CN115566762B - Self-balancing electric vehicle intelligent charge and discharge management method for local power distribution network - Google Patents
Self-balancing electric vehicle intelligent charge and discharge management method for local power distribution network Download PDFInfo
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- 238000007726 management method Methods 0.000 title claims abstract description 27
- 238000007600 charging Methods 0.000 claims abstract description 74
- 238000007599 discharging Methods 0.000 claims abstract description 22
- 238000012545 processing Methods 0.000 claims abstract description 7
- 238000000034 method Methods 0.000 claims description 26
- 238000010801 machine learning Methods 0.000 claims description 17
- 238000004458 analytical method Methods 0.000 claims description 13
- 238000007667 floating Methods 0.000 claims description 12
- 238000010248 power generation Methods 0.000 claims description 11
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- 238000010280 constant potential charging Methods 0.000 claims description 8
- 238000013499 data model Methods 0.000 claims description 7
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- 238000004364 calculation method Methods 0.000 claims description 4
- 238000006243 chemical reaction Methods 0.000 claims description 4
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 claims description 3
- 238000010277 constant-current charging Methods 0.000 claims description 3
- 238000000605 extraction Methods 0.000 claims description 3
- 229910052744 lithium Inorganic materials 0.000 claims description 3
- 238000012360 testing method Methods 0.000 claims description 3
- 238000013528 artificial neural network Methods 0.000 claims description 2
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- 238000007405 data analysis Methods 0.000 claims description 2
- 238000012544 monitoring process Methods 0.000 claims description 2
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Classifications
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0047—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with monitoring or indicating devices or circuits
- H02J7/0048—Detection of remaining charge capacity or state of charge [SOC]
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
- H02J3/322—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means the battery being on-board an electric or hybrid vehicle, e.g. vehicle to grid arrangements [V2G], power aggregation, use of the battery for network load balancing, coordinated or cooperative battery charging
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/00032—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries characterised by data exchange
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/007—Regulation of charging or discharging current or voltage
- H02J7/00712—Regulation of charging or discharging current or voltage the cycle being controlled or terminated in response to electric parameters
- H02J7/00714—Regulation of charging or discharging current or voltage the cycle being controlled or terminated in response to electric parameters in response to battery charging or discharging current
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/007—Regulation of charging or discharging current or voltage
- H02J7/00712—Regulation of charging or discharging current or voltage the cycle being controlled or terminated in response to electric parameters
- H02J7/007182—Regulation of charging or discharging current or voltage the cycle being controlled or terminated in response to electric parameters in response to battery voltage
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/007—Regulation of charging or discharging current or voltage
- H02J7/007188—Regulation of charging or discharging current or voltage the charge cycle being controlled or terminated in response to non-electric parameters
- H02J7/007192—Regulation of charging or discharging current or voltage the charge cycle being controlled or terminated in response to non-electric parameters in response to temperature
- H02J7/007194—Regulation of charging or discharging current or voltage the charge cycle being controlled or terminated in response to non-electric parameters in response to temperature of the battery
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
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- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The invention provides an intelligent charging and discharging management method for a self-balancing electric vehicle of a local power distribution network, which comprises the following steps: collecting battery data of an electric automobile; analyzing and processing the battery data; and selecting a corresponding charging mode based on the analyzed and processed battery data to finish charging and discharging management of the electric automobile. The invention regulates and controls the battery pack, can better maintain the battery, prolongs the service life of the battery, meets the complicated working condition management requirements of various electric automobiles, and ensures that the battery is safer and more reliable to use.
Description
Technical Field
The invention belongs to the technical field of battery management control of electric vehicles, and particularly relates to an intelligent charging and discharging management method of a self-balancing electric vehicle of a local power distribution network.
Background
Energy is an important precondition for human survival and development, along with continuous development of social science and technology and economy, the traditional fossil energy is increasingly exhausted, the problems of global warming, environmental pollution and the like are increasingly aggravated, and the improvement of the utilization rate of clean energy becomes the next important research subject; because the wind-solar power generation system has great dependence on climate conditions, the intermittence and uncertainty of the power generation capacity are caused, and if the wind-solar power generation system is directly connected to a power grid, the system stability is greatly affected. The micro-grid can efficiently solve the problem of large-scale scattered access of Renewable Energy (RE), and the running mode of grid connection and isolated network enhances the flexibility of the micro-grid system.
In recent years, electric vehicles (Electrical vehicle, EV) have been rapidly developed, and the value of the mobile energy storage property thereof has been gradually known. EV has been regarded as a rational utilization of clean energy, which is an effective way to solve the energy environmental problem; in order to relieve the pressure of the power grid, the country adopts a policy of time-of-use electricity price to guide a user to change the charging time, and the user is required to pay attention to the electricity price and respond autonomously, so that certain limitation exists; and a large number of charging devices at present cannot obey power grid dispatching, and the mobile energy storage characteristic of the electric automobile cannot be fully utilized.
Disclosure of Invention
In order to solve the technical problems, the invention provides an intelligent charging and discharging management method for an electric vehicle with a self-balancing local distribution network, which can realize dispatching of the electric vehicle by a local micro-grid.
In order to achieve the above purpose, the invention provides an intelligent charging and discharging management method for a self-balancing electric vehicle of a local power distribution network, which comprises the following steps:
collecting battery data of an electric automobile;
analyzing and processing the battery data;
and selecting a corresponding charging mode based on the analyzed and processed battery data to finish charging and discharging management of the electric automobile.
Optionally, the battery data includes: charge-discharge voltage, charge-discharge current, SOC, temperature, and output power.
Optionally, the method for analyzing the battery data is as follows:
filtering the battery data to obtain data related to battery performance; the filtering mode comprises the following steps: keyword filtering, machine learning classification and extraction of the core network.
Optionally, the method for processing the battery data comprises the following steps:
establishing a machine learning big data model;
and calculating and analyzing the filtered battery data based on the machine learning big data model to obtain a charging and discharging instruction and a charging mode of the electric automobile.
Optionally, the charge and discharge instruction is:
and charging the battery of the electric automobile with the current tracked by the maximum power point.
Optionally, the charging mode is: trickle, constant current, constant voltage and floating charge,
in the constant voltage charging and floating charging modes, the charging voltage is set by an external resistor voltage-dividing network; in the constant voltage charging stage, the charging current gradually decreases, and when the charging current decreases to a value set by an external resistor, a floating charging state is entered; in a floating charge state, if the voltage of the battery drops to a preset threshold value of the set constant voltage charging voltage, automatically starting a new charging period;
in the constant current charging mode, the charging current is set through an external resistor;
in the trickle charge mode, when the voltage of the battery is lower than a preset threshold value of the set constant voltage charge voltage, the battery is trickle charged by the preset threshold value of the set constant current charge current.
Optionally, the judging method for completing the charge and discharge management of the electric automobile comprises the following steps:
the battery management system of the electric automobile performs system self-checking and hardware testing firstly, detects whether the electric automobile is in a charging state after the self-checking is normal, and if the electric automobile is not in the charging state, the system is powered on;
detecting whether the system precharge is completed, if so, starting normal discharge, otherwise, entering a system protection mode;
and detecting whether the battery system is in an under-voltage state in real time in a discharging process, and controlling the maximum output power, current and temperature of a motor of the electric automobile according to a power-SOC-temperature curve of a lithium battery of the electric automobile and combining the real-time SOC, temperature and voltage of the battery if the battery system is in the under-voltage state.
Optionally, the method further comprises: and displaying the real-time charge and discharge information and fault information of the battery.
Compared with the prior art, the invention has the following advantages and technical effects:
according to the invention, a machine learning big data model is constructed according to the relations among different states of voltage, SOC, temperature, output power and the like in the charging and discharging processes of the battery, the current state value is substituted into the model in the actual use process of the battery, the battery pack is regulated and controlled, the service life of the battery can be better maintained, the service life of the battery is prolonged, and the battery is suitable for the complicated working condition management requirements of various electric vehicles, so that the battery is safer and more reliable to use.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application, illustrate and explain the application and are not to be construed as limiting the application. In the drawings:
fig. 1 is a schematic flow chart of an intelligent charging and discharging management method for a local power distribution network self-balancing electric vehicle according to an embodiment of the invention.
Detailed Description
It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowcharts, in some cases the steps illustrated or described may be performed in an order other than that illustrated herein.
Example 1
As shown in fig. 1, the invention provides an intelligent charging and discharging management method for a local power distribution network self-balancing electric automobile, which comprises the following steps:
collecting battery data of an electric automobile:
further, the battery data includes: charge-discharge voltage, charge-discharge current, SOC, temperature, and output power.
Analyzing and processing the battery data:
further, the method for analyzing the battery data comprises the following steps:
since the acquired battery data may have interference signals, these are some useless data that need to be analyzed and filtered. Common filtering modes include: keyword filtering, machine learning classification, extraction of core networks, and the like. After analysis and filtration, the result is that the data to be collected and recorded is the data related to the battery performance.
The method for processing the battery data comprises the following steps:
the embodiment can establish a machine learning big data model, analyze and learn filtered battery data by using a machine learning system, and combine the previous battery data with the modern front big data analysis so as to improve the calculation and analysis efficiency of the battery data and finally obtain the charging and discharging instructions and the charging mode of the electric automobile.
In some embodiments, the machine learning system further comprises a big data machine learning function unit, and in the process of accumulating the analysis results of the calculation of the enough battery data, the machine learning system is equivalently trained by using an expert to perform labeled learning, then the machine can implement neural network autonomous learning, and a data model is established, so that the combination of artificial analysis of the battery data and the big data machine learning is realized, and finally the machine autonomous analysis is realized.
Based on the analyzed and processed battery data, a corresponding charging mode is selected, and charging and discharging management of the electric automobile is completed:
further, the charge and discharge instruction is: and charging the battery of the electric automobile with the current tracked by the maximum power point.
The basic parameters of the micro-grid system of this embodiment include: the method comprises the steps of (1) number of electric vehicles, satisfaction of initial electric vehicle users, capacity of an energy storage device, upper limit of the state of charge of the energy storage device and lower limit of the state of charge of the electric vehicle of the state of charge of the energy storage device;
building a renewable energy source module output model to predict renewable energy source module output, and building an energy storage device load model to predict the load state of the energy storage device;
and establishing an electric automobile load characteristic model and obtaining the charge states of all electric automobiles.
The output model of the renewable energy source module is a solar radiation quantity random output model and is used for calculating the output power of the photovoltaic power generation unit. And performing direct-current step-down chopping and real-time power analysis operation on the output voltage of the photovoltaic power generation unit through a voltage conversion and power control module to obtain current tracked by a maximum power point.
The voltage conversion and power control module comprises a singlechip and a voltage converter;
the singlechip is connected with the voltage converter;
the singlechip is used for monitoring and step-down chopping the direct-current voltage output by the photovoltaic power generation unit and outputting PWM pulses to the voltage converter;
the voltage converter is used for carrying out step-down chopping on the direct-current voltage output by the photovoltaic power generation unit according to the on-off state of the PWM pulse control IGBT switching device, and inputting the converted voltage to the battery through the charging mode selection unit;
the singlechip is also used for detecting the charging voltage and the charging current of the voltage converter, programming a control strategy for tracking the maximum power point based on an approximate gradient variable step voltage disturbance observation method, and outputting an allowable signal to the charging mode selection unit through the operation and analysis and calculation of the singlechip program so as to charge the battery with the current tracked by the maximum power point.
Further, the charging mode is: trickle, constant current, constant voltage and floating charge,
in the constant voltage charging and floating charging modes, the charging voltage is set by an external resistor voltage-dividing network; in the constant voltage charging stage, the charging current gradually decreases, and when the charging current decreases to a value set by an external resistor, a floating charging state is entered; in a floating charge state, if the voltage of the battery drops to a preset threshold value of the set constant voltage charging voltage, automatically starting a new charging period;
in the constant current charging mode, the charging current is set through an external resistor;
in the trickle charge mode, when the voltage of the battery is lower than a preset threshold value of the set constant voltage charge voltage, the battery is trickle charged by the preset threshold value of the set constant current charge current.
Further, the judging method for completing the charge and discharge management of the electric automobile comprises the following steps:
aiming at the relation among voltage, current, SOC, temperature and output power in the process of charging and discharging the battery, a relation model is established by utilizing a multiple logistic regression method to carry out mathematical analysis;
based on the relation model, the output current, the power and the current temperature of the battery are regulated and controlled by combining various data indexes of the battery, so that safe and stable use of the battery is ensured.
The battery management system of the electric automobile carries out system self-checking and hardware testing firstly, detects whether the electric automobile is in a charging state after the self-checking is normal, and if the electric automobile is not in the charging state, the electric automobile is powered on;
detecting whether the system precharge is completed, if so, starting normal discharge, otherwise, entering a system protection mode;
and detecting whether the battery system is in an under-voltage state in real time in a discharging process, and controlling the maximum output power, current and temperature of a motor of the electric automobile according to a power-SOC-temperature curve of a lithium battery of the electric automobile and combining the real-time SOC, temperature and voltage of the battery if the battery system is in the under-voltage state.
Further, the present embodiment further includes: and displaying the real-time charge and discharge information and fault information of the battery.
The embodiment can send information such as voltage, current, charge-discharge amount, charge-discharge time and the like to the user mobile phone APP through the GPRS communication module.
The reasons for the faults include the fact that the charging temperature is too high, the temperature exceeding the sustainable temperature of the battery, the battery equalization is poor, the voltage exceeding the highest sustainable voltage of the single battery and the like. If the power is suddenly cut off in the charging process, the power grid can be fed back according to the battery state of the electric automobile, and the original charging state can be continued after the power is turned on.
The foregoing is merely a preferred embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions easily conceivable by those skilled in the art within the technical scope of the present application should be covered in the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (4)
1. The intelligent charging and discharging management method for the electric vehicle with the self-balancing local distribution network is used for realizing the dispatching of the regional micro-grid to the electric vehicle and is characterized by comprising the following steps of:
collecting battery data of an electric automobile;
analyzing and processing the battery data;
based on the analyzed and processed battery data, selecting a corresponding charging mode to finish charging and discharging management of the electric automobile;
the method for analyzing the battery data comprises the following steps:
filtering the battery data to obtain data related to battery performance; the filtering mode comprises the following steps: keyword filtering, machine learning classification and core network extraction;
the method for processing the battery data comprises the following steps:
according to the relation among different states of voltage, SOC, temperature and output power in the battery charging and discharging processes, a machine learning big data model is established, a machine learning system is used for analyzing and learning filtered battery data, and the previous battery data is combined with big data analysis to obtain a charging and discharging instruction and a charging mode of the electric automobile; the process of accumulating the battery data to calculate the analysis result is equivalent to that an expert performs label learning training on a machine learning system, then performs neural network autonomous learning on the machine, establishes a machine learning big data model, and finally realizes machine autonomous analysis by combining artificial analysis battery data with big data machine learning;
the charge and discharge instruction is as follows: charging a battery of the electric automobile with a current tracked by a maximum power point;
the charging mode is as follows: trickle, constant current, constant voltage and floating charge management modes;
in a constant voltage and floating charging management mode, the charging voltage is set by an external resistor voltage-dividing network; in the constant voltage charging stage, the charging current gradually decreases, and when the charging current decreases to a value set by an external resistor voltage dividing network, a floating charging state is entered; in a floating charge state, if the voltage of the battery drops to a preset threshold value of the set constant voltage charging voltage, automatically starting a new charging period;
in the constant current charging mode, the charging current is set through an external resistor;
in a trickle charge mode, when the voltage of the battery is lower than a preset threshold value of a set constant-voltage charge voltage, trickle charging the battery by using the preset threshold value of the set constant-current charge current;
the renewable energy source module output model of the microgrid is a solar radiation quantity random output model and is used for calculating the output power of the photovoltaic power generation unit; the direct-current voltage output by the photovoltaic power generation unit is subjected to step-down chopping and real-time power analysis operation through a voltage conversion and power control module, so that current tracked by a maximum power point is obtained; the voltage conversion and power control module comprises a singlechip and a voltage converter; the singlechip is connected with the voltage converter; the singlechip is used for monitoring and step-down chopping the direct-current voltage output by the photovoltaic power generation unit and outputting PWM pulses to the voltage converter; the voltage converter is used for carrying out step-down chopping on the direct-current voltage output by the photovoltaic power generation unit according to the on-off state of the PWM pulse control IGBT switching device, and inputting the converted voltage to the battery through the charging mode selection unit; the singlechip is also used for detecting charging voltage and charging current of the voltage converter, programming a control strategy for tracking the maximum power point based on an approximate gradient variable step voltage disturbance observation method, outputting an allowable signal to the charging mode selection unit through the operation and analysis and calculation of the singlechip program, and charging the battery with the current tracked by the maximum power point.
2. The method for intelligent charge and discharge management of a local power distribution network self-balancing electric vehicle according to claim 1, wherein the battery data comprises: charge-discharge voltage, charge-discharge current, SOC, temperature, and output power.
3. The intelligent charging and discharging management method for the electric automobile with the self-balancing local power distribution network according to claim 1, wherein the judging method for completing the charging and discharging management of the electric automobile is as follows:
the battery management system of the electric automobile performs system self-checking and hardware testing firstly, detects whether the electric automobile is in a charging state after the system self-checking is normal, and if the electric automobile is not in the charging state, the system is electrified;
detecting whether the system precharge is completed, if so, starting normal discharge, otherwise, entering a system protection mode;
and detecting whether the battery system is in an under-voltage state in real time in a discharging process, and controlling the maximum output power, current and temperature of a motor of the electric automobile according to a lithium battery power-SOC-temperature curve of the electric automobile and combining the real-time SOC, temperature and voltage of the battery if the battery system is in the under-voltage state.
4. The method for intelligent charge and discharge management of a local power distribution network self-balancing electric vehicle according to claim 1, further comprising: and displaying the real-time charge and discharge information and fault information of the battery.
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| CN113997805A (en) * | 2021-11-15 | 2022-02-01 | 北京理工大学深圳汽车研究院(电动车辆国家工程实验室深圳研究院) | Charging control method and system of new energy automobile, vehicle-mounted terminal and medium |
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