CN113071373B - Temperature prediction and device based on cloud intelligent interconnected big data - Google Patents
Temperature prediction and device based on cloud intelligent interconnected big data Download PDFInfo
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- CN113071373B CN113071373B CN202110447776.8A CN202110447776A CN113071373B CN 113071373 B CN113071373 B CN 113071373B CN 202110447776 A CN202110447776 A CN 202110447776A CN 113071373 B CN113071373 B CN 113071373B
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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
- B60L58/12—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]
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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
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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
- 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
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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
- 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
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02T90/10—Technologies relating to charging of electric vehicles
- Y02T90/16—Information or communication technologies improving the operation of electric vehicles
Abstract
The application provides a temperature prediction and device based on cloud intelligent interconnected big data, and relates to the technical field of automobiles. The method comprises the following steps: acquiring an initial state of charge (SOC) and an initial temperature of a power battery; determining a first predicted temperature rise amount when the power battery is charged at the end according to the initial state of charge (SOC) and the initial temperature; acquiring a second predicted temperature rise determined by the cloud server from the initial state of charge (SOC) and the initial temperature when charging is started to the end of charging; and determining the predicted temperature of the power battery at the charge cut-off time according to the first predicted temperature rise and the second predicted temperature rise. The technical scheme provided by the application can predict the self temperature rise time of the power battery, can also predict the temperature rise amount, and can reduce the heating threshold value through the predicted temperature rise amount, thereby achieving the purpose of reducing the thermal energy consumption management.
Description
Technical Field
The application relates to the technical field of automobiles, in particular to a temperature prediction and device based on cloud intelligent interconnected big data.
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, along with the increase of charging time, the battery is heated to generate more heat, on one hand, the energy is wasted, and the service life of the battery is also influenced. In addition, in the prior art, the estimation of the temperature rise of the battery is only carried out by considering the temperature prediction of the vehicle end, which often causes inaccurate temperature prediction.
Content of application
The embodiment of the application provides a temperature prediction and device based on cloud intelligent interconnected big data to solve the problem that the temperature rise prediction in the battery charging process is inaccurate.
In order to solve the technical problem, the following technical scheme is adopted in the application:
the embodiment of the application provides a temperature prediction based on interconnected big data of high in clouds intelligence, is applied to the vehicle, includes:
acquiring an initial state of charge (SOC) and an initial temperature of a power battery;
Determining a first predicted temperature rise amount when the power battery is charged at the end according to the initial state of charge (SOC) and the initial temperature;
acquiring a second predicted temperature rise determined by the cloud server from the initial state of charge (SOC) and the initial temperature when charging is started to the end of charging;
and determining the predicted temperature of the power battery when the charging is stopped according to the first predicted temperature rise and the second predicted temperature rise.
Optionally, the determining a first predicted temperature rise amount at the charge cut-off according to the initial state of charge SOC and the initial temperature includes:
determining a plurality of SOC intervals in the charging process according to the initial state of charge (SOC); each SOC interval corresponds to a time detection period;
determining the temperature rise variation corresponding to each SOC interval corresponding to the initial temperature according to a prestored first charging parameter mapping table; the first charging parameter mapping table comprises a corresponding relation between each SOC interval and temperature rise variation in a plurality of temperature intervals;
determining the first predicted temperature rise according to the temperature rise variation corresponding to each SOC interval;
the first charging parameter mapping table is updated according to the previous charging process.
Optionally, determining the first predicted temperature rise amount according to the temperature rise variation corresponding to each SOC interval includes:
according to the initial temperature and a first preset temperature, determining a difference value between the first preset temperature and the initial temperature as a first temperature variation in the heating process;
according to the temperature intervals, acquiring a plurality of temperature rise variable quantities of each SOC interval from a first charging parameter mapping table stored at present;
performing summation operation according to the temperature rise variable quantities, and determining a second temperature variable quantity in the heating process of the power battery;
determining a first predicted temperature rise amount when the power battery is charged at the end according to the first temperature variation and the second temperature variation;
the first preset temperature is a critical temperature value for dividing normal temperature and high temperature.
Optionally, the method further includes:
acquiring a target state of charge (SOC) when the power battery is charged and a plurality of SOC variable quantities in the charging process;
if the difference value between the target SOC and the initial SOC is larger than the sum of the SOC variation quantities, executing a step of determining a first predicted temperature rise quantity when the power battery is charged at the cut-off according to the initial SOC and the initial temperature;
Otherwise, determining the first predicted temperature rise amount as a difference value between a second preset temperature and the initial temperature;
wherein the second preset temperature is a critical temperature value for dividing normal temperature and high temperature.
Optionally, the determining the predicted temperature of the power battery at the time of charge cutoff according to the first predicted temperature rise amount and the second predicted temperature rise amount includes:
acquiring a first weight corresponding to the first predicted temperature rise and a second weight corresponding to the second predicted temperature rise;
and determining the predicted temperature of the power battery at the charge cut-off time according to the first weight, the second weight, the first predicted temperature rise and the second predicted temperature rise.
Optionally, obtaining a first weight corresponding to the first predicted temperature rise amount and a second weight corresponding to the second predicted temperature rise amount includes:
determining all predicted temperature rises as the sum of the first predicted temperature rise and the second predicted temperature rise;
determining the first weight according to the first predicted temperature rise and the all predicted temperature rises;
determining the second weight according to the second predicted temperature rise and the total predicted temperature rise; wherein a sum of the first weight and the second weight is 1.
The embodiment of the application further provides a temperature prediction based on cloud intelligence interconnected big data, is applied to the cloud server, includes:
acquiring an initial state of charge (SOC) and an initial temperature of a power battery of a target vehicle;
determining a second predicted temperature rise amount of the power battery when the charging is stopped according to the initial state of charge (SOC) and the initial temperature;
sending the second predicted temperature rise to the target vehicle.
Optionally, the determining a second predicted temperature rise amount corresponding to the initial state of charge SOC and the initial temperature includes:
acquiring a third predicted temperature rise amount after the plurality of vehicles of which the vehicle states are matched with the vehicle state of the target vehicle are respectively charged at the initial state of charge (SOC) and the initial temperature;
determining the second predicted temperature rise amount according to the plurality of third predicted temperature rise amounts;
wherein the vehicle state at least comprises the regional position information of the vehicle.
Optionally, the determining the second predicted temperature rise amount according to the plurality of third predicted temperature rises amounts includes:
and accumulating and summing the plurality of third predicted temperature rise amounts, and dividing the accumulated and summed result by the third predicted temperature rise amount to determine the second predicted temperature rise amount.
Optionally, the determining a second predicted temperature rise amount of the power battery at the charge cut-off further includes:
acquiring batch training data of vehicles in a charging process;
analyzing the obtained batch training data, then learning big data, and generating a prediction control instruction aiming at the initial state of charge (SOC) and a prediction control instruction aiming at the initial temperature of the vehicle according to a preset learning rule;
associating all the prediction control instructions generated by learning with the corresponding initial state of charge (SOC) and initial temperature to generate a prediction instruction database;
according to the initial state of charge (SOC) and the initial temperature, matching with the prediction instruction database to determine the second prediction temperature rise;
wherein the training data comprises: the initial state of charge SOC, the initial temperature in the vehicle charging process, the corresponding relation between a plurality of SOC intervals and each SOC interval and the temperature rise variable quantity in the vehicle charging process.
Optionally, the training data includes a driving location, an initial temperature of the power battery, and an initial state of charge SOC of the power battery, and the preset learning rule includes at least one of the following rules:
extracting charging control behaviors of a plurality of vehicles in the same area;
Extracting a plurality of historical charging control behaviors of the same vehicle at the same place;
extracting charging control behaviors of a plurality of vehicles aiming at the same initial temperature and initial state of charge (SOC);
a plurality of historical charging control behaviors of the same vehicle at the same initial temperature and initial state of charge (SOC) are extracted.
The embodiment of the present application further provides a temperature prediction apparatus, which is applied to a vehicle, and includes:
the first acquisition module is used for acquiring the initial state of charge (SOC) and the initial temperature of the power battery;
the first determining module is used for determining a first predicted temperature rise amount when the power battery is charged at the end according to the initial state of charge (SOC) and the initial temperature;
the second acquisition module is used for acquiring a second predicted temperature rise amount which is determined by the cloud server and is from the initial state of charge (SOC) and the initial temperature when charging is started to the charging is stopped;
and the second determination module is used for determining the predicted temperature of the power battery at the charge cut-off time according to the first predicted temperature rise amount and the second predicted temperature rise amount.
The embodiment of the present application further provides a temperature prediction device, which is applied to a cloud server, and includes:
the third acquisition module is used for acquiring the initial state of charge (SOC) and the initial temperature of the power battery of the target vehicle;
The third determining module is used for determining a second predicted temperature rise of the power battery when the charging is stopped according to the initial state of charge (SOC) and the initial temperature;
and the sending module is used for sending the second predicted temperature rise amount to the target vehicle.
An 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 step of temperature prediction based on cloud-based intelligent interconnection big data is implemented.
The beneficial effect of this application is:
in the technical scheme, the method comprises the steps of obtaining an initial state of charge (SOC) and an initial temperature of a power battery; determining a first predicted temperature rise amount when the power battery is charged at the end according to the initial state of charge (SOC) and the initial temperature; acquiring a second predicted temperature rise determined by the cloud server from the initial state of charge (SOC) and the initial temperature when charging is started to the end of charging; and determining the predicted temperature of the power battery at the charge cut-off time according to the first predicted temperature rise and the second predicted temperature rise. The predicted temperature rise of the vehicle end can be adjusted according to the predicted temperature rise of the cloud end, so that the purpose of data correction is achieved, and the energy consumption of heat management can be reduced according to the determined predicted temperature when the power battery is charged at the end.
Drawings
Fig. 1 is a schematic flow chart illustrating temperature prediction based on cloud-based smart interconnection big data according to an embodiment of the present disclosure;
fig. 2 is a second schematic flow chart illustrating temperature prediction based on cloud-based smart interconnect big data according to an embodiment of the present application;
FIG. 3 is a schematic flow chart of a temperature prediction apparatus according to an embodiment of the present disclosure;
fig. 4 is a second schematic flowchart of a temperature prediction 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.
The application provides a temperature prediction and device based on cloud intelligence interconnection big data to the inaccurate problem of temperature rise prediction of battery charging process.
As shown in fig. 1, an embodiment of the present application provides a temperature prediction based on cloud-based intelligent interconnected big data, which is applied to a vehicle, and includes:
step S100, acquiring an initial state of charge (SOC) and an initial temperature of a power battery;
here, by acquiring the initial SOC and the initial temperature of the power battery and providing data support for the subsequent, it is also possible to know the data comparison of the SOC and the cutoff temperature when the initial SOC is raised to the charge cutoff.
Step S200, determining a first predicted temperature rise amount when the power battery is charged at the end according to the initial state of charge (SOC) and the initial temperature;
here, the first predicted temperature rise amount is obtained through the vehicle end, reference can be made according to a predicted value of the vehicle, and certainly when the network communication fails, the first predicted temperature rise amount is mainly used as a result of the prediction, so that whether the network communication exists or not is guaranteed, the first predicted temperature rise amount caused by the charging stop of the power battery can be obtained.
Step S300, acquiring a second predicted temperature rise amount, which is determined by the cloud server and is from the initial state of charge (SOC) and the initial temperature when charging is started to the charging is stopped;
here, by acquiring the second predicted temperature rise amount sent by the cloud server, it can be ensured that the temperature rise amount acquired through the big data is used as another auxiliary or correction data under the same initial state of charge (SOC) and initial temperature, so that the first predicted temperature rise amount predicted by the vehicle end is more accurate, and the accuracy of the predicted temperature rise amount is improved.
And step S400, determining the predicted temperature of the power battery at the time of charge cut-off according to the first predicted temperature rise and the second predicted temperature rise.
According to the embodiment, the weight corresponding to each temperature rise amount is obtained according to the first predicted temperature rise amount and the second predicted temperature rise amount, and the predicted temperature of the power battery at the time of charge ending is determined, so that the predicted data of the power battery is more accurate.
It should be noted that, on the premise that the charging time is relatively abundant, the charging time is not strongly required, and the charging energy consumption is intentionally reduced, the predicted temperature at the end of the charging of the power battery can be predicted through the steps S100 to S400, the charging temperature rise of the battery can be reasonably utilized, the heating threshold value can be intelligently reduced, and the control threshold value is further corrected by combining the calculation result of the second predicted temperature rise sent by the cloud server, so that the thermal management energy consumption can be reduced.
Optionally, step S200 includes:
determining a plurality of SOC intervals in the charging process according to the initial SOC; each SOC interval corresponds to a time detection period;
here, it should be noted that, a user or a developer may divide the SOC interval in advance, for example: as the SOC interval increases, the span of the SOC interval gradually decreases, as: SOC is more than or equal to 20% and less than or equal to 40%, SOC is more than or equal to 90% and less than or equal to 95%, and the like. Of course, the initial temperature may be divided into zones, or the real-time temperature during the heating process may be divided into zones, such as: the initial temperature is less than or equal to 15 ℃ and is a temperature interval, and the initial temperature is more than 15 ℃ and is another temperature interval; or, divided according to real-time temperature.
Determining the temperature rise variation corresponding to each SOC interval corresponding to the initial temperature according to a prestored first charging parameter mapping table; the first charging parameter mapping table comprises a corresponding relation between each SOC interval and temperature rise variation in a plurality of temperature intervals;
for example, the variation of temperature rise corresponding to the first SOC interval and the second SOC interval, that is, Δ T ═ Δ T (2) - Δt (1), Δ T (2) is the temperature rise corresponding to the second SOC interval, and Δ T (1) is the temperature rise corresponding to the first SOC interval, so the total formula of the variation of temperature rise is: Δ T (i +1) Δ T (i), Δ T (i +1), and Δ T (i) are temperatures corresponding to the highest temperature of the cell when the SOC reaches SOC (i +1) and SOC (i), respectively.
That is, the step may specifically be to obtain data in corresponding temperature intervals according to the initial temperature, where each temperature interval includes temperature rise variation amounts corresponding to a plurality of SOC intervals, that is, obtain the first SOC interval and the temperature rise variation amounts of the SOC intervals after the first SOC interval.
Determining the first predicted temperature rise according to the temperature rise variation corresponding to each SOC interval; the first charging parameter mapping table is a mapping table updated according to a previous charging process.
Under the condition that the initial temperature of the power battery is lower than a first preset temperature, acquiring each SOC interval and temperature rise variation according to a currently stored charging parameter mapping table, wherein the currently stored heating parameter mapping table is a mapping table updated according to a previous heating process;
that is to say, in the optional implementation manner, the charging parameter mapping table is a mapping table that is continuously updated according to the heating process of the power battery, so that the temperature rise variation obtained in the optional implementation manner is closer to the true temperature rise variation of the power battery, and thus, the accuracy of temperature rise prediction when the heating of the power battery is stopped can be improved.
Specifically, determining the first predicted temperature rise amount according to the temperature rise variation amount corresponding to each SOC interval includes:
according to the initial temperature and a first preset temperature, determining a difference value between the first preset temperature and the initial temperature as a first temperature variation in the heating process; the first preset temperature is a critical temperature value for dividing normal temperature and high temperature.
Here, the first preset temperature value is preferably 15 ℃, that is, when the initial temperature is less than or equal to 15 ℃, it is determined that the temperature is the normal temperature, the first temperature variation is 15-T0, and the T0 is the initial temperature, and the temperature increase amount of the first part may be calculated.
According to the temperature intervals, acquiring a plurality of temperature rise variable quantities of each SOC interval from a first charging parameter mapping table stored at present;
performing summation operation according to the temperature rise variable quantities, and determining a second temperature variable quantity in the heating process of the power battery;
that is, the second temperature change amount is determined according to the following equation:
Determining a first predicted temperature rise amount when the power battery is charged at the end according to the first temperature variation and the second temperature variation;
That is, the first predicted temperature rise amount at the time of charge cutoff of the power battery is determined according to the following formula:
the delta b1(SOCs) is a first predicted temperature rise amount when the charging of the power battery is cut off.
The first predicted temperature rise amount is accumulated from SOC0+ Σ Δ SOC (i) to the charge cut-off SOC, the initial SOC value is SOC0, and when SOC is equal to or greater than SOC (i +1), SOC is equal to SOC (i + 1).
Specifically, the method further comprises:
acquiring a target state of charge (SOC) when the power battery is charged and a plurality of SOC variable quantities in the charging process;
if the difference value between the target SOC and the initial SOC is larger than the sum of the SOC variation quantities, executing a step of determining a first predicted temperature rise quantity when the power battery is charged at the cut-off according to the initial SOC and the initial temperature;
that is, if SOCE-SOC > Σ Δ SOC (i), Δ SOC (i) is the SOC charged during heating; SOCE is a target state of charge SOC when the power battery is charged and stopped; namely calculation
With the preconditions mentioned above.
Otherwise, determining the first predicted temperature rise amount as a difference value between a second preset temperature and the initial temperature;
wherein the second preset temperature is a critical temperature value for dividing normal temperature and high temperature.
Here, the second preset temperature is preferably 15 ℃, i.e., SOCE-SOC ≦ Σ Δ SOC (i), and the first predicted temperature rise amount is determined to be (15-T0), where T0 is the initial temperature of the power battery.
Of course, the calculation formula is the formula described above regardless of the state of the initial temperature drink.
Optionally, the step S400 includes:
acquiring a first weight P1 corresponding to the first predicted temperature rise amount and a second weight P2 corresponding to the second predicted temperature rise amount;
and determining the predicted temperature of the power battery at the charge cut-off according to the first weight P1, the second weight P2, the first predicted temperature rise amount and the second predicted temperature rise amount.
Converting the above into a formula, namely:
if SOCE-SOC > Σ Δ SOC (i), Δ SOC (i) is the SOC charged during heating;
Δ ts (SOCs)) (15-T0) + P1 × b1+ P2 × Σ Δ T (i +1) (the temperature rise is accumulated from SOC0+ Σ Δ SOC (i) to a charge cut-off SOCE, SOCE is a target state of charge SOC at the time of charge cut-off of the power battery, SOC initial value is SOC0, and when SOC is equal to or greater than SOC (i +1), SOC equals SOC (i + 1); b1 is the second predicted temperature rise, and the initial value of b1 is 0. Δ ts (socs) is the predicted temperature at the end of charge of the power cell.
When SOCE-SOC ≦ Σ Δ SOC (i), Δ ts (SOCs) ((15-T0)), Δ ts (SOCs) is the predicted temperature at the time of charge cut-off of the power battery.
Δ ts (socs) ═ Σ b2(i) × Δ T (i +1) if Tbat0 > 15 ℃; b2(i) is the second predicted temperature rise, Tbat0 is the initial temperature, Δ ts (socs) is the predicted temperature at the end of charge of the power battery.
Optionally, obtaining a first weight corresponding to the first predicted temperature rise amount and a second weight corresponding to the second predicted temperature rise amount includes:
determining all predicted temperature rises as the sum of the first predicted temperature rise and the second predicted temperature rise;
here, the total predicted temperature rise amount is set to (Eb1+ ET), Eb1 is the result calculated for the second predicted temperature rise amount, and ET is the result calculated for the first predicted temperature rise amount.
Determining the first weight P1 according to the first predicted temperature rise and the all predicted temperature rise;
that is, P1 is ET/(Eb1+ ET).
Determining the second weight P2 according to the second predicted temperature rise and the total predicted temperature rise; wherein a sum of the first weight and the second weight is 1.
Namely, P2 ═ Eb1/(Eb1+ ET), or P2 ═ 1-ET/(Eb1+ ET).
As shown in fig. 2, an embodiment of the present application further provides a temperature prediction based on cloud-based intelligent interconnected big data, which is applied to a cloud server, and includes:
Step S500, acquiring an initial state of charge (SOC) and an initial temperature of a power battery of a target vehicle;
step S600, determining a second predicted temperature rise of the power battery when the charging is stopped according to the initial state of charge (SOC) and the initial temperature;
and step S700, sending the second predicted temperature rise amount to the target vehicle.
According to the embodiment, the second predicted temperature rise amount which is the same as the initial state of charge (SOC) and the initial temperature of the power battery of the target vehicle can be calculated through the cloud server, so that calculation of big data is increased; the historical charging temperature rise curve can be learned through a Vehicle Identification Number (VIN) of a vehicle, namely the historical charging temperature rise curve is stored in a first charging parameter mapping table of a cloud server, the learning mode is the same as that of a vehicle end, and the temperature rise prediction is carried out by accumulating storage results in a segmented mode during the next charging.
Optionally, the step S600 includes:
acquiring a third predicted temperature rise amount after the plurality of vehicles of which the vehicle states are matched with the vehicle state of the target vehicle are respectively charged at the initial state of charge (SOC) and the initial temperature;
determining the second predicted temperature rise amount according to the plurality of third predicted temperature rise amounts;
wherein the vehicle state at least comprises the regional position information of the vehicle.
In this embodiment, the obtaining of the plurality of vehicles whose vehicle states match the vehicle state of the target vehicle may be considered to obtain a plurality of vehicles whose area position information is the same as that of the vehicle, on the premise that the same area is satisfied, obtaining the vehicles under the condition that the initial state of charge SOC and the initial temperature are the same, and calculating a third predicted temperature rise amount of the vehicle that both conditions are satisfied; and determining the second predicted temperature rise according to the plurality of third predicted temperature rises, so that the accuracy of cloud data can be ensured, the cloud historical data of the target vehicle before is obtained, the data of a plurality of vehicles is also obtained, comprehensive comparison is carried out, and the accuracy of the cloud data is ensured.
Specifically, the determining the second predicted temperature rise amount according to the plurality of third predicted temperature rise amounts includes:
and accumulating and summing the plurality of third predicted temperature rise amounts, and dividing the accumulated and summed result by the third predicted temperature rise amount to determine the second predicted temperature rise amount.
Note that b1 is Σ Δ b1(SOCs)/N, N is the number of vehicles calculated from the same parameters (e.g., the same number of vehicles in beijing, and the same initial state of charge SOC and initial temperature of the target vehicle), and b1 is the second predicted temperature increase amount.
Optionally, the step S600 further includes:
acquiring batch training data of vehicles in a charging process;
analyzing the obtained batch training data, then learning big data, and generating a prediction control instruction aiming at the initial state of charge (SOC) and a prediction control instruction aiming at the initial temperature of the vehicle according to a preset learning rule;
associating all the prediction control instructions generated by learning with the corresponding initial state of charge (SOC) and initial temperature to generate a prediction instruction database;
according to the initial state of charge (SOC) and the initial temperature, matching with the prediction instruction database to determine the second prediction temperature rise;
wherein the training data comprises: the initial state of charge SOC, the initial temperature in the vehicle charging process, the corresponding relation between a plurality of SOC intervals and each SOC interval and the temperature rise variable quantity in the vehicle charging process.
Specifically, the selection of the training data may be selected according to the purpose of charge control, for example, based on monitoring the charge control behavior of the vehicle in the same area, the driving data may be selected as the training data, otherwise, if the monitoring is based on the model of the vehicle or historical data before the vehicle, the selected working parameters may be changed according to the actual situation.
Optionally, the training data includes a driving location, an initial temperature of the power battery, and an initial state of charge SOC of the power battery, and the preset learning rule includes at least one of the following rules:
extracting charging control behaviors of a plurality of vehicles in the same area;
extracting a plurality of historical charging control behaviors of the same vehicle at the same place;
extracting charging control behaviors of a plurality of vehicles aiming at the same initial temperature and initial state of charge (SOC);
a plurality of historical charging control behaviors of the same vehicle at the same initial temperature and initial state of charge (SOC) are extracted.
The preset learning rule is set according to the purpose of charge control for the target vehicle, and may be arbitrarily changed, for example, to a rule of "extracting charge control behaviors of a plurality of vehicles in the same area", or may be further modified by adding a time limit to "extracting charge control behaviors of a plurality of vehicles in the same area in the same time zone". Therefore, the embodiments of the present application only list a few possible learning rules, and the learning rules can be set arbitrarily as required. After the preset learning rule is set, a big data learning mode is set, so that data with statistical significance and corresponding control behaviors are obtained and corresponding prediction control instructions are generated based on big data learning, and temperature rise prediction of a charging heating process can be achieved.
To sum up, the temperature prediction based on the cloud intelligent interconnection big data of this application can effectively intelligent reduce the heating energy consumption, reduces the user and uses car cost to can promote the user and intelligent use car experience.
As shown in fig. 3, an embodiment of the present application further provides a temperature prediction apparatus applied to a vehicle, including:
the first acquiring module 10 is used for acquiring an initial state of charge (SOC) and an initial temperature of the power battery;
the first determining module 20 is configured to determine a first predicted temperature rise amount when the power battery is charged at a cut-off state according to the initial state of charge SOC and the initial temperature;
the second obtaining module 30 is configured to obtain a second predicted temperature rise amount, which is determined by the cloud server and is from the initial state of charge SOC and the initial temperature when charging is started to the end of charging;
and the second determining module 40 is configured to determine the predicted temperature when the power battery is charged at the end according to the first predicted temperature rise amount and the second predicted temperature rise amount.
Optionally, the first determining module 20 includes:
the first determining unit is used for determining a plurality of SOC intervals in the charging process according to the initial SOC; each SOC interval corresponds to a time detection period;
The second determining unit is used for determining the temperature rise variation corresponding to the initial temperature in each SOC interval according to a prestored first charging parameter mapping table; the first charging parameter mapping table comprises a corresponding relation between each SOC interval and temperature rise variation in a plurality of temperature intervals;
a third determining unit, configured to determine the first predicted temperature rise amount according to the temperature rise variation amount corresponding to each SOC interval;
the first charging parameter mapping table is updated according to the previous charging process.
Optionally, the third determining unit includes:
the first determining subunit is used for determining a difference value between the first preset temperature and the initial temperature as a first temperature variation in the heating process according to the initial temperature and the first preset temperature;
the first obtaining subunit is configured to obtain, according to the temperature interval, a plurality of temperature rise variation amounts for each SOC interval from a currently stored first charging parameter mapping table;
the second determining subunit is used for performing summation operation according to the plurality of temperature rise variation quantities and determining a second temperature variation quantity in the heating process of the power battery;
the third determining subunit is used for determining a first predicted temperature rise amount when the power battery is charged at the end according to the first temperature variation and the second temperature variation;
The first preset temperature is a critical temperature value for dividing normal temperature and high temperature.
Optionally, the apparatus further comprises:
the fourth acquisition module is used for acquiring a target state of charge (SOC) when the power battery is stopped charging and a plurality of SOC variable quantities in the charging process;
a fourth determining unit, configured to, if a difference between the target state of charge SOC and the initial state of charge SOC is greater than a sum of a plurality of SOC variation amounts, perform a step of determining a first predicted temperature rise amount when the power battery is stopped charging according to the initial state of charge SOC and the initial temperature;
a fifth determining unit, configured to determine that the first predicted temperature rise amount is a difference between a second preset temperature and the initial temperature if the first predicted temperature rise amount is not the difference;
wherein the second preset temperature is a critical temperature value for dividing normal temperature and high temperature.
Optionally, the second determining module 40 includes:
a first acquiring unit configured to acquire a first weight corresponding to the first predicted temperature increase amount and a second weight corresponding to the second predicted temperature increase amount;
and a sixth determining unit configured to determine a predicted temperature at the time of charge cutoff of the power battery according to the first weight, the second weight, the first predicted temperature increase amount, and the second predicted temperature increase amount.
Optionally, the sixth determining unit includes:
a fourth determining subunit configured to determine that all of the predicted temperature increases are the sum of the first predicted temperature increase and the second predicted temperature increase;
a fifth determining subunit, configured to determine the first weight according to the first predicted temperature rise amount and the total predicted temperature rise amount;
a sixth determining subunit, configured to determine the second weight according to the second predicted temperature rise amount and the total predicted temperature rise amount; wherein a sum of the first weight and the second weight is 1.
As shown in fig. 4, an embodiment of the present application further provides a temperature prediction apparatus applied to a cloud server, including:
a third obtaining module 50, configured to obtain an initial state of charge SOC and an initial temperature of a power battery of the target vehicle;
a third determining module 60, configured to determine a second predicted temperature rise amount of the power battery at the charge cut-off according to the initial state of charge SOC and the initial temperature;
a sending module 70, configured to send the second predicted temperature rise amount to the target vehicle.
Optionally, the third determining module 60 includes:
the first obtaining submodule is used for obtaining a third predicted temperature rise amount after the plurality of vehicles with vehicle states matched with the vehicle state of the target vehicle are respectively charged at the initial state of charge (SOC) and the initial temperature;
The first determining submodule is used for determining the second predicted temperature rise according to the plurality of third predicted temperature rises;
wherein the vehicle state at least comprises the regional position information of the vehicle.
Optionally, the first determining sub-module includes:
and a seventh determining unit configured to add up and sum the plurality of third predicted temperature rise amounts, and determine the second predicted temperature rise amount by dividing a result of the addition and the number of the third predicted temperature rise amounts.
Optionally, the third determining module 60 further includes:
the second acquisition submodule is used for acquiring batch training data of the vehicles in the charging process;
the third acquisition submodule is used for analyzing the acquired batch training data, then performing big data learning, and generating a prediction control instruction aiming at the initial state of charge (SOC) of the vehicle and a prediction control instruction aiming at the initial temperature according to a preset learning rule;
the generation submodule is used for associating all the prediction control instructions generated by learning with the corresponding initial state of charge (SOC) and initial temperature to generate a prediction instruction database;
the second determining submodule is used for matching the initial state of charge (SOC) and the initial temperature with the prediction instruction database to determine the second predicted temperature rise;
Wherein the training data comprises: the initial state of charge SOC, the initial temperature in the vehicle charging process, the corresponding relation between a plurality of SOC intervals and each SOC interval and the temperature rise variable quantity in the vehicle charging process.
Optionally, the training data includes a driving location, an initial temperature of the power battery, and an initial state of charge SOC of the power battery, and the preset learning rule includes at least one of the following rules:
extracting charging control behaviors of a plurality of vehicles in the same area;
extracting a plurality of historical charging control behaviors of the same vehicle at the same place;
extracting charging control behaviors of a plurality of vehicles aiming at the same initial temperature and initial state of charge (SOC);
a plurality of historical charging control behaviors of the same vehicle at the same initial temperature and initial state of charge (SOC) are extracted.
The embodiment of the present application further provides a readable storage medium, where a program is stored, and when executed by a processor, the program implements the above-mentioned processes of the embodiment of temperature prediction based on cloud-based intelligent interconnection big data, 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, in this document, relational terms such as first and second, and the like are 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 appreciated by those skilled in the art that various changes and modifications may be made therein without departing from the principles of the application, and it is intended to cover such changes and modifications as fall within the scope of the application.
Claims (9)
1. A temperature prediction method based on cloud intelligent interconnection big data is characterized by comprising the following steps:
the method comprises the steps that a vehicle obtains an initial state of charge (SOC) and an initial temperature of a power battery;
determining a first predicted temperature rise amount of the power battery when the charging of the power battery is cut off by the vehicle according to the initial state of charge (SOC) and the initial temperature; the first predicted amount of temperature rise is taken by the vehicle;
the vehicle obtains a second predicted temperature rise amount which is determined by the cloud server and is from the initial state of charge (SOC) and the initial temperature when charging is started to the charging end;
the vehicle determines the predicted temperature of the power battery when the charging is cut off according to the first predicted temperature rise and the second predicted temperature rise;
determining a first predicted temperature rise amount at the charge cutoff according to the initial state of charge (SOC) and the initial temperature, wherein the first predicted temperature rise amount at the charge cutoff comprises the following steps:
determining a plurality of SOC intervals in the charging process according to the initial SOC; each SOC interval corresponds to a time detection period;
determining the temperature rise variation corresponding to each SOC interval corresponding to the initial temperature according to a prestored first charging parameter mapping table; the first charging parameter mapping table comprises a corresponding relation between each SOC interval and temperature rise variation in a plurality of temperature intervals;
Determining the first predicted temperature rise according to the temperature rise variation corresponding to each SOC interval;
the first charging parameter mapping table is updated according to the previous charging process;
wherein the method further comprises:
acquiring a target state of charge (SOC) when the power battery is charged and a plurality of SOC variable quantities in the charging process;
if the difference value between the target SOC and the initial SOC is larger than the sum of the SOC variation quantities, executing a step of determining a first predicted temperature rise quantity when the power battery is charged at the cut-off according to the initial SOC and the initial temperature;
otherwise, determining the first predicted temperature rise amount as a difference value between a second preset temperature and the initial temperature;
the second preset temperature is a critical temperature value for dividing normal temperature and high temperature;
wherein determining the predicted temperature at the end of charging the power battery according to the first predicted temperature rise amount and the second predicted temperature rise amount comprises:
acquiring a first weight corresponding to the first predicted temperature rise and a second weight corresponding to the second predicted temperature rise;
determining the predicted temperature of the power battery at the end of charging according to the first weight, the second weight, the first predicted temperature rise and the second predicted temperature rise;
Wherein obtaining a first weight corresponding to the first predicted temperature rise amount and a second weight corresponding to the second predicted temperature rise amount includes:
determining all predicted temperature rises as the sum of the first predicted temperature rise and the second predicted temperature rise;
determining the first weight according to the first predicted temperature rise and the all predicted temperature rises;
determining the second weight according to the second predicted temperature rise and the total predicted temperature rise; wherein a sum of the first weight and the second weight is 1.
2. The cloud-based intelligent interconnection big data temperature prediction method according to claim 1, wherein the determining the first predicted temperature rise amount according to the temperature rise variation corresponding to each SOC interval includes:
according to the initial temperature and a first preset temperature, determining a difference value between the first preset temperature and the initial temperature as a first temperature variation in the heating process;
according to the temperature intervals, acquiring a plurality of temperature rise variable quantities of each SOC interval from a first charging parameter mapping table stored at present;
performing summation operation according to the temperature rise variable quantities, and determining a second temperature variable quantity in the heating process of the power battery;
Determining a first predicted temperature rise amount when the power battery is charged at the end according to the first temperature variation and the second temperature variation;
the first preset temperature is a critical temperature value for dividing normal temperature and high temperature.
3. The cloud-based smart interconnect big data temperature prediction method according to claim 1,
the cloud server acquires an initial state of charge (SOC) and an initial temperature of a power battery of a target vehicle;
the cloud server determines a second predicted temperature rise of the power battery when the charging is stopped according to the initial state of charge (SOC) and the initial temperature;
and the cloud server sends the second predicted temperature rise to the target vehicle.
4. The cloud-based intelligent interconnection big data temperature prediction method according to claim 3, wherein the determining a second predicted temperature rise amount corresponding to the initial state of charge (SOC) and the initial temperature comprises:
acquiring a third predicted temperature rise amount after the plurality of vehicles of which the vehicle states are matched with the vehicle state of the target vehicle are respectively charged at the initial state of charge (SOC) and the initial temperature;
determining the second predicted temperature rise amount according to the plurality of third predicted temperature rise amounts;
Wherein the vehicle state at least comprises the regional position information of the vehicle.
5. The cloud-based intelligent interconnection big data temperature prediction method according to claim 4, wherein the determining the second predicted temperature rise amount according to the plurality of third predicted temperature rise amounts comprises:
and accumulating and summing the plurality of third predicted temperature rise amounts, and dividing the accumulated and summed result by the third predicted temperature rise amount to determine the second predicted temperature rise amount.
6. The cloud-based intelligent interconnected big data temperature prediction method according to claim 3, wherein the determining of the second predicted temperature rise of the power battery at the end of charging further comprises:
acquiring batch training data of vehicles in a charging process;
analyzing the obtained batch training data, then learning big data, and generating a prediction control instruction aiming at the initial state of charge (SOC) and a prediction control instruction aiming at the initial temperature of the vehicle according to a preset learning rule;
associating all the prediction control instructions generated by learning with the corresponding initial state of charge (SOC) and initial temperature to generate a prediction instruction database;
According to the initial state of charge (SOC) and the initial temperature, matching with the prediction instruction database, and determining the second prediction temperature rise;
wherein the training data comprises: the initial state of charge SOC, the initial temperature in the vehicle charging process, the corresponding relation between a plurality of SOC intervals and each SOC interval and the temperature rise variable quantity in the vehicle charging process.
7. The cloud-based intelligent interconnected big data temperature prediction method according to claim 6, wherein the training data comprises a driving place, an initial temperature of a power battery and an initial state of charge (SOC) of the power battery, and the preset learning rule comprises at least one of the following rules:
extracting charging control behaviors of a plurality of vehicles in the same area;
extracting a plurality of historical charging control behaviors of the same vehicle at the same place;
extracting charging control behaviors of a plurality of vehicles aiming at the same initial temperature and initial state of charge (SOC);
a plurality of historical charging control behaviors of the same vehicle at the same initial temperature and initial state of charge (SOC) are extracted.
8. A temperature prediction device, comprising:
the first acquisition module is used for acquiring the initial state of charge (SOC) and the initial temperature of the power battery by the vehicle;
The first determining module is used for determining a first predicted temperature rise amount when the charging of the power battery is stopped according to the initial state of charge (SOC) and the initial temperature of the vehicle; the first predicted amount of temperature rise is taken by the vehicle;
the second acquisition module is used for acquiring a second predicted temperature rise amount, which is determined by the cloud server and is from the initial state of charge (SOC) and the initial temperature when charging is started to the charging is stopped;
the second determination module is used for determining the predicted temperature of the power battery when the charging is stopped according to the first predicted temperature rise amount and the second predicted temperature rise amount;
wherein the first determining module comprises:
the first determining unit is used for determining a plurality of SOC intervals in the charging process according to the initial SOC; each SOC interval corresponds to a time detection period;
the second determining unit is used for determining the temperature rise variation corresponding to the initial temperature in each SOC interval according to a prestored first charging parameter mapping table; the first charging parameter mapping table comprises a corresponding relation between each SOC interval and temperature rise variation in a plurality of temperature intervals;
A third determining unit, configured to determine the first predicted temperature rise amount according to the temperature rise variation amount corresponding to each SOC interval;
the first charging parameter mapping table is updated according to the previous charging process;
wherein the apparatus further comprises:
the fourth acquisition module is used for acquiring a target state of charge (SOC) when the power battery is stopped charging and a plurality of SOC variable quantities in the charging process;
a fourth determining unit, configured to, if a difference between the target state of charge SOC and the initial state of charge SOC is greater than a sum of a plurality of SOC variation amounts, perform a step of determining a first predicted temperature rise amount when the power battery is stopped charging according to the initial state of charge SOC and the initial temperature;
a fifth determining unit, configured to determine that the first predicted temperature rise amount is a difference between a second preset temperature and the initial temperature if the first predicted temperature rise amount is not the difference;
the second preset temperature is a critical temperature value for dividing normal temperature and high temperature;
wherein the second determining module comprises:
a first acquiring unit configured to acquire a first weight corresponding to the first predicted temperature increase amount and a second weight corresponding to the second predicted temperature increase amount;
A sixth determining unit configured to determine a predicted temperature at the time of charge cutoff of the power battery, based on the first weight, the second weight, the first predicted temperature increase amount, and the second predicted temperature increase amount;
wherein the sixth determining unit includes:
a fourth determining subunit configured to determine that all of the predicted temperature increase amounts are the sum of the first predicted temperature increase amount and the second predicted temperature increase amount;
a fifth determining subunit, configured to determine the first weight according to the first predicted temperature rise amount and the total predicted temperature rise amount;
a sixth determining subunit, configured to determine the second weight according to the second predicted temperature rise amount and the total predicted temperature rise amount; wherein a sum of the first weight and the second weight is 1.
9. A readable storage medium, wherein the readable storage medium stores thereon a program, and the program when executed by a processor implements the steps of the cloud-based intelligent interconnection big data temperature prediction method according to any one of claims 1 to 7.
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