CN101323304A - Running status intelligent recognition system for hybrid power electric automobile - Google Patents
Running status intelligent recognition system for hybrid power electric automobile Download PDFInfo
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Abstract
The invention relates to a driving status identification system for a hybrid electric vehicle, which belongs to the field of electrical vehicle control technique. The hardware of the system comprises a central processing unit, a speed collecting and processing circuit, an analog quantity output circuit, a CAN bus communication control circuit and a serial port communication control circuit; while the software comprises a data input system, a data analysis system and a status identification system. The driving status identification system is characterized by mainly obtaining the speed signals of vehicles by the CAN, the serial port or a speed sensor, and then analyzing the speed in the central processing unit, and thus the driving status of the vehicles can be obtained and divided so as to aid the controller PCU of the electric vehicle to adjust a controls parameter; moreover, the driving status identification system can output result by the CAN, the serial port and the analogue quantity, thus having strong universality and convenience.
Description
Technical field
The present invention relates to a kind of running status intelligent recognition system of mixed power electric car, belong to the Control of Electric Vehicles technical field, can provide foundation for the conversion of various mixed power electric car controller parameter.
Background technology
Electronlmobil is an important directions of China's development of automobile, the research of mixed power electric car control policy has been become the focus of automotive research, because mixed power electric car has two propulsions source: driving engine and electrical motor, so energy distribution has just become one of research emphasis of mixed power electric car control policy.Have and be closely connected and the energy distribution of mixed power electric car is a motoring condition with automobile.
Summary of the invention
In order to overcome the deficiency of above-mentioned prior art structure, the invention provides a kind of.Purpose of the present invention is exactly that moving velocity according to automobile calculates and analyzes, thereby the state that automobile travels is at present discerned, help the control system adjustment data of automobile, make the fuel economy of mixed power electric car and emission performance obtain maximum raising.
The technical scheme that technical solution problem of the present invention is adopted is:
The running status intelligent recognition system of mixed power electric car comprises hardware and software,
Hardware comprises central process unit, vehicle speed signal collection and treatment circuit, digital quantity signal output circuit, CAN bus communication control circuit, serial communication control circuit, analogue quantity output circuit;
Software then comprises data entry system, data analysis system and state recognition system.CAN bus communication control system meets the specification of CAN2.0B.
Data entry system is the database software of setting up with matlab, can read the operating mode file of the city thoroughfare, through street etc. of various forms, can set up easily and substantial program in the data bank of city driving cycle.
Data analysis system also is by the data processor that uses matlab to write, and adopts the algorithm of analysis of neural network that the operating mode in the data bank is discerned.
State recognition system calculates and analyzes according to the automobile speed signal that self gathers or other controllers are input to immediately, thereby judges the state that travels of automobile, and evaluates;
The hardware connection diagram as shown in Figure 2, central process unit receives the information (analog quantity) of vehicle speed signal collection and treatment circuit, information (digital quantity) is passed to the digital quantity signal output circuit and information (analog quantity) is passed to analogue quantity output circuit, and central process unit carries out bidirectional information (SCI communicates by letter) with CAN bus communication control circuit and serial communication control circuit and transmits.
The software connection diagram as shown in Figure 5, the data entry system in the PC is connected with data analysis system, data analysis system is connected with state recognition system in the central process unit.
Beneficial effect of the present invention is specific as follows: owing to adopted technique scheme, the running state of hybrid electric automobile recognition system can self be measured very easily or import vehicle speed signal by the external world by CAN or serial communication, and exports the state parameter of the current running car that identifies easily to various mixed power electric car controllers.
Description of drawings
The signal transput declaration of Fig. 1 state recognition system;
Fig. 2 state recognition system ECU (Electrical Control Unit) hardware structure diagram;
The scheme drawing of Fig. 3 segmentation stack;
Fig. 4 neural network algorithm model;
Fig. 5 software system connection diagram;
Fig. 6 vehicle speed signal filter circuit scheme drawing;
Fig. 7 digital quantity output circuit scheme drawing;
Fig. 8 CAN interface circuit scheme drawing;
Fig. 9 SCI interface circuit scheme drawing;
Figure 10 analog quantity change-over circuit scheme drawing.
The specific embodiment
Be described further as follows below in conjunction with accompanying drawing to enforcement of the present invention:
Hardware using MPC566 central process unit in the running status intelligent recognition system of mixed power electric car comprises a digital quantity input channel, analog output channel, serial communication interface (SCI), CAN communication interface.The signal transput declaration of recognition system as shown in Figure 1, recognition system ECU (Electrical Control Unit) hardware configuration is as shown in Figure 2.
Hardware comprises central process unit, vehicle speed signal collection and treatment circuit, digital quantity signal output circuit, CAN bus serial communication circuit, serial communication control circuit, analogue quantity output circuit;
Central process unit adopts MPC566;
Vehicle speed signal collection and treatment circuit are shown in Figure 6 as scheming;
The digital quantity signal output circuit as shown in Figure 7;
CAN bus serial communication circuit as shown in Figure 8;
The serial communication control circuit as shown in Figure 9;
Analogue quantity output circuit as shown in figure 10.
Software in the running status intelligent recognition system of mixed power electric car comprises data entry system, data analysis system and state recognition system, and its connection mode as shown in Figure 5.
Data entry system is the database software of setting up with matlab on PC in the software system, can read the operating mode file of the city thoroughfare, through street etc. of various forms, can set up easily and substantial program in the data bank of city driving cycle.
Data analysis system also is the data processor by using matlab to write on PC, adopts the algorithm of analysis of neural network that the operating mode in the data bank is discerned.For guarantee to discern in time and accuracy, in input layer, the mode that adopts the operating mode sample of input segmentation to superpose is divided into 3 minutes small samples of one section, and every mistake once divides in one second, as shown in Figure 3.Then the speed in the small sample is calculated, the result is as shown in table 1, obtains sample parameter x required in the analysis of neural network input layer
1, x
2X
kAnd output layer is then used y
1Represent the trunk roads operating mode, y
2Represent the through street operating mode ..., y
mRepresent operating mode among the m.So just, can carry out neural network and calculate, algorithm model obtains required parameter w in the state recognition system as shown in Figure 4
11, w
12W
Km, b
1, b
2B
m
State recognition system can be immediately according to the parameter that collects, calculate, the result who obtains is the input parameter in the neural network identification, calculate, at first automobile is imported from data entry system in different operating modes such as the trunk roads in city, through streets, set up the floor data storehouse, carry out data analysis system then, calculate the parameter that needs in the recognition system, then parameter is input in the recognition system, again program is downloaded in the central process unit.In carrying out vehicle traveling process, the vehicle speed signal that can collect by car speed sensor, also can obtain vehicle speed signal from CAN or SCI, after state recognition system calculating, the state that just can obtain current running car is to be under which kind of operating mode, and the result is exported by analog channel, CAN or SCI.
State recognition system can be immediately according to the parameter in the speed of a motor vehicle computation chart 1 in 3 minutes that collect, and every mistake once calculated in one second, the result who obtains is the input parameter x of neural network in discerning
1, x
2X
k, adopt formula (1) to calculate then
The concrete operations flow process:
At first automobile is imported from data entry system in different operating modes such as the trunk roads in city, through streets, set up the floor data storehouse, carry out data analysis system then, calculate the parameter w that needs in the state recognition system
11, w
12W
Km, b
1, b
2B
m, then parameter is input in the recognition system, again program is downloaded in the central process unit.
In carrying out vehicle traveling process, the vehicle speed signal that can collect by car speed sensor, also can obtain vehicle speed signal from CAN or SCI, after state recognition system calculating, the state that just can obtain current running car is to be under which kind of operating mode, and the result is exported by analog channel, CAN or SCI.
Table 1: the sample parameter that neural network is used in the state analysis system
| Sequence number | The parameter | Meaning | Unit | |
| 1 | v_avg | Average ground speed | km/ |
|
| 2 | v_std | Vehicle speed standard is poor | km/ |
|
| 3 | v_max | Maximum speed | km/ |
|
| 4 | a_avg | Average acceleration | m/ |
|
| 5 | a_std | The acceleration/accel standard deviation | m/ |
|
| 6 | a_max | Peak acceleration | m/ |
|
| 7 | r_avg | Mean deceleration | m/ |
|
| 8 | r_std | The deceleration/decel standard deviation | m/ |
|
| 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/accel greater than 0 smaller or equal to 7m/s 2Count and account for the ratio of always counting | ％ | |
| 16 | a_7_10 | Acceleration/accel is greater than 7m/s 2Count and account for the ratio of always counting | ％ | |
| 17 | r_0_7 | Deceleration/decel more than or equal to-7 less than 0m/s 2Count and account for the ratio of always counting | ％ | |
| 18 | r_7_10 | Deceleration/decel more than or equal to-10 less than-7m/s 2Count and account for the ratio of always counting | ％ | |
| 19 | r_10_15 | Deceleration/decel is less than-10m/s 2Count and account for the ratio of always counting | ％ | |
| 20 | va_0 | Va is less than 0m 2/s 3Count and account for the ratio of always counting | ％ | |
| 21 | 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 | ％ | |
| 22 | va_3_6 | Va greater than 3 smaller or equal to 6m 2/s 3Count and account for the ratio of always counting | ％ | |
| 23 | va_6_10 | Va greater than 6 smaller or equal to 10m 2/s 3Count and account for the ratio of always counting | ％ | |
| 24 | va_10_15 | Va is greater than 10m 2/s 3Count and account for the ratio of always counting | ％ |
Illustrate: va: represent the product of acceleration/accel and speed, unit is: m
2/ s
3
Claims (5)
1. the running status intelligent recognition system of mixed power electric car, comprise hardware and software, hardware comprises central process unit, vehicle speed signal collection and treatment circuit, digital quantity signal output circuit, CAN bus communication control circuit, serial communication control circuit, analogue quantity output circuit; Software then comprises data entry system, data analysis system and state recognition system, it is characterized in that:
Data entry system is the database software of setting up with matlab, reads the city thoroughfare of various forms, the operating mode file of through street, the data bank of the city driving cycle in foundation and the substantial program;
Data analysis system also is by the data processor that uses matlab to write, and adopts the algorithm of analysis of neural network that the operating mode in the data bank is discerned;
State recognition system calculates and analyzes according to the automobile speed signal that self gathers or other controllers are input to immediately, thereby judges the state that travels of automobile, and evaluates;
Data entry system is connected with data analysis system, data analysis system is connected with state recognition system in the central process unit.
2. the running status intelligent recognition system of mixed power electric car according to claim 1, it is characterized in that having state recognition system, according to the parameter that collects, the result who obtains is the input parameter in the neural network identification, calculate, at first automobile is imported from data entry system in the different operating modes in trunk roads, the through street in city, set up the floor data storehouse, carry out data analysis system then, calculate the parameter that needs in the recognition system, then parameter is input in the recognition system, again program is downloaded in the central process unit; In carrying out vehicle traveling process, the vehicle speed signal that collects by car speed sensor, also obtain vehicle speed signal from CAN or SCI, after state recognition system calculating, just the state that obtains current running car is to be under which kind of operating mode, and the result is exported by analog channel, CAN or SCI.
3. the running status intelligent recognition system of mixed power electric car according to claim 1 is characterized in that adopting the neural network mode to carry out motoring condition identification.
4. the running status intelligent recognition system of mixed power electric car according to claim 1 when it is characterized in that being carrying out the vehicle speed signal processing, adopts the mode of segmentation stack to carry out data handing.
5. the running status intelligent recognition system of mixed power electric car according to claim 1, the parameter that adopts when it is characterized in that being to discern has average ground speed, vehicle speed standard is poor, maximum speed, average acceleration, the acceleration/accel standard deviation, peak acceleration, mean deceleration, the deceleration/decel standard deviation, the minimum deceleration degree, speed 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, acceleration/accel greater than 0 smaller or equal to 7m/s
2Count and account for the ratio of always counting, acceleration/accel greater than 7m/s
2Count account for the ratio of always counting, deceleration/decel more than or equal to-7 less than 0m/s
2Count account for the ratio of always counting, deceleration/decel more than or equal to-10 less than-7m/s
2Count and account for the ratio of always counting, deceleration/decel less than-10m/s
2Count and account for the ratio of always counting, va less than 0m
2/ s
3Count account for the ratio of always counting, va more than or equal to 0 smaller or equal to 3m
2/ s
3Count account for the ratio of always counting, va greater than 3 smaller or equal to 6m
2/ s
3Count account for the ratio of always counting, va greater than 6 smaller or equal to 10m
2/ s
3Count and account for the ratio of always counting or va greater than 10m
2/ s
3Count and account for the ratio of always counting.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102419555A (en) * | 2011-10-31 | 2012-04-18 | 重庆长安汽车股份有限公司 | Electric automobile state simulation method based on petri net |
| CN102556083A (en) * | 2011-12-16 | 2012-07-11 | 北京交通大学 | Method and system for recognizing working conditions of hybrid-power shunting engine |
| CN102582637A (en) * | 2011-12-20 | 2012-07-18 | 北京交通大学 | Operation working condition intelligent identification evaluation system for hybrid shunter |
| CN102831768A (en) * | 2012-08-15 | 2012-12-19 | 大连理工大学 | Hybrid power bus driving condition forecasting method based on internet of vehicles |
| CN103171561A (en) * | 2013-03-25 | 2013-06-26 | 广州市雄兵汽车电器有限公司 | Automobile gesture detecting method |
| CN106184224A (en) * | 2014-11-20 | 2016-12-07 | 现代自动车株式会社 | Vehicle velocity signal system of selection and vehicle velocity signal adjust verification method |
| CN108349485A (en) * | 2015-12-09 | 2018-07-31 | 威伯科有限公司 | Method for speed to be adaptively adjusted in the car and the speed adjustment equipment for executing this method |
-
2008
- 2008-07-28 CN CN 200810117260 patent/CN101323304A/en active Pending
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102419555A (en) * | 2011-10-31 | 2012-04-18 | 重庆长安汽车股份有限公司 | Electric automobile state simulation method based on petri net |
| CN102419555B (en) * | 2011-10-31 | 2013-08-07 | 重庆长安汽车股份有限公司 | Electric automobile state simulation method based on petri net |
| CN102556083A (en) * | 2011-12-16 | 2012-07-11 | 北京交通大学 | Method and system for recognizing working conditions of hybrid-power shunting engine |
| CN102582637A (en) * | 2011-12-20 | 2012-07-18 | 北京交通大学 | Operation working condition intelligent identification evaluation system for hybrid shunter |
| CN102831768A (en) * | 2012-08-15 | 2012-12-19 | 大连理工大学 | Hybrid power bus driving condition forecasting method based on internet of vehicles |
| CN102831768B (en) * | 2012-08-15 | 2014-10-15 | 大连理工大学 | Hybrid power bus driving condition forecasting method based on internet of vehicles |
| CN103171561A (en) * | 2013-03-25 | 2013-06-26 | 广州市雄兵汽车电器有限公司 | Automobile gesture detecting method |
| CN106184224A (en) * | 2014-11-20 | 2016-12-07 | 现代自动车株式会社 | Vehicle velocity signal system of selection and vehicle velocity signal adjust verification method |
| CN106184224B (en) * | 2014-11-20 | 2019-09-24 | 现代自动车株式会社 | Vehicle velocity signal selection method and vehicle velocity signal adjust verification method |
| CN108349485A (en) * | 2015-12-09 | 2018-07-31 | 威伯科有限公司 | Method for speed to be adaptively adjusted in the car and the speed adjustment equipment for executing this method |
| CN108349485B (en) * | 2015-12-09 | 2021-06-01 | 威伯科有限公司 | Method for adaptively adjusting vehicle speed in vehicle and speed adjusting device |
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Open date: 20081217 |