CN113085656A - Intelligent attenuation pre-control method and device based on big data of user habits - Google Patents

Intelligent attenuation pre-control method and device based on big data of user habits Download PDF

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Publication number
CN113085656A
CN113085656A CN202110447777.2A CN202110447777A CN113085656A CN 113085656 A CN113085656 A CN 113085656A CN 202110447777 A CN202110447777 A CN 202110447777A CN 113085656 A CN113085656 A CN 113085656A
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time
charging
determining
soc
preset
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CN113085656B (en
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梁海强
沈帅
张骞慧
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Beijing Electric Vehicle Co Ltd
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Beijing Electric Vehicle Co Ltd
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    • 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]
    • B—PERFORMING OPERATIONS; TRANSPORTING
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    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
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    • 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
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    • 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
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    • B60L58/24—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries for controlling the temperature of batteries
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    • 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]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • 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
    • G—PHYSICS
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    • B60L2240/00—Control parameters of input or output; Target parameters
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Abstract

The application provides an attenuation intelligent pre-control method and device based on big data of user habits, and relates to the technical field of automobiles. The method comprises the following steps: acquiring the current first remaining charging time, the current charging time and the predicted vehicle using time of the power battery; determining the allowable extension time length of charging according to the first residual charging time length, the current charging time and the predicted vehicle using time; determining a charging allowable input current according to the charging allowable extension time; the estimated vehicle using time is vehicle using time set by a user or vehicle using time sent by the cloud server. According to the embodiment of the application, the vehicle using time set by the user or the vehicle using time sent by the cloud server and the first remaining charging time predicted by the vehicle end are calculated, the allowable extending time is prolonged, the allowable current in the charging process is delayed, the battery attenuation is delayed, and the service life of the power battery is protected.

Description

Intelligent attenuation pre-control method and device based on big data of user habits
Technical Field
The application relates to the technical field of automobiles, in particular to an attenuation intelligent pre-control method and device based on big data of user habits.
Background
With the rapid development of science and technology and the improvement of chip manufacturing technology, electric vehicles using power batteries as energy sources appear in the field of automobiles. The electric automobile is released, so that the problem of air pollution caused by exhaust emission of the traditional fuel oil automobile is solved, and the consumption of non-renewable resources such as petroleum is reduced. In the process of charging a power battery of an electric automobile, the service life performance of the battery is accurately predicted, and the service life performance is very important for playing the real capacity of the battery and protecting the battery.
At present, in the prior art, when the battery life is predicted, an offline prediction method is generally adopted, for example, a model is established based on a life data rule under a standard condition, and then relevant data of the battery in a period of time is collected, the future life decay trend of the battery is predicted offline, the operation is complex, the battery life cannot be predicted in real time in the charging process, the prediction result is only limited to the prediction of a vehicle end, the accuracy is low, and improvement is urgently needed.
Content of application
The embodiment of the application provides an attenuation intelligent pre-control method and device based on big data of user habits, and aims to solve the problem that the accuracy of battery attenuation prediction in the prior art is low.
In order to solve the technical problem, the following technical scheme is adopted in the application:
the embodiment of the application provides an attenuation intelligent pre-control method based on big data of user habits, which is applied to vehicles and comprises the following steps:
acquiring the current first remaining charging time, the current charging time and the predicted vehicle using time of the power battery;
determining the allowable extension time length of charging according to the first residual charging time length, the current charging time and the predicted vehicle using time;
determining a charging allowable input current according to the charging allowable extension time;
the estimated vehicle using time is vehicle using time set by a user or vehicle using time sent by the cloud server.
Optionally, the obtaining a current first remaining charging duration of the power battery includes:
acquiring initial temperature of a power battery, state of charge (SOC) of a plurality of time detection points in a charging process, and acquiring temperature rise change rate, SOC variation and charging average current of a plurality of time detection periods;
and determining the first remaining charging time according to the state of charge SOC, the temperature rise change rate, the SOC change amount and the charging average current.
Optionally, the determining the first remaining charging time according to the state of charge SOC, the temperature rise change rate, the SOC change amount, and the charging average current includes:
acquiring a temperature rise rate in a charging process and a state of charge (SOC) variation in the heating process according to a currently stored heating parameter mapping table, wherein the currently stored heating parameter mapping table comprises a plurality of corresponding relations of the temperature rise rate, the SOC variation and the charging current corresponding to each SOC interval;
determining the heating time length according to the initial temperature and each temperature rise change rate;
determining second residual charging time of the plurality of time detection points after the heating is stopped according to the SOC of the plurality of time detection points after the heating is stopped, and the charging current and the limit value of each SOC corresponding interval;
and determining the first remaining charging time length according to the second remaining charging time length and the heating time length.
Optionally, the determining that the charging is allowed to be extended for a long time includes:
performing difference operation on the estimated vehicle using time and the current charging time to determine a first difference;
and performing difference operation on the first difference and the first residual charging time length to determine the allowable charging extension time length.
Optionally, the method further includes:
when the charge allowable extension time meets a preset range, determining a charge allowable input current according to the charge allowable extension time; when the charge allowable extension time does not meet a preset range, determining that the power battery is charged by normal input current;
the preset range is that the charging allowable extension time is greater than the product of a first preset value and the first residual charging time, and the charging allowable extension time is less than or equal to a second preset value.
Optionally, when the charge allowable extended time period satisfies a preset range, determining the charge allowable input current according to the charge allowable extended time period includes:
acquiring a maximum output capacity CML (constant current limit) sent by the charging equipment;
and determining the charging allowable input current according to the CML and the first residual charging time period.
Optionally, the method further includes:
in the charging process, if a first preset condition is met, determining that a constant-voltage charging mode is adopted in the charging process;
wherein the first preset condition comprises:
the charging allowable input current is smaller than a third preset value, the current SOC of the power battery is larger than the first preset SOC, and a second difference value between the predicted vehicle using time and the current charging time is larger than a fourth preset value;
the first preset SOC is an expected SOC sent by the cloud server.
Optionally, the method further includes:
in the charging process, if a second preset condition is met, determining to exit the constant voltage charging mode;
wherein the second preset condition comprises: the second difference is smaller than a fifth preset value, wherein the fifth preset value is smaller than the fourth preset value.
Optionally, the method further includes:
and if the estimated vehicle using time is within the preset night time range, determining that the charging allowable input current is the maximum allowable charging current of the power battery.
Optionally, the method further includes:
if the allowable charging extension time is less than zero, determining the maximum SOC which can be charged in the first remaining charging time;
if the chargeable maximum SOC is smaller than a second preset SOC, determining that the power battery is normally charged, wherein the maximum SOC is obtained by descending at a first preset attenuation rate from a charging cut-off SOC;
and the second preset SOC is an estimated SOC sent by the cloud server.
The embodiment of the present application further provides a charge control device, which is applied to a vehicle, and includes:
the first acquisition module is used for acquiring the current first remaining charging time, the current charging time and the predicted vehicle using time of the power battery;
the first determining module is used for determining the allowable extension time of charging according to the first residual charging time, the current charging time and the predicted vehicle using time;
the second determination module is used for determining the charging allowable input current according to the charging allowable extension time;
the estimated vehicle using time is vehicle using time set by a user or vehicle using time sent by the cloud server.
The embodiment of the application further provides an attenuation intelligent pre-control method based on big data of user habits, which is applied to a cloud server and comprises the following steps:
acquiring vehicle using time data of a target vehicle within a preset time range;
determining the predicted vehicle using time of the target vehicle according to a standard deviation formula;
transmitting the estimated vehicle usage time to the target vehicle.
Optionally, the method further includes:
acquiring the current SOC of a power battery of the target vehicle, the long-distance running probability, the average value of long-distance running and a preset standard mileage;
determining a predicted SOC according to the current SOC, the long-distance driving probability, the average value of the long-distance driving and the preset standard mileage;
transmitting the predicted SOC to the target vehicle.
Optionally, the obtaining the long-distance running probability and the average value of the long-distance running includes:
acquiring a first total travel days of the target vehicle within a preset time range and a first travel days of a preset standard mileage which is more than or equal to a preset multiple, wherein the first total travel days are more than five days;
determining the long-distance driving probability according to a first ratio of the first travel days to the first total travel days; determining the average value of long-distance running according to the total travelling distance corresponding to the first travelling days and the second ratio of the first travelling days;
and if the second trip days of the preset standard mileage smaller than the preset multiple appear for three times continuously, determining that the average values of the long-distance running probability and the long-distance running are both 0.
Optionally, the obtaining the long-distance running probability and the average value of the long-distance running further includes:
acquiring a second total number of travel days of the target vehicle within a preset time range and a third total number of travel days of a preset standard mileage, wherein the second total number of travel days is less than or equal to five days;
calculating a third ratio of the third travel days to the second travel total days, and determining the long-distance travel probability if the third ratio is greater than or equal to 60%; determining the average value of long-distance running according to the total travelling distance corresponding to the third travelling days and the fourth ratio of the third travelling days;
and if the third ratio is less than 60%, determining that the average values of the long-distance running probability and the long-distance running are both 0.
The embodiment of the present application further provides a charging control device, which is applied to a cloud server, and includes:
the second acquisition module is used for acquiring the vehicle using time data of the target vehicle within a preset time range;
the third determination module is used for determining the predicted vehicle using time of the target vehicle according to a standard deviation formula;
and the sending module is used for sending the predicted vehicle using time to the target vehicle.
The embodiment of the present application further provides a readable storage medium, where a program is stored, and when the program is executed by a processor, the steps of the attenuation intelligent pre-control method based on big data of user habit are implemented as described above.
The beneficial effect of this application is:
in the above technical solution, the method includes: acquiring the current first remaining charging time, the current charging time and the predicted vehicle using time of the power battery; determining the allowable extension time length of charging according to the first residual charging time length, the current charging time and the predicted vehicle using time; determining a charging allowable input current according to the charging allowable extension time; the estimated vehicle using time is vehicle using time set by a user or vehicle using time sent by the cloud server. The method and the device have the advantages that the current remaining time of the power battery is obtained, the current charging rate is adjusted according to the vehicle using time sent by the cloud server, the charging current of the battery can be controlled, the battery attenuation is delayed, and the service life of the battery is prolonged.
Drawings
FIG. 1 is a schematic block diagram illustrating an intelligent attenuation pre-control method based on big data of user habits according to an embodiment of the present application;
fig. 2 is a second schematic block diagram of an intelligent attenuation pre-control method based on big data of user habits according to an embodiment of the present application;
FIG. 3 is a system flowchart of an intelligent attenuation pre-control method based on big data of user habits according to an embodiment of the present application;
fig. 4 is a block diagram of a charging control apparatus according to an embodiment of the present disclosure;
fig. 5 shows a second block diagram of a charging control apparatus according to an embodiment of the present application.
Detailed Description
To make the technical problems, technical solutions and advantages to be solved by the present application clearer, the following detailed description is made with reference to the accompanying drawings and specific embodiments. In the following description, specific details such as specific configurations and components are provided only to help the embodiments of the present application be fully understood. Accordingly, it will be apparent to those skilled in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present application. In addition, descriptions of well-known functions and constructions are omitted for clarity and conciseness.
It should be appreciated that reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
In various embodiments of the present application, it should be understood that the sequence numbers of the following processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
Aiming at the problem that the accuracy of predicting the battery attenuation is low in the prior art, the application provides an intelligent attenuation pre-control method and device based on big data of user habits.
As shown in fig. 1, the present application provides an attenuation intelligent pre-control method based on big data of user habits, applied to a vehicle, including:
step 100, acquiring a current first remaining charging time, a current charging time and a predicted vehicle using time of a power battery;
step 200, determining the allowable extension time length of charging according to the first residual charging time length, the current charging time and the predicted vehicle using time;
step 300, determining a charging allowable input current according to the charging allowable extension time;
the estimated vehicle using time is vehicle using time set by a user or vehicle using time sent by the cloud server.
In the embodiment, in the charging process, according to the estimated vehicle using time, the allowable extension time length can be calculated for a first residual charging time length estimated under the set vehicle using time (or self-learning vehicle using time) and the estimated vehicle using time (BMS); based on the allowed extension time, the charge allowed current (lower than the maximum allowed current) is reversely pushed, if the time is allowed to be sufficient, the battery is recharged to the cut-off SOC (or fully charged) after being charged to a certain SOC temporary state, the fully charged placing time is shortened, and the battery decay speed is reduced. According to the method and the device, a vehicle-end controller control strategy is considered, a cloud control strategy is also considered, and the accuracy of data is guaranteed.
The vehicle-end control strategy calculates the allowable extension time according to the predicted vehicle using time (or self-learning vehicle using time) and the residual charging time estimated by the BMS, and delays the battery attenuation based on the allowable extension time and the backward-pushing charging allowable current (lower than the maximum allowable current). The cloud control strategy learns the user travel time and calculates the charging cut-off SOC according to the user travel distance.
Optionally, the obtaining a current first remaining charging duration of the power battery includes:
acquiring initial temperature of a power battery, state of charge (SOC) of a plurality of time detection points in a charging process, and acquiring temperature rise change rate, SOC variation and charging average current of a plurality of time detection periods;
and determining the first remaining charging time according to the state of charge SOC, the temperature rise change rate, the SOC change amount and the charging average current.
In this embodiment, the first remaining charging time is determined according to the state of charge SOC, the temperature rise change rate, the SOC change amount, and the charging average current, so that the accuracy of data is ensured.
Optionally, the determining the first remaining charging time according to the state of charge SOC, the temperature rise change rate, the SOC change amount, and the charging average current includes:
acquiring a temperature rise rate in a charging process and a state of charge (SOC) variation in the heating process according to a currently stored heating parameter mapping table, wherein the currently stored heating parameter mapping table comprises a plurality of corresponding relations of the temperature rise rate, the SOC variation and the charging current corresponding to each SOC interval;
determining the heating time length according to the initial temperature and each temperature rise change rate;
here, the heating time period is (Ts-T)/VTavg, where T is the initial temperature, Ts is the minimum threshold value (default value 5 ℃) for starting heating, and Vtavg is the temperature rise change rate when the battery is heated.
Determining second residual charging time of the plurality of time detection points after the heating is stopped according to the SOC of the plurality of time detection points after the heating is stopped, and the charging current and the limit value of each SOC corresponding interval;
and determining the first remaining charging time length according to the second remaining charging time length and the heating time length.
Determining the first remaining charge time period is formulated as:
tN ═ Ts-T)/VTavg + (SOC2- Δ SOC-SOC0) × C/Iavgi + { [ SOC (i +1) -SOCi ] × C/Iavgi } (assuming SOC < SOC2 after heating); wherein t is the charging remaining time, Ts is the lowest threshold (default value 5 ℃) for starting heating, Vtavg is the average temperature rise rate of the battery during heating, Δ SOC is the variation of the battery SOC from the initial heating to the temperature Ts, Iavgi is the charging average current (calculated by the uncompensated charging request current) of each SOC interval, SOCi is used for calculating the starting SOC point and the ending SOC point of the average current, i is the minimum value of 2, and the maximum value of 9; and C is the maximum available capacity of the battery corresponding to the estimated charging end temperature.
Optionally, the determining that the charging is allowed to be extended for a long time includes:
performing difference operation on the estimated vehicle using time and the current charging time to determine a first difference;
and performing difference operation on the first difference and the first residual charging time length to determine the allowable charging extension time length.
In this embodiment, the estimated vehicle-use time t1 and the current charging time t2 are subjected to difference operation to determine a first difference value, and the first difference value and the first remaining charging time period t3 are subjected to difference operation to determine the charge allowable extension time period. That is, the allowable extension time period t0 is calculated as t1-t2-t3, where t1 is the predicted vehicle-use time, t2 is the current charging time, and t3 is the remaining time tN calculated by the remaining time formula.
Optionally, the method further includes:
when the charge allowable extension time meets a preset range, determining a charge allowable input current according to the charge allowable extension time; when the charge allowable extension time does not meet a preset range, determining that the power battery is charged by normal input current;
the preset range is that the charging allowable extension time is greater than the product of a first preset value and the first residual charging time, and the charging allowable extension time is less than or equal to a second preset value.
In this embodiment, the preset range is 20% × tN < t0 ≦ 5h, where a product of the first preset value and the first remaining charging time period is 20% × tN, the t0 is a charging allowable extension time period, the second preset value is 5h, and when the charging allowable extension time period satisfies the preset range, the step of determining the charging allowable input current according to the charging allowable extension time period is performed; when the charge allowable extension time does not meet the preset range, namely t0 is less than or equal to 20% t N or t0 is greater than 5h, the charging current is not adjusted, namely the power battery is determined to be charged with normal input current.
Optionally, when the charge allowable extended time period satisfies a preset range, determining the charge allowable input current according to the charge allowable extended time period includes:
acquiring a maximum output capacity CML (constant current limit) sent by the charging equipment;
and determining the charging allowable input current according to the CML and the first residual charging time period.
In this embodiment, when the charge allowable extension period satisfies the preset range, the reference current for adjustment is calculated as follows: and determining the charging allowable input current according to the CML and the first remaining charging time period, and dividing the node step-by-step calculation time according to the CML, if the current CML is 250A, calculating the time tref (i) corresponding to the time that the CML (i) is 230, 190, 150, 110, 70 (needs 5h for charging), and i is 0,1, 2, 3, 4, 5, if tref (i) is less than (tN + t0) and less than or equal to tref (i +1), using the CML (i) (i is CML (tN + t0) with the initial value of the maximum allowable charging current of the power battery charging pile) as the current CML (used for the remaining time calculation) and used for requesting the charging current and the remaining time calculation, otherwise, not changing the CML.
When the interval division corresponding to each CML can be that the CML is less than 70A, the corresponding CML is preferably 50; when the CML is more than or equal to 70 and less than 110A, the corresponding CML is preferably 90; when the CML is more than or equal to 110 and less than 150A, the CML is preferably 130; when the CML is more than or equal to 150 and less than 190A, the corresponding CML is preferably 170; when the CML is more than or equal to 190 and less than 230A, the corresponding CML is preferably 210; 230. ltoreq. CML < 110A, preferably corresponds to a CML of 250.
Optionally, the method further includes:
in the charging process, if a first preset condition is met, determining that a constant-voltage charging mode is adopted in the charging process;
wherein the first preset condition comprises:
the charging allowable input current is smaller than a third preset value, the current SOC of the power battery is larger than the first preset SOC, and a second difference value between the predicted vehicle using time and the current charging time is larger than a fourth preset value;
the first preset SOC is an expected SOC sent by the cloud server.
In this embodiment, during the charging process, if the first preset condition is met, the charging process enters a standstill state, that is, the power battery adopts a constant voltage charging mode (constant voltage charging to stopping charging), that is, the requested voltage is the current total voltage of the power battery. The first preset condition is as follows: according to the CML and the first residual charging time period, determining that CML (tN + t0) < 70A in the charging allowable input current, the current SOC of the power battery is greater than a first preset SOC (SOCU), the SOCU of the power battery is related to the distance which a user is accustomed to going to, the initial value of the SOCU is 90%, the second difference value between the predicted vehicle using time and the current charging time is greater than a fourth preset value, and the fourth preset value is 1.5 times of the first residual charging time period or 1h added to the first residual charging time period.
Optionally, the method further includes:
in the charging process, if a second preset condition is met, determining to exit the constant voltage charging mode;
wherein the second preset condition comprises: the second difference is smaller than a fifth preset value, wherein the fifth preset value is smaller than the fourth preset value.
In this embodiment, the second preset condition is: a second difference value between the predicted vehicle using time and the current charging time is less than a fifth preset value, and the fifth preset value is 1.3 times of the first residual charging time; or the second difference between the predicted vehicle using time and the current charging time is less than a fifth preset value, and the fifth preset value is the sum of the first residual charging time and 0.5 h. And when a second preset condition is met, the charging process exits from the stagnation state, namely, the constant-voltage charging mode is determined to exit.
Optionally, the method further includes:
and if the estimated vehicle using time is within the preset night time range, determining that the charging allowable input current is the maximum allowable charging current of the power battery.
In this embodiment, the preset evening time range is preferably a time period from 23 o 'clock of the day to 7 o' clock of the next day, and the charging allowable input current is determined to be the maximum allowable charging current of the power battery, that is, the charging is requested according to the maximum allowable charging current (MAP) of the power battery.
Optionally, the method further includes:
if the allowable charging extension time is less than zero, determining the maximum SOC which can be charged in the first remaining charging time;
it should be noted that, if the difference between the estimated vehicle using time and the current charging time, i.e. the first remaining charging time is greater than or equal to 0, the SOC can be cut off; and if the difference between the expected vehicle using time and the current charging time, namely the first residual charging time is less than 0, estimating the maximum SOC which can be charged in the time period of the charging allowable extension time according to the following method.
If the chargeable maximum SOC is smaller than a second preset SOC, determining that the power battery is normally charged, wherein the maximum SOC is obtained by descending at a first preset attenuation rate from a charging cut-off SOC;
and the second preset SOC is an estimated SOC sent by the cloud server.
In this embodiment, the maximum SOC is obtained by decrementing from the charging cut-off SOC at a first preset decay rate, that is, the maximum SOC is decremented from the charging cut-off SOC at a resolution of 1%, that is, t (SOCi), and the remaining time corresponding to the decremented SOC is calculated, and the maximum SOC is deduced from the charging time, that is, t (SOCi); if t (SOCi) > the charge allowed extension period & t (SOCi-1) ≦ the charge allowed extension period, it is considered that (SOCi-1) is chargeable for the period of the charge allowed extension period. And judging whether the user travel requirement is met or not by estimating the chargeable quantity, wherein the electric quantity value is larger than a second preset SOC, and if the electric quantity value is smaller than the second preset SOC, the charging time is not prolonged, and the power battery is determined to be normally charged.
As shown in fig. 2, an embodiment of the present application further provides an attenuation intelligent pre-control method based on big data of user habits, which is applied to a cloud server, and includes:
step 400, obtaining vehicle using time data of a target vehicle within a preset time range;
step 500, determining the predicted vehicle using time of the target vehicle according to a standard deviation formula;
and step 600, sending the predicted vehicle using time to the target vehicle.
In this embodiment, in order to distinguish the working day and the holiday to calculate the user usage time habit, the preset time range is preferably to count the first preparation "ready" time of 4:00-12:00 time periods (the user-set time is preferentially used if the user sets the time) in the morning of the working day and the holiday, respectively, calculate at least 3 times according to the standard deviation formula, and calculate the maximum data length to be 30 times, that is, when the calculation times is greater than 30 times, discard the data before 30 times, retain the calculation results of the last 30 times, and send the calculated estimated usage time to the target vehicle.
The standard deviation formula here is:
Figure BDA0003037592100000111
in the formula, the values X1, X2, X3, are.
Optionally, the method further includes:
acquiring the current SOC of a power battery of the target vehicle, the long-distance running probability, the average value of long-distance running and a preset standard mileage;
determining a predicted SOC according to the current SOC, the long-distance driving probability, the average value of the long-distance driving and the preset standard mileage;
transmitting the predicted SOC to the target vehicle.
In this embodiment, a predicted SOC is determined according to the current SOC, the long-distance travel probability, the average value of the long-distance travel, and the preset standard mileage, and the predicted SOC is represented by SOCU:
that is, the formula for SOCU is:
Figure BDA0003037592100000112
wherein R is the preset standard mileage,
Figure BDA0003037592100000113
a maximum value of 1, PL being the long-distance travel probability,
Figure BDA0003037592100000114
is an average value of the long-distance running.
Optionally, PL and
Figure BDA0003037592100000115
the calculation method comprises the following steps of distinguishing Saturday days during calculation, and returning the calculation result to the vehicle end
Optionally, the obtaining the long-distance running probability and the average value of the long-distance running includes:
acquiring a first total travel days Day of the target vehicle in a preset time range and a first travel days Day DayL of a preset standard mileage which is more than or equal to a preset multiple, wherein the first total travel days are more than five days; the precondition is that distance (i) is more than or equal to 0.3 Rkm (R is the preset nominal mileage, and 0.3 is the preset multiple) when Day is more than 5 days.
Determining a long-distance driving probability PL according to a first ratio of the first travel days DayL to the first total travel days Day; determining the average value of long-distance running according to a second ratio of the total trip distance sigma DistanceL (j) corresponding to the first trip days DayL;
that is, it is expressed by the following formula:
when Day>At 5 days, distance (i) is more than or equal to 0.3 × Rkm (R is a preset nominal mileage), and the probability of long-distance driving in the next trip is PL-DayL/Day; the average distance in days of long-distance travel is the average value of long-distance travel, i.e. the average value of long-distance travel
Figure BDA0003037592100000116
And if the second trip days DayL1 of the preset standard mileage smaller than the preset multiple appears for three times, determining that the long-distance driving probability and the average value of the long-distance driving are both 0.
I.e. 3 consecutive days, Distance < 0.3R km per Day, PL-0, DayL 1-0, Day-0,
optionally, the obtaining the long-distance running probability and the average value of the long-distance running further includes:
acquiring a second total days of travel Day1 of the target vehicle within a preset time range and a third total days of travel Day DayL2 of a preset standard mileage which is greater than or equal to a preset multiple, wherein the second total days of travel Day Day1 is less than or equal to five days; the precondition is that distance (i) is more than or equal to 0.3 Rkm (R is the preset nominal mileage, and 0.3 is the preset multiple) when Day1 is less than or equal to 5 days.
Calculating a third ratio of the third travel days DayL2 to the second travel total days Day1, and determining the long-distance travel probability if the third ratio is greater than or equal to 60%; determining the average value of long-distance running according to a fourth ratio of the total trip distance sigma distanceL (j1) corresponding to the third trip days DayL 2;
that is, it is expressed by the following formula:
when Day1 is not more than 5 days, distance (i) is not less than 0.3 Rkm (R is the preset nominal mileage, 0.3 is the preset nominal mileage)
Multiple), the probability of long-distance running for the next trip is PL (DayL) 2/5; average value of long-distance running
Figure BDA0003037592100000121
Figure BDA0003037592100000122
And if the third ratio is less than 60%, determining that the average values of the long-distance running probability and the long-distance running are both 0.
In this embodiment, if the third ratio is less than 60%, it is determined that the third travel day DayL2 is less than 3 days, that is, the number of days occurring in the working day of the week is less than three days, which may be three discontinuous days, and it is determined that the long-distance travel probability and the average value of the long-distance travel are both 0. That is, PL is 0, DayL2 is 0, and Day is 0.
To sum up, this application combines the user to use the car time, reduces battery decay rate, extension battery life to can promote the intelligent car experience of user.
As shown in fig. 3, in the "vehicle-using time determination", the "vehicle-using time" is determined by the estimated vehicle-using time, the current charging time and the first remaining charging time period, the "reverse-driving allowable current" is allowed when the charging allowable extended time period satisfies the preset range, the "charging current is increased" when the charging temperature is increased to the suitable temperature in the preset evening time range, preferably, in the 7-point time period from 23 points to the next day, "charging current is maintained" in the non-preset evening time range, in the state of any condition current, it is further required to determine whether the current satisfies the stagnation condition, that is, "recharging after charging to a certain SOC is stagnated" or "charging to a certain SOC is not stagnated", the stagnation condition, that is, the stagnation condition explained in the vehicle-end method is determined, that the "vehicle-using start time for the user" is determined after the stagnation condition is determined, and when the vehicle using time is judged, if the charging allowable extension time does not meet the preset range, determining that the charging is normal currently. During normal charging, if the set vehicle-using time acquired by the vehicle end is valid, the chargeable amount can be estimated according to the time, and if the set vehicle-using time acquired by the vehicle end is invalid (namely, when the vehicle-using time is set for fault reminding), the vehicle-using time is normally full.
In summary, the method of the embodiment of the application not only obtains the current remaining time of the power battery, but also adjusts the current charging rate according to the vehicle using time sent by the cloud server, so that the charging current of the battery can be controlled, the battery attenuation is delayed, and the service life of the battery is prolonged.
As shown in fig. 4, an embodiment of the present application further provides a charge control device applied to a vehicle, including:
the first obtaining module 10 is configured to obtain a current first remaining charging time, a current charging time and a predicted vehicle using time of the power battery;
the first determining module 20 is configured to determine a charging allowable extension time according to the first remaining charging time, the current charging time and the predicted vehicle using time;
a second determining module 30, configured to determine a charging permission input current according to the charging permission extension time;
the estimated vehicle using time is vehicle using time set by a user or vehicle using time sent by the cloud server.
Optionally, the first obtaining module 10 includes:
the first acquisition unit is used for acquiring the initial temperature of the power battery and the SOC of a plurality of time detection points in the charging process, and acquiring the temperature rise change rate, the SOC variation and the charging average current of a plurality of time detection periods;
the first determining unit is used for determining the first remaining charging time according to the state of charge SOC, the temperature rise change rate, the SOC change amount and the charging average current.
Optionally, the first determining unit includes:
the system comprises a first obtaining subunit, a second obtaining subunit, a third obtaining subunit and a fourth obtaining subunit, wherein the first obtaining subunit is used for obtaining a temperature rise rate in a charging process and a state of charge (SOC) variation in the heating process according to a currently stored heating parameter mapping table, and the currently stored heating parameter mapping table comprises a plurality of corresponding relations of the temperature rise rate, the SOC variation and a charging current corresponding to each SOC interval;
the first determining subunit is used for determining the heating time length according to the initial temperature and each temperature rise change rate;
the second determining subunit is used for determining second remaining charging time lengths of the plurality of time detection points after the heating is stopped according to the SOCs of the plurality of time detection points after the heating is stopped, the charging current and the limit value of each SOC corresponding interval;
and determining the first remaining charging time length according to the second remaining charging time length and the heating time length.
Optionally, the first determining module 20 includes:
the second determining unit is used for performing difference operation on the estimated vehicle using time and the current charging time to determine a first difference;
and the third determining unit is used for performing difference operation on the first difference and the first residual charging time length and determining the charging allowable extension time length.
It should be noted that, when the charge allowable extended time length meets the preset range, the first determining module 20 is configured to determine the charge allowable input current according to the charge allowable extended time length; when the charge allowable extension time does not meet a preset range, the first determining module 20 is configured to determine that the power battery is charged with a normal input current;
the preset range is that the charging allowable extension time is greater than the product of a first preset value and the first residual charging time, and the charging allowable extension time is less than or equal to a second preset value.
Optionally, the second determining module 30 includes:
the second acquisition unit is used for acquiring the maximum output capacity CML sent by the charging equipment;
and the fourth determination unit is used for determining the charging allowable input current according to the CML and the first residual charging time.
Optionally, the apparatus further comprises:
the first determining submodule is used for determining that a constant voltage charging mode is adopted in the charging process if a first preset condition is met in the charging process;
wherein the first preset condition comprises:
the charging allowable input current is smaller than a third preset value, the current SOC of the power battery is larger than the first preset SOC, and a second difference value between the predicted vehicle using time and the current charging time is larger than a fourth preset value;
the first preset SOC is an expected SOC sent by the cloud server.
Optionally, the apparatus further comprises:
the second determining submodule is used for determining to exit the constant-voltage charging mode if a second preset condition is met in the charging process;
wherein the second preset condition comprises: the second difference is smaller than a fifth preset value, wherein the fifth preset value is smaller than the fourth preset value.
Optionally, the apparatus further comprises:
and the third determining submodule is used for determining that the charging allowable input current is the maximum allowable charging current of the power battery if the expected vehicle using time is within the preset night time range.
Optionally, the apparatus further comprises:
a fourth determining submodule, configured to determine a maximum SOC that may be charged within the first remaining charging time period if the allowable charging extension time period is less than zero;
the fifth determining submodule is used for determining that the power battery is normally charged if the maximum chargeable SOC is smaller than a second preset SOC, wherein the maximum SOC is obtained by descending at a first preset attenuation rate from a charging cut-off SOC;
and the second preset SOC is an estimated SOC sent by the cloud server.
As shown in fig. 5, an embodiment of the present application further provides a charging control device, which is applied to a cloud server, and includes:
the second obtaining module 40 is used for obtaining the vehicle using time data of the target vehicle within the preset time range;
a third determination module 50, configured to determine an expected vehicle usage time of the target vehicle according to a standard deviation formula;
a sending module 60, configured to send the predicted vehicle using time to the target vehicle.
Optionally, the apparatus further comprises:
the third acquisition module is used for acquiring the current SOC of the power battery of the target vehicle, the long-distance running probability, the average value of long-distance running and the preset standard mileage;
the fourth determination module is used for determining the predicted SOC according to the current SOC, the long-distance driving probability, the average value of the long-distance driving and the preset standard mileage;
a transmitting module to transmit the predicted SOC to the target vehicle.
Optionally, the third obtaining module includes:
a third obtaining unit, configured to obtain a first total number of travel days of the target vehicle within a preset time range and a first total number of travel days of a preset standard mileage that is greater than or equal to a preset multiple, where the first total number of travel days is greater than five days;
a fifth determining unit, configured to determine, according to a first ratio between the first travel days and the first total travel days, a long-distance travel probability; determining the average value of long-distance running according to the total travelling distance corresponding to the first travelling days and the second ratio of the first travelling days;
and if the second trip days of the preset standard mileage smaller than the preset multiple appear for three times continuously, determining that the average values of the long-distance running probability and the long-distance running are both 0.
Optionally, the third obtaining module further includes:
a fourth obtaining unit, configured to obtain a second total number of days of travel of the target vehicle within a preset time range, and a third total number of days of travel of a preset standard mileage that is greater than or equal to a preset multiple, where the second total number of days of travel is less than or equal to five days;
a sixth determining unit, configured to calculate a third ratio between the third travel days and the second travel total days, and determine that the long distance travel probability is obtained if the third ratio is greater than or equal to 60%; determining the average value of long-distance running according to the total travelling distance corresponding to the third travelling days and the fourth ratio of the third travelling days;
and a seventh determining unit configured to determine that both the long-distance travel probability and the average value of the long-distance travel are 0 if the third ratio is less than 60%.
The embodiment of the present application further provides a readable storage medium, where a program is stored on the readable storage medium, and when the program is executed by a processor, the program implements the processes of the above embodiment of the attenuation intelligent pre-control method based on big data of user habit, and can achieve the same technical effects, and in order to avoid repetition, the detailed description is omitted here. The readable storage medium may be a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal 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. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
While the foregoing is directed to the preferred embodiment of the present application, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the principles of the disclosure and, therefore, the scope of the disclosure is to be defined by the appended claims.

Claims (17)

1. The intelligent attenuation pre-control method based on big data of user habits is applied to a vehicle and comprises the following steps:
acquiring the current first remaining charging time, the current charging time and the predicted vehicle using time of the power battery;
determining the allowable extension time length of charging according to the first residual charging time length, the current charging time and the predicted vehicle using time;
determining a charging allowable input current according to the charging allowable extension time;
the estimated vehicle using time is vehicle using time set by a user or vehicle using time sent by the cloud server.
2. The intelligent attenuation pre-control method based on big user habit data according to claim 1, wherein the obtaining the current first remaining charging time of the power battery comprises:
acquiring initial temperature of a power battery, state of charge (SOC) of a plurality of time detection points in a charging process, and acquiring temperature rise change rate, SOC variation and charging average current of a plurality of time detection periods;
and determining the first remaining charging time according to the state of charge SOC, the temperature rise change rate, the SOC change amount and the charging average current.
3. The user habit big data-based attenuation intelligent pre-control method according to claim 2, wherein the determining the first remaining charging time period according to the state of charge SOC, the temperature rise change rate, the SOC change amount and the charging average current comprises:
acquiring a temperature rise rate in a charging process and a state of charge (SOC) variation in the heating process according to a currently stored heating parameter mapping table, wherein the currently stored heating parameter mapping table comprises a plurality of corresponding relations of the temperature rise rate, the SOC variation and the charging current corresponding to each SOC interval;
determining the heating time length according to the initial temperature and each temperature rise change rate;
determining second residual charging time of the plurality of time detection points after the heating is stopped according to the SOC of the plurality of time detection points after the heating is stopped, and the charging current and the limit value of each SOC corresponding interval;
and determining the first remaining charging time length according to the second remaining charging time length and the heating time length.
4. The intelligent attenuation pre-control method based on big data of user habits according to claim 1, wherein the determining the allowable extended charging time period comprises:
performing difference operation on the estimated vehicle using time and the current charging time to determine a first difference;
and performing difference operation on the first difference and the first residual charging time length to determine the allowable charging extension time length.
5. The intelligent attenuation pre-control method based on big data of user habits according to claim 1, wherein the method further comprises:
when the charge allowable extension time meets a preset range, determining a charge allowable input current according to the charge allowable extension time; when the charge allowable extension time does not meet a preset range, determining that the power battery is charged by normal input current;
the preset range is that the charging allowable extension time is greater than the product of a first preset value and the first residual charging time, and the charging allowable extension time is less than or equal to a second preset value.
6. The intelligent attenuation pre-control method based on big data of user habits according to claim 5, wherein when the charge allowable extended duration satisfies a preset range, the determining the charge allowable input current according to the charge allowable extended duration comprises:
acquiring a maximum output capacity CML (constant current limit) sent by the charging equipment;
and determining the charging allowable input current according to the CML and the first residual charging time period.
7. The intelligent attenuation pre-control method based on big data of user habits according to claim 1, wherein the method further comprises:
in the charging process, if a first preset condition is met, determining that a constant-voltage charging mode is adopted in the charging process;
wherein the first preset condition comprises:
the charging allowable input current is smaller than a third preset value, the current SOC of the power battery is larger than the first preset SOC, and a second difference value between the predicted vehicle using time and the current charging time is larger than a fourth preset value;
the first preset SOC is an expected SOC sent by the cloud server.
8. The intelligent attenuation pre-control method based on big data of user habits according to claim 7, wherein the method further comprises:
in the charging process, if a second preset condition is met, determining to exit the constant voltage charging mode;
wherein the second preset condition comprises: the second difference is smaller than a fifth preset value, wherein the fifth preset value is smaller than the fourth preset value.
9. The intelligent attenuation pre-control method based on big data of user habits according to claim 1, wherein the method further comprises:
and if the estimated vehicle using time is within the preset night time range, determining that the charging allowable input current is the maximum allowable charging current of the power battery.
10. The intelligent attenuation pre-control method based on big data of user habits according to claim 3, wherein the method further comprises:
if the allowable charging extension time is less than zero, determining the maximum SOC which can be charged in the first remaining charging time;
if the chargeable maximum SOC is smaller than a second preset SOC, determining that the power battery is normally charged, wherein the maximum SOC is obtained by descending at a first preset attenuation rate from a charging cut-off SOC;
and the second preset SOC is an estimated SOC sent by the cloud server.
11. A charge control device, applied to a vehicle, comprising:
the first acquisition module is used for acquiring the current first remaining charging time, the current charging time and the predicted vehicle using time of the power battery;
the first determining module is used for determining the allowable extension time of charging according to the first residual charging time, the current charging time and the predicted vehicle using time;
the second determination module is used for determining the charging allowable input current according to the charging allowable extension time;
the estimated vehicle using time is vehicle using time set by a user or vehicle using time sent by the cloud server.
12. The intelligent attenuation pre-control method based on big data habit of a user is applied to a cloud server and comprises the following steps:
acquiring vehicle using time data of a target vehicle within a preset time range;
determining the predicted vehicle using time of the target vehicle according to a standard deviation formula;
transmitting the estimated vehicle usage time to the target vehicle.
13. The intelligent attenuation pre-control method based on big data of user habits according to claim 12, wherein the method further comprises:
acquiring the current SOC of a power battery of the target vehicle, the long-distance running probability, the average value of long-distance running and a preset standard mileage;
determining a predicted SOC according to the current SOC, the long-distance driving probability, the average value of the long-distance driving and the preset standard mileage;
transmitting the predicted SOC to the target vehicle.
14. The method for intelligent attenuation pre-control based on big data of user habits according to claim 13, wherein the obtaining of the long-distance driving probability and the average value of the long-distance driving comprises:
acquiring a first total travel days of the target vehicle within a preset time range and a first travel days of a preset standard mileage which is more than or equal to a preset multiple, wherein the first total travel days are more than five days;
determining the long-distance driving probability according to a first ratio of the first travel days to the first total travel days; determining the average value of long-distance running according to the total travelling distance corresponding to the first travelling days and the second ratio of the first travelling days;
and if the second trip days of the preset standard mileage smaller than the preset multiple appear for three times continuously, determining that the average values of the long-distance running probability and the long-distance running are both 0.
15. The method for intelligent attenuation pre-control based on big data of user habits according to claim 14, wherein the obtaining of the long-distance driving probability and the average value of the long-distance driving further comprises:
acquiring a second total number of travel days of the target vehicle within a preset time range and a third total number of travel days of a preset standard mileage, wherein the second total number of travel days is less than or equal to five days;
calculating a third ratio of the third travel days to the second travel total days, and determining the long-distance travel probability if the third ratio is greater than or equal to 60%; determining the average value of long-distance running according to the total travelling distance corresponding to the third travelling days and the fourth ratio of the third travelling days;
and if the third ratio is less than 60%, determining that the average values of the long-distance running probability and the long-distance running are both 0.
16. The utility model provides a charge control device which characterized in that is applied to high in the clouds server, includes:
the second acquisition module is used for acquiring the vehicle using time data of the target vehicle within a preset time range;
the third determination module is used for determining the predicted vehicle using time of the target vehicle according to a standard deviation formula;
and the sending module is used for sending the predicted vehicle using time to the target vehicle.
17. A readable storage medium, characterized in that the readable storage medium stores thereon a program, which when executed by a processor implements the steps of the method for intelligent pre-control of attenuation based on big data of user habit according to any one of claims 1 to 10, or which when executed by a processor implements the steps of the method for intelligent pre-control of attenuation based on big data of user habit according to any one of claims 12 to 15.
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