CN104022552A - Intelligent detection method for electric vehicle charging control - Google Patents
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Abstract
The invention provides an intelligent detection method for electric vehicle charging control, which comprises the following steps: A. the information acquisition submodule acquires information from a front-end charging terminal to form a data flow record, and acquires the information once at intervals in the whole charging process; B. background modeling analysis is carried out to form a background analysis model; C. the access control submodule sends control instruction information to the front-end charging terminal according to a preset charging mode value obtained by the background analysis model; D. the intelligent charging control detection module constructs a decision tree classification model by using an incremental classification decision tree method based on data stream records collected in real time; E. comparing the local analysis model with the background analysis model; F. and E, transmitting the comparison result of the step E to background analysis system software, and pre-judging a classification target value by referring to the local analysis model to optimize the background analysis model. The invention can achieve the function of local high-efficiency detection, and the background system can continuously adjust and optimize the background analysis model according to the received result.
Description
Technical Field
The invention relates to an intelligent detection method for electric vehicle charging control.
Background
The development of new energy vehicles is one of the main means for dealing with energy crisis and environmental protection in all countries around the world, and with the rapid improvement of the service life, energy density and other performances of lithium ion power batteries, various types of new energy vehicles gradually enter a large-scale demonstration application stage, and some types of new energy vehicles already enter a commercialization stage.
At present, domestic electric automobile battery replacement stations are developed to a commercial promotion and operation stage with a certain scale from an initial Olympic games and world exposition technical verification stage, industrialization of relevant core charging equipment, equipment replacement, monitoring system software and hardware and the like is realized in the development process, the requirements of battery replacement and charging can be met from equipment performance indexes, product process degree and reliability, and one of the main problems is as follows from the practical situation of current popularization and application: the charging load of the power change station is greatly influenced by the rule of vehicle operation, and most electric vehicles have a peak period and a peak period in the morning and evening in the operation, so that the load of the power change station has large load fluctuation, the time of peak load is close to the peak period of other conventional loads, the peak load and the valley load of a city power distribution network are not facilitated, certain difficulty is brought to the access of an external power supply of the power change station, meanwhile, the charging power price of the power change station is mostly peak power price, and the operation cost of the power change station is high
The reason for such problems is mainly that the vehicle charging management strategy is simple and the economy is poor. At present, the electric automobile power exchanging station in actual operation adopts a power charging and exchanging management strategy that the vehicle is returned to the station and the power is exchanged immediately, and then the vehicle is charged immediately after the power exchanging, and the problems of high charging cost, large load fluctuation and the like of the power exchanging station are not considered aiming at the operation characteristics of the electric automobile, the battery charging cost and the load characteristics of the power exchanging station. In fact, the charging control strategy which aims at reducing the charging electricity fee and improving the load characteristics is feasible in the charging station, because the operating vehicle has a high-peak and low-peak operation rule, and the charging time and power of the standby battery can be adjusted in a certain range, so that the load characteristics and the charging economy of the charging station can be improved to a great extent.
Disclosure of Invention
In view of the shortcomings in the prior art, the invention aims to provide an intelligent detection method for charging control of an electric vehicle.
In order to achieve the above object, the present invention provides an intelligent detection method for electric vehicle charging control, which includes the following steps:
A. information acquisition: the information acquisition submodule acquires information from the front-end charging terminal to form a data flow record, wherein the data flow record comprises battery information, vehicle information and user information; in the whole charging process, information is collected once at intervals; for each data stream record, a time stamp is used as a unique identifier to distinguish the data stream records;
B. background analysis and detection, forming a background analysis model: transmitting the battery information, the vehicle information and the user information to background analysis system software; the background analysis system software retrieves a consistent preset charging mode model from the background analysis model according to the battery information, the vehicle information and the user information;
C. the instruction controls the front end to charge: the access control submodule sends control instruction information to the front-end charging terminal according to a preset charging mode value obtained by the background analysis model;
D. local analysis and detection, forming a local analysis model: the intelligent charging control detection module constructs a decision tree classification model by using an incremental classification decision tree method based on data stream records collected in real time;
E. comparing the local analysis model with the background analysis model; the central processing unit compares the pre-judgment classification target value of the locally constructed incremental decision model with a preset charging mode value returned by the background analysis system software, and judges whether the two values are consistent;
F. optimizing a background analysis model: and E, transmitting the comparison result of the step E to a background system software system, and pre-judging the classification target value by referring to the local analysis model to optimize the background analysis model.
The intelligent charging system for realizing the method of the invention is divided into three parts: (1) front-end charging terminal: the system consists of power supplementing equipment such as a battery power supplementing device and a power supply, wherein the power supplementing equipment comprises equipment running state information, user information, vehicle information, battery information and the like; (2) intelligent charging control detection module: the system comprises a front-end charging terminal, a background analysis system software and a front-end charging module, wherein the front-end charging terminal is used for connecting the front-end charging terminal with the background analysis system software, acquiring information of the front end and transmitting charging control information back to the front end according to a charging mode model corresponding to the background system; (3) background analysis system software: and analyzing and establishing various flexible charging mode models according to a large amount of existing historical data.
The intelligent charging control detection module is a key device for connecting an intelligent quick power supply background software system of the electric automobile and an intelligent quick power supply terminal, and normal operation of the intelligent quick power supply terminal and coordination of the intelligent quick power supply terminal and a power grid are realized through effective data transmission and control instruction issuing. Meanwhile, the intelligent charging control needs to balance the charging requirements of users and optimize the charging performance, factors influencing all parameters and the interrelation among all parameters are fully considered, and the weight is reasonably configured. And the charging time, the charging amount and the demand elasticity of each electric automobile are considered, the charging scheme of each electric automobile is intelligently analyzed, the priority and the interruption mechanism are reasonably determined, and the functions of real-time interaction and remote control with user information are realized. The intelligent charging detection control module comprises:
the information acquisition submodule comprises: the method is used for collecting relevant information from the front-end charging terminal, wherein the relevant information comprises battery information (table I), vehicle information (table II) and user information (table III). And integrating the data of the three tables to obtain the data parameter set required to be collected.
The wireless communication sub-module: for transmitting data via IEEE802.11 wireless transmission protocol in a specific area.
An access control submodule: and the front-end terminal is used for transmitting the information of the background control system back to the front-end terminal according to the preset safety access authority.
A cache submodule: the device is used for storing the acquired data, the intermediate data in the analysis process and the control information returned by the background.
Table one: battery information
| Serial number | Name (R) | Description of the invention |
| 1 | Time stamp | Information acquisition time point |
| 2 | Battery ID | Unique identification number ID of battery |
| 3 | State of the battery | The state of the battery: charging or discharging |
| 4 | Percentage of electric quantity | The electric quantity of the battery is 0-100 |
| 5 | Voltage of | Voltage value of battery |
| 6 | Electric current | Current value of battery |
| 7 | Fault information | Presetting a fault information value |
| 8 | High temperature value | Maximum temperature value of battery |
| 9 | Low temperature value | Lowest temperature value of battery |
| 10 | Model number | Signal type of battery |
| 11 | Presetting a charging mode | Analysis according to a background analysis systemThe resulting preset charging mode |
Table two: vehicle information
| Serial number | Name (R) | Description of the invention |
| 1 | Vehicle state | The state of the battery: charging or discharging |
| 2 | Mileage of driving | The electric quantity of the battery is 0-100 |
| 3 | Voltage of | Value of voltage |
| 4 | Maximum power | Maximum power of electric vehicle |
| 5 | Model number | Type of vehicle |
| 6 | The affiliated user ID | Unique identification number ID of user ID |
| 7 | Using battery ID | Unique identification number ID of battery |
Table three: user information
| Serial number | Name (R) | Description of the invention |
| 1 | User ID | Unique identification number ID of user ID |
| 2 | Age (age) | Age group of user |
| 3 | Occupation of the world | Assigned preset value |
| 4 | Hobby | Assigned preset value |
| 5 | Age of driver | Time of driving vehicle |
| 6 | Sex | Gender of user |
In the present invention, a decision tree classification model is constructed by using an incremental classification decision tree method, and the construction process may be, for example, as shown in fig. 1. Wherein (X, y) is a data record integrating information of people, battery and vehicle, X is a vector containing all attribute values, and y is a corresponding object class. XiFor an attribute name in a record, X1Is the first attribute name (e.g., battery voltage), xijIs an attribute specific value, x11Is attribute X1For example, 110V. n isminThe preset values are as follows: for determining whether to perform node splitting evaluation (the same attribute value belongs to the same leaf node with the minimum number).
The information gain is a mathematical calculation formula. Let S be a set of S data samples. Assume that the class label attribute has m different values. siIs of class CiThe number of samples of (1), the entropy or expected information of the sample set is
Suppose that attribute A has v different values { a }1,a2,a3,...avB, }; s can be divided into v subsets S by attribute A1,S2,S3,...SvIn which S isjTaking the attribute A in S as ajA subset of samples of (a); sijIs the subset SjMiddle class is CiThe entropy or expected information divided into multiple intervals by the attribute A is
Wherein the itemIs a subset SjEqual to the subset SjDivided by the total number of samples in S. Given subset Sj，
Wherein,is SjMiddle sampleBelong to class CiThe probability of (c). The information gain divided by the attribute a is gain (a) I(s)1,s2,s3,...am)-E(A)。
HB is the Hoeffding constraint ε. Wherein Hoeffding is a decision tree learning method. In particular, the classification problem is defined as a training set of N samples given in the form (x, y), y being a discrete class description. And x is a vector with d attributes. Each attribute may be represented symbolically or numerically. The goal is to build a model, y f (x), to predict y with high accuracy from future x. For example, x may be a record of a customer's purchases, and y indicates whether the customer is given a daily record of the item. Or x is a cell phone record and y indicates whether it is fraudulent. The most efficient and most used classification method is the decision tree learning method. The learner generalizes the model through a decision tree in which each node contains a test for an attribute. Each tree leads to one possible output result of the tree. Each leaf contains a prediction class. The method for obtaining Y ═ DT (x) is to make the sample to be tested descend from root node to leaf node, and test each node by using its specific attribute. And entering the corresponding branches according to the test result. A decision tree is derived by recursively testing nodes, starting with the root node, and replacing leaves. For the selection of the test attributes of each node, certain heuristic rules are applied to compare all available attributes of the node, and the most suitable one is selected. Decision-making learners such as ID3, C4.5, and CART all assume that all training sets can exist in main memory at the same time. The number of samples that they can learn is severely limited. Hard disk based decision tree learners like SLIQ and SPRINT assume that all samples are present on the hard disk. They are learned by repeated readings. When the size of the training set is increased dramatically, learning such a complex tree is costly. These methods fail completely if the data set is large enough to be accommodated by the hard disk. The goal is to train a decision tree learner for the mass data set. It requires a one-time study in an extremely short, constant timeFor each sample. This would allow direct mining of online data resources and building potentially complex trees at an acceptable computational cost. A test attribute is selected for each node while ensuring that a small subset of all training samples is considered sufficient. Thus, for a given data chain, the first few samples are used to test the root node, and the remainder are used to generate the leaves. And selects test attributes for the leaves, and so on recursively. And solving the problem that how many samples should be selected by competition to obtain the test genus win by applying Hoeffding constraint. If a true value of the random variable R is within the range of R. Assume that there are n independent observations for r and calculate their average. The Hoeffding constraint is that for confidence 1-delta, the true value of the variable r is at leastWherein
The process is a dynamic update model process, which has the advantages that a large amount of historical data does not need to be calculated, modeling can be performed, and the model changes along with the change of new data.
In the invention, the intelligent charging control detection module is based on a decision tree analysis model of the high-speed data stream, has the characteristic of processing the data stream in blocks, and is suitable for constructing the decision analysis model under the condition of limited computing resources; and the decision analysis model is used as a background detection auxiliary auditing mechanism of the intelligent quick power supply system, and the analysis model can be adaptively updated according to newly arrived data streams based on the analysis model of the expert knowledge base.
In the invention, the intelligent charging control detection module combines the functions of information acquisition, automatic detection and scheduling control together through an incremental modeling method of a local module. The local modeling uses the data collected in real time to obtain the pre-judged control information, and the background analysis system software uses the historical data analysis model to retrieve the similarity to obtain the preset control information. The intelligent charging detection control module sends a control instruction of the charging module to the front-end charging terminal by combining and comparing real-time data with historical data, and adjusts a charging mode according to information of a battery, a vehicle and personnel; meanwhile, the local real-time analysis result is transmitted back to the background analysis system for improving the background analysis model.
According to another embodiment of the present invention, the battery information in the data stream record comprises: timestamp, battery ID, battery state, percentage of charge, voltage, current, fault information, high temperature value, low temperature value, model, preset charging mode.
According to another embodiment of the present invention, the vehicle information in the data stream record includes: vehicle state, mileage, voltage, maximum power, model, user ID, battery ID.
According to another embodiment of the present invention, the user information in the data stream record comprises: user ID, age, occupation, hobby, driving age, gender.
According to another embodiment of the present invention, the decision model is continuously updated with newly acquired data stream increments throughout the charging process.
According to another embodiment of the present invention, the time interval between two adjacent times of collecting information is: 1-10 seconds.
According to another embodiment of the present invention, step B comprises the steps of:
b1, calling a wireless communication sub-module through a central processing unit;
b2, the central processor transmits the battery ID, the vehicle ID and the user ID to the wireless communication submodule;
b3, the wireless communication sub-module transmits the battery ID, the vehicle ID and the user ID to the background analysis system software through a wireless communication protocol;
b4, the background analysis system software retrieves a corresponding preset charging mode model from the background analysis model according to the battery ID, the vehicle ID and the user ID.
According to another embodiment of the present invention, step C comprises the steps of:
and C1, the central processing unit transmits the preset charging mode value transmitted back by the background to the access control submodule.
And C2, the access control submodule sends control instruction information to the front-end charging terminal according to the preset charging mode value.
According to another embodiment of the present invention, step E comprises the steps of:
e1, transmitting a preset charging mode model obtained by the background analysis system software back to the intelligent charging detection control module through the wireless communication sub-module;
e2, the wireless communication sub-module transmits the received background return data to the central processing unit;
e3, comparing the pre-judging classification target value of the locally constructed increment decision model with the preset charging mode value returned by the background analysis system software by the central processing unit, judging whether the two values are consistent, and returning the judgment result to the wireless communication submodule.
Compared with the prior art, the invention has the following beneficial effects:
1. the method for constructing the decision tree based on the increment is integrated into a local intelligent charging control detection module, and the data stream updated in real time is analyzed. The method is different from the traditional decision tree model building method, the traditional method needs to completely import all data into a database for analysis, and when new data is added, all data (historical data and newly added data) needs to be modeled again. The modeling of the incremental method is based on cache, and a dynamic incremental modeling method is used, when new data arrives, only the new added data is analyzed, and the analysis result is integrated with the established model, so that the dynamic modeling function is achieved;
2. modeling the acquired real-time data stream by utilizing an incremental decision model locally constructed by the intelligent charging control detection module, pre-judging a classification target value (a preset charging mode) according to the model, comparing the pre-judged classification target value with a preset charging mode value returned by background analysis system software, and returning a result to a background system by analyzing the difference between the pre-judged classification target value and the preset charging mode value. The background system can continuously adjust and optimize the background analysis model according to the received result while achieving the local high-efficiency detection function.
The present invention will be described in further detail with reference to the accompanying drawings.
Drawings
FIG. 1 is a process for constructing a decision tree classification model using a method for incrementally classifying decision trees;
fig. 2 is an intelligent charging system implementing the method of embodiment 1;
fig. 3 shows the working steps of the intelligent charging control detection module.
Detailed Description
Example 1
As shown in fig. 2, the intelligent charging system implementing the method of the present embodiment is divided into three major parts: (1) front-end charging terminal: the system consists of power supplementing equipment such as a battery power supplementing device and a power supply, wherein the power supplementing equipment comprises equipment running state information, user information, vehicle information, battery information and the like; (2) intelligent charging control detection module: the system comprises a front-end charging terminal, a background analysis system software and a front-end charging module, wherein the front-end charging terminal is used for connecting the front-end charging terminal with the background analysis system software, acquiring information of the front end and transmitting charging control information back to the front end according to a charging mode model corresponding to the background system; (3) background analysis system software: and analyzing and establishing various flexible charging mode models according to a large amount of existing historical data.
The intelligent charging control detection module includes: 1. the information acquisition submodule comprises: the system comprises a front-end charging terminal, a front-end charging terminal and a back-end charging terminal, wherein the front-end charging terminal is used for acquiring related information from the front-end charging terminal, and the related information comprises battery information, vehicle information and user information; integrating the data of the three tables to obtain a data parameter set required to be collected; 2. the wireless communication sub-module: for transmitting data via an IEEE802.11 wireless transmission protocol in a specific area; 3. an access control submodule: the system comprises a front-end terminal, a background control system and a back-end terminal, wherein the front-end terminal is used for transmitting information of the background control system back to the front-end terminal according to a preset security access authority; 4. a cache submodule: the device is used for storing the acquired data, the intermediate data in the analysis process and the control information returned by the background.
As shown in fig. 3, the main steps (method of the present embodiment) of the intelligent charging control detection module are as follows:
step 001: the information acquisition submodule acquires information from a front-end charging terminal, the information is continuously acquired in the whole charging process, and a timestamp is used as a unique identifier for each data stream record to distinguish the data stream records;
step 002: the collected data passes through a central processing unit and calls a wireless communication submodule;
step 003: the central processing unit transmits the battery ID, the vehicle ID and the user ID information to the communication submodule;
step 004: the communication submodule transmits the battery ID, the vehicle ID and the user ID information to background analysis system software through a wireless communication protocol;
step 005: based on the data stream collected in real time, a decision tree classification model is constructed by using an incremental classification decision tree method;
step 006: integrating all relevant data into data flow data with the acquisition timestamp as a unique identifier through the battery ID, the vehicle ID and the user ID, and taking a preset charging mode in the battery information as a classification target value; continuously using an updating decision model of newly acquired data stream increment in the whole charging process;
step 007: the background system searches a corresponding preset charging mode model in the background analysis model according to the battery ID, the vehicle ID and the user ID, and transmits the model back to the intelligent charging detection control module through the wireless communication sub-module;
step 008: the communication sub-module transmits the received background return data to the central processing unit;
step 009: the central processing unit compares the pre-judgment classification target value (the preset charging mode) of the locally constructed incremental decision model with the preset charging mode value returned by the background analysis system software, judges whether the two values are consistent or not, and returns the judgment result to the wireless communication submodule;
step 010: the wireless communication sub-module transmits the comparison result to a background system software system through a wireless communication protocol for improving an analysis module of the background system;
step 011: the central processing unit transmits the preset charging mode value transmitted back by the background to the safety access control submodule;
step 012: and the access control submodule sends control instruction information to the front-end charging terminal according to the preset charging mode value.
Although the present invention has been described with reference to the preferred embodiments, it is not intended to limit the scope of the invention. It will be appreciated by those skilled in the art that changes may be made without departing from the scope of the invention, and it is intended that all matter contained in the above description or shown in the accompanying drawings shall be interpreted as illustrative and not in a limiting sense.
Claims (9)
1. An intelligent detection method for electric vehicle charging control comprises the following steps:
A. information acquisition: the information acquisition submodule acquires information from a front-end charging terminal to form a data stream record, wherein the data stream record comprises battery information, vehicle information and user information; in the whole charging process, information is collected once at intervals; for each data stream record, a time stamp is used as a unique identifier to distinguish the data stream records;
B. background modeling analysis, forming a background analysis model: transmitting the battery information, the vehicle information and the user information to background analysis system software; the background analysis system software retrieves a consistent preset charging mode model from the background analysis model according to the battery information, the vehicle information and the user information;
C. the instruction controls the front end to charge: the access control submodule sends control instruction information to the front-end charging terminal according to a preset charging mode value obtained by the background analysis model;
D. local analysis and detection, forming a local analysis model: the intelligent charging control detection module constructs a decision tree classification model by using an incremental classification decision tree method based on data stream records collected in real time;
E. comparing the local analysis model with the background analysis model; the central processing unit compares the pre-judgment classification target value of the locally constructed incremental decision model with a preset charging mode value returned by the background analysis system software, and judges whether the two values are consistent;
F. optimizing a background analysis model: and E, transmitting the comparison result of the step E to background analysis system software, and pre-judging a classification target value by referring to the local analysis model to optimize the background analysis model.
2. The method of claim 1, wherein the battery information in the data stream record comprises: timestamp, battery ID, battery state, percentage of charge, voltage, current, fault information, high temperature value, low temperature value, model, preset charging mode.
3. The method of claim 1, wherein the vehicle information in the data stream record comprises: vehicle state, mileage, voltage, maximum power, model, user ID, battery ID.
4. The method of claim 1, wherein the user information in the data stream record comprises: user ID, age, occupation, hobby, driving age, gender.
5. The method of claim 1, wherein the decision model is incrementally updated with newly acquired data streams throughout the charging process.
6. The method of claim 1, wherein the time interval between two adjacent information acquisitions is: 1-10 seconds.
7. The method of claim 1, wherein the step B comprises the steps of:
b1, calling a wireless communication sub-module through a central processing unit;
b2, the central processor transmits the battery ID, the vehicle ID and the user ID to the wireless communication submodule;
b3, the wireless communication sub-module transmits the battery ID, the vehicle ID and the user ID to the background analysis system software through a wireless communication protocol;
b4, the background analysis system software retrieves a corresponding preset charging mode model from the background analysis model according to the battery ID, the vehicle ID and the user ID.
8. The method of claim 1, wherein the step C comprises the steps of:
and C1, the central processing unit transmits the preset charging mode value transmitted back by the background to the access control submodule.
And C2, the access control submodule sends control instruction information to the front-end charging terminal according to the preset charging mode value.
9. The method of claim 1, wherein the step E comprises the steps of:
e1, transmitting a preset charging mode model obtained by the background analysis system software back to the intelligent charging detection control module through the wireless communication sub-module;
e2, the wireless communication sub-module transmits the received background return data to the central processing unit;
e3, comparing the pre-judging classification target value of the locally constructed increment decision model with the preset charging mode value returned by the background analysis system software by the central processing unit, judging whether the two values are consistent, and returning the judgment result to the wireless communication submodule.
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