CN117538767A - Electric vehicle charging and discharging process vehicle-pile cooperative state monitoring system and method - Google Patents
Electric vehicle charging and discharging process vehicle-pile cooperative state monitoring system and method Download PDFInfo
- Publication number
- CN117538767A CN117538767A CN202410026183.8A CN202410026183A CN117538767A CN 117538767 A CN117538767 A CN 117538767A CN 202410026183 A CN202410026183 A CN 202410026183A CN 117538767 A CN117538767 A CN 117538767A
- Authority
- CN
- China
- Prior art keywords
- vehicle battery
- preset
- life time
- charging
- less
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 61
- 238000012544 monitoring process Methods 0.000 title claims abstract description 59
- 238000007599 discharging Methods 0.000 title claims abstract description 55
- 230000008569 process Effects 0.000 title claims abstract description 35
- 238000012545 processing Methods 0.000 claims abstract description 46
- 238000012937 correction Methods 0.000 claims description 83
- 239000011159 matrix material Substances 0.000 claims description 45
- 238000004891 communication Methods 0.000 claims description 10
- 238000012549 training Methods 0.000 claims description 10
- 238000005516 engineering process Methods 0.000 claims description 9
- 238000007781 pre-processing Methods 0.000 claims description 9
- 238000012360 testing method Methods 0.000 claims description 6
- 230000002159 abnormal effect Effects 0.000 claims description 3
- 238000005259 measurement Methods 0.000 abstract description 2
- 230000006870 function Effects 0.000 description 9
- 230000008859 change Effects 0.000 description 8
- 230000032683 aging Effects 0.000 description 4
- 230000005611 electricity Effects 0.000 description 3
- 230000036541 health Effects 0.000 description 3
- 238000010801 machine learning Methods 0.000 description 3
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 2
- 230000003044 adaptive effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 230000003862 health status Effects 0.000 description 2
- 229910052744 lithium Inorganic materials 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 238000013178 mathematical model Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000002485 combustion reaction Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000009977 dual effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000012417 linear regression Methods 0.000 description 1
- 229910001416 lithium ion Inorganic materials 0.000 description 1
- 230000003137 locomotive effect Effects 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 230000002035 prolonged effect Effects 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000000611 regression analysis Methods 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
- 238000012706 support-vector machine Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
- G01R31/3842—Arrangements for monitoring battery or accumulator variables, e.g. SoC combining voltage and current measurements
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L3/00—Electric devices on electrically-propelled vehicles for safety purposes; Monitoring operating variables, e.g. speed, deceleration or energy consumption
- B60L3/0023—Detecting, eliminating, remedying or compensating for drive train abnormalities, e.g. failures within the drive train
- B60L3/0046—Detecting, eliminating, remedying or compensating for drive train abnormalities, e.g. failures within the drive train relating to electric energy storage systems, e.g. batteries or capacitors
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/30—Constructional details of charging stations
- B60L53/31—Charging columns specially adapted for electric vehicles
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/60—Monitoring or controlling charging stations
- B60L53/62—Monitoring or controlling charging stations in response to charging parameters, e.g. current, voltage or electrical charge
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
- B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
- B60L58/16—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to battery ageing, e.g. to the number of charging cycles or the state of health [SoH]
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/374—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] with means for correcting the measurement for temperature or ageing
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2240/00—Control parameters of input or output; Target parameters
- B60L2240/40—Drive Train control parameters
- B60L2240/54—Drive Train control parameters related to batteries
- B60L2240/545—Temperature
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2240/00—Control parameters of input or output; Target parameters
- B60L2240/40—Drive Train control parameters
- B60L2240/54—Drive Train control parameters related to batteries
- B60L2240/547—Voltage
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2240/00—Control parameters of input or output; Target parameters
- B60L2240/40—Drive Train control parameters
- B60L2240/54—Drive Train control parameters related to batteries
- B60L2240/549—Current
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Life Sciences & Earth Sciences (AREA)
- Sustainable Development (AREA)
- Sustainable Energy (AREA)
- Secondary Cells (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The invention relates to the technical field of electric variable measurement, in particular to a vehicle-pile collaborative state monitoring system and method in the charging and discharging process of an electric vehicle. Comprising the following steps: the vehicle battery monitoring module is used for collecting state parameters of the vehicle battery in real time; the charging pile monitoring module is used for monitoring state parameters of the charging pile in real time; the monitoring module is used for processing the state parameters of the vehicle battery and the state parameters of the charging pile and judging the charging and discharging states of the vehicle battery according to a preset formula; and the processing module is used for predicting the service life of the vehicle battery according to the charge and discharge state of the vehicle battery, the state parameter of the vehicle battery and the state parameter of the charging pile. According to the invention, the service life of the battery is reasonably predicted through the parameters of the vehicle-pile cooperative state, and the service life prediction of the battery is corrected by combining a plurality of influence parameters, so that the service life of the battery is accurately and effectively estimated.
Description
Technical Field
The invention relates to the technical field of electric variable measurement, in particular to a vehicle-pile collaborative state monitoring system and method in the charging and discharging process of an electric vehicle.
Background
With the continuous improvement of the performance of the storage battery, the electric vehicle is widely applied, and in a period of time in the future, the main stream of vehicle development is increased year by year with the holding quantity of the electric vehicle, and the trend of the internal combustion locomotive is partially replaced. Meanwhile, as the technology of the electric vehicle is more and more mature, the management and service of the electric vehicle are more standard; the standards of electric vehicles will also appear in a series, standardized modern mode, and electric vehicles will start a revolution in the vehicle era.
However, in the prior art, in the process of charging and discharging the electric automobile, the battery health status assessment always has difficulties, the main influencing factors include nonlinear change of battery parameters, change of battery performance along with change of time and use environment, and it is difficult to establish an accurate mathematical model, and the battery aging process has randomness and unpredictability, no definite aging rule exists at present, and the difficulties of effectively integrating various monitoring data and reasonably analyzing to assess the battery health status exist. Therefore, how to provide a system and a method for monitoring the cooperative state of a vehicle and a pile in the charging and discharging process of an electric vehicle is a technical problem which needs to be solved by a person skilled in the art.
Disclosure of Invention
The invention aims to provide a vehicle-pile cooperative state monitoring system and method for an electric vehicle in a charging and discharging process.
In order to achieve the above object, the present invention provides the following technical solutions:
an electric automobile charge-discharge process car-stake collaborative state monitoring system includes:
the vehicle battery monitoring module is used for collecting state parameters of a vehicle battery in real time, wherein the state parameters of the vehicle battery comprise battery terminal voltage Vd, battery charge and discharge current Id and battery temperature Td;
the charging pile monitoring module is used for monitoring state parameters of the charging pile in real time, wherein the state parameters of the charging pile comprise charging pile voltage Vc, charging pile current Ic and charging pile power Pc;
the data communication module is used for transmitting the state parameters of the vehicle battery and the state parameters of the charging pile through a wireless communication technology;
the monitoring module is used for receiving the state parameters of the vehicle battery and the state parameters of the charging pile, processing the state parameters of the vehicle battery and the state parameters of the charging pile, and judging the charging and discharging states of the vehicle battery according to a preset formula;
And the processing module is used for predicting the service life of the vehicle battery according to the charge and discharge state of the vehicle battery, the state parameter of the vehicle battery and the state parameter of the charging pile.
In some embodiments of the present application, when the monitoring module determines the charge and discharge states of the vehicle battery according to a preset formula, the monitoring module includes:
calculating the residual capacity of the vehicle battery according to the battery terminal voltage Vd and the battery charging and discharging current Id, and judging the charging and discharging state of the vehicle battery according to the residual capacity of the vehicle battery;
the preset formula is as follows:
SOC=(Vbat-Vmin)/(Vmax-Vmin)100%；
wherein SOC is a remaining power of the vehicle battery, vbat is a terminal voltage of the vehicle battery, vmin is a minimum allowable voltage of the vehicle battery, and Vmax is a maximum allowable voltage of the vehicle battery; wherein,
when the residual electric quantity of the vehicle battery is more than 50%, judging that the vehicle battery is in a charging state;
when the residual electric quantity of the vehicle battery is less than 50%, judging that the vehicle battery is in a discharging state;
and when the residual electric quantity of the vehicle battery is equal to 50%, judging that the vehicle battery is in a full charge state.
In some embodiments of the present application, the monitoring module is further configured to generate a charging curve according to a state parameter of the charging post when the vehicle battery is determined to be in a charging state, and obtain a charging rate vd of the vehicle battery according to the charging curve;
The processing module is further configured to predict a lifetime of the vehicle battery based on a charge rate vd of the vehicle battery;
a preset charge rate matrix T0 and a preset residual life time matrix A are preset in the processing module, A (A1, A2, A3 and A4) is set for the preset residual life time matrix A, wherein A1 is a first preset residual life time, A2 is a second preset residual life time, A3 is a third preset residual life time, A4 is a fourth preset residual life time, and A1 is more than A2 and less than A3 and less than A4 and less than 5 years;
setting T0 (T01, T02, T03, T04) for the preset charge rate matrix T0, wherein T01 is a first preset charge rate, T02 is a second preset charge rate, T03 is a third preset charge rate, T04 is a fourth preset charge rate, and T01 is less than T02 and less than T03 is less than T04;
the processing module is used for selecting corresponding residual life time as the life of the predicted vehicle battery according to the relation between vd and the preset charge rate matrix T0;
when vd < T01, selecting the fourth preset remaining life time A4 as a predicted life of the vehicle battery;
when T01 is less than or equal to vd and less than T02, selecting the third preset remaining life time A3 as the life of the vehicle battery;
When T02 is less than or equal to vd and less than T03, selecting the second preset remaining life time A2 as the life of the vehicle battery;
and when T03 is less than or equal to vd and less than T04, selecting the first preset residual life time A1 as the life of the vehicle battery.
In some embodiments of the present application, the processing module is further configured to obtain a historical charge number k of the vehicle battery;
the processing module is also internally provided with a preset historical charging frequency matrix R0 and a preset residual life time correction coefficient matrix B, and B (B1, B2, B3 and B4) is set for the preset residual life time correction coefficient matrix B, wherein B1 is a first preset residual life time correction coefficient, B2 is a second preset residual life time correction coefficient, B3 is a third preset residual life time correction coefficient, B4 is a fourth preset residual life time correction coefficient, and B1 is more than 0.6 and less than B2 and B3 and less than B4 and less than 1;
setting R0 (R01, R02, R03 and R04) for the preset historical charging frequency matrix R0, wherein R01 is a first preset historical charging frequency, R02 is a second preset historical charging frequency, R03 is a third preset historical charging frequency, R04 is a fourth preset historical charging frequency, and R01 is less than R02 and less than R03 is less than R04;
The processing module is further used for selecting corresponding residual life time correction coefficients according to the relation between k and the preset historical charging frequency matrix R0 so as to correct each preset residual life time;
when k is smaller than R01, the fourth preset residual life time correction coefficient B4 is selected to correct the fourth preset residual life time A4, and the corrected residual life time is A4B4；
When R01 is less than or equal to k and less than R02, selecting the third preset residual life time correction coefficient B3 to correct the third preset residual life time A3, wherein the corrected residual life time is A3B3；
When R02 is less than or equal to k and less than R03, selecting the second preset residual life time correction coefficient B2 to correct the second preset residual life time A2, wherein the corrected residual life time is A2B2；
When R03 is less than or equal to k and less than R04, selecting the first preset residual life time correction coefficient B1 to correct the first preset residual life time A1, wherein the corrected residual life time is A1B1。
In some embodiments of the present application, the monitoring module is further configured to generate a discharge curve according to a state parameter of the vehicle battery and a remaining power of the vehicle battery when the vehicle battery is determined to be in a discharge state, and obtain a discharge rate vt of the vehicle battery according to the discharge curve;
The processing module is also internally provided with a preset discharge rate matrix W0 and a preset residual life time secondary correction coefficient matrix C, and C (C1, C2, C3 and C4) is set for the preset residual life time secondary correction coefficient matrix C, wherein C1 is a first preset residual life time secondary correction coefficient, C2 is a second preset residual life time secondary correction coefficient, C3 is a third preset residual life time secondary correction coefficient, C4 is a fourth preset residual life time secondary correction coefficient, and C1 is more than 0.6 and less than C2 and C3 and less than C4 and less than 1;
setting W0 (W01, W02, W03, W04) for the preset discharge rate matrix W0, wherein W01 is a first preset discharge rate, W02 is a second preset discharge rate, W03 is a third preset discharge rate, W04 is a fourth preset discharge rate, and W01 is less than W02 and less than W03 is less than W04;
the processing module is further used for selecting a corresponding secondary correction coefficient of the residual life time according to the relation between vt and the preset discharge rate matrix W0 so as to carry out secondary correction on each corrected preset residual life time;
when vt is less than W01, selecting the fourth preset remaining life time secondary correction coefficient C4 to perform secondary correction on the corrected fourth preset remaining life time A4, wherein the corrected remaining life time is A4 B4/>C4；
When W01 is less than or equal to vt and less than W02, selecting the third preset remaining life time secondary correction coefficient C3 to perform secondary correction on the corrected third preset remaining life time A3, wherein the corrected remaining life time is A3B3/>C3；
When W02 is less than or equal to vt and less than W03, selecting the second preset remaining life time secondary correction coefficient C2 to perform secondary correction on the corrected second preset remaining life time A2, wherein the corrected remaining life time is A2B2/>C2；
When W03 is less than or equal to vt and less than W04, selecting the first preset remaining life time secondary correction coefficient C1 to perform secondary correction on the corrected first preset remaining life time A1, wherein the corrected remaining life time is A1B1/>C1。
In some embodiments of the present application, the processing module is further configured to predict a failure of the vehicle battery based on the predicted lifetime of the vehicle battery; wherein,
the processing module performs fault prediction on the vehicle battery, including:
acquiring historical state parameters of the vehicle battery, wherein the historical state parameters of the vehicle battery comprise battery historical terminal voltage, battery charge-discharge historical current and battery historical temperature;
arranging the acquired historical state parameters of the vehicle battery according to a time sequence, and preprocessing the arranged historical state parameters;
Learning and training data in the historical state parameters based on SVR, and establishing a fault prediction model;
performing fault prediction on the vehicle battery according to the fault prediction model and the state parameters of the vehicle battery;
and carrying out residual error threshold value test on the result of the fault prediction of the vehicle battery according to the fault prediction model, and carrying out fault early warning.
In some embodiments of the present application, the preprocessing the arranged historical state parameters by the processing module includes:
removing abnormal value data in the historical state parameters, carrying out interval processing on the data in the historical state parameters, and carrying out nonlinear judgment on the data in the historical state parameters to generate a sample form for supporting the learning training of a vector machine.
In order to achieve the above purpose, the present invention further provides a method for monitoring the cooperative state of the vehicle and the pile in the charging and discharging process of the electric vehicle, which is applied to the system for monitoring the cooperative state of the vehicle and the pile in the charging and discharging process of the electric vehicle, and comprises the following steps:
collecting state parameters of a vehicle battery in real time, wherein the state parameters of the vehicle battery comprise battery terminal voltage Vd, battery charging and discharging current Id and battery temperature Td;
Monitoring state parameters of a charging pile in real time, wherein the state parameters of the charging pile comprise charging pile voltage Vc, charging pile current Ic and charging pile power Pc;
transmitting the state parameters of the vehicle battery and the state parameters of the charging pile through a wireless communication technology;
receiving the state parameters of the vehicle battery and the state parameters of the charging pile, processing the state parameters of the vehicle battery and the state parameters of the charging pile, and judging the charging and discharging states of the vehicle battery according to a preset formula;
and predicting the service life of the vehicle battery according to the charge and discharge state of the vehicle battery, the state parameter of the vehicle battery and the state parameter of the charging pile.
In some embodiments of the present application, when determining the charge and discharge state of the vehicle battery according to a preset formula, the method includes:
calculating the residual capacity of the vehicle battery according to the battery terminal voltage Vd and the battery charging and discharging current Id, and judging the charging and discharging state of the vehicle battery according to the residual capacity of the vehicle battery;
the preset formula is as follows:
SOC=(Vbat-Vmin)/(Vmax-Vmin)100%；
wherein SOC is a remaining power of the vehicle battery, vbat is a terminal voltage of the vehicle battery, vmin is a minimum allowable voltage of the vehicle battery, and Vmax is a maximum allowable voltage of the vehicle battery; wherein,
When the residual electric quantity of the vehicle battery is more than 50%, judging that the vehicle battery is in a charging state;
when the residual electric quantity of the vehicle battery is less than 50%, judging that the vehicle battery is in a discharging state;
and when the residual electric quantity of the vehicle battery is equal to 50%, judging that the vehicle battery is in a full charge state.
In some embodiments of the present application, further comprising:
performing fault prediction on the vehicle battery according to the predicted service life of the vehicle battery; wherein,
performing fault prediction on the vehicle battery, including:
acquiring historical state parameters of the vehicle battery, wherein the historical state parameters of the vehicle battery comprise battery historical terminal voltage, battery charge-discharge historical current and battery historical temperature;
arranging the acquired historical state parameters of the vehicle battery according to a time sequence, and preprocessing the arranged historical state parameters;
learning and training data in the historical state parameters based on SVR, and establishing a fault prediction model;
performing fault prediction on the vehicle battery according to the fault prediction model and the state parameters of the vehicle battery;
and carrying out residual error threshold value test on the result of the fault prediction of the vehicle battery according to the fault prediction model, and carrying out fault early warning.
The invention provides a vehicle-pile cooperative state monitoring system and a method in the charging and discharging process of an electric vehicle, which have the beneficial effects that compared with the prior art:
according to the invention, the charging rate and the discharging rate are calculated through the state parameters of the vehicle battery and the state parameters of the charging pile, so that the service life of the battery is reasonably predicted, the service life of the battery can be accurately estimated by combining with the adaptive correction of related parameters, and the fault early warning is performed according to the service life of the battery by combining with the machine learning mode.
Drawings
FIG. 1 is a functional block diagram of a vehicle-pile collaborative state monitoring system for an electric vehicle charging and discharging process in an embodiment of the invention;
fig. 2 is a flowchart of a method for monitoring a co-operation state of a vehicle and a pile in a charging and discharging process of an electric vehicle according to an embodiment of the invention.
Detailed Description
The following describes in further detail the embodiments of the present invention with reference to the drawings and examples. The following examples are illustrative of the invention and are not intended to limit the scope of the invention.
In the description of the present application, it should be understood that the terms "center," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate description of the present application and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present application.
The terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present application, unless otherwise indicated, the meaning of "a plurality" is two or more.
In the description of the present application, it should be noted that, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be the communication between the inner sides of the two elements. The specific meaning of the terms in this application will be understood by those of ordinary skill in the art in a specific context.
The long charging time is one of the pain points of popularization of new energy automobiles, one of the solutions is to improve the charging efficiency, namely the so-called quick charging technology, and supplement electricity to 80% in 30 minutes or 15 minutes, but the requirement on the safety of a power battery is relatively high, and the service life of the battery is also influenced, and the second is to change the electricity mode, so that the method has the advantages of improving the energy efficiency, improving the utilization efficiency of the power battery, controlling the safety and facilitating the commercialization of the power battery in theory, but also has the problems of cost control, management system, standardization and the like, and not all the automobiles are suitable for the electricity mode.
The common charging mode of the electric automobile adopts a quick charging technology, so that the battery health state is difficult to evaluate in the charging and discharging process of the electric automobile, the battery performance is changed along with the change of time and the use environment due to the nonlinear change of the battery parameters, an accurate mathematical model is difficult to establish, the battery aging process has randomness and unpredictability, no definite aging rule exists at present, and the difficulty of effectively integrating various monitoring data to reasonably analyze to evaluate the battery health state exists.
Therefore, the invention provides a vehicle-pile cooperative state monitoring system and method for the charging and discharging process of the electric vehicle, which reasonably predicts the service life of the battery through parameters of the vehicle-pile cooperative state, corrects the service life prediction of the battery by combining a plurality of influence parameters, and accurately and effectively evaluates the service life of the battery.
Referring to fig. 1, the disclosed embodiment of the invention provides a vehicle-pile collaborative state monitoring system for an electric vehicle charging and discharging process, comprising:
the vehicle battery monitoring module is used for collecting state parameters of a vehicle battery in real time, wherein the state parameters of the vehicle battery comprise battery terminal voltage Vd, battery charge and discharge current Id and battery temperature Td;
The charging pile monitoring module is used for monitoring state parameters of the charging pile in real time, wherein the state parameters of the charging pile comprise charging pile voltage Vc, charging pile current Ic and charging pile power Pc;
the data communication module is used for transmitting the state parameters of the vehicle battery and the state parameters of the charging pile through a wireless communication technology;
the monitoring module is used for receiving the state parameters of the vehicle battery and the state parameters of the charging pile, processing the state parameters of the vehicle battery and the state parameters of the charging pile, and judging the charging and discharging states of the vehicle battery according to a preset formula;
and the processing module is used for predicting the service life of the vehicle battery according to the charge and discharge state of the vehicle battery, the state parameter of the vehicle battery and the state parameter of the charging pile.
In a specific embodiment of the present application, when the monitoring module determines the charge and discharge states of the vehicle battery according to a preset formula, the monitoring module includes:
calculating the residual capacity of the vehicle battery according to the battery terminal voltage Vd and the battery charging and discharging current Id, and judging the charging and discharging states of the vehicle battery according to the residual capacity of the vehicle battery;
the preset formula is:
SOC=(Vbat-Vmin)/(Vmax-Vmin)100%；
wherein, SOC is the residual quantity of the vehicle battery, vbat is the terminal voltage of the vehicle battery, vmin is the minimum allowable voltage of the vehicle battery, and Vmax is the maximum allowable voltage of the vehicle battery; wherein,
When the residual electric quantity of the vehicle battery is more than 50%, judging that the vehicle battery is in a charging state;
when the residual electric quantity of the vehicle battery is less than 50%, judging that the vehicle battery is in a discharging state;
and when the residual electric quantity of the vehicle battery is equal to 50%, judging that the vehicle battery is in a full charge state.
It should be noted that the above technical solution is applied to a lithium battery, and the parameter acquisition in the solution may be obtained in practical application by combining a Battery Management System (BMS) or an electrical parameter sensor that directly reads the battery, so as to obtain parameters such as terminal voltage and charge-discharge current of the battery.
In a specific embodiment of the present application, the monitoring module is further configured to generate a charging curve according to a state parameter of the charging pile when the vehicle battery is determined to be in a charging state, and obtain a charging rate vd of the vehicle battery according to the charging curve;
the processing module is also used for predicting the service life of the vehicle battery according to the charging rate vd of the vehicle battery;
the method comprises the steps that a preset charging rate matrix T0 and a preset residual life time matrix A are preset in a processing module, A (A1, A2, A3 and A4) is set for the preset residual life time matrix A, wherein A1 is a first preset residual life time, A2 is a second preset residual life time, A3 is a third preset residual life time, A4 is a fourth preset residual life time, and A1 is more than A2 and less than A3 and less than A4 and less than 5 years;
For a preset charge rate matrix T0, setting T0 (T01, T02, T03 and T04), wherein T01 is a first preset charge rate, T02 is a second preset charge rate, T03 is a third preset charge rate, T04 is a fourth preset charge rate, and T01 is less than T02 and less than T03 is less than T04;
the processing module is used for selecting corresponding residual life time as the life of the predicted vehicle battery according to the relation between vd and the preset charge rate matrix T0;
when vd is less than T01, selecting a fourth preset residual life time A4 as the life of the predicted vehicle battery;
when T01 is less than or equal to vd and less than T02, selecting a third preset residual life time A3 as the life of the predicted vehicle battery;
when T02 is less than or equal to vd and less than T03, selecting a second preset residual life time A2 as the life of the predicted vehicle battery;
when T03 is less than or equal to vd and less than T04, selecting the first preset residual life time A1 as the life of the predicted vehicle battery.
It will be appreciated that the faster the charge rate, the shorter the battery life. The method is characterized in that the battery plate is subjected to a charging process, and the battery plate is subjected to a charging process according to the charging process, so that the battery plate is subjected to a charging process according to the charging process. Through prediction, the service life change condition of the battery under different charging rates can be known, so that reasonable charging suggestions are provided for users, the service life of the battery is prolonged, and the endurance mileage and performance of the electric automobile are improved. In addition, the method has guiding significance for battery manufacturers and electric automobile manufacturers, and is helpful for promoting the development of the electric automobile industry.
In a specific embodiment of the present application, the processing module is further configured to obtain a historical charging number k of the vehicle battery;
the processing module is also internally provided with a preset historical charging frequency matrix R0 and a preset residual life time correction coefficient matrix B, and B (B1, B2, B3 and B4) is set for the preset residual life time correction coefficient matrix B, wherein B1 is a first preset residual life time correction coefficient, B2 is a second preset residual life time correction coefficient, B3 is a third preset residual life time correction coefficient, B4 is a fourth preset residual life time correction coefficient, and B1 is more than 0.6 and less than B2 and B3 and less than B4 and less than 1;
setting R0 (R01, R02, R03 and R04) for a preset historical charging frequency matrix R0, wherein R01 is a first preset historical charging frequency, R02 is a second preset historical charging frequency, R03 is a third preset historical charging frequency, R04 is a fourth preset historical charging frequency, and R01 is less than R02 and less than R03 is less than R04;
the processing module is also used for selecting corresponding residual life time correction coefficients according to the relation between k and a preset historical charging frequency matrix R0 so as to correct each preset residual life time;
when k is smaller than R01, a fourth preset residual life time correction coefficient B4 is selected to correct the fourth preset residual life time A4, and the corrected residual life time is A4 B4；
When R01 is less than or equal to k and less than R02, a third preset residual life time correction coefficient B3 is selected to correct the third preset residual life time A3, and the corrected residual life time is A3B3；
When R02 is less than or equal to k and less than R03, selecting a second preset residual life time correction coefficient B2 to correct the second preset residual life time A2, wherein the corrected residual life time is A2B2；
When R03 is less than or equal to k and less than R04, a first preset residual life time correction coefficient B1 is selected to correct the first preset residual life time A1, and the corrected residual life time is A1B1。
It will be appreciated that for lithium ion batteries, the lifetime under normal use conditions is typically 3-5 years, however, this is also affected by the number of charges, which when increased results in a reduced lifetime.
In a specific embodiment of the present application, the monitoring module is further configured to generate a discharge curve according to a state parameter of the vehicle battery and a remaining capacity of the vehicle battery when the vehicle battery is determined to be in a discharge state, and obtain a discharge rate vt of the vehicle battery according to the discharge curve;
The processing module is also internally provided with a preset discharge rate matrix W0 and a preset residual life time secondary correction coefficient matrix C, and C (C1, C2, C3 and C4) is set for the preset residual life time secondary correction coefficient matrix C, wherein C1 is a first preset residual life time secondary correction coefficient, C2 is a second preset residual life time secondary correction coefficient, C3 is a third preset residual life time secondary correction coefficient, C4 is a fourth preset residual life time secondary correction coefficient, and C1 is more than 0.6 and less than C2 and less than C3 and less than C4 and less than 1;
for a preset discharge rate matrix W0, setting W0 (W01, W02, W03, W04), wherein W01 is a first preset discharge rate, W02 is a second preset discharge rate, W03 is a third preset discharge rate, W04 is a fourth preset discharge rate, and W01 is less than W02 and less than W03 is less than W04;
the processing module is also used for selecting a corresponding secondary correction coefficient of the residual life time according to the relation between vt and the preset discharge rate matrix W0 so as to carry out secondary correction on each corrected preset residual life time;
when vt is less than W01, selecting a fourth preset remaining life time secondary correction coefficient C4 to perform secondary correction on the corrected fourth preset remaining life time A4, wherein the corrected remaining life time is A4 B4/>C4；
When W01 is less than or equal to vt and less than W02, selecting a third preset remaining life time secondary correction coefficient C3 to perform secondary correction on the corrected third preset remaining life time A3, wherein the corrected remaining life time is A3B3/>C3；
When W02 is less than or equal to vt and less than W03, selecting a second preset remaining life time secondary correction coefficient C2 to perform secondary correction on the corrected second preset remaining life time A2, wherein the corrected remaining life time is A2B2/>C2；
When W03 is less than or equal to vt and less than W04, selecting a first preset remaining life time secondary correction coefficient C1 to perform secondary correction on the corrected first preset remaining life time A1, and the corrected remaining life timeResidual life time of A1B1/>C1。
It can be understood that the discharge rate has a significant effect on the service life of the battery of the vehicle, the faster the discharge rate is, the shorter the service life of the battery is, because the rapid discharge can cause unbalanced chemical reaction inside the battery, the reduction of the battery performance is accelerated, the slower the discharge rate is, the longer the service life of the battery is, the service life of the battery is predicted by the discharge rate, and the service life change condition of the battery under different discharge rates can be known, thereby providing basis for the management and maintenance of the battery, being beneficial to optimizing the service condition of the battery, prolonging the service life of the battery, improving the performance of the battery, improving the overall performance of the vehicle, finding the potential problem of the battery in advance, and carrying out timely maintenance and replacement.
In a specific embodiment of the present application, the processing module is further configured to predict a failure of the vehicle battery based on the predicted lifetime of the vehicle battery; wherein,
the processing module performs fault prediction on a vehicle battery, including:
acquiring historical state parameters of a vehicle battery, wherein the historical state parameters of the vehicle battery comprise battery historical terminal voltage, battery charge-discharge historical current and battery historical temperature;
arranging the acquired historical state parameters of the vehicle battery according to a time sequence, and preprocessing the arranged historical state parameters;
learning and training data in the historical state parameters based on SVR, and establishing a fault prediction model;
performing fault prediction on the vehicle battery according to the fault prediction model and the state parameters of the vehicle battery;
and carrying out residual threshold value test on the result of the fault prediction of the vehicle battery according to the fault prediction model, and carrying out fault early warning.
In one specific embodiment of the present application, performing fault prediction on a vehicle battery and establishing a fault prediction model includes:
the SVM is used for regression analysis, namely a Support Vector Regression (SVR), and the implementation scheme is that data X of an input space is mapped into a high-dimensional characteristic space G through a nonlinear mapping phi, and linear regression is carried out in the space. Given training set t= { (x) １ ，ｙ １ ），…，（ｘ ｌ ，ｙ ｌ ）｝∈（Ｒ ｎ ×γ） ｌ Wherein x is ｉ ∈Ｒ ｎ ，ｙ ｉ E γ=r, i=1, …, l; the regression problem is simplified by introducing a insensitive loss function, which is also called epsilon-SVR, which is a standard algorithm supporting vector machine regression, and the function is estimated by using the following formula:
；
wherein b is the paranoid amount.
Taking extremum to the optimization target when optimizing the estimation function:
；
such that:
；
wherein: c is a punishment factor used for realizing the compromise of experience risks and confidence risks; the larger C is, the higher the fitting capability to the data is; zeta type toy ｉ And xi ＊ ｉ Is a relaxation factor used to control the linear inseparable boundary. Epsilon is used to control regression approximation errors and generalization ability of the model, which is defined as:
；
introducing a Lagrangian function, and finally obtaining a regression function by utilizing a dual form of an optimization problem:
；
wherein: alpha, alpha ＊ ｉ ≥０；γ，γ ＊ ｉ ＝≥０；ｉ＝１，…ｌ。
From the KKT theorem, it is possible to obtain:
；
thus, for nonlinear support vector machine regression, the regression function is expressed as:
；
the kernel function selects a Radial Basis Function (RBF):
。
it can be understood that the invention combines the machine learning mode to grasp the battery state in real time, establishes a fault prediction model based on SVR, predicts the health state according to the current data and the history data, and predicts the possible faults.
In a specific embodiment of the present application, the preprocessing module performs preprocessing on the arranged historical state parameters, including:
removing abnormal value data in the historical state parameters, carrying out interval processing on the data in the historical state parameters, and carrying out nonlinear judgment on the data in the historical state parameters to generate a sample form for supporting the learning training of the vector machine.
Based on the same technical concept, referring to fig. 2, the invention further correspondingly provides a method for monitoring the vehicle-pile cooperative state in the charging and discharging process of the electric vehicle, which is applied to a system for monitoring the vehicle-pile cooperative state in the charging and discharging process of the electric vehicle, and comprises the following steps:
collecting state parameters of a vehicle battery in real time, wherein the state parameters of the vehicle battery comprise battery terminal voltage Vd, battery charging and discharging current Id and battery temperature Td;
monitoring state parameters of the charging pile in real time, wherein the state parameters of the charging pile comprise charging pile voltage Vc, charging pile current Ic and charging pile power Pc;
transmitting state parameters of the vehicle battery and state parameters of the charging pile through a wireless communication technology;
receiving state parameters of the vehicle battery and state parameters of the charging pile, processing the state parameters of the vehicle battery and the state parameters of the charging pile, and judging the charging and discharging states of the vehicle battery according to a preset formula;
And predicting the service life of the vehicle battery according to the charge and discharge state of the vehicle battery, the state parameter of the vehicle battery and the state parameter of the charging pile.
In one embodiment of the present application, when determining a charge/discharge state of a vehicle battery according to a preset formula, the method includes:
calculating the residual capacity of the vehicle battery according to the battery terminal voltage Vd and the battery charging and discharging current Id, and judging the charging and discharging states of the vehicle battery according to the residual capacity of the vehicle battery;
the preset formula is:
SOC=(Vbat-Vmin)/(Vmax-Vmin)100%；
wherein, SOC is the residual quantity of the vehicle battery, vbat is the terminal voltage of the vehicle battery, vmin is the minimum allowable voltage of the vehicle battery, and Vmax is the maximum allowable voltage of the vehicle battery; wherein,
when the residual electric quantity of the vehicle battery is more than 50%, judging that the vehicle battery is in a charging state;
when the residual electric quantity of the vehicle battery is less than 50%, judging that the vehicle battery is in a discharging state;
and when the residual electric quantity of the vehicle battery is equal to 50%, judging that the vehicle battery is in a full charge state.
It should be noted that the above technical solution is applied to a lithium battery, and the parameter acquisition in the solution may be obtained in practical application by combining a Battery Management System (BMS) or an electrical parameter sensor that directly reads the battery, so as to obtain parameters such as terminal voltage and charge-discharge current of the battery.
In a specific embodiment of the present application, further comprising:
performing fault prediction on the vehicle battery according to the predicted service life of the vehicle battery; wherein,
performing fault prediction on a vehicle battery, comprising:
acquiring historical state parameters of a vehicle battery, wherein the historical state parameters of the vehicle battery comprise battery historical terminal voltage, battery charge-discharge historical current and battery historical temperature;
arranging the acquired historical state parameters of the vehicle battery according to a time sequence, and preprocessing the arranged historical state parameters;
learning and training data in the historical state parameters based on SVR, and establishing a fault prediction model;
performing fault prediction on the vehicle battery according to the fault prediction model and the state parameters of the vehicle battery;
and carrying out residual threshold value test on the result of the fault prediction of the vehicle battery according to the fault prediction model, and carrying out fault early warning.
In summary, the invention calculates the charging rate and the discharging rate through the state parameters of the vehicle battery and the state parameters of the charging pile, so as to reasonably predict the service life of the battery, and can accurately evaluate the service life of the battery by combining with the adaptive correction of the related parameters, and can perform fault early warning according to the service life of the battery by combining with the machine learning mode. The invention has the advantages of intelligence, accuracy and the like.
The foregoing is merely an example of the present invention and is not intended to limit the scope of the present invention, and all changes made in the structure according to the present invention should be considered as falling within the scope of the present invention without departing from the gist of the present invention.
It will be clear to those skilled in the art that, for convenience and brevity of description, the specific working process of the system described above and the related description may refer to the corresponding process in the foregoing method embodiment, which is not repeated here.
It should be noted that, in the system provided in the foregoing embodiment, only the division of the foregoing functional modules is illustrated, in practical application, the foregoing functional allocation may be performed by different functional modules, that is, the modules or steps in the embodiment of the present invention are further decomposed or combined, for example, the modules in the foregoing embodiment may be combined into one module, or may be further split into multiple sub-modules, so as to complete all or part of the functions described above. The names of the modules and steps related to the embodiments of the present invention are merely for distinguishing the respective modules or steps, and are not to be construed as unduly limiting the present invention.
Those of skill in the art will appreciate that the various illustrative modules, method steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the program(s) corresponding to the software modules, method steps, may be embodied in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art. To clearly illustrate this interchangeability of electronic hardware and software, various illustrative components and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as electronic hardware or software depends upon the particular application and design constraints imposed on the solution. Those skilled in the art may implement the described functionality using different approaches for each particular application, but such implementation is not intended to be limiting.
The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus/apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus/apparatus.
Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will fall within the scope of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the present invention.
Claims (10)
1. The utility model provides an electric automobile charge-discharge process car-stake collaborative state monitoring system which characterized in that includes:
the vehicle battery monitoring module is used for collecting state parameters of a vehicle battery in real time, wherein the state parameters of the vehicle battery comprise battery terminal voltage Vd, battery charge and discharge current Id and battery temperature Td;
the charging pile monitoring module is used for monitoring state parameters of the charging pile in real time, wherein the state parameters of the charging pile comprise charging pile voltage Vc, charging pile current Ic and charging pile power Pc;
the data communication module is used for transmitting the state parameters of the vehicle battery and the state parameters of the charging pile through a wireless communication technology;
The monitoring module is used for receiving the state parameters of the vehicle battery and the state parameters of the charging pile, processing the state parameters of the vehicle battery and the state parameters of the charging pile, and judging the charging and discharging states of the vehicle battery according to a preset formula;
and the processing module is used for predicting the service life of the vehicle battery according to the charge and discharge state of the vehicle battery, the state parameter of the vehicle battery and the state parameter of the charging pile.
2. The system for monitoring the co-operating state of a vehicle-pile in the charging and discharging process of an electric vehicle according to claim 1, wherein the monitoring module judges the charging and discharging state of the vehicle battery according to a preset formula, comprises:
calculating the residual capacity of the vehicle battery according to the battery terminal voltage Vd and the battery charging and discharging current Id, and judging the charging and discharging state of the vehicle battery according to the residual capacity of the vehicle battery;
the preset formula is as follows:
SOC=(Vbat-Vmin)/(Vmax-Vmin)100%；
wherein SOC is a remaining power of the vehicle battery, vbat is a terminal voltage of the vehicle battery, vmin is a minimum allowable voltage of the vehicle battery, and Vmax is a maximum allowable voltage of the vehicle battery; wherein,
When the residual electric quantity of the vehicle battery is more than 50%, judging that the vehicle battery is in a charging state;
when the residual electric quantity of the vehicle battery is less than 50%, judging that the vehicle battery is in a discharging state;
and when the residual electric quantity of the vehicle battery is equal to 50%, judging that the vehicle battery is in a full charge state.
3. The electric vehicle-pile cooperative state monitoring system according to claim 2, wherein,
the monitoring module is further used for generating a charging curve according to the state parameters of the charging pile when the vehicle battery is judged to be in a charging state, and acquiring the charging rate vd of the vehicle battery according to the charging curve;
the processing module is further configured to predict a lifetime of the vehicle battery based on a charge rate vd of the vehicle battery;
a preset charge rate matrix T0 and a preset residual life time matrix A are preset in the processing module, A (A1, A2, A3 and A4) is set for the preset residual life time matrix A, wherein A1 is a first preset residual life time, A2 is a second preset residual life time, A3 is a third preset residual life time, A4 is a fourth preset residual life time, and A1 is more than A2 and less than A3 and less than A4 and less than 5 years;
Setting T0 (T01, T02, T03, T04) for the preset charge rate matrix T0, wherein T01 is a first preset charge rate, T02 is a second preset charge rate, T03 is a third preset charge rate, T04 is a fourth preset charge rate, and T01 is less than T02 and less than T03 is less than T04;
the processing module is used for selecting corresponding residual life time as the life of the predicted vehicle battery according to the relation between vd and the preset charge rate matrix T0;
when vd < T01, selecting the fourth preset remaining life time A4 as a predicted life of the vehicle battery;
when T01 is less than or equal to vd and less than T02, selecting the third preset remaining life time A3 as the life of the vehicle battery;
when T02 is less than or equal to vd and less than T03, selecting the second preset remaining life time A2 as the life of the vehicle battery;
and when T03 is less than or equal to vd and less than T04, selecting the first preset residual life time A1 as the life of the vehicle battery.
4. The electric vehicle-pile cooperative state monitoring system according to claim 3, wherein,
the processing module is also used for acquiring historical charging times k of the vehicle battery;
the processing module is also internally provided with a preset historical charging frequency matrix R0 and a preset residual life time correction coefficient matrix B, and B (B1, B2, B3 and B4) is set for the preset residual life time correction coefficient matrix B, wherein B1 is a first preset residual life time correction coefficient, B2 is a second preset residual life time correction coefficient, B3 is a third preset residual life time correction coefficient, B4 is a fourth preset residual life time correction coefficient, and B1 is more than 0.6 and less than B2 and B3 and less than B4 and less than 1;
Setting R0 (R01, R02, R03 and R04) for the preset historical charging frequency matrix R0, wherein R01 is a first preset historical charging frequency, R02 is a second preset historical charging frequency, R03 is a third preset historical charging frequency, R04 is a fourth preset historical charging frequency, and R01 is less than R02 and less than R03 is less than R04;
the processing module is further used for selecting corresponding residual life time correction coefficients according to the relation between k and the preset historical charging frequency matrix R0 so as to correct each preset residual life time;
when k is smaller than R01, the fourth preset residual life time correction coefficient B4 is selected to correct the fourth preset residual life time A4, and the corrected residual life time is A4B4；
When R01 is less than or equal to k and less than R02, selecting the third preset residual life time correction coefficient B3 to correct the third preset residual life time A3, wherein the corrected residual life time is A3B3；
When R02 is less than or equal to k and less than R03, selecting the second preset residual life time correction coefficient B2 to correct the second preset residual life time A2, wherein the corrected residual life time is A2B2；
When R03 is less than or equal to k and less than R04, selecting the first preset residual life time correction coefficient B1 to correct the first preset residual life time A1, wherein the corrected residual life time is A1 B1。
5. The electric vehicle-pile cooperative state monitoring system according to claim 4, wherein,
the monitoring module is further used for generating a discharge curve according to the state parameters of the vehicle battery and the residual electric quantity of the vehicle battery when the vehicle battery is judged to be in a discharge state, and acquiring the discharge rate vt of the vehicle battery according to the discharge curve;
the processing module is also internally provided with a preset discharge rate matrix W0 and a preset residual life time secondary correction coefficient matrix C, and C (C1, C2, C3 and C4) is set for the preset residual life time secondary correction coefficient matrix C, wherein C1 is a first preset residual life time secondary correction coefficient, C2 is a second preset residual life time secondary correction coefficient, C3 is a third preset residual life time secondary correction coefficient, C4 is a fourth preset residual life time secondary correction coefficient, and C1 is more than 0.6 and less than C2 and C3 and less than C4 and less than 1;
setting W0 (W01, W02, W03, W04) for the preset discharge rate matrix W0, wherein W01 is a first preset discharge rate, W02 is a second preset discharge rate, W03 is a third preset discharge rate, W04 is a fourth preset discharge rate, and W01 is less than W02 and less than W03 is less than W04;
The processing module is further used for selecting a corresponding secondary correction coefficient of the residual life time according to the relation between vt and the preset discharge rate matrix W0 so as to carry out secondary correction on each corrected preset residual life time;
when vt is less than W01, selecting the fourth preset remaining life time secondary correction coefficient C4 to perform secondary correction on the corrected fourth preset remaining life time A4, wherein the corrected remaining life time is A4B4/>C4；
When W01 is less than or equal to vt and less than W02, selecting the third preset remaining life time secondary correction coefficient C3 to perform secondary correction on the corrected third preset remaining life time A3, wherein the corrected remaining life time is A3B3/>C3；
When W02 is less than or equal to vt and less than W03, selecting the second preset remaining life time secondary correction coefficient C2 to perform secondary correction on the corrected second preset remaining life time A2, wherein the corrected remaining life time is A2B2/>C2；
When W03 is less than or equal to vt and less than W04, selecting the first preset remaining life time secondary correction coefficient C1 to perform secondary correction on the corrected first preset remaining life time A1, wherein the corrected remaining life time is A1B1/>C1。
6. The electric vehicle-pile cooperative state monitoring system according to claim 5, wherein,
The processing module is further used for carrying out fault prediction on the vehicle battery according to the predicted service life of the vehicle battery; wherein,
the processing module performs fault prediction on the vehicle battery, including:
acquiring historical state parameters of the vehicle battery, wherein the historical state parameters of the vehicle battery comprise battery historical terminal voltage, battery charge-discharge historical current and battery historical temperature;
arranging the acquired historical state parameters of the vehicle battery according to a time sequence, and preprocessing the arranged historical state parameters;
learning and training data in the historical state parameters based on SVR, and establishing a fault prediction model;
performing fault prediction on the vehicle battery according to the fault prediction model and the state parameters of the vehicle battery;
and carrying out residual error threshold value test on the result of the fault prediction of the vehicle battery according to the fault prediction model, and carrying out fault early warning.
7. The system for monitoring the co-operating state of a vehicle-pile in the charging and discharging process of an electric vehicle according to claim 6, wherein the processing module pre-processes the arranged historical state parameters, and comprises:
Removing abnormal value data in the historical state parameters, carrying out interval processing on the data in the historical state parameters, and carrying out nonlinear judgment on the data in the historical state parameters to generate a sample form for supporting the learning training of a vector machine.
8. The method for monitoring the cooperative state of the vehicle and the pile in the charging and discharging process of the electric vehicle is applied to the system for monitoring the cooperative state of the vehicle and the pile in the charging and discharging process of the electric vehicle as claimed in any one of claims 1 to 7, and is characterized by comprising the following steps:
collecting state parameters of a vehicle battery in real time, wherein the state parameters of the vehicle battery comprise battery terminal voltage Vd, battery charging and discharging current Id and battery temperature Td;
monitoring state parameters of a charging pile in real time, wherein the state parameters of the charging pile comprise charging pile voltage Vc, charging pile current Ic and charging pile power Pc;
transmitting the state parameters of the vehicle battery and the state parameters of the charging pile through a wireless communication technology;
receiving the state parameters of the vehicle battery and the state parameters of the charging pile, processing the state parameters of the vehicle battery and the state parameters of the charging pile, and judging the charging and discharging states of the vehicle battery according to a preset formula;
And predicting the service life of the vehicle battery according to the charge and discharge state of the vehicle battery, the state parameter of the vehicle battery and the state parameter of the charging pile.
9. The method for monitoring the co-operating state of a vehicle and a pile in the charging and discharging process of an electric vehicle according to claim 8, wherein when judging the charging and discharging state of the vehicle battery according to a preset formula, the method comprises:
calculating the residual capacity of the vehicle battery according to the battery terminal voltage Vd and the battery charging and discharging current Id, and judging the charging and discharging state of the vehicle battery according to the residual capacity of the vehicle battery;
the preset formula is as follows:
SOC=(Vbat-Vmin)/(Vmax-Vmin)100%；
wherein SOC is a remaining power of the vehicle battery, vbat is a terminal voltage of the vehicle battery, vmin is a minimum allowable voltage of the vehicle battery, and Vmax is a maximum allowable voltage of the vehicle battery; wherein,
when the residual electric quantity of the vehicle battery is more than 50%, judging that the vehicle battery is in a charging state;
when the residual electric quantity of the vehicle battery is less than 50%, judging that the vehicle battery is in a discharging state;
and when the residual electric quantity of the vehicle battery is equal to 50%, judging that the vehicle battery is in a full charge state.
10. The method for monitoring the cooperative state of a vehicle and a pile in the charging and discharging process of an electric vehicle according to claim 9, further comprising:
performing fault prediction on the vehicle battery according to the predicted service life of the vehicle battery; wherein,
performing fault prediction on the vehicle battery, including:
acquiring historical state parameters of the vehicle battery, wherein the historical state parameters of the vehicle battery comprise battery historical terminal voltage, battery charge-discharge historical current and battery historical temperature;
arranging the acquired historical state parameters of the vehicle battery according to a time sequence, and preprocessing the arranged historical state parameters;
learning and training data in the historical state parameters based on SVR, and establishing a fault prediction model;
performing fault prediction on the vehicle battery according to the fault prediction model and the state parameters of the vehicle battery;
and carrying out residual error threshold value test on the result of the fault prediction of the vehicle battery according to the fault prediction model, and carrying out fault early warning.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410026183.8A CN117538767B (en) | 2024-01-09 | 2024-01-09 | Electric vehicle charging and discharging process vehicle-pile cooperative state monitoring system and method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410026183.8A CN117538767B (en) | 2024-01-09 | 2024-01-09 | Electric vehicle charging and discharging process vehicle-pile cooperative state monitoring system and method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117538767A true CN117538767A (en) | 2024-02-09 |
| CN117538767B CN117538767B (en) | 2024-03-22 |
Family
ID=89792304
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410026183.8A Active CN117538767B (en) | 2024-01-09 | 2024-01-09 | Electric vehicle charging and discharging process vehicle-pile cooperative state monitoring system and method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117538767B (en) |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108037462A (en) * | 2017-12-14 | 2018-05-15 | 株洲广锐电气科技有限公司 | Storage battery health status quantization method and system |
| CN109164397A (en) * | 2018-09-21 | 2019-01-08 | 华北电力大学（保定） | Consider that appraisal procedure is lost in the service life of lithium battery of charge rate and environment temperature |
| CN109342949A (en) * | 2018-11-06 | 2019-02-15 | 长沙理工大学 | Lithium-ion-power cell remaining life on-line prediction method in charging process |
| KR101949449B1 (en) * | 2017-11-07 | 2019-02-18 | 주식회사 스마트이앤엠 | Method and apparatus for estimating battery life |
| US20190361077A1 (en) * | 2018-05-25 | 2019-11-28 | Denso Corporation | Battery life estimation system and method |
| CN110901470A (en) * | 2019-11-29 | 2020-03-24 | 安徽江淮汽车集团股份有限公司 | Method, device and equipment for predicting service life of battery of electric vehicle and storage medium |
| CN113779750A (en) * | 2021-07-22 | 2021-12-10 | 广东劲天科技有限公司 | Battery life prediction method and system based on charging state and charging pile |
| CN114609536A (en) * | 2022-03-10 | 2022-06-10 | 广州万城万充新能源科技有限公司 | Charging pile historical data-based battery life prediction method and system |
| CN114932836A (en) * | 2022-06-06 | 2022-08-23 | 华人运通(山东)科技有限公司 | Method and device for monitoring charging state of vehicle and vehicle |
| CN116882981A (en) * | 2023-09-07 | 2023-10-13 | 深圳市海雷新能源有限公司 | Intelligent battery management system based on data analysis |
-
2024
- 2024-01-09 CN CN202410026183.8A patent/CN117538767B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101949449B1 (en) * | 2017-11-07 | 2019-02-18 | 주식회사 스마트이앤엠 | Method and apparatus for estimating battery life |
| CN108037462A (en) * | 2017-12-14 | 2018-05-15 | 株洲广锐电气科技有限公司 | Storage battery health status quantization method and system |
| US20190361077A1 (en) * | 2018-05-25 | 2019-11-28 | Denso Corporation | Battery life estimation system and method |
| CN109164397A (en) * | 2018-09-21 | 2019-01-08 | 华北电力大学（保定） | Consider that appraisal procedure is lost in the service life of lithium battery of charge rate and environment temperature |
| CN109342949A (en) * | 2018-11-06 | 2019-02-15 | 长沙理工大学 | Lithium-ion-power cell remaining life on-line prediction method in charging process |
| CN110901470A (en) * | 2019-11-29 | 2020-03-24 | 安徽江淮汽车集团股份有限公司 | Method, device and equipment for predicting service life of battery of electric vehicle and storage medium |
| CN113779750A (en) * | 2021-07-22 | 2021-12-10 | 广东劲天科技有限公司 | Battery life prediction method and system based on charging state and charging pile |
| CN114609536A (en) * | 2022-03-10 | 2022-06-10 | 广州万城万充新能源科技有限公司 | Charging pile historical data-based battery life prediction method and system |
| CN114932836A (en) * | 2022-06-06 | 2022-08-23 | 华人运通(山东)科技有限公司 | Method and device for monitoring charging state of vehicle and vehicle |
| CN116882981A (en) * | 2023-09-07 | 2023-10-13 | 深圳市海雷新能源有限公司 | Intelligent battery management system based on data analysis |
Non-Patent Citations (2)
| Title |
|---|
| TIANTIAN XU 等: "A Deployment Model of Charging Pile based on Random Forest for Shared Electric Vehicle in Smart Cities", 《2018 14TH INTERNATIONAL CONFERENCE ON MOBILE AD-HOC AND SENSOR NETWORKS》, 31 December 2018 (2018-12-31), pages 49 - 54, XP033539051, DOI: 10.1109/MSN.2018.00015 * |
| 田玉婷 等: "考虑寿命衰减及电价机制的电池储能系统技术经济研究", 《四川电力技术》, vol. 45, no. 5, 31 October 2022 (2022-10-31), pages 1 - 6 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117538767B (en) | 2024-03-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| EP2629109B1 (en) | Electrical storage device | |
| CN102231546B (en) | Battery management system with balanced charge and discharge functions and control method thereof | |
| CN111239630A (en) | Energy storage battery service life prediction method and management system | |
| JP7446990B2 (en) | Method for estimating state of charge of power storage device and state of charge estimation system for power storage device | |
| Hussein | Adaptive artificial neural network-based models for instantaneous power estimation enhancement in electric vehicles’ Li-ion batteries | |
| Kubiak et al. | Calendar aging of a 250 kW/500 kWh Li-ion battery deployed for the grid storage application | |
| CN110324383B (en) | Cloud server, electric automobile and management system and method of power battery in electric automobile | |
| US11835589B2 (en) | Method and apparatus for machine-individual improvement of the lifetime of a battery in a battery-operated machine | |
| EP4206712A1 (en) | Apparatus and method for diagnosing battery | |
| CN116973782B (en) | New energy automobile maintenance and fault monitoring and diagnosing method based on machine learning | |
| CN114329760A (en) | Vehicle-mounted lithium ion battery modeling and fault diagnosis method based on digital twinning | |
| Bohlen | Impedance-based battery monitoring | |
| US20230231396A1 (en) | Method and apparatus for executing a charging operation of a device battery | |
| US9702941B2 (en) | Method and devices for making available information for the purpose of performing maintenance and servicing of a battery | |
| US11733313B2 (en) | Method and apparatus for operating a system for providing states of health of electrical energy stores for a multiplicity of devices with the aid of machine learning methods | |
| CN117538767B (en) | Electric vehicle charging and discharging process vehicle-pile cooperative state monitoring system and method | |
| CN114784397A (en) | Automobile charging method and system based on multi-time scale battery fault | |
| US20230324463A1 (en) | Method and Apparatus for Operating a System for Detecting an Anomaly of an Electrical Energy Store for a Device by Means of Machine Learning Methods | |
| CN116826887A (en) | Battery equalization method and equalization device | |
| CN117388737A (en) | Method, device, equipment and storage medium for evaluating battery health state | |
| CN111308352A (en) | Method for estimating battery attenuation of lithium ions | |
| CN115951225A (en) | Battery equalization optimization capacity estimation method and device | |
| CN114545259A (en) | Storage battery capacity evaluation method, computer device, and storage medium | |
| KR20230098257A (en) | Power battery charging method and battery management system | |
| EP4462139A1 (en) | Battery management apparatus and operation method therefor |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |