embodiments of the method, the present application also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the vehicle power system energy consumption prediction method according to any of the above-described embodiments.
The computer readable media of the present embodiments, including both permanent and non-permanent, removable and non-removable media, may be used to implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device.
The storage medium of the foregoing embodiments stores computer instructions for causing the computer to execute the method for predicting energy consumption of a vehicle power system according to any one of the foregoing embodiments, and has the advantages of the corresponding method embodiments, which are not described herein.
Those of ordinary skill in the art will appreciate that: the discussion of any of the embodiments above is merely exemplary and is not intended to suggest that the scope of the application (including the claims) is limited to these examples; the technical features of the above embodiments or in the different embodiments may also be combined within the idea of the present application, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of brevity.
Additionally, well-known power/ground connections to Integrated Circuit (IC) chips and other components may or may not be shown within the provided figures, in order to simplify the illustration and discussion, and so as not to obscure the embodiments of the present application. Furthermore, the devices may be shown in block diagram form in order to avoid obscuring the embodiments of the present application, and this also takes into account the fact that specifics with respect to implementation of such block diagram devices are highly dependent upon the platform on which the embodiments of the present application are to be implemented (i.e., such specifics should be well within purview of one skilled in the art). Where specific details (e.g., circuits) are set forth in order to describe example embodiments of the application, it should be apparent to one skilled in the art that embodiments of the application can be practiced without, or with variation of, these specific details. Accordingly, the description is to be regarded as illustrative in nature and not as restrictive.
While the present application has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of those embodiments will be apparent to those skilled in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.
The present embodiments are intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims. Accordingly, any omissions, modifications, equivalents, improvements and/or the like which are within the spirit and principles of the embodiments are intended to be included within the scope of the present application.
Claims (10)
1. A method for predicting energy consumption of a vehicle powertrain, the method comprising:
obtaining navigation information from the current position of the vehicle to the target position, wherein the navigation information at least comprises the position of the vehicle, the remaining distance, the total number of road sections and the road section information;
performing data preprocessing on the acquired navigation information;
according to the preprocessed navigation information, estimating the initial energy consumption of a vehicle driving list section based on a longitudinal dynamics model constructed in advance;
according to the current road condition information and the vehicle parameters, driving characteristic factors are self-learned, and initial energy consumption of corresponding characteristic road sections is corrected;
and estimating the total energy consumption of the road section to be driven in front according to the initial energy consumption of the corrected single road section, and carrying out dynamic correction according to self-learning of driving characteristic factors.
2. The vehicle powertrain energy consumption prediction method according to claim 1, characterized in that:
the road section information at least comprises a road section ID, a road section length, a road section type, a road section speed limit, a road section congestion state, a road section congestion recommended speed, a road section inner sampling point gradient and a road section inner sampling point distance.
3. The vehicle power system energy consumption prediction method according to claim 2, characterized in that the data preprocessing of the acquired navigation information includes:
setting the navigation information of each n road sections as a data packet by taking the navigation information of the remaining road sections as a unit;
and extracting information of the same field from each road section information to form a single array signal so as to divide the information into a plurality of data packets, and periodically transmitting the data packets in turn until all the route planning information is transmitted.
4. A vehicle powertrain energy consumption prediction method according to claim 3, characterized by further comprising:
after preprocessing is completed, on the basis of original navigation information, the total number of segmentation of navigation information data packets and the number information of the current transmission data packet are newly added and issued along with each segmentation data packet to characterize the current data packet transmission process and state.
5. The method according to claim 2, wherein the estimating the initial energy consumption of the vehicle driving list section based on the pre-constructed longitudinal dynamics model based on the pre-processed navigation information comprises:
calculating the equivalent gradient and the distance of the road section based on the sampling point information in the single road section;
estimating virtual vehicle speed of the vehicle on the road section according to the road section type, the road section speed limit, the road section congestion condition, the road section gradient and the power parameters of the vehicle;
estimating initial energy consumption of a power system for a vehicle to travel on the road section based on a longitudinal dynamics model;
and calculating the initial energy consumption of all road sections, and superposing the initial energy consumption to obtain the total initial energy consumption of the vehicle running to the destination.
6. The method for predicting energy consumption of a vehicle power system according to claim 1, wherein the performing driving characteristic factor self-learning according to current road condition information and vehicle parameters and correcting initial energy consumption of a corresponding characteristic road segment comprises:
calculating the energy consumption value of a vehicle power system in real time based on motor data, obtaining an actual energy consumption value corresponding to a road section after each road section runs, and comparing the actual energy consumption value with the initial energy consumption of the road section to obtain an energy consumption correction coefficient under the road condition characteristics of the road section;
and carrying out real-time correction on the initial energy consumption matched with all road section characteristics of the rest route based on the energy consumption correction coefficient to obtain the corrected predicted energy consumption data of all road sections.
7. The method according to claim 1, wherein the estimating the total energy consumption of the preceding road section to be driven based on the corrected initial energy consumption of the single road section and the dynamic correction based on the self-learning of the driving characteristic factor comprises:
the predicted energy consumption of the single-path segment based on the real-time correction is overlapped to obtain the predicted total energy consumption of the power system from the current position of the vehicle to the destination;
and the vehicle runs in a navigation state, and the correction coefficient of the road corresponding to the characteristic road section is updated in real time in a rolling way according to the running process and is stored.
8. A vehicle power system energy consumption prediction apparatus, comprising:
the information acquisition module is configured to acquire navigation information from the current position of the vehicle to the target position, wherein the navigation information at least comprises the vehicle position, the remaining distance, the total number of road segments and the road segment information;
the preprocessing module is configured to perform data preprocessing on the acquired navigation information;
the initial energy consumption calculation module is configured to estimate the initial energy consumption of the vehicle driving list section based on a pre-constructed longitudinal dynamics model according to the preprocessed navigation information;
the correction module is configured to perform driving characteristic factor self-learning according to the current road condition information and the vehicle parameters and correct the initial energy consumption of the corresponding characteristic road section;
and the energy consumption prediction module is configured to estimate the total energy consumption of the road section to be driven in front according to the initial energy consumption of the corrected single road section and dynamically correct the road section according to the self-learning of the driving characteristic factor.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the vehicle powertrain energy consumption prediction method of any of claims 1-7 when the program is executed by the processor.
10. A non-transitory computer readable storage medium, wherein the non-transitory computer readable storage medium stores computer instructions for causing a computer to perform the vehicle powertrain energy consumption prediction method of any of claims 1-7.
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