CN109141459A - A kind of electric car navigation system and method with Analysis of Electricity prediction - Google Patents
A kind of electric car navigation system and method with Analysis of Electricity prediction Download PDFInfo
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- CN109141459A CN109141459A CN201811142438.8A CN201811142438A CN109141459A CN 109141459 A CN109141459 A CN 109141459A CN 201811142438 A CN201811142438 A CN 201811142438A CN 109141459 A CN109141459 A CN 109141459A
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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
- G01C21/3469—Fuel consumption; Energy use; Emission aspects
Abstract
The invention belongs to technical field of new energy, are related to a kind of electric car navigation system and method with Analysis of Electricity prediction.The navigation system includes vehicle carried data collecting system, memory module, controller and man-machine interface；Vehicle carried data collecting system includes CAN bus communication module, GPS and network module and environment temperature detection module, for acquiring user's driving behavior data；Controller acquires user's driving behavior data by vehicle carried data collecting system, user's driving behavior data obtain various features parameter typing memory module relevant to automobile power consumption after Data Analysis Services process, and the time after accumulative driving behavior sample using charging carries out Machine self-learning to provide power consumption prediction；User initiates stroke demand to controller by man-machine interface, and row route planning and machine learning training pattern are provided corresponding power consumption and feed back to man-machine interface by controller.The present invention is most short, the fastest path that user recommends, and the consumption the smallest path of power consumption.
Description
Technical field
The invention belongs to technical field of new energy, are related to a kind of electric car navigation system with Analysis of Electricity prediction
System and method.
Background technique
In recent years, electric car has started one environmentally friendly upsurge in China, and under the driving of Environmental Factors, country helps energetically
Ev industry is held, the construction of electric car charging infrastructure is promoted.For electric car, environmental protection the advantage is that,
And its disadvantage is that the deficiency of course continuation mileage.Under such situation, it is necessary to make rational planning for route to save electric car
Power consumption, the power consumption of each stroke of electric car is accurately predicted, so as to allow user reasonably to select route,
More humanized, intelligentized service is provided for user.
Since existing patent only has the navigation system predicted oil consumption, but oil consumption and the prediction of power consumption have similitude,
Now the difference of existing oil consumption prediction technique and power consumption prediction technique of the invention is listed below.
" a kind of vehicle mounted guidance side based on optimum oil consumption disclosed in Chinese invention patent application number 201610212578.2
Method ", according to the trip purpose of user automatically retrieval goes out all feasible routes, entire road network is divided into several sections, often
It is first to construct for the acquisition of the optimum oil consumption between any two points that section can be uniquely determined wherein by two neighborhood of nodes
Static road network oil consumption model only considers that the static informations such as category of roads, section distance combine the theoretical oil consumption of every class vehicle, asks
Every class vehicle makes each knot by flooding approach and using section oil consumption as " cost " in the theoretical oil consumption of each sections of road out
Point can obtain the fuel consumption information of system-wide net, and find the least road of wherein oil consumption by corresponding algorithm combination traffic environment
Line, and by the analysis to current traffic condition, real-time drive advice is provided for user.
" a kind of vehicle green path navigation system and side disclosed in Chinese invention patent application number 201610116137.3
Method ", vehicle-mounted end travel destination according to driver, all possible running section are obtained using electronic map, according to monitoring client
The road conditions parameter of offer is got the oil consumption attribute value of this vehicle, including fuel consumption per hundred kilometers and vehicle idling oil consumption by CAN bus
Rate, and according to the oil consumption weight of each segmental arc in green path model acquisition road network, using Dijkstra optimal path algorithm
Oil consumption the smallest path when acquisition arrives at the destination.In addition, vehicle-mounted end sends monitoring client for vehicle location and is stored in data
Library carries out visualization tracking to vehicle and historical track plays back, while vehicle flowrate and traffic conditions progress reality to each section
When monitor, issue the congested traffic condition in each section.
The above patented technology is based on automobile stable state oil consumption characteristic, takes node division to different paths to carry out each node
Between oil consumption classification, the influence of weather, season, the series of factors such as vehicle transient state acceleration and deceleration is not considered, so estimation essence
It spends relatively low.On the other hand, electric car has its particularity compared to conventional fuel oil car, according to the article " base of author Zhang Hengjia
It is illustrated in pure electric automobile performance estimating method and universal feasibility study in real example ", influence of the environment temperature to power consumption is remote
Greater than fuel-engined vehicle, the lower power consumption of electric car high speed is very high but lower in low speed section power consumption, therefore accurate electric car
Practical energy conservation and environmental protection benefit of the power consumption forecasting system for the popularization of electric car and in be of great significance.
China's electric car yield and ownership rank first in the world at present, but product quality spread in performance is uneven, and 2017
Nian Qi, China have been strictly required that the electric car of new listing must install remote monitoring system, but using monitoring system to energy consumption
The scheme of progress modelling evaluation is simultaneously indefinite.In conjunction with remote monitoring system, the Analysis of Electricity prediction for having self-learning function is added
Algorithm realizes auto-navigation system, has two aspect advantages, on the one hand it is long-range to can use current electric car for the system hardware
Monitoring hardware, without increasing hardware cost；On the other hand the system constantly can carry out self study according to the energy consumption data of acquisition,
It realizes more effective accurately electric car Estimation of energy consumption, provides beneficial reference for automobile user, be ev industry
Development provides more reasonable accurate data.
Based on technology is mainly predicted with oil consumption at present, such as above two patented technology is carried out both for the oil consumption of fuel vehicle
Prediction.Current oil consumption prediction is primarily present four disadvantages:
1. the navigation system for not thering is the power consumption for electric car to be predicted；
2. pair oil consumption influence factor considers less, the key factors such as temperature, weather for ignoring；
3. mainly using stable state oil consumption map, lack transient state oil consumption evaluation method；
4. not having self-learning function, the ride characteristic of particular vehicle can not be adapted to.
" a kind of pure electric automobile actual travel energy consumption survey disclosed in Chinese invention patent application number 201611246090.8
Examination, evaluation and prediction technique " proposes a kind of electric car method for estimating power consumption, but this method is heavy also without temperature etc. is considered
Factor is wanted, and is linear method, can not consider the influence of non-linear factor, and the specific route energy combined that do not provide and navigate
Consume prediction technique.
The present invention uses machine learning algorithm, planing machine self-learning networks, and the factors such as height above sea level, temperature, weather are added,
More comprehensively electric car power consumption prediction is realized, provides the power consumption prediction of optional route for user, auxiliary electric automobile is driven
The person of sailing further decreases the power consumption of vehicle.
Summary of the invention
To solve the above problems, the present invention propose it is a kind of with Analysis of Electricity prediction electric car navigation system and side
Method is based on power consumption required for Machine self-learning automatically analyzes path according to the driving habit of user and various features data.
Technical scheme is as follows:
It is a kind of with Analysis of Electricity prediction electric car navigation system, including vehicle carried data collecting system, memory module,
Controller and man-machine interface；
The vehicle carried data collecting system, including CAN bus communication module, GPS and network module and environment temperature
Detection module, three are connected with controller, and vehicle carried data collecting system is for acquiring user's driving behavior data；Described
CAN bus communication module is connect with automobile CAN-bus interface, obtain vehicle speed information, the power consumption information of vehicle-mounted electrical equipment and
Battery SOC information；The environment temperature detecting module is for obtaining environment temperature；The GPS and network module is for adopting
Collection height above sea level, location information simultaneously obtain Weather information by network；In no GPS signal and network signal, acquisition data, which generate, to be lacked
It loses, remove the invalid data of the period and starts new data acquisition after waiting GPS signal and network signal to reconnect again；
The controller is connected with memory module, and controller is read out memory module, is written and clear operation；Control
User's driving behavior data that device processed is acquired by vehicle carried data collecting system, user's driving behavior data are through Data Analysis Services
Various features parameter typing memory module relevant to automobile power consumption is obtained after process, utilizes charging after adding up driving behavior sample
Time carry out Machine self-learning with provide power consumption prediction；
The man-machine interface is connected with controller, and user initiates stroke demand, control to controller by man-machine interface
Row route planning and machine learning training pattern are provided corresponding power consumption and feed back to man-machine interface by device, by man-machine interface to user
Feed back stroke and power consumption information；
The Data Analysis Services process is the examination to invalid data and removing, the effective original vehicle speed information warp of general
Cross the characteristic parameter obtained in each driving section after filtering removing raw noise；
The Machine self-learning process is self study, needs to detect whether charged state is tied during Machine self-learning
Beam, the termination machine self study process if end of charging；The charging terminates state-detection and includes whether charging is completed and charge
Whether disconnect.
Design parameter is as follows: the capacity of memory module is not less than 16GB；Preferably, CAN bus communication module obtains speed
The acquisition frequency of information is 1HZ, and the power consumption information of vehicle-mounted electrical equipment and the acquisition moment of battery SOC information are 0.01HZ；
The frequency acquisition of environment temperature detecting module is preferably 0.01HZ；Preferably, GPS and network module 110 acquire height above sea level and position
The frequency acquisition of information is 1HZ, and the frequency acquisition for acquiring Weather information is 0.01HZ.
A kind of electric car air navigation aid with Analysis of Electricity prediction, specific as follows:
(1) foundation in floor data library:
Step 200, start, enter step 205；
Step 205, judge vehicle whether and meanwhile meet three conditions: vehicle is in starting state, vehicle is not in charging
State, GPS and network signal are normal；When it is meet three conditions simultaneously when, then 210 are entered step, when satisfaction three simultaneously
When condition, 215 are entered step；
Step 210, it waits, return step 205；
Step 215, timer is set；
Step 220, idle loop is carried out, waiting timer interrupts, and enters step the interruption subroutine of 250~step 295；
Step 250, timer interruption, interruption start；
Step 255, vehicle speed information is read by CAN bus communication module, passes through GPS and network module reading position, sea
Pull out information；
Step 260, flag bit flag++, for recording into the number interrupted；
Step 265, whether judgement symbol position flag is equal to 100, if so, entering step 270；If it is not, being directly entered step
Rapid 280；
Step 270, temperature, weather, battery SOC and mobile unit power consumption information are read, enters step 275；
Step 275, flag bit flag is reset；
Step 280, rejudge vehicle whether and meanwhile meet three conditions: vehicle is in starting state, vehicle is not in
Charged state, GPS and network signal are normal, if so, entering step 285；If it is not, entering step 290；
Step 285, it interrupts and returns；
Step 290, Off Timer is interrupted；
Step 295, idle loop is jumped out, enters step 225；
Step 225, whether inspection data has missing, if so, 230 are entered step, if it is not, entering step 235；
Step 230, invalid run-length data, return step 205 are removed；
Step 235, run-length data is handled, by effective original vehicle speed information after filtering removes raw noise
It obtains the characteristic parameter in each driving section and is stored into memory module；
Step 240, judge whether vehicle is in starting state, if so, return step 205, if it is not, entering step 245；
Step 245, terminate.
(2) application in floor data library:
Step 300, start, enter step 305；
Step 305, whether detection vehicle is in charged state, if being in charged state, enters machine learning process, into
Enter step 315；If being not in charged state, 310 are entered step；
Step 310, to be charged, return step 305 is waited；
Step 315, whether judgement sample increment reaches setting value, if so, carrying out step 320；If it is not, entering step 350；
Step 320, self study is carried out, controller reads sample and is trained to the training pattern of Machine self-learning, enters
Step 325；
Step 325, it waits self study to terminate, enters step 330；
Step 330, judge whether self study terminates, if so, entering step 335；If it is not, entering step 340；
Step 335, self study result is stored, enters step 350；
Step 340, charged state is monitored, Vehicular charging process terminates or charged state disconnects if monitoring, judges to use
Family needs interim power-off vehicle, enters step 345；If vehicle is still in charged state, return step 325；
Step 345, stop self study behavior, enter step 350；
Step 350, terminate.
(3) user calculates power consumption using navigation:
Step 405, during user's traffic navigation, user inputs initial position and terminal in man-machine interface first；
Step 410, controller operation electronic map software provides feasible route；
Step 415, route power consumption is analyzed using the training pattern of Machine self-learning；
Step 420, the power consumption information return man-machine interface predicted is identified for reference.
Preferably, in step 215, timer is set as 1s；Preferably, in step 315, sample increment setting value is 10.
Beneficial effects of the present invention:
1, the navigation system with power consumption prediction is provided for electric car；User is in the shortest path recommended and most
While fast path, the consumption the smallest path of power consumption can be also obtained.
2, consider that influence of the factors to power consumption, the prediction accuracies such as height above sea level, temperature, weather are higher.
3, from 2017, China has been strictly required that the electric car of new listing must install remote monitoring system, the present invention
Actual use cost is relatively low.
4, Machine self-learning process makes entire power consumption prediction model as user is become more using the increase of vehicle number
Add the driving behavior for meeting user, tracking adapts to the energy consumption characteristics of particular vehicle, so that prediction is more and more accurate.
Detailed description of the invention
Fig. 1 is a kind of schematic diagram of the electric car navigation system with Analysis of Electricity prediction of the present invention.
Fig. 2 is the flow diagram of the main program during floor data library is established.
Fig. 3 is the flow diagram of the interruption subroutine during floor data library is established.
Fig. 4 is the application flow schematic diagram in floor data library.
Fig. 5 is the flow diagram that user calculates power consumption using navigation.
Fig. 6 is the example schematic diagram of self-learning networks.
In figure: 100 vehicle carried data collecting systems；105CAN bus communication module, 110GPS and network module；115 environment
Temperature detecting module；120 controllers；125 memory modules；Man-machine interface 130.
Specific embodiment
Below in conjunction with attached drawing and technical solution, a specific embodiment of the invention is further illustrated.
It is a kind of schematic diagram figure of the electric car navigation system with Analysis of Electricity prediction of the present invention, packet as shown in Figure 1
Include vehicle carried data collecting system 100, memory module 125, controller 120 and man-machine interface 130；The vehicle carried data collecting system
System 100, including CAN bus communication module 105, GPS and network module 110 and environment temperature detection module 115.
Vehicle carried data collecting system 100 is connected with controller 120, and controller 120 is connected with memory module 125, utilizes vehicle
Data collection system 100 is carried, user's driving behavior data inputting memory module 125, the Data Data analysis processing of acquisition are acquired
After obtain various features parameter relevant to automobile power consumption, and after adding up enough driving behavior samples using charging time
Machine self-learning is carried out to provide more accurate power consumption prediction.Man-machine interface 130 is connected with controller 120, and user passes through man-machine
Boundary's Controller-oriented initiates stroke demand, and controller passes through man-machine interface to user feedback stroke and power consumption information.
The technical functionality of controller 120 is as follows: (a) in vehicle travel process, being obtained by CAN bus communication module 105
Take vehicle speed information, the power consumption information of vehicle-mounted electrical equipment and battery SOC (state-of-charge) information, it is preferable that vehicle speed information
It obtains frequency and is selected as 1HZ, the power consumption information of vehicle-mounted electrical equipment and the acquisition moment of battery SOC information are set as 0.01HZ；
(b) in vehicle travel process, ambient temperature information is acquired by environment temperature detecting module 115, frequency acquisition is preferably
0.01HZ；(c) in vehicle travel process, height above sea level, location information are acquired by GPS and network module 110, frequency acquisition is excellent
It is selected as 1HZ, while Weather information is obtained by the module, frequency acquisition is preferably 0.01HZ；(d) logical with memory module 125
Letter is read out memory module 125, is written and clear operation；(e) in data acquisition, initial data is counted
According to analyzing and obtain various features parameter relevant to power consumption；(f) it during Vehicular charging, is carried out using various features parameter
The model training of Machine self-learning；(g) when user there are navigation needs, user initial position and terminal are obtained by man-machine interface
Information carries out route planning and provides corresponding power consumption using machine learning training pattern and feed back to man-machine interface.
A kind of electric car air navigation aid with Analysis of Electricity prediction, foundation (such as Fig. 2 and figure including floor data library
Shown in 3), the application (as shown in Figure 4) in floor data library and user using navigation calculate power consumption (as shown in Figure 5)
The foundation in floor data library, including main program and interruption subroutine, using interrupt carry out data sampling thus it is big
The design of capture program is simplified greatly.Specific method is: after meeting sampling condition, being set according to the data sampling rate of high-frequency data
It sets timer and enters idle loop waiting timer and generate internal interrupt, read data in interruption subroutine after entering the interrupt and adopt
Sample value, and low-frequency data is acquired by flag flag bit；Main program sky is returned in the case where continuing to meet sampling condition to follow
Ring continues waiting for next interruption, and when being unsatisfactory for sampling condition, Off Timer interrupts and jumps out idle loop and executes under main program
Then one step carries out the judgement of data validity by main program and carries out data processing and storage.
The example of a preferred self-learning networks is given below, as shown in fig. 6, given characteristic parameter is preferred knot
Fruit.
Tremendous influence in view of environment temperature to battery performance, preferred ambient temperature is as self study characteristic parameter；
In view of weather conditions such as rainy day, snowy day roll the influence of resistance coefficient to vehicle, preferably weather conditions are as self study
Characteristic parameter；
In view of the mobile unit such as influence of air-conditioning, car light to power consumption, the power consumption information conduct of preferably vehicle-mounted electrical equipment
Self study characteristic parameter；
Influence in view of the path gradient to power consumption, preferred slope is as self study characteristic parameter；
Influence in view of mileage travelled to power consumption, preferably mileage travelled are as self study characteristic parameter；
Influence in view of running time to power consumption, preferably running time are as self study characteristic parameter；
Influence in view of dead time to power consumption, preferably dead time are as self study characteristic parameter；
Influence in view of the acceleration time to power consumption, preferably acceleration time are as self study characteristic parameter；
Influence in view of deceleration time to power consumption, preferably deceleration time are as self study characteristic parameter；
Influence in view of average speed to power consumption, preferably average speed are as self study characteristic parameter；
Influence in view of accelerating sections average acceleration to power consumption, preferably accelerating sections average acceleration is as self study feature
Parameter；
Influence in view of braking section average retardation rate to power consumption, preferably braking section average retardation rate is as self study feature
Parameter；
Wherein, features described above parameter is obtained in run-length data processing in step 230, above-mentioned environment temperature characteristic parameter
It is acquired by environment temperature detecting module 115, above-mentioned weather characteristics parameter is obtained by GPS and network module 110, above-mentioned vehicle-mounted electricity consumption
The power consumption information of equipment is obtained by CAN bus communication module 105, and above-mentioned gradient feature parameter is acquired by GPS and network module 110
Elevation data obtained through data processing；Above-mentioned mileage travelled, dead time, the acceleration time, deceleration time, is put down at running time
The vehicle that equal speed, accelerating sections average acceleration, braking section average retardation rate characteristic parameter are obtained by CAN bus communication module 105
Fast data are obtained through data processing.
Claims (4)
1. a kind of electric car navigation system with Analysis of Electricity prediction, which is characterized in that the electric car, which navigates, is
System includes vehicle carried data collecting system, memory module, controller and man-machine interface；
The vehicle carried data collecting system, including CAN bus communication module, GPS and network module and environment temperature detection
Module, three are connected with controller, and vehicle carried data collecting system is for acquiring user's driving behavior data；The CAN
Bus communication module is connect with automobile CAN-bus interface, obtains vehicle speed information, the power consumption information of vehicle-mounted electrical equipment and battery
SOC information；The environment temperature detecting module is for obtaining environment temperature；The GPS and network module is for acquiring sea
It pulls out, location information and Weather information obtained by network；In no GPS signal and network signal, acquisition data generate missing, clearly
Except the period invalid data and start new data again after waiting GPS signal and network signal to reconnect and acquire；
The controller is connected with memory module, and controller is read out memory module, is written and clear operation；Controller
The user's driving behavior data acquired by vehicle carried data collecting system, user's driving behavior data are through Data Analysis Services process
After obtain various features parameter typing memory module relevant to automobile power consumption, add up driving behavior sample after using charging when
Between carry out Machine self-learning with provide power consumption prediction；
The man-machine interface is connected with controller, and user initiates stroke demand to controller by man-machine interface, and controller will
Row route planning and machine learning training pattern provide corresponding power consumption and feed back to man-machine interface, by man-machine interface to user feedback
Stroke and power consumption information；
The Data Analysis Services process be the examination to invalid data and removing, by effective original vehicle speed information through filtering
The characteristic parameter in each driving section is obtained after wave removing raw noise；
The Machine self-learning process is self study, needs to detect whether charged state terminates during Machine self-learning,
The termination machine self study process if end of charging；The charging terminates state-detection and includes whether charging is completed and whether charge
It disconnects.
2. a kind of electric car navigation system with Analysis of Electricity prediction according to claim 1, which is characterized in that tool
Body parameter is as follows: the capacity of memory module is not less than 16GB；CAN bus communication module obtain vehicle speed information acquisition frequency be
1HZ, the power consumption information of vehicle-mounted electrical equipment and the acquisition moment of battery SOC information are 0.01HZ；Environment temperature detecting module
Frequency acquisition be 0.01HZ；It is 1HZ that GPS and network module 110, which acquire height above sea level and the frequency acquisition of location information, acquires weather
The frequency acquisition of information is 0.01HZ.
3. a kind of electric car air navigation aid with Analysis of Electricity prediction, which is characterized in that specific as follows:
(1) foundation in floor data library:
Step 200, start, enter step 205；
Step 205, judge vehicle whether and meanwhile meet three conditions: vehicle is in starting state, vehicle is not in charged state,
GPS is normal with network signal；When it is meet three conditions simultaneously when, then 210 are entered step, when three conditions of satisfaction simultaneously
When, enter step 215；
Step 210, it waits, return step 205；
Step 215, timer is set；
Step 220, idle loop is carried out, waiting timer interrupts, and enters step the interruption subroutine of 250~step 295；
Step 250, timer interruption, interruption start；
Step 255, vehicle speed information is read by CAN bus communication module, is believed by GPS and network module reading position, height above sea level
Breath；
Step 260, flag bit flag++, for recording into the number interrupted；
Step 265, whether judgement symbol position flag is equal to 100, if so, entering step 270；If it is not, being directly entered step
280；
Step 270, temperature, weather, battery SOC and mobile unit power consumption information are read, enters step 275；
Step 275, flag bit flag is reset；
Step 280, rejudge vehicle whether and meanwhile meet three conditions: vehicle is in starting state, vehicle is not in charging
State, GPS and network signal are normal, if so, entering step 285；If it is not, entering step 290；
Step 285, it interrupts and returns；
Step 290, Off Timer is interrupted；
Step 295, idle loop is jumped out, enters step 225；
Step 225, whether inspection data has missing, if so, 230 are entered step, if it is not, entering step 235；
Step 230, invalid run-length data, return step 205 are removed；
Step 235, run-length data is handled, effective original vehicle speed information is obtained after filtering removes raw noise
Characteristic parameter in each driving section is simultaneously stored into memory module；
Step 240, judge whether vehicle is in starting state, if so, return step 205, if it is not, entering step 245；
Step 245, terminate；
(2) application in floor data library:
Step 300, start, enter step 305；
Step 305, whether detection vehicle is in charged state, if being in charged state, enters machine learning process, into step
Rapid 315；If being not in charged state, 310 are entered step；
Step 310, to be charged, return step 305 is waited；
Step 315, whether judgement sample increment reaches setting value, if so, carrying out step 320；If it is not, entering step 350；
Step 320, self study is carried out, controller reads sample and is trained to the training pattern of Machine self-learning, enters step
325；
Step 325, it waits self study to terminate, enters step 330；
Step 330, judge whether self study terminates, if so, entering step 335；If it is not, entering step 340；
Step 335, self study result is stored, enters step 350；
Step 340, charged state is monitored, Vehicular charging process terminates or charged state disconnects if monitoring, judges that user needs
Will interim power-off vehicle, enter step 345；If vehicle is still in charged state, return step 325；
Step 345, stop self study behavior, enter step 350；
Step 350, terminate；
(3) user calculates power consumption using navigation:
Step 405, during user's traffic navigation, user inputs initial position and terminal in man-machine interface first；
Step 410, controller operation electronic map software provides feasible route；
Step 415, route power consumption is analyzed using the training pattern of Machine self-learning；
Step 420, the power consumption information return man-machine interface predicted is identified for reference.
4. a kind of electric car air navigation aid with Analysis of Electricity prediction according to claim 3, which is characterized in that step
In rapid 215, timer is set as 1s；In step 315, sample increment setting value is 10.
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| CN111626510A (en) * | 2020-05-27 | 2020-09-04 | 广西职业技术学院 | Theoretical calculation and analysis method for fuel consumption of hybrid electric vehicle |
| CN112002124A (en) * | 2020-07-20 | 2020-11-27 | 联合汽车电子有限公司 | Vehicle travel energy consumption prediction method and device |
| CN116989817A (en) * | 2023-09-26 | 2023-11-03 | 常州满旺半导体科技有限公司 | Energy equipment safety detection data transmission system and method based on data analysis |
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