CN118228851A - Prediction method of vehicle consumption, electronic equipment, medium and vehicle - Google Patents
Prediction method of vehicle consumption, electronic equipment, medium and vehicle Download PDFInfo
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Abstract
The application provides a prediction method of vehicle consumption, electronic equipment, a medium and a vehicle. The prediction method of the vehicle consumption comprises the following steps: acquiring current path planning information and average energy consumption information of a vehicle, wherein the path planning information comprises position coordinates and altitude of a starting point and a passing point on a planned path; and obtaining the power consumption of the vehicle reaching the route point when the vehicle runs on the planned route based on the average energy consumption information and the route planning information. The method predicts the power consumption based on the average energy consumption and the path planning information including the altitude, so that the power consumption of the vehicle when the vehicle runs on the planned path can be accurately predicted, and the endurance anxiety of a driver is reduced.
Description
Technical Field
The present application relates to the field of vehicles, and more particularly, to a method for predicting power consumption of a vehicle, an electronic device, a medium, and a vehicle.
Background
Vehicle to X (V2X) is a key technology of future intelligent transportation systems. The system enables the communication between the vehicles, the vehicles and the base station, and between the base station and the base station, thereby obtaining a series of traffic information such as real-time road conditions, road information, pedestrian information and the like, so as to improve driving safety, reduce congestion, improve traffic efficiency and the like.
At present, the driving mileage of an electric automobile is calculated according to the residual electric quantity of a battery and the average hundred kilometers of power consumption of the automobile, and when the automobile is in a region with a gentle terrain and a small altitude drop, the calculation error of the mode is small. However, in mountain areas or on trips with large elevation drop, the calculation mode has large errors, and great endurance anxiety is brought to drivers.
Disclosure of Invention
The present application has been made in view of the above-described problems. The application provides a prediction method of vehicle power consumption, electronic equipment, medium and vehicle, which can predict the power consumption of the vehicle when the vehicle runs on a planned path and reduce the endurance anxiety of drivers.
According to an aspect of the present application, there is provided a prediction method of a vehicle power consumption, the prediction method including:
acquiring current path planning information and average energy consumption information of a vehicle, wherein the path planning information comprises position coordinates and altitude of a starting point and a passing point on a planned path;
and obtaining the predicted power consumption of the vehicle reaching the route point when the vehicle runs on the planned route based on the average energy consumption information and the route planning information.
In one embodiment of the present application, based on the average energy consumption information and the path planning information, obtaining a predicted power consumption for a vehicle reaching a route point when the vehicle travels on the planned path includes:
Determining first energy consumption of the vehicle reaching the route point according to the position coordinates of the starting point and the route point and the average energy consumption information;
And obtaining the predicted power consumption of the vehicle reaching the passing point when the vehicle runs on the planned path according to the altitude of the starting point and the passing point and the first energy consumption.
In one embodiment of the present application, the obtaining, based on the average energy consumption information and the path planning information, a predicted power consumption of the vehicle reaching a route point when the vehicle travels on the planned path includes:
Determining a second energy consumption of the vehicle to the route point according to the altitude of the starting point and the route point;
and obtaining the predicted power consumption of the vehicle reaching the passing point when the vehicle runs on the planned path according to the position coordinates of the starting point and the passing point and the second energy consumption.
In one embodiment of the present application, the prediction method further includes: and determining average energy consumption information according to the pre-trained model and the acquired vehicle electric quantity consumption parameters, wherein the vehicle electric quantity consumption parameters comprise driving habit data.
In one embodiment of the present application, the driving habit data includes one or more of acceleration and deceleration frequency data, air conditioning habit temperature data, and light habit data.
In one embodiment of the present application, the vehicle power consumption parameter further includes altitude data of the planned path, and the determining average power consumption information according to the pre-trained model and the acquired vehicle power consumption parameter includes:
Determining gradient parameters on the planned path according to the altitude data;
According to the gradient parameters and the driving habit data, determining interaction parameters, wherein the interaction parameters are used for reflecting the influence degree of the gradient on the driving habit;
And determining average energy consumption information according to the interaction parameters, the driving habit data and the pre-trained model.
In one embodiment of the present application, the determining the interaction parameter according to the gradient parameter and the driving habit data includes: and calculating the product of acceleration and deceleration frequency data in the driving habit data and the gradient parameter to obtain the interaction parameter.
In one embodiment of the present application, the prediction method further includes:
acquiring available electric quantity of a vehicle;
And determining the predicted mileage of the vehicle when the vehicle runs on the planned path according to the available electric quantity and the predicted consumed electric quantity.
In one embodiment of the present application, the route points are plural, and determining a predicted mileage when the vehicle travels on the planned path according to the available electricity quantity and the predicted electricity consumption quantity includes:
sequentially determining the difference value of the predicted consumed electric quantity and the available electric quantity reaching each path point according to the direction of the planned path;
And determining the predicted mileage according to the position coordinates of the passing point and the starting point with the first negative difference value.
In one embodiment of the present application, the passing point includes an end point of a planned path, and the determining a predicted mileage when the vehicle travels on the planned path according to the available electricity amount and the predicted electricity consumption amount includes:
and when the difference value of the predicted consumed electric quantity and the available electric quantity reaching each path point is positive, determining the predicted mileage according to the position coordinates of the end point and the starting point.
According to a second aspect of the present application, there is provided an electronic apparatus comprising a memory and a processor, the memory having stored thereon a computer program to be executed by the processor, which, when executed by the processor, causes an apparatus mounted with the processor to execute the above-described method of predicting vehicle power consumption.
According to a third aspect of the present application, there is provided a storage medium having stored thereon a computer program which, when executed, causes the computer to execute the above-described vehicle electricity consumption prediction method.
According to a fourth aspect of the present application, there is provided a vehicle comprising the electronic device described above or the storage medium described above.
According to the application, the power consumption is predicted based on the average energy consumption and the path planning information including the altitude, so that the power consumption of the vehicle when the vehicle runs on the planned path can be accurately predicted, and the endurance anxiety of a driver is reduced.
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The above and other objects, features and advantages of the present invention will become more apparent from the following more particular description of embodiments of the present invention, as illustrated in the accompanying drawings. The accompanying drawings are included to provide a further understanding of embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, and not constitute a limitation to the invention. In the drawings, like reference numerals generally refer to like parts or steps.
FIG. 1 is a schematic block diagram of an electronic device for implementing a method of predicting vehicle power consumption in accordance with an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a method of predicting vehicle power consumption in accordance with one embodiment of the application;
Fig. 3 is a schematic view of an application scenario of a method for predicting a vehicle power consumption according to an embodiment of the application;
FIG. 4 is a schematic flow chart of a method of predicting vehicle power consumption in accordance with another embodiment of the application;
FIG. 5 is a schematic flow chart diagram of a method of predicting vehicle power consumption in accordance with yet another embodiment of the application;
FIG. 6 is a schematic block diagram of a vehicle according to an embodiment of the present application;
Fig. 7 is a schematic structural view of a vehicle according to another embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are only some embodiments of the present invention and not all embodiments of the present invention, and it should be understood that the present invention is not limited by the example embodiments described herein. Based on the embodiments of the invention described in the present application, all other embodiments that a person skilled in the art would have without inventive effort shall fall within the scope of the invention.
The driving mileage of the electric automobile is calculated according to the residual electric quantity of the battery and the average hundred kilometers of power consumption of the automobile, and when the automobile is in a region with a gentle terrain and a small elevation drop, the calculation error of the mode is small. However, in mountain areas or on trips with large elevation drop, the calculation mode has large errors, and great endurance anxiety is brought to drivers. The application provides a prediction method, electronic equipment, medium and vehicle for the power consumption of a vehicle when the vehicle runs on a road with altitude drop, and the power consumption on a current planning path is accurately predicted, so that the worry of cruising can be reduced, and a user has better experience.
First, an example electronic apparatus 100 for implementing a method and apparatus for predicting vehicle power consumption according to an embodiment of the present invention will be described with reference to fig. 1.
As shown in fig. 1, the electronic device 100 includes a processor 110, a memory 120, and a communication interface 130, as shown in fig. 1. Wherein the processor 110, memory 120, and communication interface 130 may communicate with each other via a communication bus 140 and/or other form of connection mechanism (not shown).
It should be noted that the components and structures of the electronic device 100 shown in fig. 1 are exemplary only and not limiting, as the electronic device may have other components and structures as desired.
Optionally, the communication interface 130 may also include a transmitter and/or a receiver.
The processor 110 may be a micro control unit (Microcontroller Unit, MCU), a central Processing unit (Central Processing Unit, CPU), a digital signal processor (DIGITAL SIGNAL Processing, DSP), a single chip and embedded devices or other forms of Processing units having data Processing and/or instruction execution capabilities, and may control other components in an autopilot vehicle system to perform desired functions.
Memory 120 may be various forms of computer-readable storage media, such as volatile memory and/or nonvolatile memory. Volatile memory can include, for example, random Access Memory (RAM), cache memory (cache), synchronous dynamic random access memory (Synchronous Dynamic Random Access Memory, SDRAM), and the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory (Flash), etc. One or more computer program instructions may also be stored on the computer readable storage medium, and the memory 120 may execute the program instructions to implement a method for predicting vehicle consumption in an embodiment of the present invention described below.
Next, a method of predicting the amount of power consumption of a vehicle according to an embodiment of the present application will be described with reference to fig. 2.
As shown in fig. 2, the method for predicting the vehicle power consumption provided by the application comprises the following steps:
in step S210, current path planning information and average energy consumption information of the vehicle are acquired, where the path planning information includes position coordinates and altitude of a start point and a pass point on a planned path.
The vehicle is an electric vehicle, and the electric vehicle is provided with a positioning sensor which can be a global positioning system (Global Positioning System, GPS), a global navigation satellite system (Global Navigation SATELLITE SYSTEM, GNSS), an inertial navigation system (Inertial Navigation System, INS) or the like. The positioning sensor collects position information of the vehicle so that the vehicle can conduct path planning based on the current position.
In particular, path planning may be accomplished through a vehicle-loaded navigation service system, and accordingly, path planning information may be obtained from the navigation service system. Here, the planned route is a travel route plan determined by a navigation service system of the vehicle based on the current position of the electric vehicle and the position information of the destination.
Or the path planning can be completed through navigation service software arranged on a server, the vehicle can access the server to acquire path planning information through communication equipment loaded by the vehicle, such as V2X equipment for realizing communication between the vehicle-vehicle and the vehicle-infrastructure, and the server can be a single server, a server cluster formed by a plurality of servers or a cloud server.
For example, referring to fig. 3, fig. 3 shows an application scenario of a method for predicting vehicle power consumption according to an embodiment of the present application. As shown in fig. 3, the application scenario may include a vehicle, an on-board unit (OBU) configured on the vehicle and capable of implementing V2X communication and application, a Road Side Unit (RSU) configured on a road side and capable of implementing V2X communication and application, and a network cloud device. A. B, C, D, E are the altitude extreme points on the planned path, respectively. The specific application scenario may be that the vehicle interacts with the cloud device through the OBU and the RSU, obtains path planning information, and predicts power consumption.
The passing points in the application are all points on the planned path, and the position coordinates of all points are different, but the altitude can be the same or different. In addition, in order to improve the prediction accuracy, the approach points may further include one or more extreme points of altitude on the planned path, where the extreme points include both extreme points of altitude of the entire planned path and extreme points of each altitude change section. The location coordinates may be longitude and latitude data. The position coordinates and the altitude of the multiple path points can be obtained by accessing map data in navigation service or by accessing navigation service software through V2X equipment.
The method for acquiring the position coordinates and the altitude of the plurality of path points can be realized by a plurality of methods, and the method for acquiring the altitude-related data is not particularly limited.
The average energy consumption information in the present application is used to represent the power consumption of the vehicle per kilometer or per hundred kilometers.
Average energy consumption information can be obtained when the vehicle runs on the planned path through an average energy consumption prediction model obtained through pre-training; specifically, the average energy consumption prediction model can be established based on a logistic regression algorithm, and is obtained after training by adopting a training sample constructed based on driving habit data of a driver.
The average energy consumption prediction model is predefined, the training sample of the average energy consumption prediction model is constructed based on driving habit data collected in the normal running process of the vehicle, the driving habit data of a driver is also used for generating a test sample, and the training sample and the test sample are used for training and testing the model to obtain a trained average energy consumption prediction model. The training and optimization of the model may be performed periodically, for example, the storage unit may record and save each trip data (such as driving habits of the driver, air-conditioning temperature setting, etc.), and perform optimization of the model parameters periodically based on the latest collected sample data.
In step S220, based on the average energy consumption information and the path planning information, a predicted power consumption for the vehicle to reach the route point when traveling on the planned path is obtained.
The path planning information comprises position coordinates of a plurality of path points on the planned path, and after the path planning information of the vehicle is acquired, the length of the planned path and the length between any two points in the planned path can be determined based on the position coordinates of the plurality of path points on the planned path.
Therefore, based on the average energy consumption and the path planning information, the predicted power consumption when the vehicle runs on the planned path can be obtained based on the average energy consumption, the altitudes of a plurality of path points on the planned path and the relevant lengths of the planned path.
The step of the method for predicting the power consumption of the vehicle may be to predict the power consumption of a subsequent upcoming trip according to an initial navigation path planning trip (a trip determined corresponding to a departure location and a destination location) before the electric vehicle travels. For example, before departure, the consumed electric quantity of the electric automobile is accurately predicted according to the influence of the current determined upcoming navigation path planning journey on the consumed electric quantity.
The step of the method for predicting the power consumption of the vehicle may be to predict the power consumption of a subsequent upcoming trip according to a current navigation path planning trip (a trip determined corresponding to a current position and a destination position of the electric vehicle) in the running process of the electric vehicle.
When the electric vehicle is started before or during running, the electricity consumption of the vehicle when running on a planned path is predicted through average energy consumption information, the altitude of a path point and the path length, and finally a more accurate electricity consumption prediction result is obtained, so that a user can use the vehicle more reliablely, the anxiety of continuous voyage is reduced, and the user has better experience.
Next, a method of predicting the amount of power consumption of a vehicle according to another embodiment of the present application will be described with reference to fig. 4.
S410, establishing an average energy consumption prediction model based on a logistic regression algorithm, and training by adopting a training sample constructed based on driving habit data.
In the present embodiment, the vehicle power consumption parameter includes driving habit data of the driver.
Training samples constructed based on vehicle power consumption parameters are employed, comprising:
Acquiring historical driving data from a storage unit, wherein the historical driving data comprises driving mileage and driving habit data of a driver;
Acquiring historical average energy consumption;
Acquiring real-time electric quantity of a vehicle;
And (5) fusing data, and constructing a sample platform.
Training the average energy consumption prediction model, comprising:
setting a regression algorithm network model, taking collected historical data such as driving mileage, driving habit of a driver, real-time electric quantity of a vehicle and the like as the input of the model, and training according to the current driving mileage as the output to obtain proper parameters.
Logistic regression is a machine learning algorithm for predicting the values of one or more target variables based on one or more prediction variables, and the average energy consumption prediction model in this embodiment is used to predict average energy consumption from driving habit data, and the predicted values are mapped to probability values on (0, 1) using a sigmoid function as shown in the following formula (1):
Where p is the probability of average energy consumption occurrence, e is the base of natural logarithms, and z is the linear combination of driving habit data. Here, z=β 0+β1x1+β2x2+......+βnxn, β is a coefficient of the interactive item, x is driving habit data, and n is the number of driving habit data.
The output parameters in training need to be normalized, and Z-Score normalization (Standard) can be adopted: specifically, the mean value and standard deviation of the driving mileage are calculated through a formula (2), and the driving mileage is converted into a distribution with the mean value of 0 and the standard deviation of 1. The formula (2) is as follows:
X_normalized ＝ (X - mean) / std (2)
Where x_normalized is normalized data, X is raw data, mean is the mean of the raw data, and std is the standard deviation of the raw data.
And finally, calculating the average energy consumption according to the trained parameters and the real-time electric quantity.
S420, acquiring current path planning information of the vehicle, wherein the path planning information comprises position coordinates and altitude of a plurality of path points on a planned path.
Specifically, the position coordinates of the planned path, such as geographic coordinate information, altitude, planned path mileage, etc., may be acquired by the V2X device.
S430, obtaining average energy consumption when the vehicle runs on the planned path through an average energy consumption prediction model obtained through training.
S440, determining the height difference of each height change section based on the altitude of a plurality of passing points on the planned path.
Here, the altitude difference refers to a difference between the section end altitude and the section start altitude of the altitude change section. In each interval, the altitude change trend is the same, for example, the altitude gradually increases or the altitude gradually decreases; the trend of variation in adjacent sections is different.
It should be noted that, on the planned path, one or more height variation intervals may be included. When only one altitude change section is included, the altitude change section may be an altitude increase section or an altitude decrease section. Accordingly, the altitude difference may be a positive altitude difference in the altitude increase section or a negative altitude difference in the altitude decrease section. When a plurality of altitude change sections are included, the altitude change sections include an altitude increase section and an altitude decrease section. Accordingly, the altitude difference includes a positive altitude difference of the altitude increase section and a negative altitude difference of the altitude decrease section.
S450, determining energy loss or energy surplus when the vehicle runs in the altitude change section based on the altitude difference of each altitude change section.
Specifically, the energy loss caused by each altitude rise is determined based on the positive altitude difference of each altitude rise section; the energy surplus caused by each altitude decrease is determined based on the negative altitude difference for each altitude decrease interval.
S460, obtaining the first energy consumption based on the average energy consumption and the path length.
Specifically, the product of the average energy consumption and the path length is the first energy consumption.
And S470, correcting the first energy consumption based on the surplus energy and/or the energy loss generated on the planned path to obtain the predicted consumed electric quantity when the vehicle runs on the planned path.
Specifically, when the planned path only includes one altitude change section, if the altitude change section is an altitude rise section, the sum of the first energy consumption and the energy loss of the altitude rise section is the predicted consumed electric quantity when the planned path runs; if the altitude change section is an altitude decrease section, the difference between the first energy consumption and the energy surplus in the altitude decrease section is the predicted power consumption when the vehicle travels on the planned path.
When the planned path includes a plurality of altitude change sections, the energy consumption P when the vehicle is traveling on the planned path is calculated according to the following equation (3)
P＝P1+Psum Damage to - Psum Surplus of (3)
Wherein, P 1 is the first energy consumption, P sum Damage to is the sum of the energy losses of each altitude increase section on the planned path, and P sum Surplus of is the sum of the energy surplus of each altitude decrease section on the planned path.
In this embodiment, the average energy consumption prediction model obtained based on driving habit data can accurately predict average energy consumption, and the primarily estimated power consumption is corrected through altitude data, so that the power consumption can be predicted more accurately, and better driving experience is provided for a driver.
Next, a method of predicting the amount of power consumption of a vehicle according to still another embodiment of the present application will be described with reference to fig. 5.
S510, establishing an average energy consumption prediction model based on a logistic regression algorithm, and training by adopting a training sample constructed based on driving habit data and altitude data of a driver.
In this embodiment, the method for constructing the training sample includes:
Acquiring driving habit data, historical average energy consumption data and real-time electric quantity of a vehicle of a driver; here, the driving habit data includes at least acceleration and deceleration frequency data, and may include, but is not limited to, air conditioning habit temperature data and light habit data;
Acquiring altitude data of a driving circuit corresponding to driving habit data;
obtaining interaction parameters based on altitude data and driving habit data;
And constructing a training sample based on the driving habit data, the interaction parameters, the historical average energy consumption data and the real-time electric quantity of the vehicle.
Because the acceleration and deceleration frequency is influenced by the altitude, the related influence needs to be considered, for example, driving habit data of a driver is collected firstly, including data of acceleration and deceleration frequency, air conditioner setting habit temperature, lamplight habit and the like, and related altitude data; for each sample data point, calculating the product of the acceleration and deceleration frequency and the altitude to obtain the interaction parameter. For example, assuming that the acceleration and deceleration frequency variable is x 1 and the altitude variable is x 2, the interaction parameter is x 1*x2. The interaction parameters are added to the linear combination of arguments. The z-value calculation formula corresponding to the logistic regression model is the following formula (4):
z＝β0+β1x1+β2x2+β3(x1*x2)……+βnxn (4)
where β 3 is the coefficient of the interaction parameter.
S520, acquiring current path planning information of the vehicle, wherein the path planning information comprises position coordinates and altitude of a plurality of passing points on a planned path.
And S530, obtaining the average energy consumption of the vehicle when the vehicle runs on the planned path through the average energy consumption prediction model and the altitude obtained through pre-training.
Specifically, gradient parameters on the planned path may be determined from altitude data; according to the gradient parameters and the driving habit data, determining interaction parameters, wherein the interaction parameters are used for reflecting the influence degree of the gradient on the driving habit; and inputting the interaction parameters and the driving habit data into a pre-trained model to obtain average energy consumption information.
S540, obtaining second energy consumption of the vehicle reaching the route point when the vehicle runs on the planned route based on the average energy consumption and the route planning information.
Specifically, based on the average energy consumption and the path planning information, obtaining an arrival route point at which the vehicle arrives at the route point when traveling on the planned path, includes:
Determining a path length of the planned path based on the path planning information; the path length refers to the length of the route from the planned path start point to the planned path end point.
Obtaining second energy consumption when the vehicle runs on the planned path based on the path length and the average energy consumption; here, the path length and the average energy consumption are the second energy consumption when the vehicle is traveling on the planned path.
In the embodiment, the average energy consumption prediction is performed based on the average energy consumption prediction model obtained through training of the altitude data and the driving habit data, so that the consumed electric quantity can be predicted more quickly and accurately based on the path length, and the endurance anxiety of a driver is reduced.
S550, correcting the second energy consumption obtained in S540 based on the altitudes of a plurality of passing points on the planned path to obtain the predicted power consumption of the vehicle when the vehicle runs on the planned path. The method specifically comprises the following steps:
determining the height difference of each height change section based on the altitude of a plurality of passing points on the planned path;
determining energy loss or energy surplus of the vehicle when the vehicle runs in the height change section based on the height difference of each height change section;
And correcting the second energy consumption obtained in the step S540 based on the surplus energy and/or the energy loss generated on the planned path to obtain the predicted consumed electric quantity when the vehicle runs on the planned path.
The specific method for correcting the second energy consumption obtained in S540 based on the altitude data can be referred to the description in the previous embodiment, and will not be described in detail herein.
Average energy consumption prediction is carried out on the basis of an average energy consumption prediction model obtained through training of altitude data and driving habit data, and the average energy consumption is corrected through altitude data, so that the consumed electric quantity can be predicted more accurately, and better driving experience is provided for a driver.
According to one embodiment of the application, the prediction method further comprises:
acquiring available electric quantity of a vehicle;
and determining the predicted mileage of the vehicle when the vehicle runs on the planned path according to the available electric quantity and the predicted consumed electric quantity.
In this embodiment, the method for obtaining the available electric quantity of the vehicle may include: the method comprises the steps of obtaining the residual electric quantity of the vehicle, determining the redundant electric quantity based on the consumed electric quantity, and determining the available electric quantity based on the residual electric quantity and the redundant electric quantity. Specifically, the difference between the remaining power and the redundant power is the available power.
Specifically, the remaining power of the vehicle may be collected by a power monitoring system (such as a power monitoring system of a battery) or by providing a data collection device such as a sensor or the like.
In this embodiment, the redundant power is to reserve a part of the power in the remaining power in order to prevent some uncertainties in the actual journey, such as extreme value occurrence, bad weather, special road conditions, and the like. The determination of the redundant power supply may take a variety of forms. Specifically, the redundant power may be a fixed value determined according to the power consumption, for example, when the power consumption is greater than a preset threshold, the redundant power is A1; when the consumed electric quantity is smaller than or equal to a preset threshold value, the redundant electric quantity is A2. The redundant power may also be a variable value determined according to the power consumption, for example, based on a preset ratio system, the product of the power consumption and the ratio coefficient is cancelled as the redundant power.
Through setting up redundant electric quantity, can improve the reliability that the vehicle was gone, avoid leading to the vehicle unable destination that reaches because of appearing special circumstances.
According to one embodiment of the present application, a plurality of passing points are provided, and a predicted mileage when a vehicle travels on a planned path is determined according to an available electric quantity and a predicted consumed electric quantity, including:
sequentially determining the difference value of the predicted consumed power and the available power reaching each path point according to the direction of the planned path;
And determining the predicted mileage according to the position coordinates of the passing point and the starting point with the first negative difference value.
When the difference between the predicted power consumption and the available power consumption is negative, the predicted power consumption is larger than the available power consumption, and the passing point at the moment is the farthest position point where the vehicle can run by using the available power consumption. And thereby determine the predicted mileage by the position coordinates of the point and the starting point.
The mileage is predicted by comparing the predicted electricity consumption of each passing point with the available electricity, so that the prediction accuracy is higher, the algorithm is simple, and the method is easy to realize.
When the available electric quantity of the vehicle running on the planned path is insufficient, driving decision assistance can be provided for a driver by predicting the drivable mileage of the vehicle, so that the driver can plan the selection of the charging point in advance, and the driving experience is improved.
According to one embodiment of the present application, a passing point includes an end point of a planned path, and determining a predicted mileage of a vehicle when the vehicle travels on the planned path based on an available power amount and a predicted power consumption amount includes: when the difference value between the predicted consumed power and the available power reaching each route point is positive, determining the predicted mileage according to the position coordinates of the end point and the starting point.
In this embodiment, when the difference between the predicted power consumption and the available power consumption reaching each route point is positive, the available power consumption is greater than the predicted power consumption for the vehicle to travel to the terminal, so that it can be determined that the vehicle can travel to the terminal using the available power consumption, and then the predicted mileage can be determined according to the position coordinates of the terminal and the starting point.
According to one embodiment of the application, obtaining a predicted mileage of a vehicle traveling on a planned path based on average energy consumption and available electricity includes:
and determining the height difference of each height change section based on the altitude of a plurality of passing points on the planned path. Here, the altitude change section includes an altitude increase section and an altitude decrease section. Accordingly, the altitude difference includes a positive altitude difference of the altitude increase section and a negative altitude difference of the altitude decrease section.
The energy loss or the energy surplus when the vehicle travels in the altitude change section is determined based on the altitude difference of each altitude change section. Specifically, the energy loss caused by each altitude rise is determined based on the positive altitude difference of each altitude rise section; the energy surplus caused by each altitude decrease is determined based on the negative altitude difference for each altitude decrease interval.
The actual section energy consumption of the vehicle in each altitude change section is determined based on the average energy consumption, the energy consumption and/or the energy surplus. Specifically, the estimated energy consumption of the interval can be determined based on the average energy consumption and the interval length, and the actual interval energy consumption can be obtained by correcting the estimated energy consumption through the energy consumption and/or the energy surplus. Specifically, when the planned path only comprises an altitude elevation section, obtaining actual section energy consumption based on average energy consumption, available electric quantity and energy loss; when the planned path only comprises the altitude reduction interval, obtaining the actual interval energy consumption based on the average energy consumption, the available electric quantity and the energy surplus; when the planned path includes both the altitude increase section and the altitude decrease section, the actual section energy consumption is obtained based on the average energy consumption, the available electric quantity, the energy consumption, and the energy surplus.
Based on the actual interval energy consumption and the available electric quantity, the method for determining the predicted mileage of the vehicle when the vehicle runs on the planned path specifically comprises the following steps:
determining a final height change interval which can be reached by the vehicle when the vehicle runs under the available electric quantity and the final interval available electric quantity when the vehicle runs in the final height change interval based on the actual interval energy consumption and the available electric quantity;
determining the actual average energy consumption of the final height change section based on the section length of the final height change section and the corresponding actual section energy consumption;
determining the driving distance of the vehicle in the final height change zone based on the final zone available electric quantity and the actual average energy consumption of the final height change zone;
And determining the driving mileage of the vehicle on the planned path based on the distance from the starting point to the final altitude change section and the driving distance of the vehicle in the final altitude change section.
The predicted mileage obtained based on the altitude can be estimated more accurately, and errors caused by altitude changes are reduced.
According to one embodiment of the application, the energy loss and energy surplus are also determined based on the mass of the whole vehicle and the system efficiency.
Specifically, the energy gap due to the height difference can be calculated according to the following formula (5):
ΔE＝mghη (5)
Where Δe is the energy loss of the electric vehicle battery, m is the vehicle mass, g is the gravitational acceleration, h is the altitude difference, and η is the system efficiency.
Here, the altitude difference is an altitude difference between the end point and the start point of the section. When the height difference is positive, the energy difference is positive, and the energy loss is caused at the moment; when the height difference is negative, the energy difference is negative, and the absolute value of the energy difference is the energy surplus.
The energy loss and the energy surplus are determined based on the height difference, the whole vehicle quality and the system efficiency, so that the accuracy of electric quantity prediction and distance prediction can be improved.
According to one embodiment of the application, the redundant power level is determined based on a predetermined redundancy factor of the consumed power level.
Specifically, the redundant power is equal to the product of the consumed power and the redundancy coefficient. The redundancy factor value can be obtained by statistics based on historical predicted power consumption data and actual power consumption data. Illustratively, the redundancy factor can range from 10% to 25%.
The redundant electric quantity is determined through the redundant coefficient of the consumed electric quantity, so that the characteristics of the vehicle and the driver are more met, the accuracy of the predicted mileage of the vehicle can be improved, and better driving experience is brought to the driver.
According to one embodiment of the application, the prediction method further comprises: and when the predicted consumed electric quantity is larger than the available electric quantity, generating charging prompt information and/or driving mileage prompt information.
When the consumed electric quantity is larger than the available electric quantity, the remaining electric quantity of the vehicle is considered to be insufficient, and the vehicle is charged first, so that charging prompt information is generated.
In order to facilitate the vehicle owner to find a proper charging point, a driving mileage prompt message can be generated, so that the vehicle owner can search available charging piles or charging points on a planned path according to the driving mileage.
When the consumed electric quantity is less than or equal to the available electric quantity, the remaining electric quantity of the vehicle is considered to be sufficient, and the destination can be reached smoothly.
The embodiment of the application also provides electronic equipment, which comprises a memory and a processor, wherein the memory stores a computer program run by the processor, and the computer program, when run by the processor, enables a device provided with the processor to execute the method for predicting the vehicle consumption electric quantity according to any embodiment.
The embodiment of the application also provides a storage medium, and a computer program is stored on the storage medium, and runs on the computer, so that the computer executes the method for predicting the electric quantity consumed by the vehicle according to any embodiment.
The embodiment of the application also provides a vehicle which comprises the electronic equipment or the storage medium. Fig. 6 schematically illustrates a vehicle according to an embodiment of the application, the vehicle comprising an electronic device. Fig. 7 exemplarily shows a vehicle including a storage medium according to an embodiment of the present application.
Although the illustrative embodiments have been described herein with reference to the accompanying drawings, it is to be understood that the above illustrative embodiments are merely illustrative and are not intended to limit the scope of the present invention thereto. Various changes and modifications may be made therein by one of ordinary skill in the art without departing from the scope and spirit of the invention. All such changes and modifications are intended to be included within the scope of the present invention as set forth in the appended claims.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described device embodiments are merely illustrative, e.g., the division of the elements is merely a logical functional division, and there may be additional divisions when actually implemented, e.g., multiple elements or components may be combined or integrated into another device, or some features may be omitted or not performed.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in order to streamline the invention and aid in understanding one or more of the various inventive aspects, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof in the description of exemplary embodiments of the invention. However, the method of the present invention should not be construed as reflecting the following intent: i.e., the claimed invention requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
It will be understood by those skilled in the art that all of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or units of any method or apparatus so disclosed, may be combined in any combination, except combinations where the features are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings), may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features but not others included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the claims, any of the claimed embodiments may be used in any combination.
Various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that some or all of the functions of some of the modules in an item analysis device according to embodiments of the present invention may be implemented in practice using a microprocessor or Digital Signal Processor (DSP). The present invention can also be implemented as an apparatus program (e.g., a computer program and a computer program product) for performing a portion or all of the methods described herein. Such a program embodying the present invention may be stored on a computer readable medium, or may have the form of one or more signals. Such signals may be downloaded from an internet website, provided on a carrier signal, or provided in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, third, etc. do not denote any order. These words may be interpreted as names.
The foregoing description is merely illustrative of specific embodiments of the present invention and the scope of the present invention is not limited thereto, and any person skilled in the art can easily think about variations or substitutions within the scope of the present invention. The protection scope of the invention is subject to the protection scope of the claims.
Claims (13)
1. A method for predicting power consumption of a vehicle, the method comprising:
acquiring current path planning information and average energy consumption information of a vehicle, wherein the path planning information comprises position coordinates and altitude of a starting point and a passing point on a planned path;
and obtaining the predicted power consumption of the vehicle reaching the route point when the vehicle runs on the planned route based on the average energy consumption information and the route planning information.
2. The method of predicting the power consumption of a vehicle according to claim 1, wherein obtaining the predicted power consumption of the vehicle reaching a route point when the vehicle travels on the planned route based on the average power consumption information and the route planning information, comprises:
Determining first energy consumption of the vehicle reaching the route point according to the position coordinates of the starting point and the route point and the average energy consumption information;
And obtaining the predicted power consumption of the vehicle reaching the passing point when the vehicle runs on the planned path according to the altitude of the starting point and the passing point and the first energy consumption.
3. The method for predicting the power consumption of a vehicle according to claim 1, wherein the obtaining the predicted power consumption of the vehicle reaching the route point when the vehicle travels on the planned route based on the average power consumption information and the route planning information includes:
Determining a second energy consumption of the vehicle to the route point according to the altitude of the starting point and the route point;
and obtaining the predicted power consumption of the vehicle reaching the passing point when the vehicle runs on the planned path according to the position coordinates of the starting point and the passing point and the second energy consumption.
4. The prediction method of the vehicle consumption amount according to claim 1, characterized in that the prediction method further includes: and determining average energy consumption information according to the pre-trained model and the acquired vehicle electric quantity consumption parameters, wherein the vehicle electric quantity consumption parameters comprise driving habit data.
5. The method for predicting power consumption of a vehicle according to claim 4, wherein the driving habit data includes one or more of acceleration and deceleration frequency data, air conditioning habit temperature data, and light habit data.
6. The method of predicting vehicle power consumption of claim 4, wherein the vehicle power consumption parameters further comprise altitude data of the planned path, and wherein determining average energy consumption information based on the pre-trained model and the acquired vehicle power consumption parameters comprises:
Determining gradient parameters on the planned path according to the altitude data;
According to the gradient parameters and the driving habit data, determining interaction parameters, wherein the interaction parameters are used for reflecting the influence degree of the gradient on the driving habit;
And determining average energy consumption information according to the interaction parameters, the driving habit data and the pre-trained model.
7. The method of predicting power consumption of a vehicle of claim 6, wherein determining the interaction parameter based on the grade parameter and the driving habit data comprises: and calculating the product of acceleration and deceleration frequency data in the driving habit data and the gradient parameter to obtain the interaction parameter.
8. The prediction method of the vehicle consumption amount according to any one of claims 1 to 7, characterized in that the prediction method further includes:
acquiring available electric quantity of a vehicle;
And determining the predicted mileage of the vehicle when the vehicle runs on the planned path according to the available electric quantity and the predicted consumed electric quantity.
9. The method of predicting the power consumption of a vehicle according to claim 8, wherein the route points are plural, and determining the predicted mileage when the vehicle travels on the planned route based on the available power and the predicted power consumption includes:
sequentially determining the difference value of the predicted consumed electric quantity and the available electric quantity reaching each path point according to the direction of the planned path;
And determining the predicted mileage according to the position coordinates of the passing point and the starting point with the first negative difference value.
10. The method of predicting power consumption of a vehicle of claim 8, wherein the waypoint includes an end point of a planned path, and wherein determining a predicted mileage of the vehicle while traveling on the planned path based on the available power and the predicted power consumption includes:
and when the difference value of the predicted consumed electric quantity and the available electric quantity reaching each path point is positive, determining the predicted mileage according to the position coordinates of the end point and the starting point.
11. An electronic device comprising a memory and a processor, the memory having stored thereon a computer program to be run by the processor, which, when run by the processor, causes an apparatus mounted with the processor to perform the method of predicting the power consumption of a vehicle according to any one of claims 1-10.
12. A storage medium having stored thereon a computer program, the computer program being run on a computer, the computer program when run causing the computer to perform the method of predicting the vehicle consumption of electricity as claimed in any one of claims 1-10.
13. A vehicle, characterized in that it comprises the electronic device of claim 11 or the storage medium of claim 12.
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| CN202311286318.6A CN118228851A (en) | 2023-09-28 | 2023-09-28 | Prediction method of vehicle consumption, electronic equipment, medium and vehicle |
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