CN108981733B - Speed prediction method of electric vehicle charging navigation system - Google Patents

Speed prediction method of electric vehicle charging navigation system Download PDF

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CN108981733B
CN108981733B CN201810409048.6A CN201810409048A CN108981733B CN 108981733 B CN108981733 B CN 108981733B CN 201810409048 A CN201810409048 A CN 201810409048A CN 108981733 B CN108981733 B CN 108981733B
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variance
noise
state
speed
speed prediction
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CN108981733A (en
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王瑞
姜淏予
葛泉波
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Hangzhou Zhongheng Electric Co., Ltd
Hangzhou zhonghengyun energy Internet Technology Co., Ltd
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Hangzhou Zhongheng Cloud Energy Internet Technology Co ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
    • G01C21/3492—Special cost functions, i.e. other than distance or default speed limit of road segments employing speed data or traffic data, e.g. real-time or historical
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
    • G01P3/00—Measuring linear or angular speed; Measuring differences of linear or angular speeds

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  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
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Abstract

The invention discloses a speed prediction method of an electric automobile charging navigation system. It comprises the following steps: (1) establishing a vehicle speed prediction system model; (2) determining an initial value of state quantity of a system model and an initial value of covariance of the state quantity, and setting an expectation value and an initial value of variance of system process noise and observation noise; (3) solving a one-step predicted value of the state, the error covariance of the state and a volume point propagated by a nonlinear observation equation; (4) calculating the expectation and variance of the observed noise, calculating the state estimation value and the error covariance of the vehicle speed prediction system model at the moment, and repeatedly executing the step N0Secondly, taking the final result as the result of the state estimation value and the error covariance thereof; (5) estimating process noise expectation at time k using Sage-Husa method
Figure DDA0001642177450000011
Sum variance
Figure DDA0001642177450000012
The method can be used for rapidly and accurately predicting the speed of the electric automobile in the future when the electric automobile runs along a certain planned path, and has the advantages of small calculation amount, high calculation speed and low requirement on storage space.

Description

Speed prediction method of electric vehicle charging navigation system
Technical Field
The invention relates to the technical field of speed prediction in a navigation system for searching for charging piles of electric automobiles, in particular to a speed prediction method of a navigation system for charging electric automobiles.
Background
Compared with the traditional fuel oil automobile, the electric automobile has the advantages of environmental protection, high efficiency and the like, but has the defects of long charging time and short endurance mileage under the influence of battery capacity. Therefore, in the actual daily driving process, the owner of the electric automobile often has the requirement of searching for the charging pile. In the navigation process of finding the best surrounding charging pile for the electric vehicle and planning the best path, whether the current remaining electric quantity of the electric vehicle can reach the target charging pile needs to be considered firstly. Therefore, the navigation system is required to accurately predict the speed of the automobile in the process of traveling along a planned path, further calculate the time required by the automobile to reach a destination along the planned path and the energy consumption according to the predicted speed, and finally make a decision of an optimal target charging pile and plan the optimal path.
In actual city driving, the biggest factor that can influence the running speed of an automobile is the congestion condition of a road. Therefore, prediction of the traveling speed of a vehicle based on the congestion status of a real-time road is a widely accepted criterion. The current prediction algorithms for the running speed of the automobile based on the congestion condition of the real-time road are many, such as algorithms of a neural network, Kalman filtering and the like. The neural network algorithm is based on data, a large amount of training is needed in the prediction process, the more training times, the more accurate the result is, but the calculation amount and the storage amount are excessive, and the navigation decision-making time is too long. The Kalman filtering technology is based on a model, adopts a recursion algorithm flow, and has small memory space. The volumetric kalman filtering technique can be used for non-linear systems and is accurate to a high degree, however, it has the disadvantage that the variance of the process noise and the observation noise needs to be known in the process of velocity prediction with volumetric kalman filtering. Therefore, the variance of the two needs to be estimated by using an adaptive filtering technology, and the most commonly used adaptive filtering algorithm for variance estimation includes two methods, namely, Sage-Husa and variational bayes, but in actual engineering, the estimation effect of the two methods is sometimes not ideal, the accuracy is not high, and even the filtering is scattered.
Therefore, in the electric vehicle charging navigation system, the algorithm is difficult to quickly and accurately predict the speed of the electric vehicle when the electric vehicle runs along a certain planned path in the future, and the accuracy of the speed prediction plays an important role in the navigation system.
Disclosure of Invention
The invention provides a speed prediction method of an electric vehicle charging navigation system, which can be used for rapidly and accurately predicting the speed of an electric vehicle when the electric vehicle runs along a planned path in the future, and has the advantages of small calculation amount, high calculation speed, low requirement on storage space and capability of better meeting the actual use requirement of electric vehicle charging navigation.
In order to solve the problems, the invention adopts the following technical scheme:
the invention discloses a speed prediction method of an electric automobile charging navigation system, which comprises the following steps:
s1: establishing a vehicle speed prediction system model which comprises a state equation taking the vehicle speed as a state quantity and an observation equation established according to the relation between the target road congestion condition and the vehicle speed;
s2: obtaining the congestion condition of the target road at the current time from a traffic information center so as to determine the initial value of the state quantity of the vehicle speed prediction system model
Figure BDA0001642177430000021
And its covariance initial value
Figure BDA0001642177430000022
Simultaneous setting of expected initial values of process noise for a vehicle speed prediction system model
Figure BDA0001642177430000023
Variance initial value
Figure BDA0001642177430000031
And observing a desired initial value of noise
Figure BDA0001642177430000032
Variance initial value
Figure BDA0001642177430000033
S3: using a volumetric Kalman filter, a one-step prediction of a state is calculated
Figure BDA0001642177430000034
And its error covariance Pk|k-1And calculating updated state volume points of the propagation of the nonlinear observation equation
Figure BDA0001642177430000035
Pre-measurement volumetric point
Figure BDA0001642177430000036
S4: calculating an expectation of observed noise
Figure BDA0001642177430000037
Sum variance
Figure BDA0001642177430000038
S5: calculating the state estimation value of the vehicle speed prediction system model at the moment
Figure BDA0001642177430000039
And its error covariance Pm k|kM is initially 1;
s6: judging whether m is less than N0If so, m is m +1, jumping to step S4, otherwise, executing step S7;
s7: taking the final result as a state estimation value
Figure BDA00016421774300000310
And its error covariance Pk|kAs a result of (1), i.e.
Figure BDA00016421774300000311
Figure BDA00016421774300000312
The speed predicted value at the final k moment is obtained;
s8: estimating the period of process noise at time k using the Sage-Husa methodInspection of
Figure BDA00016421774300000313
Sum variance
Figure BDA00016421774300000314
Preferably, the formula of the vehicle speed prediction system model is as follows:
Figure BDA00016421774300000315
wherein the state quantity XkIndicating the speed of the vehicle at time k, the observed quantity ZkIndicating the number of vehicles observed on the road at time K, L indicating the length of the road, K*A congestion coefficient indicating a maximum traffic flow; v*Represents the maximum speed limit, w, of the road vehiclek-1And vkRepresenting process noise and observation noise.
Preferably, the congestion coefficient K of the link at the current time is calculated according to the length of the link and the number of vehicles at the current time*。
Preferably, the step S4 includes the steps of:
s41: calculating an expectation of observing noise using the Sage-Husa method
Figure BDA0001642177430000041
The variance of the observed noise is calculated by using the Sage-Husa method and is recorded as
Figure BDA0001642177430000042
S42: calculating the variance of the observed noise by using a variational Bayes method, and recording the variance as
Figure BDA0001642177430000043
S43: will be provided with
Figure BDA0001642177430000044
And
Figure BDA0001642177430000045
the result is summed as the final estimate of the observed noise variance at that time
Figure BDA0001642177430000046
The method estimates the process noise variance of the system by using a Sage-Husa method, and jointly estimates the observation noise variance of the system by using the Sage-Husa and a variational Bayes method. And then, based on the current congestion condition of the road, predicting the speed of the electric automobile running along the road in a future period by using a cubature Kalman filtering method.
The invention has the beneficial effects that: (1) the speed of the electric automobile when driving along a certain planned path in the future can be predicted quickly and accurately, the calculation amount is small, the calculation speed is high, meanwhile, the requirement on the storage space is low, and the actual use requirement of the electric automobile charging navigation is met better. (2) By means of joint estimation of the Sage-Husa method and the variational Bayesian method, the problem of poor estimation effect of a single method is solved, and variance estimation accuracy is improved.
Drawings
FIG. 1 is a flow chart of the present invention.
Detailed Description
The technical scheme of the invention is further specifically described by the following embodiments and the accompanying drawings.
Example (b): the speed prediction method of the electric vehicle charging navigation system of the embodiment, as shown in fig. 1, includes the following steps:
s1: establishing a vehicle speed prediction system model, wherein the formula of the vehicle speed prediction system model is as follows:
Figure BDA0001642177430000051
wherein the state quantity XkIndicating the speed of the vehicle at time k, the observed quantity ZkRepresents kThe number of vehicles observed on the road at the time, L representing the length of the road, K*A congestion coefficient (for example, 30) indicating a maximum traffic flow; v*Representing the maximum speed limit (e.g. 60KM/h), w of the road vehiclek-1And vkRepresenting process noise and observation noise (whose statistical properties are unknown and which need to be estimated using adaptive filtering);
s2: obtaining the congestion condition of the target road at the current time from a traffic information center so as to determine the initial value of the state quantity of the vehicle speed prediction system model
Figure BDA0001642177430000052
And its covariance initial value
Figure BDA0001642177430000053
Simultaneous setting of expected initial values of process noise for a vehicle speed prediction system model
Figure BDA0001642177430000054
Variance initial value
Figure BDA0001642177430000055
And observing a desired initial value of noise
Figure BDA0001642177430000056
Variance initial value
Figure BDA0001642177430000057
S3: using a volumetric Kalman filter, a one-step prediction of a state is calculated
Figure BDA0001642177430000058
And its error covariance Pk|k-1And calculating updated state volume points of the propagation of the nonlinear observation equation
Figure BDA0001642177430000059
Pre-measurement volumetric point
Figure BDA00016421774300000510
S4: calculating an expectation of observed noise
Figure BDA00016421774300000511
Sum variance
Figure BDA00016421774300000512
S5: calculating the state estimation value of the vehicle speed prediction system model at the moment
Figure BDA00016421774300000513
And its error covariance Pm k|kM is initially 1;
s6: judging whether m is less than N0If so, m is m +1, jumping to step S4, otherwise, executing step S7;
s7: taking the final result as a state estimation value
Figure BDA00016421774300000514
And its error covariance Pk|kAs a result of (1), i.e.
Figure BDA00016421774300000515
Figure BDA00016421774300000516
The speed predicted value at the final k moment is obtained;
s8: estimating process noise expectation at time k using Sage-Husa method
Figure BDA0001642177430000061
Sum variance
Figure BDA0001642177430000062
Calculating the congestion coefficient K of the road section at the current moment according to the length of the road section and the number of vehicles at the current moment*。
Step S4 includes the following steps:
s41: calculation Using Sage-Husa methodTo come out of expectation of observation noise
Figure BDA0001642177430000063
The variance of the observed noise is calculated by using the Sage-Husa method and is recorded as
Figure BDA0001642177430000064
S42: calculating the variance of the observed noise by using a variational Bayes method, and recording the variance as
Figure BDA0001642177430000065
S43: will be provided with
Figure BDA0001642177430000066
And
Figure BDA0001642177430000067
the result is summed as the final estimate of the observed noise variance at that time
Figure BDA0001642177430000068
The method estimates the process noise variance of the system by using a Sage-Husa method, and jointly estimates the observation noise variance of the system by using the Sage-Husa and a variational Bayes method. And then, based on the current congestion condition of the road, predicting the speed of the electric automobile running along the road in a future period by using a cubature Kalman filtering method.

Claims (3)

1. A speed prediction method of an electric vehicle charging navigation system is characterized by comprising the following steps:
s1: establishing a vehicle speed prediction system model which comprises a state equation taking the vehicle speed as a state quantity and an observation equation established according to the relation between the target road congestion condition and the vehicle speed;
s2: obtaining the congestion condition of the target road at the current time from a traffic information center so as to determine the initial value of the state quantity of the vehicle speed prediction system model
Figure FDA0002581347280000011
And its covariance initial value
Figure FDA0002581347280000012
Simultaneous setting of expected initial values of process noise for a vehicle speed prediction system model
Figure FDA0002581347280000013
Variance initial value
Figure FDA0002581347280000014
And observing a desired initial value of noise
Figure FDA0002581347280000015
Variance initial value
Figure FDA0002581347280000016
S3: using a volumetric Kalman filter, a one-step prediction of a state is calculated
Figure FDA0002581347280000017
And its error covariance Pk|k-1And calculating updated state volume points of the propagation of the nonlinear observation equation
Figure FDA0002581347280000018
Pre-measurement volumetric point
Figure FDA0002581347280000019
S4: calculating an expectation of observed noise
Figure FDA00025813472800000110
Sum variance
Figure FDA00025813472800000111
S5: calculating the state estimation value of the vehicle speed prediction system model at the moment
Figure FDA00025813472800000112
And its error covariance Pm k|kM is initially 1;
s6: judging whether m is less than N0If so, m is m +1, jumping to step S4, otherwise, executing step S7;
s7: taking the final result as a state estimation value
Figure FDA00025813472800000113
And its error covariance Pk|kAs a result of (1), i.e.
Figure FDA00025813472800000114
Figure FDA00025813472800000115
The speed predicted value at the final k moment is obtained;
s8: estimating process noise expectation at time k using Sage-Husa method
Figure FDA00025813472800000116
Sum variance
Figure FDA00025813472800000117
2. The speed prediction method of the electric vehicle charging navigation system according to claim 1, wherein the formula of the vehicle speed prediction system model is as follows:
Figure FDA0002581347280000021
wherein the state quantity XkIndicating the speed of the vehicle at time k, the observed quantity ZkTo representThe number of vehicles observed on the road at time K, L representing the length of the road, K*A congestion coefficient indicating a maximum traffic flow; v*Represents the maximum speed limit, w, of the road vehiclek-1And vkRepresenting process noise and observation noise.
3. The method for predicting the speed of the electric vehicle charging navigation system according to claim 1 or 2, wherein the step S4 comprises the steps of:
s41: calculating an expectation of observing noise using the Sage-Husa method
Figure FDA0002581347280000022
The variance of the observed noise is calculated by using the Sage-Husa method and is recorded as
Figure FDA0002581347280000023
S42: calculating the variance of the observed noise by using a variational Bayes method, and recording the variance as
Figure FDA0002581347280000024
S43: will be provided with
Figure FDA0002581347280000025
And
Figure FDA0002581347280000026
the result is summed as the final estimate of the observed noise variance at that time
Figure FDA0002581347280000027
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