CN113642768A - A vehicle driving energy consumption prediction method based on working condition reconstruction - Google Patents
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Abstract
The invention discloses a vehicle running energy consumption prediction method based on working condition reconstruction, which comprises the steps of extracting characteristic parameters and carrying out cluster analysis on vehicle historical working condition data, establishing a working condition characteristic parameter database, and selecting typical working condition segments; collecting real-time working condition data of a vehicle, constructing a mapping relation between the working condition characteristic data and road and traffic characteristic parameters, and establishing a prediction model of the vehicle working condition characteristic parameters based on the road and traffic characteristics; predicting characteristic parameters of roads and traffic conditions by using a prediction model; comparing the predicted vehicle working condition characteristic parameters with a database, determining the working condition of a future driving route, and reconstructing a vehicle speed-time curve of the future driving route; the energy required for the future driving route is estimated. The working condition characteristic data acquisition method adopted by the invention can predict through the model according to the road and traffic characteristics on the planned route of the vehicle, ensures the universality and the accuracy of the prediction model, and has strong working condition adaptability and practicability.
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 vehicle running energy consumption prediction method based on working condition reconstruction.
Background
For energy-sensitive pure electric vehicles or hybrid electric vehicles, the acquisition of future energy requirements of planned paths in advance and the like are main bases for accurately estimating remaining mileage, setting of charging schemes, power of hybrid systems, energy management and optimal control and the like. The existing intelligent energy management system can realize the prediction of the driving condition or the driving power consumption requirement of the vehicle for a period of time or a distance in the future by utilizing an intelligent learning algorithm, and can further realize the self-adaptive condition control of the vehicle-mounted energy based on the prediction. At present, most methods do not have the capability of predicting the long-time running energy consumption of the vehicle, and most of the existing methods are used for predicting the short-time energy demand by identifying the running condition or predicting the energy consumption based on historical data. The methods have the advantages of small calculation amount and suitability for short-time control optimization under the scene of relatively stable working conditions. However, the methods have the defects that the prediction time is short, the global planning of the vehicle-mounted energy on the planned driving route is not facilitated, the real-time performance is poor, the influence of road traffic change on the automobile energy consumption prediction cannot be reflected in real time, and the energy prediction result is delayed or misjudged under the scene with large change of working conditions.
Disclosure of Invention
The invention aims to provide a vehicle running energy consumption prediction method based on working condition reconstruction, which can predict working condition characteristic parameters according to road and traffic characteristics of a vehicle planned road, predict a model of future working conditions on the basis of the predicted working condition characteristic parameters, and reconstruct a vehicle speed time sequence of a future period of time by using typical working condition type segments, thereby calculating the vehicle running energy consumption of the future road section. The prediction model established by the invention has the characteristics of strong adaptability, good universality and high accuracy of the prediction result.
Therefore, the technical scheme adopted by the invention is as follows:
s1, extracting characteristic parameters and performing cluster analysis on vehicle historical working condition data, establishing a working condition characteristic parameter database, and selecting typical working condition segments; s2, collecting vehicle working condition data, constructing a mapping relation between the working condition characteristic data and road and traffic characteristic parameters, and establishing a prediction model of the vehicle working condition characteristic parameters based on the road and traffic characteristics; s3, obtaining quantitative characteristic parameters of roads and traffic according to the planned driving route, and predicting the characteristic parameters of the roads and traffic conditions by using the prediction model; s4, comparing the predicted vehicle working condition characteristic parameters with a database, determining the working condition of the future driving route, and reconstructing the vehicle speed-time curve of the future driving route by using the typical working condition curve; and S5, estimating the energy required by the future driving distance according to the vehicle speed-time curve.
Further, before the extracting of the characteristic parameters from the vehicle historical operating condition data in step S1, the method further includes dividing the vehicle historical operating condition data into segments, and dividing the segment time length into 100-200S.
Further, the characteristic parameters extracted in step S1 are relative positive acceleration, oscillation frequency/100 m, acceleration time ratio, deceleration time ratio, and time ratio at which the speed per hour is lower than 15 km/h.
Further, the cluster analysis and the establishment of the working condition characteristic parameter database in step S1 are specifically performed by performing cluster analysis on the extracted characteristic parameters by using a maximum expected cluster analysis method of a gaussian mixture model and establishing a database; the database comprises four types of typical working conditions, namely a congestion working condition, an urban working condition, a suburban working condition and a high-speed working condition.
Further, the typical condition segment in step S1 is the segment closest to the center of the gaussian distribution.
Further, in step S2, the road and traffic characteristic parameters include a road type, location information, and a traffic state, where the road type includes an expressway, an urban road, and a suburban road, the location information is longitude and latitude coordinates provided by a GPS/GIS, and the traffic state includes four states of smooth traffic, light traffic, medium traffic, and heavy traffic, and represents a traffic quantization state together with the road type.
Further, the prediction model is a GA-BP neural network prediction model.
Further, in the step S3, the acquiring of the quantitative characteristic parameters of the road and the traffic according to the planned driving route specifically includes selecting the driving route before the vehicle starts, calculating road information and traffic information on the selected route through a cloud service terminal GIS/ITS module, and transmitting the road information and the traffic information to the prediction model.
Further, the step S4 of determining the operating condition of the future driving route is specifically, in step S41, expressing the characteristic parameters of 4 typical operating conditions into a matrix; s42, adopting a most value normalization method to perform dimension removing processing on the vehicle working condition characteristic parameters extracted in the S1; s43, when fuzzy recognition of the working conditions is carried out on the basis of the predicted vehicle working condition characteristic parameters, setting weight coefficients for 5 vehicle working condition characteristic parameters according to different influence degrees of different characteristic quantities on working condition application indexes, and taking reduction of automobile energy consumption as a target; and S44, representing the object to be identified in a vector form, wherein the identification principle adopts an indirect method following a proximity selection principle, the proximity adopts a distance proximity, and the working condition type of the object to be identified can be determined according to the minimum value of the distance.
Further, the vehicle speed-time curve in step S5 is a vehicle speed-time sequence reconstructed by using the typical operating condition type sequence, specifically, during the driving process of the vehicle, the vehicle driving energy consumption is calculated by using the equations (1) and (2), so as to obtain the predicted energy consumption
Wherein, PdPower is required for driving; e is the energy consumption of the travelling crane; g is the acceleration of gravity; m is the mass of the whole vehicle; u. ofaIs the vehicle speed; a is the road gradient; cdCoefficient of air resistance; a is the frontal area of the vehicle; f is a rolling resistance coefficient; delta is the coefficient of rotational inertia, du/dt is the linear acceleration, etaTFor mechanical efficiency of the drive train, corresponding eta is used according to the type of operating mode predictedT。
Compared with the prior art, the invention has the following beneficial effects:
(1) compared with the energy consumption prediction made after historical data which depends on the running of the vehicle per se for a certain period is accumulated, the working condition characteristic data acquisition method adopted by the invention can carry out model prediction according to the road and traffic characteristics on the planned route of the vehicle, ensures the universality and the accuracy of a prediction model, and has strong working condition adaptability and practicability.
(2) The energy prediction method is based on a driving condition characteristic data acquisition method fusing road and traffic characteristic information, and can reflect the influence of dynamic change conditions of the road and traffic conditions on the driving energy consumption of the vehicle while ensuring the prediction accuracy.
(3) The invention considers the difference of the mechanical transmission efficiency of the automobile under different driving working conditions, adopts the mechanical efficiency of the corresponding transmission system to predict the driving energy consumption of the automobile aiming at different working condition types, and greatly improves the prediction accuracy of the driving energy consumption of the automobile.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a diagram of specific implementation steps of an energy consumption prediction method based on condition type prediction according to an embodiment of the present invention;
FIG. 2 is a diagram of a condition characteristic parameter prediction network according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of predicted operating condition types according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a vehicle speed time series based on the reconstruction of predicted operating condition types according to an embodiment of the invention;
FIG. 5 is a schematic diagram of energy consumption prediction based on condition type prediction according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "comprises" and "comprising," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
As shown in fig. 1 and fig. 2, the embodiment is a method for predicting vehicle running energy consumption based on condition reconstruction, comprising the following steps,
and S1, extracting characteristic parameters and carrying out cluster analysis on the historical working condition data of the vehicle, establishing a working condition characteristic parameter database, and selecting typical working condition segments.
Generally, before extracting characteristic parameters of the vehicle historical working condition data, the method further comprises the step of dividing the vehicle running segment of the vehicle historical working condition data, and the dividing method further analyzes the working condition characteristics on the basis of the fixed-step working condition data. In actual road driving conditions, all information of the road working conditions is uncertain and unknown, so that theoretically, local characteristics of the working conditions are not easily covered by short working condition characteristic segments, and the accuracy is favorably improved. However, the calculation amount is large, and frequent switching of the equivalent factor parameters can cause severe changes of the working conditions of the vehicle power parts, so that the selection of the proper step length is particularly important. Therefore, the time length of the working condition segment is divided into 100-200 s.
The extraction of characteristic parameters is a parameter which has obvious influence on energy consumption, and the selection of proper working condition characteristic parameters is the key for accurately and reasonably describing the running working condition. If the characteristic parameters are selected too much, the calculated amount of data extracted and analyzed on line in real time by the vehicle is too large, and the method is difficult to realize; however, the accuracy of the working condition identification is difficult to ensure due to the fact that the selection of the characteristic parameters is too few. Whereas the vehicle behavior is different due to different characteristic parameters. Therefore, different characteristic parameters should be selected for the functions and purposes (such as fuel economy improvement and evaluation, power control strategy design, travel time prediction) of different design conditions. Since the patent is aimed at energy prediction, parameters are chosen that have a significant impact on fuel consumption, including relative positive acceleration, number of oscillations/100 m, acceleration time ratio, deceleration time ratio and time ratio at speeds below 15 km/h.
As a preferred embodiment, the embodiment performs cluster analysis on the collected data features by using a maximum Expectation (EM) cluster analysis method of a Gaussian Mixture Model (GMM), and establishes a working condition database; the number of clusters is usually 4-6. If the number of clusters is too large, the difference among all the classes is small, and the effect of reducing the dimension cannot be well achieved; otherwise, the difference of the samples in each category is large, and the accuracy of the cluster analysis is affected. Considering urban road traffic conditions, living habits and existing research progress in China, the working conditions are divided into 4 types (congestion working conditions, urban working conditions, suburban working conditions and high-speed working conditions), the working conditions are respectively corresponding to 1-4 working conditions, and a corresponding typical working condition characteristic parameter matrix is established. The calculation steps of the maximum Expectation (EM) clustering analysis algorithm of the Gaussian Mixture Model (GMM) are as follows:
(1) the vehicle condition history data set X containing n data objects is input { X1, X2, …, xn }, the number of clusters k is selected and gaussian distribution parameters (mean and variance) of each cluster are randomly initialized. It is also possible to observe the data first to give a relatively accurate mean and variance
(2) Given the gaussian distribution of each cluster, the probability of each data point belonging to each cluster is calculated. The closer a point is to the center of the gaussian distribution, the more likely it belongs to the cluster;
(3) calculating gaussian distribution parameters based on these probabilities such that the probability of a data point is maximized, these new parameters can be calculated using a weighting of the probability of a data point, the weighting being the probability that a data point belongs to the cluster;
(4) iterations 2 and 3 are repeated until the mean and variance converge in the iterations,
(5) all samples are traversed by using the Gaussian parameters (mean and variance) obtained by calculation, and the samples are classified into the class with the highest probability.
It should be noted that the exemplary condition segments selected in this embodiment should have wide representativeness and well reflect the condition characteristics, so the exemplary condition segment is selected to be the segment closest to the center of the gaussian distribution.
S2, collecting vehicle working condition data, constructing a mapping relation between the working condition characteristic data and road and traffic characteristic parameters, and establishing a prediction model of the vehicle working condition characteristic parameters based on the road and traffic characteristics.
When the road and the traffic environment are characterized, the road types are mainly described by adopting expressways, urban roads and suburban roads, the speed limit condition of the roads is considered, and the longitude and latitude coordinates provided by a GPS/GIS are adopted as position information. The traffic state is represented by adopting quantitative grades according to a traffic congestion degree evaluation method in national standards, namely, four states of smooth traffic, light congestion, moderate congestion and severe congestion are correspondingly represented by 1,2, 3 and 4, and the traffic state such as average speed is embodied by matching with road types.
In this embodiment, the prediction model is a neural network prediction model, and specifically, the neural network used is a GA-BP neural network. For convenience of illustration, the structure of the GA-BP neural network described in this embodiment is exemplified as follows, and actually, the parameters are not limited to the values listed below.
(1) The GA-BP neural network topological model is a three-layer feedforward neural network according to an empirical formula of nodes of an implicit layer
Determining the number of hidden layer nodes, wherein a is a constant between 0 and 10, and further determining the number of the hidden layer nodes according to debugging experience on the basis, wherein the number of the input layer nodes corresponds to the input quantity, the number of the input layer nodes is 4, and the number of the output layer nodes is 5; the training function of the BP neural network is rainlm.
(2) The neural network normalization adopts a default processing mode, and the processing mode is as follows:
in the formula, xminIs the smallest number, x, in the data seriesmaxIs the maximum number in the sequence, yminAnd ymaxIs the specified normalized range.
(3) The neural network is input as five groups of quantities related to distance sequences, specifically, road position X is { X1, X2, …, xn }, road type N is { N1, N2, …, nk }, road speed limit is { v1, v2, …, vk }, and traffic condition S is { S1, S2, …, sk }; the output of the neural network is relative positive acceleration, oscillation times per 100m, acceleration time ratio, deceleration time ratio and time ratio of the speed per hour lower than 15 km/h;
(4) the GA-BP neural network model adopts a BP algorithm to calculate errors of an input layer, a hidden layer and an output layer, the step length is 0.1, and iteration times are counted; judging whether the iteration of the algorithm is finished, if the iteration is finished, meeting one of the following conditions, finishing the iteration: condition 1, reaching iteration times of 100; condition 2, the predicted error is reduced to within 10-8 of the target error value;
(5) the GA-BP neural network model evolution algebra is 10, and the population scale is 30; the crossover probability is 0.3; the mutation probability was 0.1.
And S3, obtaining quantitative characteristic parameters of roads and traffic according to the planned driving route, and predicting the characteristic parameters of the roads and traffic conditions by using the prediction model.
Generally, before a vehicle starts, a driver selects a driving route, road characteristic information and traffic information on the determined route are calculated through a big data cloud service terminal GIS/ITS module, and relevant information is transmitted to a road condition prediction model.
And S4, comparing the predicted vehicle working condition characteristic parameters with a database, determining the working condition of the future driving route, and reconstructing the vehicle speed-time curve of the future driving route by using the typical working condition curve.
Specifically, according to the characteristic parameters of the working conditions predicted in step S03, the working conditions are classified into the working condition types in the working condition database by the fuzzy recognition-based working condition algorithm, as shown in fig. 4. The specific algorithm steps are as follows:
s41, the characteristic parameters of 4 typical working conditions are expressed as an array, which can be expressed as:
and S42, taking the dimension inconsistency of the 5 characteristic parameters into consideration, and performing de-dimension treatment on the characteristic parameters by adopting a min-max normalization method. The normalized transfer function is as follows:
where max is the maximum value of the sample data and min is the minimum value of the sample data.
The normalized result is expressed as
S43, when fuzzy recognition of working conditions is carried out based on the predicted working condition characteristic parameters, considering different influence degrees of different characteristic quantities on working condition application indexes, taking reduction of automobile energy consumption as an example, weight coefficients are set for 5 characteristic parameters, and the weight coefficients respectively correspond to a relative positive acceleration, an oscillation frequency/100 m, an acceleration time ratio, a deceleration time ratio and a time ratio of a speed lower than 15 km/h:
w＝(w1,w2,w3,w4,w5)＝(0.40,0.30,0.10,0.10,0.10)
and S44, representing the object to be recognized in a vector form, wherein the recognition principle adopts an indirect method following a proximity selection principle, and the approach degree adopts distance approach degree. Let the object to be recognized be u0＝(x1,x2,…,x5) And the distance between the cluster center and the typical working condition cluster center is as follows:
in the formula xjFor an object u to be recognized0J ═ 1,2, …, 5; x is the number ofijClustering the center u for typical conditionsiI is 1,2, …,4, j is 1,2, …, 5; w is ajJ is the weight of the characteristic parameter, 1,2, …, 5. According to di(u0,ui) The minimum value of the number of the objects to be identified can be determined.
And S5, estimating the energy required by the future driving distance according to the vehicle speed-time curve.
Specifically, as shown in fig. 5, the vehicle speed-time sequence used is a vehicle speed-time sequence reconstructed using a typical operating condition type sequence. In the running process of the vehicle, the power consumed by the vehicle is calculated by using a formula (1) to obtain actual power and predicted power, and the running energy consumption of the vehicle is calculated by using a formula (2) to obtain predicted energy consumption.
In the formula, PdPower is required for driving; e is the energy consumption of the travelling crane; g is the acceleration of gravity; m is the mass of the whole vehicle; u. ofaIs the vehicle speed; a is the road gradient; cdCoefficient of air resistance; a is the frontal area of the vehicle; f is a rolling resistance coefficient; delta is the coefficient of rotational inertia, du/dt is the linear acceleration, etaTIs the mechanical efficiency of the drive train. Wherein η in said formulaTBy varying amounts, since the vehicle is at different operating conditions etaTDifferent. EtaTThe value should be measured and calculated in advance according to different working condition types, wherein the working conditions are divided into 4 types corresponding to 4 accurate etaT. Then, when predicting, according to the predicted working condition type, corresponding eta is adopted when calculatingT. The influence of the road gradient on the power is ignored, i.e. i is taken to be 0.
The invention is oriented to a specific man-vehicle-road system, selects working condition characteristic parameters which have great influence on vehicle energy consumption, constructs a full-path working condition pre-constructed model of the vehicle, and then reconstructs the vehicle speed based on the predicted working condition type to calculate the vehicle running energy consumption. Compared with the method for identifying the working condition by depending on the latest running historical data of the vehicle, the working condition characteristic prediction of the method takes the road characteristics and the traffic condition sequence on the planned route as input, the universality and the accuracy of a prediction model are ensured, meanwhile, the method can realize the rolling update of the traffic road state data and the long-time prediction of the running energy consumption, the influence of the road and traffic dynamic change conditions on the running state of the vehicle can be reflected in time while the long-time energy consumption accuracy of the vehicle is ensured, and meanwhile, the method considers the influence of the mechanical transmission efficiency difference of different working conditions on the energy consumption prediction, thereby greatly improving the accuracy of the vehicle running energy consumption prediction.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
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