CN110610260A - Driving energy consumption prediction system, method, storage medium and equipment - Google Patents
Driving energy consumption prediction system, method, storage medium and equipment Download PDFInfo
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Abstract
The invention discloses a driving energy consumption prediction system, a driving energy consumption prediction method, a storage medium and equipment, wherein the prediction method comprises the steps of obtaining historical working condition data of a planned driving route; constructing a training sample data set based on historical working condition data; carrying out data training on the training sample data set, and establishing a vehicle speed characteristic BP neural network model and a driving energy consumption BP neural network model; the method comprises the steps of obtaining real-time working condition information on a planned driving route, inputting the real-time working condition information into a vehicle speed characteristic BP neural network model for prediction to obtain vehicle speed characteristic data of future driving, then inputting the vehicle speed characteristic data into a vehicle energy consumption BP neural network model for prediction to obtain the data of the future driving energy consumption, and achieving on-line prediction of the vehicle energy consumption. The method can realize the on-line effective prediction of the energy consumption of the vehicles under the driving conditions of different road environments and traffic states, and helps to improve the efficiency of intelligent energy management of the vehicles.
Description
Technical Field
The invention relates to the technical field of vehicle-mounted intelligent energy management in intelligent traffic systems and intelligent networking environments, in particular to a system, a method, a storage medium and equipment for predicting driving energy consumption on any planned path on an intelligent automobile.
Background
The IEMS (Intelligent Energy Management System) is a necessary requirement for the development of Intelligent internet vehicles, ITS (Intelligent transportation System) and the like, and aims to enable vehicles to realize efficient, Energy-saving and optimized utilization of vehicle-mounted Energy in an online self-adaptive manner in different driving scenes, especially for current new Energy vehicles such as electric vehicles and hybrid electric vehicles. The consumption condition of the vehicle-mounted energy is mainly influenced by the running conditions of a certain road and a certain traffic environment, so that the intelligent energy management system has the key point of realizing the self-adaptive control of different running conditions.
The traditional vehicle energy management system is mainly based on the transient power point optimization control of the real-time working point of the power system because the driving condition of the future vehicle cannot be known. The existing intelligent energy management system can realize the running speed prediction, running power demand prediction or running condition identification in a short time (usually within 3-5 minutes) before the vehicle by using an intelligent learning algorithm, and can further realize the self-adaptive condition control of the vehicle-mounted energy based on the running speed prediction, the running power demand prediction or the running condition identification, but the self-adaptive condition control is only local optimization control in a prediction time domain. Therefore, at present, the management of the vehicle-mounted energy mainly focuses on the aspects of instantaneous optimization and local optimization control, and the global optimization control of the vehicle-mounted energy is still very little. And the global planning and the optimal control of the vehicle-mounted energy on the planned driving route can be further realized based on the prediction of the driving energy consumption on the future planned driving route, and the utilization efficiency of the vehicle-mounted energy is greatly improved.
Disclosure of Invention
Aiming at the defects of the prior art, the invention carries out online prediction on the driving energy consumption under different driving conditions on any planned driving route based on the road environment, the traffic state and the historical/real-time big data of vehicle operation so as to help realize the global planning and the optimal control of the vehicle-mounted energy.
The invention provides a driving energy consumption prediction system on one hand, which comprises a data acquisition subsystem, an off-line training subsystem and an on-line prediction subsystem; the data acquisition subsystem is used for acquiring and recording road environment parameters, traffic state parameters and vehicle operation data on a planned driving route; the off-line training subsystem is used for dividing a planned driving route into a plurality of road sections, extracting and calculating road environment parameters, traffic state parameters, vehicle speed characteristic parameters and energy consumption values of all the road sections, and establishing a sample data set; building a BP neural network model, and respectively training and verifying the data set through the BP neural network to obtain a verified BP neural network; and the online prediction subsystem is used for dividing the planned driving route into road sections, extracting road environment parameters, traffic state parameters and road section driving mileage values of all the road sections, and predicting the driving energy consumption on the planned driving route through the verified BP neural network.
Further, the offline training subsystem comprises an offline data processing module and a model training module; the off-line data processing module is used for dividing a planned driving route into a plurality of road sections, extracting and calculating road environment parameters, traffic state parameters, vehicle speed characteristic parameters and energy consumption values of the road sections, and respectively establishing a vehicle speed characteristic prediction sample data set and a driving energy consumption prediction sample data set; the model training module is used for dividing the sample data set into a training data set and a test data set, respectively building a vehicle speed characteristic BP neural network model and a driving energy consumption BP neural network model, and respectively training the training data set; and verifying the effectiveness of the trained BP neural network through the test data set.
Further, the online prediction subsystem comprises an online data processing module and a real-time prediction module; the online data processing module is used for dividing dynamic road sections of a planned driving route in real time and extracting road environment parameters, traffic state parameters and road section driving mileage values of all the road sections; the real-time prediction module is used for predicting the road section vehicle speed characteristic parameters on the future driving route in real time according to the vehicle speed characteristic BP neural network model obtained after training and the parameters extracted by the data processing module, then predicting the energy consumption values of the road sections in the future in real time by taking the predicted vehicle speed characteristic parameters as the input values of the driving energy consumption BP driving energy consumption, summing the energy consumption values of the road sections and realizing the prediction of the driving energy consumption of the future driving route.
The invention provides a driving energy consumption prediction method on the other hand, which comprises the following steps: acquiring historical working condition data of a planned driving route, wherein the historical working condition data comprises road environment parameters, traffic state parameters and vehicle operation data; constructing a training sample data set based on the historical working condition data; carrying out data training on the training sample data set, and establishing a vehicle speed characteristic BP neural network model and a driving energy consumption BP neural network model; and acquiring real-time working condition information on a planned driving route, inputting the real-time working condition information into the vehicle speed characteristic BP neural network model for prediction to obtain vehicle speed characteristic data of future driving, and then inputting the vehicle speed characteristic data into the vehicle energy consumption BP neural network model for prediction to obtain the data of the future driving energy consumption, so as to realize the online prediction of the vehicle energy consumption.
Further, the constructing of the training sample data set specifically includes: dividing a planned driving route into a plurality of road sections, and extracting characteristic parameters of the road sections by taking a single road section as a unit, wherein the characteristic parameters comprise road environment parameters, traffic state parameters, vehicle speed characteristic parameters, driving mileage values of the road sections and vehicle driving energy consumption values; and analyzing the influence relation between the energy consumption value and the vehicle speed characteristic parameter by using a stepwise linear regression method, and determining a main vehicle speed characteristic parameter for establishing a prediction model.
Further, the dividing of the planned driving route into a plurality of road segments specifically includes dividing the planned driving route into different traffic congestion levels, and dividing a section of driving mileage with the same traffic congestion level into a road segment sample.
Further, the road environment parameters comprise a road type, a road gradient and a road speed limit; the traffic state parameter is a traffic jam level; the driving mileage value of the road section is the distance from the starting point of the planned driving route to the middle point of the road section; the vehicle speed characteristic parameters are statistics of a vehicle speed sequence in a certain time period, and comprise average speed, average acceleration, speed standard deviation, average acceleration, acceleration standard deviation, acceleration time proportion, deceleration time proportion, constant speed time proportion, idling/parking time proportion, maximum vehicle speed and maximum acceleration.
Further, in the extracting of the road environment parameters of the road section, the extracting method of the road type and the road speed limit parameter data specifically includes: when the road type or the road speed limit in the road section divided according to the road section dividing method is unique, the road type or the road speed limit parameter data of each road section is a parameter value corresponding to the road type or the road speed limit; when the road type or the road speed limit in the road section divided according to the road section dividing method is not unique, the road type or the road speed limit parameter value of the road section is determined as follows: and multiplying the length proportion of different road types or road speed limits in the road section by the parameter values corresponding to the respective road types or road speed limits, and summing all the parameter values to obtain the final road type or road speed limit parameter value of the road section.
The invention also provides a storage medium, which comprises a program stored in the storage medium, and when the program runs, the equipment where the storage medium is located is controlled to execute the driving energy consumption prediction method in any one of the above technical schemes.
The invention also provides driving energy consumption detection equipment which comprises a processor, wherein the processor is used for running a program, and the program executes any one of the driving energy consumption prediction methods in the technical scheme when running.
The method predicts the driving energy consumption on the future planned driving route based on the road environment, the traffic state and the driving mileage information which can be acquired in real time, and has strong working condition adaptability and practicability; the main vehicle speed characteristic parameters are screened based on a stepwise linear regression method, so that no colinearity exists between the selected vehicle speed characteristic parameters, unnecessary input of a prediction model is reduced, and the prediction efficiency is improved while the prediction precision is ensured; the total driving energy consumption prediction on the planned driving route can further help to realize the global planning and optimization of the vehicle-mounted energy, and the utilization efficiency of the vehicle-mounted energy is greatly improved.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description, serve to explain the invention and not to limit the invention. In the drawings, there is shown in the drawings,
FIG. 1 is a block diagram of a routine vehicle energy consumption prediction system in accordance with one embodiment of the present invention;
FIG. 2 is a flow chart of another embodiment routine vehicle energy consumption prediction method of the present invention;
FIG. 3 is a histogram of the effect factor distribution of vehicle speed characteristics on energy consumption for the embodiment of FIG. 2;
FIG. 4 is a diagram illustrating a predicted vehicle speed characteristic parameter in the embodiment of FIG. 2;
FIG. 5 is a graph of energy consumption prediction results for vehicles based on real vehicle speed characteristic parameters and based on predicted vehicle speed characteristic parameters.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1
The embodiment provides an intelligent driving energy consumption prediction system, fig. 1 is a schematic diagram of the intelligent driving energy consumption prediction system of the embodiment, and referring to fig. 1, the system includes a data acquisition subsystem, an offline training subsystem and an online prediction subsystem.
The data acquisition subsystem is used for acquiring and recording road environment parameter data, traffic state parameter data and vehicle operation data on a planned driving route;
the off-line training subsystem is used for dividing a planned driving route into a plurality of road sections, extracting and calculating road environment parameters, traffic state parameters, vehicle speed characteristic parameters and energy consumption values of the road sections, analyzing the vehicle speed characteristic parameters and establishing a sample data set; and building a BP neural network model, and respectively training and verifying the data set through the BP neural network to obtain the verified BP neural network.
The online prediction subsystem is used for dividing the planned driving route into road sections, extracting the road, traffic characteristic parameters and road section driving mileage values of all the road sections, and predicting the driving energy consumption on the planned driving route through the verified BP neural network.
Further, the data acquisition subsystem comprises a road and traffic data acquisition module and a vehicle operation data acquisition module;
the road and traffic data acquisition module is used for acquiring and recording road environment parameters, traffic state parameters and mileage information of a planned driving route by adopting a vehicle-mounted GPS positioning device and a GIS information receiving device; and the vehicle operation data acquisition module is used for acquiring and recording the operation speed of the vehicle on a planned driving route by adopting a CAN bus and a vehicle speed sensor.
Further, the off-line training subsystem comprises an off-line data processing module and a model training module;
the off-line data processing module is used for dividing a planned driving route into a plurality of road sections, extracting and calculating road environment characteristic parameters, traffic state characteristic parameters, road section driving mileage values, vehicle speed characteristic parameters and energy consumption values of the road sections, analyzing the influence relation between the vehicle speed characteristic parameters and the energy consumption by utilizing a stepwise linear regression method, and respectively establishing a vehicle speed characteristic prediction sample data set and a driving energy consumption prediction sample data set;
the model training module is used for dividing the sample data set into a training data set and a test data set, respectively building a vehicle speed characteristic BP neural network model and a driving energy consumption BP neural network model, and training the training data set through the BP neural network; and verifying the effectiveness of the trained BP neural network through a test data set.
Furthermore, the online prediction subsystem comprises an online data processing module and a real-time prediction module;
the online data processing module is used for dynamically dividing the planned driving route into road sections and extracting road environment parameters, traffic state parameters and road section driving mileage values of all the road sections;
and the real-time prediction module is used for predicting the road section vehicle speed characteristic parameters on the future driving route in real time according to the vehicle speed characteristic BP neural network model obtained after training and the parameters extracted by the data processing module, then predicting the energy consumption values of the road sections in the future in real time by taking the predicted vehicle speed characteristic parameters as the input of the driving energy consumption BP, and finally summing the energy consumption values of the road sections to finish the prediction of the driving energy consumption of the future driving route.
Example 2
As shown in fig. 2, a driving energy consumption prediction method includes:
step 1, obtaining historical working condition data of a planned driving route, wherein the historical working condition data comprises road environment parameters, traffic state parameters and vehicle operation data;
collecting road environment parameters such as road type, road gradient, road speed limit and traffic state parameters such as traffic jam grade and the like by utilizing a vehicle-mounted GPS positioning device and a GIS information receiving device; the CAN bus and the vehicle speed sensor are used for acquiring vehicle running data such as running distance, vehicle speed and the like.
Step 2, constructing a training sample data set based on the acquired original data, and specifically comprising the following steps:
because the traffic states of different road sections on the planned driving route are different and time-varying, and the energy consumption conditions of vehicle operation are different under different traffic states, a section of driving mileage with the same traffic jam level on the planned driving route is divided into a road section sample;
further, for the divided road section samples, extracting and calculating road environment parameters, traffic state parameters, vehicle speed characteristic parameters and energy consumption values of the road sections, wherein
The road section driving mileage value is the distance between the middle point of the road section and the starting point of the planned driving route; the road type, the road gradient, the road speed limit and the traffic jam level are directly obtained by the vehicle-mounted GPS positioning device and the GIS information receiving device, and generally no further processing is needed. However, the road type, the road speed limit and the traffic congestion level parameter value are discrete state values of limited types, after the road sections are divided according to the road section dividing method, the traffic congestion level parameter value of each road section can be uniquely determined, but the situation that the road type or the road speed limit of some road sections is not unique may occur, at this time, the road type and the road speed limit parameter value of the road section need to be further processed according to the following method:
and multiplying the length proportion of different road types or road speed limits in the road section by the parameter values corresponding to the respective road types or road speed limits, and summing the parameter values multiplied by the length proportions to obtain the final road type or road speed limit parameter value of the road section.
In the embodiment, for the subsequent analysis of the relationship between the vehicle speed characteristic parameter and the energy consumption, 9 basic vehicle speed characteristic parameters are selected for calculation:
the vehicle speed characteristic parameters specifically include: p0Is the idle/stop time ratio; paIs an acceleration time ratio; pdIs a deceleration time proportion; pyThe time proportion is uniform; average velocity VmStandard deviation of velocity VsAverage acceleration amAverage deceleration dmAcceleration standard deviation As。
First, assuming that the operation time of a link sample is T, the acceleration at each time is obtained, and the idling/stopping time T is counted0Acceleration travel time TaDeceleration running time TdAnd a constant speed driving time Ty：
In the formula ai,i+1Acceleration in m/s2 for the ith and (i + 1) th seconds; u. ofi,i+1Speed of i +1 second, uiThe speed of the ith second is km/h; k is the number of all speed data points of the road section sample;
T0the total number of data points with the speed of 0 in the road section sample;
Tathe total points of the road section sample with the acceleration not less than 0.15m/s 2;
Tdthe total points of the road section sample with the acceleration not more than-0.15 m/s 2;
Ty＝T-T0-Ta-Td
further, all the vehicle speed characteristic parameters are calculated as follows:
the energy consumption value is calculated from the speed sequence data of the road section sample, firstly, the required power for the vehicle to run is calculated by using an automobile power balance equation, and then the energy consumption in a period of time is calculated by using an energy calculation formula, wherein the specific power and energy calculation formula is as follows:
wherein m is the mass of the automobile, g is the acceleration of gravity, etaTFor transmission efficiency, i is road grade, CDIs an air resistance coefficient, A is the windward area of the automobile, f is a rolling resistance coefficient, delta is a rotating mass conversion coefficient,for straight-line running acceleration, PePower required for vehicle running, uaThe vehicle running speed is denoted as E, and energy is denoted as E.
Generally, in order to reduce the complexity of a prediction model and improve the prediction efficiency, a stepwise linear regression method is used for analyzing the influence relationship between each vehicle speed characteristic parameter and energy consumption, and vehicle speed characteristic values which have main influence on the energy consumption are screened out for subsequent prediction model establishment.
The basic principle of the stepwise regression is to introduce variables into the model one by one, perform an F test after introducing an explanatory variable, perform a t test on the already selected explanatory variables one by one, and delete the originally introduced explanatory variable when the introduced explanatory variable becomes no longer significant due to the introduction of the explained variable later, so as to ensure that the regression equation only contains significant variables before introducing new variables each time, which is a repeated process until no significant explanatory variable is selected into the regression equation or no insignificant explanatory variable is removed from the regression equation, so as to ensure that the explanatory variable finally retained in the model is important and no serious multiple collinearity exists. The method comprises the following specific steps: firstly, the explained variables (energy) are used for carrying out simple regression on each considered explained variable (vehicle speed characteristic parameter), then the regression equation corresponding to the explained variable which has the largest contribution to the explained variable is used as the basis, and other explained variables are gradually introduced, the introduction sequence principle is that the variable has a larger check value than other variables when entering a model, each step can obtain a regression equation until the optimal regression equation is obtained, namely, the optimal explained variable set is screened out.
The specific analysis results are shown in table 1:
TABLE 1 results of stepwise regression analysis
According to the analysis results in the table 1, the influence factors of the vehicle speed characteristic parameters on the energy are calculated:
wherein, Δ R2The lifting amplitude of a decision coefficient R2 of a regression equation after new variables are introduced for each step; r2 lAnd determining coefficients of the optimal regression equation finally obtained by regression analysis.
As shown in fig. 4, it can be seen that the regression analysis is finally retained as acceleration time ratio, average speed, speed standard deviation, average acceleration, average deceleration, acceleration standard deviation; the larger the influence factor value is, the higher the influence degree on the energy consumption is, and according to the distribution condition of the influence factors in fig. 4, the first three vehicle speed features having main influence are selected for subsequent modeling, namely, acceleration time proportion, average speed and speed standard deviation.
And after the vehicle speed characteristic parameters are determined, establishing a final vehicle speed characteristic prediction sample data set and a driving energy consumption prediction sample data set.
Step 3, carrying out data training on the sample data set to establish a vehicle speed characteristic BP neural network model and a driving energy consumption BP neural network model;
training the vehicle speed characteristic prediction sample data set by using a BP neural network, and establishing a vehicle speed characteristic BP neural network model; and training the driving energy consumption prediction sample data set by using a BP neural network, and establishing a driving energy consumption BP neural network model.
The BP neural network establishing method comprises the following steps:
(1) selecting an input variable of a network, and determining the number m of nodes of an input layer;
(2) determining the number of hidden layers and the number of nodes of the hidden layers;
(3) determining a learning rate, an initial weight and an initial threshold;
(4) determining the number of nodes of an output layer;
(5) and training the neural network.
In this embodiment, for the vehicle speed characteristic BP neural network model, the input variables are road type, road speed limit, road gradient, traffic congestion level and travel mileage, the number of nodes in the input layer is 5, the number of hidden layers is 2, the learning rate is 0.02, the initial weight and the initial threshold are default values, and since the three vehicle speed characteristic parameters are individually predicted, the number of nodes in the output layer is 1.
For the driving energy consumption BP neural network model, input variables are acceleration time proportion, average speed and speed standard deviation, the number of nodes of an input layer is 3, the number of hidden layers is 2, the learning rate is 0.02, initial weight and initial threshold are default values, an output variable is energy, and the number of nodes of an output layer is 1.
The node number m of the hidden layer has three estimation methods as follows:
(1)
(2)m＝log2n
(3)
wherein n is the number of nodes of an input layer, l is the number of nodes of an output layer, and delta is a constant between 0 and 10, and the number m of the nodes of the hidden layer is obtained by an estimation method and a trial and error method.
The number m of hidden layer nodes of the BP neural network model for the vehicle speed characteristics is determined to be 25, and the number m of hidden layer nodes of the BP neural network model for the driving energy consumption is determined to be 20.
The process of training the neural network specifically comprises the following steps:
for the vehicle speed characteristic BP neural network model, randomly selecting 75% from the sample data set as a training sample, using 25% as a test sample, and using the road type, the road speed limit, the road gradient, the traffic jam grade and the driving mileage of the training sample as network input; the vehicle speed characteristic of a training sample is used as network output, a standard BP model is adopted, the number of hidden layers is selected to be 2, the number of nodes of an input layer is 5, the number of nodes of an output layer is 1, the number of nodes of the hidden layers is 25, a first layer transfer function is selected to be a tansig function, a second layer transfer function is a purelin function, a training function is an improved training function thingdm for driving quantity gradient descent, and training of a neural network is completed through data learning.
For the running energy consumption BP neural network model, randomly selecting 75% from the sample data set as a training sample, using 25% as a test sample, and using the acceleration time proportion, the average speed and the speed standard deviation of the training sample as network input; the energy consumption of a training sample is used as network output, a standard BP model is adopted, the number of hidden layers is selected to be 2, the number of nodes of an input layer is 3, the number of nodes of an output layer is 1, the number of nodes of the hidden layers is 20, a first layer transfer function is selected to be a tansig function, a second layer transfer function is a purelin function, a training function is an improved training function thingdm for driving quantity gradient descent, and training of a neural network is completed through data learning.
And then testing the trained vehicle speed characteristic BP neural network model and the train running energy consumption BP neural network model by using test sample data, wherein the test results of the vehicle speed characteristic BP neural network model are shown in fig. 4(a), fig. 4(b) and fig. 4(c), and the test results of the train running energy consumption BP neural network model are shown in fig. 5 (a).
And (b) predicting the driving energy consumption by taking the predicted vehicle speed characteristic parameter data as the input of the driving energy consumption BP neural network model, wherein the result is shown in figure 5(b), the left vertical axis in the figure is the accumulated energy consumption of the road section, and the right vertical axis in the figure is the absolute error of the accumulated energy consumption.
Fig. 5(a) shows a final absolute error of about 100KJ of the prediction result using the actual vehicle speed characteristic parameter as the input of the vehicle energy consumption BP neural network model, and fig. 5(b) shows a final absolute error of about 160KJ of the prediction result using the predicted vehicle speed characteristic parameter as the input of the vehicle energy consumption BP neural network model. Therefore, although the driving energy consumption prediction accuracy based on the predicted vehicle speed characteristic parameter is reduced compared with the former, the prediction accuracy is still quite high, and the final relative error is about 7%.
And 4, acquiring real-time road and traffic information parameters on the planned driving route, inputting the real-time road and traffic information parameters into the vehicle speed characteristic BP neural network model for prediction to obtain vehicle speed characteristic data of future driving, and then inputting the predicted vehicle speed characteristic data into the vehicle energy consumption BP neural network model for prediction to obtain the future driving energy consumption data, so that the online prediction of the vehicle energy consumption is realized.
Because the traffic states of different road sections on the planned driving route are different and time-varying, and the energy consumption conditions of vehicle operation are different under different traffic states, a section of driving mileage with the same traffic jam level on the planned driving route is divided into a road section.
Based on real-time traffic jam grade data on a planned route acquired by a vehicle-mounted GPS positioning device and a GIS information receiving device, the planned driving route is dynamically divided into a plurality of road sections according to the method, and other parameter data of each road section are further determined, wherein the other parameter data comprise road types, road gradients, road speed limits and road section driving mileage values.
The road section driving mileage value is the distance between the middle point of the road section and the starting point of the planned driving route; the road type, the road gradient, the road speed limit and the traffic jam level are directly obtained by the vehicle-mounted GPS positioning device and the GIS information receiving device, and generally no further processing is needed. However, the road type, the road speed limit and the traffic congestion level parameter value are discrete state values of limited types, after the road sections are divided according to the road section dividing method, the traffic congestion level parameter value of each road section can be uniquely determined, but the situation that the road type or the road speed limit of some road sections is not unique may occur, at this time, the road type and the road speed limit parameter value of the road section need to be further processed according to the following method: the length proportion of different road types or road speed limits in the road section is multiplied by the parameter values corresponding to the respective road types or road speed limits, and the parameter values multiplied by the length proportion are summed to obtain the final road type or road speed limit parameter value of the road section.
And inputting the acquired parameter data into a vehicle speed characteristic BP neural network model for prediction to obtain vehicle speed characteristic data of each road section in the future. And inputting the predicted vehicle speed characteristic data into the running energy consumption BP neural network model for prediction to obtain energy consumption data of each future road section, summing the predicted energy consumption data of all the road sections to obtain total energy consumption on the planned running route, or summing the predicted energy consumption data of any number of continuous road sections to obtain energy consumption in any running mileage on the planned running route.
The embodiment also provides a storage medium, which includes a program stored in the storage medium, and when the program runs, the device where the storage medium is located is controlled to execute any one of the driving energy consumption prediction methods in the above technical solutions.
The embodiment also provides driving energy consumption detection equipment, which comprises a processor, wherein the processor is used for running a program, and the program executes any one of the driving energy consumption prediction methods in the technical schemes when running.
The technical means disclosed in the invention scheme are not limited to the technical means disclosed in the above embodiments, but also include the technical scheme formed by any combination of the above technical features.
Claims (10)
1. A driving energy consumption prediction system is characterized in that: the system comprises a data acquisition subsystem, an offline training subsystem and an online prediction subsystem; wherein
The data acquisition subsystem is used for acquiring and recording road environment parameters, traffic state parameters and vehicle operation data on a planned driving route;
the off-line training subsystem is used for dividing a planned driving route into a plurality of road sections, extracting and calculating road environment parameters, traffic state parameters, vehicle speed characteristic parameters and energy consumption values of all the road sections, and establishing a sample data set; building a BP neural network model, and respectively training and verifying the data set through the BP neural network to obtain a verified BP neural network;
and the online prediction subsystem is used for dividing the planned driving route into road sections, extracting road environment parameters, traffic state parameters and road section driving mileage values of all the road sections, and predicting the driving energy consumption on the planned driving route through the verified BP neural network.
2. The driving energy consumption prediction system according to claim 1, characterized in that: the off-line training subsystem comprises an off-line data processing module and a model training module; wherein
The off-line data processing module is used for dividing a planned driving route into a plurality of road sections, extracting and calculating road environment parameters, traffic state parameters, vehicle speed characteristic parameters and energy consumption values of the road sections, and respectively establishing a vehicle speed characteristic prediction sample data set and a driving energy consumption prediction sample data set;
the model training module is used for dividing the sample data set into a training data set and a test data set, respectively building a vehicle speed characteristic BP neural network model and a driving energy consumption BP neural network model, and respectively training the training data set;
and verifying the effectiveness of the trained BP neural network through the test data set.
3. The driving energy consumption prediction system according to claim 1, characterized in that: the online prediction subsystem comprises an online data processing module and a real-time prediction module; wherein
The online data processing module is used for dividing the planning driving route into dynamic road sections in real time and extracting road environment parameters, traffic state parameters and road section driving mileage values of all the road sections;
the real-time prediction module is used for predicting the road section vehicle speed characteristic parameters on the future driving route in real time according to the vehicle speed characteristic BP neural network model obtained after training and the parameters extracted by the data processing module, then predicting the energy consumption values of the road sections in the future in real time by taking the predicted vehicle speed characteristic parameters as the input values of the driving energy consumption BP driving energy consumption, summing the energy consumption values of the road sections and realizing the prediction of the driving energy consumption of the future driving route.
4. The driving energy consumption prediction method is characterized by comprising the following steps:
acquiring historical working condition data of a planned driving route, wherein the historical working condition data comprises road environment parameters, traffic state parameters and vehicle operation data;
constructing a training sample data set based on the historical working condition data;
carrying out data training on the training sample data set, and establishing a vehicle speed characteristic BP neural network model and a driving energy consumption BP neural network model;
and acquiring real-time working condition information on a planned driving route, inputting the real-time working condition information into the vehicle speed characteristic BP neural network model for prediction to obtain vehicle speed characteristic data of future driving, and then inputting the vehicle speed characteristic data into the vehicle energy consumption BP neural network model for prediction to obtain the data of the future driving energy consumption, so as to realize the online prediction of the vehicle energy consumption.
5. The driving energy consumption prediction method according to claim 4, wherein the constructing of the training sample data set specifically comprises: dividing a planned driving route into a plurality of road sections, and extracting characteristic parameters of the road sections by taking a single road section as a unit, wherein the characteristic parameters comprise road environment parameters, traffic state parameters, vehicle speed characteristic parameters, driving mileage values of the road sections and vehicle driving energy consumption values; and analyzing the influence relation between the energy consumption value and the vehicle speed characteristic parameter by using a stepwise linear regression method, and determining a main vehicle speed characteristic parameter for establishing a prediction model.
6. The driving energy consumption prediction method according to claim 5, wherein the dividing the planned driving route into a plurality of road segments is specifically dividing the planned driving route into different traffic congestion levels, and dividing a section of driving mileage with the same traffic congestion level into a road segment sample.
7. The driving energy consumption prediction method according to claim 5, wherein the road environment parameters include a road type, a road gradient and a road speed limit; the traffic state parameter is a traffic jam level; the driving mileage value of the road section is the distance from the starting point of the planned driving route to the middle point of the road section; the vehicle speed characteristic parameters are statistics of a vehicle speed sequence in a certain time period, and comprise average speed, average acceleration, speed standard deviation, average acceleration, acceleration standard deviation, acceleration time proportion, deceleration time proportion, constant speed time proportion, idling/parking time proportion, maximum vehicle speed and maximum acceleration.
8. The driving energy consumption prediction method according to claim 6, wherein the method for extracting the road type and the road speed limit parameter data from the extracted road environment parameters of the road section specifically comprises: when the road type or the road speed limit in the road section divided according to the road section dividing method is unique, the road type or the road speed limit parameter data of each road section is a parameter value corresponding to the road type or the road speed limit; when the road type or the road speed limit in the road section divided according to the road section dividing method is not unique, the road type or the road speed limit parameter value of the road section is determined as follows: and multiplying the length proportion of different road types or road speed limits in the road section by the parameter values corresponding to the respective road types or road speed limits, and summing all the parameter values to obtain the final road type or road speed limit parameter value of the road section.
9. A storage medium, characterized by: the energy consumption forecasting method comprises a program stored in the storage medium, and when the program runs, the program controls a device where the storage medium is located to execute the energy consumption forecasting method of any one of claims 4 to 8.
10. The utility model provides a driving energy consumption check out test set which characterized in that: the vehicle energy consumption prediction method comprises a processor, wherein the processor is used for running a program, and the program is used for executing the vehicle energy consumption prediction method according to any one of claims 4-8.
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