CN101419679B - Intelligent identification Method for running state of hybrid electric automobile - Google Patents
Intelligent identification Method for running state of hybrid electric automobile Download PDFInfo
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- CN101419679B CN101419679B CN2008102391682A CN200810239168A CN101419679B CN 101419679 B CN101419679 B CN 101419679B CN 2008102391682 A CN2008102391682 A CN 2008102391682A CN 200810239168 A CN200810239168 A CN 200810239168A CN 101419679 B CN101419679 B CN 101419679B
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Abstract
The invention discloses an intelligent method for identifying the driving state of a hybrid electric vehicle, belonging to the electric vehicle control technical field. Neural network is mainly adopted by the method for the identification. The working process is mainly divided into two stages of a learning period and a working period. During the learning period, the standard driving condition of the vehicle is collected firstly and segmented; each sample is calculated to obtain a series of sample parameters; the sample parameters are inserted in a neural network calculation formula to obtain parameters required for the intelligent identification control. During the working period, the cut ridge filtering processing is implemented on the obtained speeds; then the recursive average filtering processing is carried out; the speeds within a certain period of time are stored and are calculated; the calculated results are inserted in the neural network calculation formula to obtain the current driving state of the vehicle. With obvious identification effect on the vehicle driving state, the method for identifying the driving state of the hybrid electric vehicle can help a controller of the electric vehicle to reasonably and effectively regulate control parameters, thereby further improving the fuel economy and emission performance of the electric vehicle.
Description
Technical field
The present invention relates to a kind of intelligent identification Method of current driving state of hybrid-electric car, belong to the Control of Electric Vehicles technical field.
Background technology
Electric automobile is an important directions of China's development of automobile, and the research of mixed power electric car control strategy has been become one of focus of automotive research, and the energy assignment problem during running car is the emphasis of Control of Electric Vehicles strategy study.Compare with the conventional engines automobile, the control strategy and the motoring condition of mixed power electric car are closely connected, and present domestic equipment and the method for also motoring condition not being discerned, so mixed power electric car does not obtain maximum the embodiment in the advantage aspect fuel economy and the emission behavior.The present invention is by handling and calculate the speed of operation of automobile, thereby identified the transport condition of automobile effectively, helps vehicle control unit of electric vehicle better the energy of automobile to be distributed.
Summary of the invention:
The objective of the invention is to be achieved through the following technical solutions:
Because method and the technology motoring condition not discerned at present, influenced the raising of energy distribution and the automobile fuel ecomomy and the emission behavior of mixed power electric car to a certain extent, in order to remedy this deficiency, the invention provides a kind of method of Intelligent Recognition running state of hybrid electric automobile.By gathering the travel speed of automobile a period of time, adopt neural net method that it is calculated and analyzes then, thereby identify the current transport condition of automobile, help the whole-control system of automobile self to adjust parameter, make the fuel economy of mixed power electric car and emission behavior get a greater increase.
Adopt the course of work of neural network identification automobile running working condition to be divided into two stages, it at first is the learning period, it mainly is by the car gage driving cycle is learnt, thereby determine the parameters in the state recognition program: be under-stream period then, mainly be to gather the speed of operation of automobile and handle, the current driving state that calculates automobile by the state recognition program is to belong to which kind of standard driving cycle again.
First level-learning phase, by realizing with the matlab coding that in PC its method step is:
Step 1: existing automobile trunk road, through street country or provincial standard driving cycle of input at first, respectively the driving cycle of automobile trunk road, through street is carried out segmentation then, each segment is as a sample, and the time of each sample can be overlapping.If setting the sampling time of each small sample is t
sSecond, every t
fOnce sample second, t
s=n
it
f, then its division result is 0～t
sBe one section operating mode second, t
f～(t
s+ t
f) second be one section operating mode, 2t
f～(2t
f+ t
s) second be one section operating mode ..., (nt
f)～(nt
f+ t
s) second be one section operating mode, and the result after every kind of operating mode segmentation is stored, be divided into the n1 section as if the major trunk roads operating mode, then the major trunk roads operating mode is stored as array v
Z1, v
Z2... v
Zn1If the through street operating mode is divided into the n2 section, the through street operating mode is stored as array v
K1, v
K2..., v
Kn2
Step 2: each segment operating mode array is handled, at first passed through the speed calculation acceleration of operating mode, when acceleration is that timing is stored as array a
Z1, a
Z2..., a
Zn1, a
K1, a
K2..., a
Kn2..., when acceleration is stored as array r when negative
Z1, r
Z2..., r
Zn1, r
K1, r
K2..., r
Kn2..., and the product of the speed of each sampled point and acceleration is stored as array va
Z1, va
Z2..., va
Zn1, va
K1, va
K2..., va
Kn2....Again the array of speed and positive acceleration is calculated maximal value, mean value and the standard deviation of its data, the array of negative acceleration is calculated minimum value, mean value and the standard deviation of its data, the array of acceleration and speed product is calculated the mean value of its data.Again the array of the product of speed, positive acceleration, negative acceleration, speed and acceleration is carried out node division and data statistics, and calculate the number percent that every part accounts for the time;
Step 3: adopt the parameter that calculates in the above-mentioned steps 2, as the training sample x of neural network learning
1, x
2X
k, i.e. input layer; Adopt y
1Represent the trunk roads operating mode, y
2Represent the through street operating mode ..., y
mRepresent operating mode among the m, as the output layer of neural network; Adopt formula (1) to calculate then, try to achieve the weight w on each connection weight of neural network
Im, b
m
Second stage under-stream period write the running status intelligent recognizer by adopt C in PC, downloads to then in the Intelligent Recognition controller and realizes, its method step is:
Step 4: at first speed is carried out the limit filtration method and handle: currently collect the speed of a motor vehicle with the preceding speed of a motor vehicle of once trying to achieve compares, as if variable quantity greater than v
c, the speed of a motor vehicle of once trying to achieve before then getting is if change less than v
c, the then current speed of a motor vehicle that collects; Then speed being carried out the recurrence average filter method handles: as if the sample frequency at the phase one medium velocity is t
fOnce then get preceding n second
cThe mean value of the inferior speed of a motor vehicle is as current vehicle speed;
Step 5: the speed of a motor vehicle of storage certain hour forms speed of a motor vehicle array.If in the phase one, select t for use
sThe sample of second carries out neural network weight and calculates, t before then storing after each sampling in vehicle traveling process
sThe speed of a motor vehicle in second forms one and has n
iThe array of individual velocity amplitude is as a speed segment;
Step 6: above-mentioned speed segment is calculated according to the step 2 in the phase one, obtained neural network and calculate required parameters;
Step 7: adopt formula (1) to carry out neural network then and calculate, promptly obtain current motoring condition y
m
The invention has the beneficial effects as follows: obvious to the motoring condition recognition effect, can help electric vehicle controller to regulate controlled variable rationally and effectively, thereby further improve the fuel economy and the emission behavior of electric automobile.
Description of drawings:
Fig. 1 realizes the Intelligent Recognition process flow diagram for the present invention adopts neural network;
Fig. 2 is certain major urban arterial highway driving cycle;
Fig. 3 is certain city expressway driving cycle;
Fig. 4 adopts the recognition effect figure of this intelligent identification Method for the present invention.
Embodiment:
The present invention is further described as follows below in conjunction with accompanying drawing:
As shown in Figure 1, adopt neural network to carry out Intelligent Recognition and need carry out two stages, first stage is the learning period, it mainly is the required controlled variable of computational intelligence recognizer, second stage write the Intelligent Recognition program exactly, download to then just can be real-time in the controller the transport condition to automobile discern.Below we will be explained in detail respectively above-mentioned two stages in conjunction with example:
In first stage, the learning period is shown in the first half of Fig. 1:
Step 1: gather automobile trunk road and through street standard driving cycle.Then automobile running working condition is carried out segmentation, obtain little speed fragment, i.e. small sample.In this instance analysis, the sampling time of each small sample is t
sSelected t for use 180 seconds
fSelected for 1 second for use, so n
i=180, t
s=n
it
fSegmentation result is: 0～180 is a sample segment, and 1～181 is a sample segment, and the like.And the result after every kind of operating mode segmentation stored, the major trunk roads operating mode is divided into 800 sections, is stored as array v
Z1, v
Z2... v
800The through street operating mode is divided into 64 sections, is stored as array v
K1, v
K2..., v
64
Step 2: the parameter of calculating each speed fragment.At first pass through the speed calculation acceleration of operating mode, when acceleration is that timing is stored as array a
Z1, a
Z2..., a
Zn1, a
K1, a
K2..., a
Kn2, when acceleration is stored as array r when negative
Z1, r
Z2..., r
Zn1, r
K1, r
K2..., r
Kn2, and the product of the speed of each sampled point and acceleration is stored as array va
Z1, va
Z2..., va
Zn1, va
K1, va
K2..., va
Kn2Again the array of speed and positive acceleration is calculated maximal value, mean value and the standard deviation of its data, the array of negative acceleration is calculated minimum value, mean value and the standard deviation of its data, the array of speed and acceleration product is calculated the mean value of its data.Again to speed, positive acceleration, negative acceleration, the array of the product of speed and acceleration is carried out node division and data statistics, and calculate the number percent that every part accounts for the time, for example: if speed is divided node is 4, be 1m/s, 5m/s, 10m/s, 15m/s, then computing velocity accounts for the ratio that sampling is always counted smaller or equal to the sampling number of 1m/s, speed accounts for the ratio of always counting greater than 1 smaller or equal to counting of 5m/s, speed is counted smaller or equal to 10m/s greater than 5 and is accounted for the ratio of always counting, speed accounts for the ratio of always counting greater than 10 smaller or equal to counting of 15m/s, speed accounts for the ratio of always counting greater than counting of 15m/s; If it is 1 that positive acceleration is divided node, i.e. 7m/s
2, then calculate acceleration greater than 0 smaller or equal to 7m/s
2Count and account for the ratio of always counting, acceleration greater than 7m/s
2Count and account for the ratio of always counting; If it is 1 that negative acceleration is divided node, promptly-7m/s
2, then calculate retarded velocity more than or equal to-7 less than 0m/s
2Count and account for the ratio of always counting, retarded velocity less than-7m/s
2Count and account for the ratio of always counting; If the product node to speed and acceleration is 3, i.e. 3m
2/ s
3, 6m
2/ s
3, 10m
2/ s
3, then the product of computing velocity and acceleration is less than 0m
2/ s
3Count account for ratio, speed and the acceleration of always counting product greater than 3 smaller or equal to 6m
2/ s
3Count account for ratio, speed and the acceleration of always counting product greater than 6 smaller or equal to 10m
2/ s
3Count account for ratio, speed and the acceleration of always counting product greater than 10m
2/ s
3Count and account for the ratio of always counting.Final major trunk roads condition calculating obtains the matrix of a 800*24, and the through street condition calculating obtains the matrix of a 64*24;
Step 3: calculate the parameter in the neural network identification.Two matrixes that in the step 2 major trunk roads and through street obtained as input layer; Define major trunk roads y then
1Be 1, through street y
2Be 0, as output layer.Calculate w by formula (1)
ImBe matrix [12.214;-12.269;-12.183; 6.0995; 2.3818; 2.3416;-13.32;-13.214;-13.924; 5.8685; 5.7457;-0.06623; 6.0199; 2.8603; 1.3746; 4.3112; 3.712; 2.9215;-5.1756;-11.405;-10.7;-3.1999;-2.5333;-3.1603], b
m=7.5095;
In second stage, under-stream period is shown in the Lower Half of Fig. 1:
Step 4: the speed of a motor vehicle that collects is carried out Filtering Processing.At first speed being carried out the limit filtration method handles: if v
cSelect 20km/h for use, compare with the preceding speed of a motor vehicle of once trying to achieve, greater than 20km/h, the speed of a motor vehicle of once trying to achieve before then getting if change less than 20km/h, is then got the current speed of a motor vehicle that collects as if variable quantity; Then speed being carried out the recurrence average filter method handles: if n
cSelect 10 for use, the mean value of then getting the preceding 10 times speed of a motor vehicle is as current vehicle speed;
Step 5: t before the storage
iThe speed of a motor vehicle in promptly 180 seconds obtains a speed array;
Step 6:, obtain a matrix to numerical value in the above-mentioned velocity group reckoner 1;
Step 7: just can obtain the current driving state to the matrix substitution neural network computing formula (1) that obtains.
As shown in Figure 2, ordinate is a speed among the figure, and horizontal ordinate is the time, and curve is the standard driving cycle of certain major urban arterial highway of China.
As shown in Figure 3, ordinate is a speed among the figure, and horizontal ordinate is the time, and curve is the standard driving cycle of certain city expressway of China.
As shown in Figure 4, the left side ordinate is a speed among the figure, and the right side ordinate is a transport condition, the horizontal ordinate express time, solid line is that certain model electric automobile is at certain city actual travel rate curve among the figure, dotted line is the transport condition result who identifies among the figure, and recognition result is 1 on 0～2132 second and 2602～3035 seconds, is illustrated on the major trunk roads and travels, recognition result is 0 on the other times section, be illustrated on the through street and travel, identical substantially with the actual travel situation, recognition effect is obvious.
Table 1: sample calculation desired parameters during neural network is calculated
| Sequence number | The parameter symbol | Meaning | Unit |
| 1 | v_avg | Average speed | km/h |
| 2 | v_std | Vehicle speed standard is poor | km/h |
| 3 | v_max | Max. speed | km/h |
| 4 | a_avg | Average acceleration | m/s 2 |
| 5 | a_std | The acceleration standard deviation | m/s 2 |
| 6 | a_max | Peak acceleration | m/s 2 |
| 7 | r_avg | Average retardation rate | m/s 2 |
| 8 | r_std | The retarded velocity standard deviation | m/s 2 |
| 9 | r_min | The minimum deceleration degree | m/ |
| 10 | V_0_1 | Speed accounts for the ratio that sampling is always counted smaller or equal to the sampling number of 1m/s | ％ |
| 11 | v_1_5 | Speed accounts for the ratio of always counting greater than 1 smaller or equal to counting of 5m/s | ％ |
| 12 | v_5_10 | Speed is counted smaller or equal to 10m/s greater than 5 and is accounted for the ratio of always counting | ％ |
| 13 | v_10_15 | Speed accounts for the ratio of always counting greater than 10 smaller or equal to counting of 15m/s | ％ |
| 14 | v_15_25 | Speed accounts for the ratio of always counting greater than counting of 15m/s | ％ |
| 15 | a_0_7 | Acceleration greater than 0 smaller or equal to 7m/s 2Count and account for the ratio of always counting | ％ |
| 16 | a_7 | Acceleration is greater than 7m/s 2Count and account for the ratio of always counting | ％ |
| 17 | r_0_7 | Retarded velocity more than or equal to-7 less than 0m/s 2Count and account for the ratio of always counting | ％ |
| 18 | r_7 | Retarded velocity is less than-7m/s 2Count and account for the ratio of always counting | ％ |
| 29 | va_0 | Va is less than 0m 2/s 3Count and account for the ratio of always counting | ％ |
| 20 | va_0_3 | Va more than or equal to 0 smaller or equal to 3m 2/s 3Count and account for the ratio of always counting | ％ |
| 21 | va_3_6 | Va greater than 3 smaller or equal to 6m 2/s 3Count and account for the ratio of always counting | ％ |
| 22 | va_6_10 | Va greater than 6 smaller or equal to 10m 2/s 3Count and account for the ratio of always counting | ％ |
| 23 | va_10 | Va is greater than 10m 2/s 3Count and account for the ratio of always counting | ％ |
| 24 | va_avg | The mean value of Va | m 2/s 3 |
Illustrate: va: represent the product of acceleration and speed, unit is: m
2/ s
3
Claims (5)
1. the intelligent identification Method of a running state of hybrid electric automobile is characterized in that: adopt neural network identification automobile running working condition, its course of work is divided into two stages:
First stage is the learning period, by realizing with the matlab coding that in PC its method step is:
Step 1: at first input has the country or the provincial standard driving cycle of automobile trunk road, through street now, respectively the driving cycle of automobile trunk road, through street is carried out segmentation then, each segment is as a sample, and the time of each sample can be overlapping; If setting the sampling time of each small sample is t
sSecond, every t
fOnce sample second, t
s=n
it
f, then its division result is 0～t
sBe one section operating mode second, t
f～(t
f+ t
s) second be one section operating mode, 2t
f～(2t
f+ t
s) second be one section operating mode ..., (nt
f)～(nt
f+ t
s) be one section operating mode, and the result after every kind of operating mode segmentation is stored second; If the major trunk roads operating mode is divided into the n1 section, then the major trunk roads operating mode is stored as array v
Z1, v
Z2... v
Zn1If the through street operating mode is divided into the n2 section, the through street operating mode is stored as array v
K1, v
K2..., v
Kn2
Step 2: each segment operating mode array is handled, at first passed through the speed calculation acceleration of operating mode, when acceleration is that timing is stored as array a
Z1, a
Z2..., a
Zn1, a
K1, a
K2..., a
Kn2..., when acceleration is stored as array r when negative
Z1, r
Z2..., r
Zn1, r
K1, r
K2..., r
Kn2..., and the product of the speed of each sampled point and acceleration is stored as array va
Z1, va
Z2..., va
Zn1, va
K1, va
K2..., va
Kn2..., again the array of speed and positive acceleration is calculated maximal value, mean value and the standard deviation of its data, the array of negative acceleration is calculated minimum value, mean value and the standard deviation of its data, the array of acceleration and speed product is calculated the mean value of its data; Again the array of the product of speed, positive acceleration, negative acceleration, speed and acceleration is carried out node division and data statistics, and calculate the number percent that every part accounts for T.T.;
Step 3: adopt the parameter that calculates in the above-mentioned steps 2, as the training sample x of neural network learning
1, x
2X
k, i.e. input layer; Adopt y
1Represent the major trunk roads operating mode, y
2Represent the through street operating mode ..., y
mRepresent m kind operating mode, as the output layer of neural network; Adopt formula (1) to calculate then, try to achieve the weight w on each connection weight of neural network
Im, b
m
Second stage is under-stream period, writes the running status intelligent recognizer by adopt C in PC, downloads to then in the Intelligent Recognition controller and realizes, its method step is:
Step 4: at first speed is carried out the limit filtration method and handle: currently collect the speed of a motor vehicle with the preceding speed of a motor vehicle of once trying to achieve compares, as if variable quantity greater than v
c, the speed of a motor vehicle that once calculates before then getting is if change less than v
c, then get the current speed of a motor vehicle that collects; Then speed being carried out the recurrence average filter method handles: as if the sample frequency at the phase one medium velocity is t
fOnce then get preceding n second
cThe mean value of the inferior speed of a motor vehicle is as current vehicle speed;
Step 5: the speed of a motor vehicle of storage certain hour forms speed of a motor vehicle array; If in the phase one, select t for use
sThe sample of second carries out neural network weight and calculates, t before then storing after each sampling in vehicle traveling process
sThe speed of a motor vehicle in second forms one and has n
iThe array of individual velocity amplitude is as a speed segment;
Step 6: above-mentioned speed segment is calculated according to the step 2 in the phase one, obtained neural network and calculate required parameters;
Step 7: adopt formula (1) to carry out neural network then and calculate, promptly obtain current motoring condition y
m
2. the method for a kind of Intelligent Recognition running state of hybrid electric automobile according to claim 1 is characterized in that: when adopting neural network to calculate the motoring condition parameter designing, the driving cycle of automobile is divided the time t of division
sBe 120～300 seconds, interval time t
fBe 0.5～3 second, and the time of dividing can be overlapping.
3. the intelligent identification Method of a kind of running state of hybrid electric automobile according to claim 1, it is characterized in that: when adopting neural network to calculate the motoring condition parameter, in to time segment computation process, product to speed, acceleration, speed and acceleration has carried out node division, and dividing node is 1～10.
4. the intelligent identification Method of a kind of running state of hybrid electric automobile according to claim 1, it is characterized in that: when adopting neural network to calculate the motoring condition parameter, selected average velocity for use, the standard deviation of the speed of a motor vehicle, max. speed, average acceleration, the standard deviation of acceleration, peak acceleration, average retardation rate, the minimum deceleration degree, the standard deviation of retarded velocity, the mean value of the product of speed and acceleration and to the speed of a motor vehicle, acceleration, after the product of speed and acceleration carried out node division, each several part accounted for the input of the ratio of T.T. as neural network learning.
5. the intelligent identification Method of a kind of running state of hybrid electric automobile according to claim 1, it is characterized in that: when adopting the transport condition of neural network identification automobile, at the variation characteristic of the speed of a motor vehicle in the reality, at first the speed of a motor vehicle has been carried out the limit filtration method and handled its variable quantity v
cBe 18～30km/h, again the speed of a motor vehicle carried out the recurrence average filter method then and handled, select the speed number n for use
cBe 10～25.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9802599B2 (en) | 2016-03-08 | 2017-10-31 | Ford Global Technologies, Llc | Vehicle lane placement |
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| CN102496079B (en) * | 2011-12-12 | 2015-04-08 | 北京航空航天大学 | Monitoring method for energy consumption and emission on roads |
| CN102566416A (en) * | 2011-12-20 | 2012-07-11 | 北京交通大学 | Intelligent identification and evaluation method for operation condition of hybrid shunting locomotive |
| CN102592330B (en) * | 2012-01-19 | 2014-08-06 | 上海交通大学 | Method for extracting road vehicle running status based on battery data of electric vehicle |
| CN103279795B (en) * | 2013-06-13 | 2016-01-13 | 中南大学 | A kind of Forecasting Methodology of occupant crash's damage |
| CN104802737B (en) * | 2015-03-25 | 2017-05-10 | 清华大学 | Mobile phone based vehicle abnormality driving behavior detection method |
| CN106546763B (en) * | 2015-09-23 | 2019-05-21 | 联创汽车电子有限公司 | Speed calculation method |
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| CN110514255B (en) * | 2019-09-06 | 2021-02-02 | 重庆市城投金卡信息产业(集团)股份有限公司 | Automobile emission detection method and detection system thereof |
| CN110514254B (en) * | 2019-09-06 | 2021-02-02 | 重庆市城投金卡信息产业(集团)股份有限公司 | Method and system for detecting discharge amount of bicycle |
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| US9802599B2 (en) | 2016-03-08 | 2017-10-31 | Ford Global Technologies, Llc | Vehicle lane placement |
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