CN117118004B - Automatic regulation and control system of intelligent charger - Google Patents
Automatic regulation and control system of intelligent charger Download PDFInfo
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- CN117118004B CN117118004B CN202310842185.XA CN202310842185A CN117118004B CN 117118004 B CN117118004 B CN 117118004B CN 202310842185 A CN202310842185 A CN 202310842185A CN 117118004 B CN117118004 B CN 117118004B
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- 238000007600 charging Methods 0.000 claims abstract description 326
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- 238000004458 analytical method Methods 0.000 claims abstract description 18
- 238000011002 quantification Methods 0.000 claims abstract description 10
- 238000013139 quantization Methods 0.000 claims abstract description 10
- 230000001105 regulatory effect Effects 0.000 claims abstract description 5
- 230000035772 mutation Effects 0.000 claims description 19
- 238000007667 floating Methods 0.000 claims description 17
- 238000010280 constant potential charging Methods 0.000 claims description 14
- 238000010277 constant-current charging Methods 0.000 claims description 11
- 238000012549 training Methods 0.000 claims description 11
- 238000009825 accumulation Methods 0.000 claims description 10
- 238000012216 screening Methods 0.000 claims description 7
- 238000012360 testing method Methods 0.000 claims description 7
- 230000002238 attenuated effect Effects 0.000 claims description 4
- 238000001514 detection method Methods 0.000 claims description 4
- 238000012545 processing Methods 0.000 claims description 4
- 230000001186 cumulative effect Effects 0.000 claims description 3
- 238000005070 sampling Methods 0.000 claims description 3
- 238000011156 evaluation Methods 0.000 claims description 2
- 239000000284 extract Substances 0.000 claims description 2
- 238000010276 construction Methods 0.000 claims 1
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- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010281 constant-current constant-voltage charging Methods 0.000 description 1
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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
- 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
- H02J7/00036—Charger exchanging data with battery
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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
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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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Abstract
The invention discloses an automatic regulation and control system of an intelligent charger, which comprises a charging parameter acquisition module, a charging model building module, a charging model prediction module, a deviation loss analysis module, a performance quantification identification module and a supply and demand balance management and control module. According to the invention, loss judgment is carried out by adopting the predicted charging duration and the tested charging duration, so as to judge the deviation degree of the charging duration in the current charging state, the battery temperature variation and the average electric quantity variation of the battery in the actual charging duration of the battery with the deviation degree larger than the set threshold value are screened, the battery performance in the charging process is quantitatively evaluated by adopting a battery charging performance quantization model, the continuous power supply risk degree of the charger is obtained by combining the battery electric quantity and the deviation degree, the charging parameters of the charger are dynamically regulated and controlled in an equalizing manner according to the continuous power supply risk degree, and the adjustment of the maximum charging parameters is carried out on the premise of ensuring the charging safety, so that the intelligent dynamic regulation of the charging parameters is realized.
Description
Technical Field
The invention belongs to the technical field of chargers, and relates to an automatic regulation and control system of an intelligent charger.
Background
The charger is a charging device which adopts a high-frequency power supply technology and uses an advanced intelligent dynamic adjustment charging technology. To the consumer that adopts the battery need frequently charge and discharge, the purpose of charging is to let the electric quantity of the interior battery of consumer resume the capacity in time to satisfy the use of consumer, the charge mode of using frequently: the current charging mode adopts a constant current technology in the early stage, the battery voltage is rapidly increased, the charging voltage is automatically converted into constant voltage charging when reaching the upper limit voltage, and the charging terminal stage is converted into floating charging, so that the battery is uniformly charged, and the capacity of the battery is ensured to be recovered.
The battery storage, external environment, circuit components and other reasons of the existing electric equipment cause abnormal battery temperature in the electric equipment and abrupt change of electric quantity change in unit time in the charging process under different charging modes, the prediction of the charging duration of the battery temperature and the electric quantity change condition in the battery in the charging process cannot be established, the continuous power supply risk degree of the battery of the electric equipment connected with the charger in the current charging mode cannot be quantitatively analyzed according to the predicted charging duration, the deviation degree and the like, further intelligent balanced regulation and control of the charging parameters in the current charging mode cannot be performed according to the continuous power supply risk degree, the problem of low charging efficiency due to continuous power supply risk increase and power supply stop exists, and intelligent regulation and control of the charging parameters in the safety charging process are lacking.
Disclosure of Invention
The invention aims to provide an automatic regulation and control system of an intelligent charger, which solves the problems in the prior art.
The aim of the invention can be achieved by the following technical scheme:
An automatic regulation and control system of an intelligent charger comprises a charging parameter acquisition module, a charging model building module, a charging model prediction module, a deviation loss analysis module, a performance quantification identification module and a supply and demand balance management and control module, wherein the charging parameter acquisition module is used for acquiring parameter information of a battery of electric equipment connected with the charger and a charging parameter of the charger;
the charging model building module trains a charger to train and model the charging amount of the electric equipment and the battery temperature variation in unit time under different charging modes;
The method comprises the steps that a charging model prediction module obtains the current electric quantity and the battery temperature of electric equipment in a charging state, determines a charging mode based on the current electric quantity, and predicts the time length required by the electric equipment from the current electric quantity to the set electric quantity by combining the average change quantity of the electric quantity and the change quantity of the battery temperature in unit time in a charging process;
the deviation loss analysis module carries out loss analysis on the predicted consumed time length and the actual charging time length of the electric equipment, predicts the deviation degree between the charging time length and the actual charging time length, judges whether the deviation degree is larger than a set threshold value, and if the deviation degree is larger than the set threshold value, extracts the real-time temperature of the battery of the electric equipment under the actual charging time length of the electric equipment under the deviation degree and sends the real-time temperature to the performance quantification identification module;
the performance quantization identification module is used for extracting the real-time temperature of the battery under the condition that the deviation degree is larger than the actual charging time length of the electric equipment with the set deviation degree threshold value, obtaining the battery temperature variation in unit time and the actual electric quantity average variation of the battery in unit time through processing, screening the electric quantity average variation in unit time and a temperature variation curve, and carrying out quantization evaluation on the performance accumulation mutation coefficient of the battery in the charging process of the electric equipment by adopting a battery charging performance quantization model;
the supply and demand balance management and control module is used for extracting the performance accumulation mutation coefficient in the battery charging process analyzed by the performance quantification and identification module, predicting a continuous power supply risk coefficient corresponding to continuous steady-state power supply according to the current electric quantity, the deviation degree and the performance accumulation mutation coefficient of the battery, and carrying out balance dynamic regulation and control on the charging parameters of the charger based on the power supply risk coefficient.
Further, predicting the charging time of the electric equipment:
And when When the electric quantity is setLess thanIn the time-course of which the first and second contact surfaces,Equal to 0 andIf the electric quantity is setLess thanAnd is greater than or equal toIn the time-course of which the first and second contact surfaces,If the electric quantity is setLess thanAnd is greater than or equal toIn the time-course of which the first and second contact surfaces,; When (when)When the electric quantity is setLess thanAnd is greater than or equal toIn the time-course of which the first and second contact surfaces,And is also provided withEqual to 0, if the electric quantity is setLess thanAnd is greater than or equal toIn the time-course of which the first and second contact surfaces,，Expressed as the current powerThe actual battery temperature of the lower powered device,Represented as a training battery from zero charge to chargeThe temperature of the battery of the lower electric equipment,、AndThe interference proportion coefficients of the battery temperature to the charging duration in the constant-current charging mode, the constant-voltage charging mode and the floating charging mode are respectively shown, e is a natural number,Represented as the current power of the powered device,The electric quantity of the electric equipment is expressed when the electric equipment is switched from constant voltage charging to floating charging,The electric quantity is expressed as the electric quantity of the fully charged electric equipment,Expressed as the time required for the amount of change in the unit power in the constant current charging mode,Expressed as the time required for the amount of change in the unit electric power in the constant voltage charging mode,The time required for the unit electric quantity change amount in the floating charge mode is represented by D1, the number of unit time corresponding to the battery charging duration in the constant current charge mode is represented by D2, the number of unit time corresponding to the battery charging duration in the constant voltage charge mode is represented by D3, and the number of unit time corresponding to the battery charging duration in the floating charge mode is represented by D1.
Further, the degree of deviation between the predicted charge duration and the actual charge duration is: When (when) The smaller the predicted charge duration of the powered device is, the closer the predicted charge duration is to the actual charge duration,Denoted as the j-th test powered device slave electric quantity at battery temperature wTo set electric quantityThe actual charge time period required is set,Slave electrical quantity of electric equipment at battery temperature wTo set electric quantityThe required charging time length, L, is expressed as the slave electric quantity of the electric equipment at the battery temperature wTo set electric quantityIs a test number of times (1).
Further, the battery charging performance quantization model is:， expressed as a cumulative mutation coefficient of the battery performance of the electric equipment under the current charging duration, Expressed as the average temperature of the battery per unit time for which the current charge duration tb is located,AndRespectively expressed as a minimum value and a maximum value of the battery temperature per unit time in which the current charge duration tb of the training set-up is located,Expressed as an average change amount of the actual electric quantity per unit time of the current charge duration tb,The average change quantity of the electric quantity in the unit time of the charging duration tb of the training building is represented, e is a natural number,AndRespectively expressed as a dangerous interference factor of the previous unit time and the previous two unit times of the unit time of the current charging duration tb,Expressed as a length of time per unit time.
Further, the method comprises the steps of,，，Expressed as the amount of change in the battery temperature in the previous unit time at the unit time of the current charge duration tb,Expressed as the battery temperature variation in the first two unit times of the current charge duration tb,AndRespectively expressed as a minimum battery temperature variation and a maximum battery temperature variation allowed per unit time within a theoretical safety range,AndRespectively expressed as the battery temperature in the previous unit time and the previous two unit times for the unit time in which the current charge duration tb is located.
Further, a steady state power supply risk model is continued:， Expressed as a power supply risk factor corresponding to a continuous steady state power supply, Indicated as the duration of continuous steady state power supply required to achieve mode switching,Denoted as the power supply risk scale factor in the f-th charging mode, f=1, 2,3, respectively denoted as constant current, constant voltage and floating charging modes, the corresponding power supply risk scale factors being 1.34,1.12,1.2,For determining the charging mode to which the current battery corresponds,Denoted as the maximum battery charge required for the f-th charging mode.
Further, the charging parameters of the charger are regulated and controlled in a balanced and dynamic mode, and the regulation and control method comprises the following steps:
Step 1, judging whether a continuous power supply risk coefficient corresponding to continuous steady-state power supply in a current charging mode is larger than a set lower limit continuous power supply risk threshold, and if so, executing step 2;
step 2, analyzing a sustainable safe power supply duration S corresponding to a set upper limit continuous power supply risk threshold value in a current charging mode, wherein the sustainable safe power supply duration S is adopted The calculation formula of (2) reversely deduces the sustainable safe power supply duration S, wherein S is smaller thanThe upper-limit continuous power supply risk threshold is larger than the lower-limit continuous power supply risk threshold;
Step 3, based on sustainable safe power supply duration S The ratio of the relative difference values of the charging modes is analyzed;
step 4, selecting a charging parameter in a current charging mode, reducing and adjusting the charging parameter in the current charging mode according to the charging parameter variation in the charging mode, wherein the charging parameter in the adjusted charging mode is larger than a set charging parameter lower limit threshold;
Step 5, judging whether the accumulated mutation coefficients of the battery charging performance in two continuous adjacent fixed sampling time periods are attenuated or not after the charging parameters are adjusted, if so, executing step 6, and if not, executing step 4;
Step 6, continuously supplying power by using the new charging parameters after the charging mode adjustment, and if the power supply risk coefficient corresponding to the continuous steady-state power supply is smaller than the lower limit continuous power supply risk threshold value in the continuous power supply process, recovering the charging parameters before the charging mode adjustment of the charging parameter variation;
And 7, judging whether the charging parameter in the charging mode is smaller than the set charging parameter lower limit threshold, if so, stopping the charger from continuously charging the charging equipment until the battery temperature of the electric equipment is lower than the battery temperature corresponding to the battery electric quantity in the current charging mode.
Further, the system also comprises an interference parameter detection module for detecting the temperature in the charger, an interference preview analysis module and a combined regulation and control module;
The interference pre-modeling analysis module is used for screening temperature grades corresponding to the temperatures of the chargers, determining temperature association influence coefficients corresponding to the temperature grades, extracting power supply risk coefficients corresponding to continuous steady-state power supply in the current charging state, and predicting combined charging risk coefficients between the chargers and the batteries of the electric equipment in the current state;
The combined regulation and control module is used for judging whether the combined charging risk coefficient is larger than a set combined charging risk threshold and whether the continuous power supply risk coefficient is larger than a set lower limit continuous power supply risk threshold, stopping the charger to continuously supply power to the electric equipment if the combined charging risk coefficient is larger than the set combined charging risk threshold, and adopting a regulation and control method of the supply and demand balance management and control module to carry out balance dynamic regulation and control on the charging parameters of the charger if the combined charging risk coefficient is smaller than the set combined charging risk threshold and the continuous power supply risk coefficient is larger than the set lower limit continuous power supply risk threshold.
The invention has the beneficial effects that:
According to the automatic regulation and control system of the intelligent charger, provided by the invention, the charge duration of the electric equipment from the current electric quantity to the required consumption of the set electric quantity is predicted by analyzing the charge mode of the electric equipment in the charge state and the average change quantity of the electric quantity and the temperature change quantity of the battery in unit time in the charge process, and loss judgment is carried out by adopting the predicted charge duration and the tested charge duration so as to judge the deviation degree of the charge duration in the current charge state, so that early-stage data processing is carried out for intelligent regulation and control of the charger, the temperature change quantity and the average change quantity data information of the electric quantity in the charge process can be screened and intercepted according to the deviation degree, and the effective data of the temperature change quantity and the electric quantity change quantity in the charge process are improved.
The battery performance in the charging process is quantitatively evaluated by screening the battery temperature variation and the electric quantity average variation under the condition that the deviation degree is greater than the actual charging duration of the battery with the set threshold value, and the battery electric quantity and the deviation degree are combined to obtain the continuous power supply risk degree of the charger, and the charging parameters of the charger are dynamically regulated and controlled in an equalizing manner according to the continuous power supply risk degree, so that the adjustment of the maximum charging parameters can be carried out on the premise of ensuring the charging safety, the charging safety and the charging efficiency are effectively coordinated, the intelligent dynamic regulation and control of the charging parameters are realized, and the supply and demand requirements of the charger and electric equipment in the charging process are met.
The combined analysis is carried out by adopting the temperature association influence coefficient mapped by the temperature grade under the charger and the power supply risk coefficient corresponding to the continuous steady-state power supply of the electric equipment connected with the charger so as to predict the combined charging risk, and the double judgment is carried out according to the combined charging risk and the continuous power supply risk, so that the intelligent automatic regulation and control of the charger in the charging process is realized, the charging parameters are in accordance with the dynamic regulation under the charging safety, and the intelligent characteristic is realized.
Detailed Description
The technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
An automatic regulation and control system of an intelligent charger comprises a charging parameter acquisition module, a charging model building module, a charging model prediction module, a deviation loss analysis module, a performance quantification identification module and a supply and demand balance management and control module.
The charging parameter acquisition module is used for acquiring parameter information of a battery of electric equipment connected with the charger and charging parameters of the charger, wherein the battery parameter information comprises battery capacity, constant-current charging electric quantity, constant-voltage charging electric quantity, floating charging electric quantity, current electric quantity, temperature and the like, and the charging parameters of the charger comprise charging current, charging voltage and the like under different charging modes.
The charging model building module trains charging time and charging quantity of the electric equipment in different charging modes by using the charger, obtains average change quantity of electric quantity of the battery in unit time in different charging modes, models the average change quantity of the electric quantity in unit time in the charging process, draws a temperature change curve for battery temperature of the electric equipment in different charging modes, and builds a battery temperature change quantity model of the electric equipment in unit time in different charging modes.
When the charger is charged, the charging mode is switched and selected according to the electric quantity of the electric equipment, and the charging mode comprises constant-current charging, constant-voltage charging and floating charging.
The charging model prediction module obtains the current electric quantity and the battery temperature of the electric equipment in a charging state by adopting a charger connected with the electric equipment, determines a charging mode based on the current electric quantity, predicts the time length required by the electric equipment from the current electric quantity to the set electric quantity by combining the average change quantity of the electric quantity in unit time and the change quantity of the battery temperature in the charging process, and can predict the time length required by the full charge of the electric equipment in different charging modes.
Predicting the charging time of the electric equipment:
And when When the electric quantity is setLess thanIn the time-course of which the first and second contact surfaces,Equal to 0 andIf the electric quantity is setLess thanAnd is greater than or equal toIn the time-course of which the first and second contact surfaces,If the electric quantity is setLess thanAnd is greater than or equal toIn the time-course of which the first and second contact surfaces,；
When (when)When in use, ifIn the time-course of which the first and second contact surfaces,And is also provided withEqual to 0, ifIn the time-course of which the first and second contact surfaces,The charge duration prediction of the electric equipment does not consider the discharge condition of the electric equipment,Expressed as the current powerThe actual battery temperature of the lower powered device,Represented as a training battery from zero charge to chargeThe temperature of the battery of the lower electric equipment,、AndThe interference proportion coefficients are respectively expressed as interference proportion coefficients of battery temperature to charging time under a constant-current charging mode, a constant-voltage charging mode and a floating charging mode, the interference proportion coefficients are obtained through experiments, are approximate data and inaccurate data, take values of 0.7,0.5,0.56 and e as natural numbers,Represented as the current power of the powered device,The electric quantity of the electric equipment is represented when the electric equipment is switched from constant-current charging to constant-voltage charging,The electric quantity of the electric equipment is expressed when the electric equipment is switched from constant voltage charging to floating charging,The electric quantity is expressed as the electric quantity of the fully charged electric equipment,Expressed as the time required for the amount of change in the unit power in the constant current charging mode,Expressed as the time required for the amount of change in the unit electric power in the constant voltage charging mode,Expressed as a time required for a change amount of the unit electric quantity in the float charging mode, D1 expressed as a number of unit time corresponding to a charging time period of the battery in the constant current charging mode, D2 expressed as a number of unit time corresponding to a charging time period of the battery in the constant voltage charging mode, D3 expressed as a number of unit time corresponding to a charging time period of the battery in the float charging mode,Represented as the charge duration accumulated from the current amount of electricity to the end of constant-current charge,Represented as the charge duration that is accumulated from the current amount of charge to the end of constant voltage charge.
The deviation loss analysis module is used for continuously testing the actual charging time length required by the electric equipment from the current electric quantity to the set electric quantity under the current battery temperature along with the change of the battery temperature for a plurality of times, carrying out loss analysis on the predicted time length required by the electric equipment from the current electric quantity to the set electric quantity and the actual charging time length, predicting the deviation degree between the charging time length and the actual charging time length, judging whether the deviation degree is larger than a set threshold value, if the deviation degree is larger than the set threshold value, extracting the real-time temperature of the battery under the actual charging time length of the electric equipment under the deviation degree, and sending the real-time temperature to the performance quantification identification module, and sending the deviation degree to the supply and demand balance control module.
Predicting the deviation degree between the charging duration and the actual charging duration: When (when) The smaller the predicted charge duration of the powered device is, the closer the predicted charge duration is to the actual charge duration,Denoted as the j-th test powered device slave electric quantity at battery temperature wTo set electric quantityThe actual charge time period required is set,Slave electrical quantity of electric equipment at battery temperature wTo set electric quantityThe required charging time length, L, is expressed as the slave electric quantity of the electric equipment at the battery temperature wTo set electric quantityIs a test number of times (1).
By adopting deviation analysis, the actual charging time length and the predicted charging time length in the charging process of the specified electric quantity can be analyzed, so that the difference between the actual charging time length and the predicted charging time length can be determined, and once the deviation is large, the surface temperature of the battery and the actually charged electric quantity in the surface charging process are not matched with the predicted charging time length, so that a reliable deviation basis is provided for the later charging performance analysis.
The performance quantitative identification module is used for extracting the real-time temperature of the battery under the condition that the deviation degree is larger than the actual charging time length of the electric equipment with the set deviation degree threshold value, obtaining the battery temperature variation in unit time and the actual electric quantity average variation of the battery in unit time through processing, screening the electric quantity average variation and the temperature variation curve in unit time in the charging process of training and building, quantitatively evaluating the performance accumulation mutation coefficient of the battery in the charging process of the electric equipment by adopting the battery charging performance quantitative model, reflecting the performance mutation condition of the battery in the charging process of the electric equipment by adopting the performance accumulation mutation coefficient, realizing the quantitative identification of the performance mutation condition of the battery, controlling the intelligent regulation and control of a charger connected with the electric equipment, avoiding the situation that the electric quantity of the battery does not reach the expected electric quantity and cannot regulate and control the charging parameters, and increasing the risk caused by continuous non-adjustable charging parameters.
The battery charging performance quantization model is as follows:， expressed as a cumulative mutation coefficient of the battery performance of the electric equipment under the current charging duration, Expressed as the average temperature of the battery per unit time for which the current charge duration tb is located,AndRespectively expressed as a minimum value and a maximum value of the battery temperature per unit time in which the current charge duration tb of the training set-up is located,Expressed as an average change amount of the actual electric quantity per unit time of the current charge duration tb,The average change quantity of the electric quantity in the unit time of the charging duration tb of the training building is represented, e is a natural number,AndRespectively expressed as a dangerous interference factor of the previous unit time and the previous two unit times of the unit time of the current charging duration tb,Expressed as a length of time per unit time.
Wherein,，，Expressed as the amount of change in the battery temperature in the previous unit time at the unit time of the current charge duration tb,Expressed as the battery temperature variation in the first two unit times of the current charge duration tb,AndRespectively expressed as a minimum battery temperature variation and a maximum battery temperature variation allowed per unit time within a theoretical safety range,AndRespectively expressed as the battery temperature in the previous unit time and the previous two unit times for the unit time in which the current charge duration tb is located.
The supply-demand balance management and control module is used for extracting a performance accumulation mutation coefficient in the battery charging process analyzed by the performance quantification identification module, predicting a continuous power supply risk coefficient corresponding to continuous steady-state power supply according to the current electric quantity and the deviation degree of the battery and the performance accumulation mutation coefficient, carrying out balance dynamic regulation and control on charging parameters of a charger based on the power supply risk coefficient, ensuring that electric equipment dynamically adjusts the charging parameters without stopping charging, and ensuring that the power supply risk coefficient is smaller than an upper limit continuous power supply risk threshold value in the adjusted battery continuous charging state.
Continuous steady-state power supply risk model:， Expressed as a power supply risk factor corresponding to a continuous steady state power supply, Indicated as the duration of continuous steady state power supply required to achieve mode switching,Denoted as the power supply risk scale factor in the f-th charging mode, f=1, 2,3, respectively denoted as constant current, constant voltage and floating charging modes, the corresponding power supply risk scale factors being 1.34,1.12,1.2,For determining the charging mode to which the current battery corresponds,Denoted as the maximum battery charge required for the f-th charging mode.
The charging parameters of the charger are regulated and controlled in a balanced and dynamic mode, and the regulation and control method comprises the following steps:
Step 1, judging whether a continuous power supply risk coefficient corresponding to continuous steady-state power supply in a current charging mode is larger than a set lower limit continuous power supply risk threshold, and if so, executing step 2;
step 2, analyzing a sustainable safe power supply duration S corresponding to a set upper limit continuous power supply risk threshold value in a current charging mode, wherein the sustainable safe power supply duration S is adopted The calculation formula of (2) reversely deduces the sustainable safe power supply duration S, wherein S is smaller thanThe upper-limit continuous power supply risk threshold is larger than the lower-limit continuous power supply risk threshold;
Step 3, based on sustainable safe power supply duration S The variation of the charging parameters of the charging mode is analyzed, and the variation of the charging parameters adjusted in the constant current mode is analyzed: the amount of variation of the charging parameter adjusted in the constant voltage mode: Charging parameter fluctuation amount adjusted in floating charging mode: ；
step 4, selecting a charging parameter in a current charging mode, reducing and adjusting the charging parameter in the current charging mode according to the charging parameter variation in the charging mode, wherein the charging parameter in the adjusted charging mode is larger than a set charging parameter lower limit threshold;
Step 5, judging whether the accumulated mutation coefficients of the battery charging performance in two continuous adjacent fixed sampling time periods are attenuated or not after the charging parameters are adjusted, if so, executing step 6, and if not, executing step 4;
Step 6, continuously supplying power by using the new charging parameters after the charging mode adjustment, and if the power supply risk coefficient corresponding to the continuous steady-state power supply is smaller than the lower limit continuous power supply risk threshold value in the continuous power supply process, recovering the charging parameters before the charging mode adjustment of the charging parameter variation;
And 7, judging whether the charging parameter in the charging mode is smaller than the set charging parameter lower limit threshold, if so, stopping the charger from continuously charging the charging equipment until the battery temperature of the electric equipment is lower than the battery temperature corresponding to the battery electric quantity in the current charging mode.
The continuous power supply risk coefficient in the current charging mode is judged, the charging parameter variation in the charging mode is determined based on the allowable continuous safe power supply duration in the current charging mode and the duration of switching from the current mode to the next charging mode, the current charging parameter is reduced and adjusted according to the charging parameter variation, automatic dynamic regulation is carried out according to whether the accumulated mutation coefficient of the charging performance of the battery after the charging parameter adjustment is attenuated or not, the adjustment of the maximum charging parameter can be carried out on the premise of ensuring the charging safety, the balance of the charging safety and the charging efficiency is effectively coordinated, the intelligent dynamic regulation of the charging parameter is realized, and the supply and demand requirements of a charger and electric equipment in the charging process are met.
Example two
The first embodiment is to judge the risk of the battery in the charging process of the electric equipment by the charger, automatically regulate and control the input charging parameters of the electric equipment by the charger according to the identified risk of the electric equipment battery, and not consider the intelligent regulation and control of the charger due to abnormal temperature of the charger in the charging process.
The system also comprises an interference parameter detection module, wherein the interference parameter detection module adopts a temperature sensor to detect the temperature of the electric equipment when the charger connected with the electric equipment charges the electric equipment.
The interference pre-modeling analysis module is used for screening temperature grades corresponding to the temperatures of the chargers, determining temperature association influence coefficients corresponding to the temperature grades, extracting power supply risk coefficients corresponding to continuous steady-state power supply in the current charging state, predicting combined charging risk coefficients H between the chargers and the batteries of the electric equipment in the current state, and specifically determining the temperature association influence coefficientsAnd x=1, 2,3,4 and 5, the risk of the charger in the charging process and the risk of the electric equipment battery in the charging process are combined, so that the combined risk in the charging process can be comprehensively analyzed, the risk correlation between the charger and the electric equipment battery is established, and all risk sources can be accurately combined.
The temperature-related influence coefficient is obtained by adopting a large amount of experimental training, and the temperature levels of the chargers are respectively as follows: u1, u2, u3, u4, u5, the temperature-related influence coefficients corresponding to the charging temperature levels are respectively: g1, g2, g3, g4, g5, and g1 < g2 < g3 < g4 < g5.
The combined regulation and control module is used for judging whether the combined charging risk coefficient is larger than a set combined charging risk threshold and whether the continuous power supply risk coefficient is larger than a set lower limit continuous power supply risk threshold, if the combined charging risk coefficient is larger than the set combined charging risk threshold, stopping the charger to continuously supply power to the electric equipment, and if the combined charging risk coefficient is smaller than the set combined charging risk threshold and the continuous power supply risk coefficient is larger than the set lower limit continuous power supply risk threshold, adopting a regulation and control method of the supply-demand balance management and control module to carry out balance dynamic regulation and control on charging parameters of the charger, and can carry out combined risk judgment on batteries of electric equipment and the charger powered by the electric equipment so as to realize balance dynamic regulation of the charging parameters.
The formula is a formula for acquiring a large amount of data to perform software simulation to obtain the latest real situation, preset parameters in the formula are set by a person skilled in the art according to the actual situation, the size of the proportionality coefficient is a specific numerical value obtained by quantizing each parameter, the subsequent comparison is convenient, and the size of the proportionality coefficient is only required to not influence the proportionality relation between the parameters and the quantized numerical value.
The foregoing is merely illustrative and explanatory of the principles of the invention, as various modifications and additions may be made to the specific embodiments described, or similar thereto, by those skilled in the art, without departing from the principles of the invention or beyond the scope of the appended claims.
Claims (5)
1. The automatic regulation and control system of the intelligent charger comprises a charging parameter acquisition module for acquiring parameter information of a battery of electric equipment connected with the charger and charging parameters of the charger, and is characterized by further comprising a charging model building module, a charging model prediction module, a deviation loss analysis module, a performance quantification identification module and a supply and demand balance management and control module;
the charging model building module trains a charger to train and model the charging amount of the electric equipment and the battery temperature variation in unit time under different charging modes;
The method comprises the steps that a charging model prediction module obtains the current electric quantity and the battery temperature of electric equipment in a charging state, determines a charging mode based on the current electric quantity, and predicts the time length required by the electric equipment from the current electric quantity to the set electric quantity by combining the average change quantity of the electric quantity and the change quantity of the battery temperature in unit time in a charging process;
the deviation loss analysis module carries out loss analysis on the predicted consumed time length and the actual charging time length of the electric equipment, predicts the deviation degree between the charging time length and the actual charging time length, judges whether the deviation degree is larger than a set threshold value, and if the deviation degree is larger than the set threshold value, extracts the real-time temperature of the battery of the electric equipment under the actual charging time length of the electric equipment under the deviation degree and sends the real-time temperature to the performance quantification identification module;
the performance quantization identification module is used for extracting the real-time temperature of the battery under the condition that the deviation degree is larger than the actual charging time length of the electric equipment with the set deviation degree threshold value, obtaining the battery temperature variation in unit time and the actual electric quantity average variation of the battery in unit time through processing, screening the electric quantity average variation in unit time and a temperature variation curve, and carrying out quantization evaluation on the performance accumulation mutation coefficient of the battery in the charging process of the electric equipment by adopting a battery charging performance quantization model;
The battery charging performance quantization model is as follows: ，/> expressed as cumulative mutation coefficient of battery performance of electric equipment under current charging time length,/> Expressed as average battery temperature per unit time for the current charge duration tb/>And/>Respectively representing the minimum value and the maximum value of the battery temperature in unit time of the current charging duration tb of the training construction,/>Expressed as the average change of the actual electric quantity in the unit time of the current charging duration tb/>, andThe average change quantity of the electric quantity in unit time of the charging time tb of training building is represented, e is a natural number,/>, andAnd/>Dangerous interference factors respectively expressed as the previous unit time and the previous two unit times of the unit time of the current charging duration tb,/>A duration expressed as a unit time;
The supply-demand balance management and control module is used for extracting a performance accumulation mutation coefficient in the battery charging process analyzed by the performance quantification and identification module, predicting a continuous power supply risk coefficient corresponding to continuous steady-state power supply according to the current electric quantity and the deviation degree of the battery and the performance accumulation mutation coefficient, and carrying out balance dynamic regulation and control on the charging parameters of the charger based on the power supply risk coefficient;
wherein, last steady state power supply risk model: ，/> representing a power supply risk coefficient corresponding to continuous steady-state power supply,/> Representing the duration of sustained steady state power supply required to achieve mode switching,/>Denoted as the power supply risk scale factor in the f-th charging mode, f=1, 2,3, respectively denoted as constant current, constant voltage and floating charging modes, and the corresponding power supply risk scale factors are 1.34,1.12,1.2,/>, respectivelyFor determining the charging mode corresponding to the current battery,/>A maximum battery charge indicated as required for the f-th charging mode;
the charging parameters of the charger are regulated and controlled in a balanced and dynamic mode, and the regulation and control method comprises the following steps:
Step 1, judging whether a continuous power supply risk coefficient corresponding to continuous steady-state power supply in a current charging mode is larger than a set lower limit continuous power supply risk threshold, and if so, executing step 2;
step 2, analyzing a sustainable safe power supply duration S corresponding to a set upper limit continuous power supply risk threshold value in a current charging mode, wherein the sustainable safe power supply duration S is adopted The sustainable safe power supply duration S is reversely deduced, and S is smaller than/>The upper-limit continuous power supply risk threshold is larger than the lower-limit continuous power supply risk threshold;
Step 3, based on sustainable safe power supply duration S The ratio of the relative difference values of the charging modes is analyzed;
step 4, selecting a charging parameter in a current charging mode, reducing and adjusting the charging parameter in the current charging mode according to the charging parameter variation in the charging mode, wherein the charging parameter in the adjusted charging mode is larger than a set charging parameter lower limit threshold;
Step 5, judging whether the accumulated mutation coefficients of the battery charging performance in two continuous adjacent fixed sampling time periods are attenuated or not after the charging parameters are adjusted, if so, executing step 6, and if not, executing step 4;
Step 6, continuously supplying power by using the new charging parameters after the charging mode adjustment, and if the power supply risk coefficient corresponding to the continuous steady-state power supply is smaller than the lower limit continuous power supply risk threshold value in the continuous power supply process, recovering the charging parameters before the charging mode adjustment of the charging parameter variation;
And 7, judging whether the charging parameter in the charging mode is smaller than the set charging parameter lower limit threshold, if so, stopping the charger from continuously charging the charging equipment until the battery temperature of the electric equipment is lower than the battery temperature corresponding to the battery electric quantity in the current charging mode.
2. The automatic regulation and control system of an intelligent charger according to claim 1, wherein the charging duration of the electric equipment is predicted:
and when/> If the electric quantity/>Less than/>Time,/>Equal to 0 and/>If the electric quantity/>, is setLess than/>And is greater than or equal to/>Time,/>If the electric quantity/>, is setLess than/>And is greater than or equal to/>Time,/>; When/>If the electric quantity/>Less than/>And is greater than or equal to/>In the time-course of which the first and second contact surfaces,And/>Equal to 0, if the electric quantity/>, is setLess than/>And is greater than or equal to/>Time,/>，/>Expressed as current charge/>Actual battery temperature of lower electric equipment,/>Expressed as training battery from zero charge to charge/>The temperature of the battery of the lower electric equipment,、/>And/>The interference ratio coefficients of the battery temperature to the charging duration in a constant-current charging mode, a constant-voltage charging mode and a floating charging mode are respectively shown, e is a natural number, and is/areExpressed as the current power of the electric equipment,/>Expressed as the electric quantity of electric equipment when the electric equipment is switched from constant-current charging to constant-voltage charging,/>Expressed as the electric quantity of electric equipment when the electric equipment is switched from constant voltage charging to floating chargingExpressed as the electric quantity of the fully charged electric equipmentExpressed as the time required for the change amount of the unit electric quantity in the constant current charging mode,/>Expressed as time required for a change amount of a unit electric quantity in a constant voltage charging mode,/>The time required for the unit electric quantity change amount in the floating charge mode is represented by D1, the number of unit time corresponding to the battery charging duration in the constant current charge mode is represented by D2, the number of unit time corresponding to the battery charging duration in the constant voltage charge mode is represented by D3, and the number of unit time corresponding to the battery charging duration in the floating charge mode is represented by D1.
3. The automatic regulation and control system of an intelligent charger according to claim 2, wherein the degree of deviation between the predicted charging duration and the actual charging duration is: When/> The smaller the charge duration of the predicted electric equipment is, the closer the charge duration of the predicted electric equipment is to the actual charge duration is, and the greater the charge duration of the predicted electric equipment isExpressed as j-th test powered device from power to set power/>, at battery temperature wThe actual charge time required,/>Represented as predicted consumer slave charge/>, at battery temperature wTo set electric quantity/>The required charge duration, L, is expressed as the slave charge/>, of the consumer at the battery temperature wTo set electric quantity/>Is a test number of times (1).
4. The automatic regulation and control system of an intelligent charger of claim 1, wherein,，/>，/>Expressed as the battery temperature change per unit time immediately before the current charge duration tbExpressed as the battery temperature change in the first two units of time per unit of time in which the current charge duration tb is located,/>And/>Respectively expressed as a minimum battery temperature variation and a maximum battery temperature variation allowed per unit time within a theoretical safety range,And/>Respectively expressed as the battery temperature in the previous unit time and the previous two unit times for the unit time in which the current charge duration tb is located.
5. The automatic regulation and control system of an intelligent charger according to claim 1, further comprising an interference parameter detection module for detecting the temperature in the charger, an interference preview analysis module and a joint regulation and control module;
The interference pre-modeling analysis module is used for screening temperature grades corresponding to the temperatures of the chargers, determining temperature association influence coefficients corresponding to the temperature grades, extracting power supply risk coefficients corresponding to continuous steady-state power supply in the current charging state, and predicting combined charging risk coefficients between the chargers and the batteries of the electric equipment in the current state;
The combined regulation and control module is used for judging whether the combined charging risk coefficient is larger than a set combined charging risk threshold and whether the continuous power supply risk coefficient is larger than a set lower limit continuous power supply risk threshold, stopping the charger to continuously supply power to the electric equipment if the combined charging risk coefficient is larger than the set combined charging risk threshold, and adopting a regulation and control method of the supply and demand balance management and control module to carry out balance dynamic regulation and control on the charging parameters of the charger if the combined charging risk coefficient is smaller than the set combined charging risk threshold and the continuous power supply risk coefficient is larger than the set lower limit continuous power supply risk threshold.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN205195325U (en) * | 2015-11-04 | 2016-04-27 | 天长市万德福电子有限公司 | Controlling means and have regulation function's charging system charges |
| WO2018120236A1 (en) * | 2016-12-30 | 2018-07-05 | 华为技术有限公司 | Battery charging method, charging device and terminal equipment |
| CN113191547A (en) * | 2021-04-29 | 2021-07-30 | 浙江吉利控股集团有限公司 | New energy vehicle power battery charging optimization method and system |
| CN114284589A (en) * | 2022-03-01 | 2022-04-05 | 深圳市海雷新能源有限公司 | Lithium battery intelligent monitoring management system based on internet |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN205195325U (en) * | 2015-11-04 | 2016-04-27 | 天长市万德福电子有限公司 | Controlling means and have regulation function's charging system charges |
| WO2018120236A1 (en) * | 2016-12-30 | 2018-07-05 | 华为技术有限公司 | Battery charging method, charging device and terminal equipment |
| CN113191547A (en) * | 2021-04-29 | 2021-07-30 | 浙江吉利控股集团有限公司 | New energy vehicle power battery charging optimization method and system |
| CN114284589A (en) * | 2022-03-01 | 2022-04-05 | 深圳市海雷新能源有限公司 | Lithium battery intelligent monitoring management system based on internet |
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