CN111762062A - Multi-factor automobile battery temperature pre-regulation and control method based on Internet of vehicles big data - Google Patents

Multi-factor automobile battery temperature pre-regulation and control method based on Internet of vehicles big data Download PDF

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Publication number
CN111762062A
CN111762062A CN202010644824.8A CN202010644824A CN111762062A CN 111762062 A CN111762062 A CN 111762062A CN 202010644824 A CN202010644824 A CN 202010644824A CN 111762062 A CN111762062 A CN 111762062A
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flow
temperature
ref
dif
sys
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CN111762062B (en
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霍宇涛
庞晓文
饶中浩
周寿斌
姜庆海
朱明海
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Huafu Jiangsu Lithium Electricity New Technology Co ltd
China University of Mining and Technology CUMT
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Huafu Jiangsu Lithium Electricity New Technology Co ltd
China University of Mining and Technology CUMT
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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
    • B60L58/24—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
    • 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
    • B60L58/24—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
    • B60L58/26—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 by cooling
    • 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
    • B60L58/24—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
    • B60L58/27—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 by heating
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D23/00—Control of temperature
    • G05D23/19—Control of temperature characterised by the use of electric means
    • G05D23/20—Control of temperature characterised by the use of electric means with sensing elements having variation of electric or magnetic properties with change of temperature
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/60—Heating or cooling; Temperature control
    • H01M10/61—Types of temperature control
    • H01M10/613—Cooling or keeping cold
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/60—Heating or cooling; Temperature control
    • H01M10/61—Types of temperature control
    • H01M10/615—Heating or keeping warm
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/60—Heating or cooling; Temperature control
    • H01M10/62—Heating or cooling; Temperature control specially adapted for specific applications
    • H01M10/625—Vehicles
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/60—Heating or cooling; Temperature control
    • H01M10/63—Control systems
    • H01M10/633—Control systems characterised by algorithms, flow charts, software details or the like
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/60—Heating or cooling; Temperature control
    • H01M10/63—Control systems
    • H01M10/635—Control systems based on ambient temperature
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/60—Heating or cooling; Temperature control
    • H01M10/65—Means for temperature control structurally associated with the cells
    • H01M10/656—Means for temperature control structurally associated with the cells characterised by the type of heat-exchange fluid
    • H01M10/6567—Liquids
    • 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
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M2220/00—Batteries for particular applications
    • H01M2220/20—Batteries in motive systems, e.g. vehicle, ship, plane
    • 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
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/10—Energy storage using batteries
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries

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  • Engineering & Computer Science (AREA)
  • Manufacturing & Machinery (AREA)
  • Chemical & Material Sciences (AREA)
  • Chemical Kinetics & Catalysis (AREA)
  • Electrochemistry (AREA)
  • General Chemical & Material Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Sustainable Development (AREA)
  • Sustainable Energy (AREA)
  • Power Engineering (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Automation & Control Theory (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Electric Propulsion And Braking For Vehicles (AREA)
  • Secondary Cells (AREA)

Abstract

The invention discloses a multi-factor automobile battery temperature pre-regulating and controlling method based on Internet of vehicles big data, the system temperature T _ sys is transmitted to a comparison link 1 through a signal transmission line and is compared with a target temperature T _ ref to obtain a first difference value T _ dif, obtaining a fitting relation curve of the first difference value T _ dif and the flow V _ ref by a fitting mode, obtaining the required flow V _ ref by an interpolation method, in the comparison link 2, the system coolant flow V _ sys is compared with the required flow V _ ref to obtain a second difference V _ dif between the system coolant flow V _ sys and the required flow V _ ref, and the second difference V _ dif is sent to the flow controller, so that the flow controller controls the flow regulating device to output the current coolant flow of the system according to the second difference V _ dif to realize the pre-regulation and control of the temperature of the automobile battery.

Description

Multi-factor automobile battery temperature pre-regulation and control method based on Internet of vehicles big data
Technical Field
The invention relates to the technical field of control systems, in particular to a multi-factor automobile battery temperature pre-regulating and controlling method based on Internet of vehicles big data.
Background
The performance of the power battery has very high requirements on the working temperature, the service life of the battery and the capacity of the battery can be greatly reduced by overhigh or overlow temperature, the reasonable working temperature is ensured, the service life of the battery is prolonged, the capacity of the battery is improved, and the basic national policy of sustainable development is met.
However, the existing temperature control system has poor performance and low sensitivity, cannot adapt to the high sensitivity of the power battery to temperature change, cannot guarantee the capacity and the service life of the power battery, and urgently needs a temperature control system with high sensitivity and high stability to guarantee the service life and the battery capacity of the battery.
There is proposed a flow control device and a program which control a drive circuit by setting a target flow rate and a series of output units, and a control system designed to consider the control of the flow rate solely from the aspect of pressure, cannot control the flow rate by a method which a user uses habit and pre-adjustment, considers the flow rate solely from the aspect of voltage, causes unnecessary waste, and is low in system sensitivity and requires correction and improvement. The control method is easy to be disturbed by external factors to cause the failure of the control method, and meanwhile, the precision of the control method is low due to the adoption of a specified algorithm, so that the flow demand cannot be predicted in a real-time state. It can be seen that the traditional regulation and control scheme often has the problem of low control accuracy.
Disclosure of Invention
Aiming at the problems, the invention provides a self-stable high-precision and high-sensitivity multi-factor automobile battery temperature pre-regulation and control method based on the Internet of vehicles big data.
In order to realize the aim of the invention, the invention provides a multi-factor automobile battery temperature pre-regulation and control method based on Internet of vehicles big data, which comprises the following steps:
s20, in the current sampling period, obtaining a system temperature T _ sys measured by a temperature sensor and a system cooling liquid flow V _ sys measured by a flow sensor, transmitting the system temperature T _ sys to a comparison link 1 through a signal transmission line, and comparing the system temperature T _ sys with a target temperature T _ ref to obtain a first difference T _ dif between the target temperature T _ ref and the system temperature T _ sys;
s30, transmitting the difference value T _ dif of the signal transmission line to an interpolation table look-up link, obtaining a corresponding relation between a first difference value T _ dif of the system temperature and the flow V _ ref required by reaching the target temperature T _ ref in an online training mode, obtaining a fitting relation curve of the first difference value T _ dif and the flow V _ ref in a fitting mode, and obtaining the required flow V _ ref by adopting an interpolation method after receiving the first difference value T _ dif;
s40, the required flow V _ ref is transmitted to a comparison link 2 through a signal transmission line, and in the comparison link 2, the system cooling liquid flow V _ sys and the required flow V _ ref are compared to obtain a second difference value V _ dif between the system cooling liquid flow V _ sys and the required flow V _ ref;
and S50, sending the second difference V _ dif to the flow controller, and enabling the flow controller to control the flow adjusting device to output the current cooling liquid flow of the system according to the second difference V _ dif.
In one embodiment, the method for pre-adjusting and controlling the battery temperature of the multi-factor automobile based on the big data of the internet of vehicles further includes:
and S60, when the liquid cooling system receives the system cooling liquid flow rate V _ sys of the flow rate adjusting device, the liquid cooling system operates to cool or keep the temperature of the system, and the cooled system temperature T _ sys is output.
In one embodiment, before step S20, the method further includes:
and S10, setting a target temperature T _ ref for system operation, transmitting a target temperature signal of the system to the comparison link 1, putting the signal in each sampling period into a register for storage, and calculating and predicting the sampling period of the next cycle.
In one embodiment, the flow controller is provided with a PID control module, and flow control is carried out through a PID control algorithm.
Specifically, the flow controller is arranged in the control cabinet and is connected with a flow supply system; the output signal of the flow controller is used for controlling the rotating speed of a water pump in the flow supply system or the opening of a proportional valve, so that the control of the cooling liquid flow of the liquid cooling system is realized.
In one embodiment, a frequency converter is arranged in the liquid cooling system, and the frequency converter is connected with the booster pump and used for controlling the rotating speed of the booster pump so as to adjust the flow rate of the liquid cooling system.
The multi-factor automobile battery temperature pre-regulation and control method based on the internet of vehicles big data comprises the steps of obtaining a system temperature T _ sys measured by a temperature sensor and a system cooling liquid flow V _ sys measured by a flow sensor in a current sampling period, transmitting the system temperature T _ sys to a comparison link 1 through a signal transmission line, comparing the system temperature T _ sys with a target temperature T _ ref to obtain a first difference value T _ dif between the target temperature T _ ref and the system temperature T _ sys, transmitting the difference value T _ dif to an interpolation table look-up link through the signal transmission line, obtaining a corresponding relation between the first difference value T _ dif of the system temperature and the flow V _ ref required by reaching the target temperature T _ ref in an online training mode, obtaining a fitting relation curve between the first difference value T _ dif and the flow V _ ref in a fitting mode, and obtaining the required flow V _ ref by adopting an interpolation method after receiving the first difference value T _ dif, the required flow V _ ref is transmitted to a comparison link 2 through a signal transmission line, in the comparison link 2, the system cooling liquid flow V _ sys and the required flow V _ ref are compared to obtain a second difference value V _ dif between the system cooling liquid flow V _ sys and the required flow V _ ref, and the second difference value V _ dif is sent to a flow controller, so that the flow controller controls a flow regulating device to output the current cooling liquid flow of the system according to the second difference value V _ dif, the temperature of the automobile battery is pre-regulated and controlled, the system where the automobile battery is located always runs in a proper temperature range, the stability and the high efficiency of the system running are ensured, and the accuracy of temperature pre-regulation and control on the automobile battery can be improved; the sensor has the characteristics of high stability, good sensitivity and good robustness, is suitable for various application environments, and effectively avoids a series of accidents caused by out-of-control temperature.
Drawings
FIG. 1 is a flow chart of a multi-factor automotive battery temperature pre-conditioning method based on Internet of vehicles big data according to an embodiment;
FIG. 2 is a schematic diagram of an electrical configuration of a temperature control system according to one embodiment;
FIG. 3 is a schematic view of an embodiment of a temperature control system usage environment;
FIG. 4 is a schematic diagram of the operation of the temperature control system according to one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
Referring to fig. 1, fig. 1 is a flowchart of a multi-factor car battery temperature pre-regulation method based on car networking big data according to an embodiment, and the method includes the following steps:
s20, in the current sampling period, obtaining the system temperature T _ sys measured by the temperature sensor and the system cooling liquid flow V _ sys measured by the flow sensor, transmitting the system temperature T _ sys to the comparison link 1 through the signal transmission line, and comparing the system temperature T _ sys with the target temperature T _ ref to obtain a first difference T _ dif between the target temperature T _ ref and the system temperature T _ sys.
The system is a system (such as a temperature control system) for pre-regulating the temperature of the automobile battery.
The system is provided with a temperature sensor and a flow sensor, and is used for acquiring the temperature T _ sys and the cooling liquid flow V _ sys of the system in each sampling period, transmitting the temperature T _ sys of the system to a comparison link 1 through a signal transmission line, and comparing the temperature T _ sys with a target temperature T _ ref to obtain a difference value T _ dif between the target temperature T _ ref and the current actual temperature T _ sys of the system.
S30, the difference value T _ dif of the signal transmission line is transmitted to an interpolation table look-up link, the corresponding relation between the first difference value T _ dif of the system temperature and the flow V _ ref required by reaching the target temperature T _ ref is obtained in an online training mode, a fitting relation curve of the first difference value T _ dif and the flow V _ ref is obtained in a fitting mode, and after the first difference value T _ dif is received, the required flow V _ ref is obtained by adopting an interpolation method.
The above steps transmit the difference signal T _ dif to the interpolation table look-up link through the signal transmission line, obtain the corresponding relation between the system temperature difference T _ dif and the flow V _ ref required for reaching the target temperature T _ ref through the on-line training mode, obtain the fitting relation curve of T _ dif and V _ ref through the fitting mode, and after receiving the T _ dif signal transmitted by the comparison link 1, obtain the required flow V _ ref by the interpolation method.
In one example, the data source in the interpolation table look-up link has two parts, one part is from a mathematical model of a flow supply system established by a manufacturer, and the mathematical model is theoretically analyzed to obtain basic characteristics of the flow supply system including flow and pressure; and analyzing the influence of the flow supply system parameters on the cooling performance of the liquid cooling system to obtain the outlet temperature distribution of the liquid cooling system under various flow parameters. The other part of the method is that the system automatically learns in the actual use process of the user, a database is obtained according to the use habit of the user, and a factory calculation model is finely adjusted to obtain an accurate temperature-flow corresponding relation.
And S40, transmitting the required flow V _ ref to a comparison link 2 through a signal transmission line, and in the comparison link 2, comparing the system cooling liquid flow V _ sys with the required flow V _ ref to obtain a second difference V _ dif between the system cooling liquid flow V _ sys and the required flow V _ ref.
And S50, sending the second difference V _ dif to the flow controller, and enabling the flow controller to control the flow adjusting device to output the current cooling liquid flow of the system according to the second difference V _ dif.
In the above steps, the flow controller receives the V _ dif signal, and the flow controller controls the flow regulating device to output the current flow V _ sys of the system.
The multi-factor automobile battery temperature pre-regulation and control method based on the internet of vehicles big data comprises the steps of obtaining a system temperature T _ sys measured by a temperature sensor and a system cooling liquid flow V _ sys measured by a flow sensor in a current sampling period, transmitting the system temperature T _ sys to a comparison link 1 through a signal transmission line, comparing the system temperature T _ sys with a target temperature T _ ref to obtain a first difference value T _ dif between the target temperature T _ ref and the system temperature T _ sys, transmitting the difference value T _ dif to an interpolation table look-up link through the signal transmission line, obtaining a corresponding relation between the first difference value T _ dif of the system temperature and the flow V _ ref required by reaching the target temperature T _ ref in an online training mode, obtaining a fitting relation curve between the first difference value T _ dif and the flow V _ ref in a fitting mode, and obtaining the required flow V _ ref by adopting an interpolation method after receiving the first difference value T _ dif, the required flow V _ ref is transmitted to a comparison link 2 through a signal transmission line, in the comparison link 2, the system cooling liquid flow V _ sys and the required flow V _ ref are compared to obtain a second difference value V _ dif between the system cooling liquid flow V _ sys and the required flow V _ ref, and the second difference value V _ dif is sent to a flow controller, so that the flow controller controls a flow regulating device to output the current cooling liquid flow of the system according to the second difference value V _ dif, the temperature of the automobile battery is pre-regulated and controlled, the system where the automobile battery is located always runs in a proper temperature range, the stability and the high efficiency of the system running are ensured, and the accuracy of temperature pre-regulation and control on the automobile battery can be improved; the sensor has the characteristics of high stability, good sensitivity and good robustness, is suitable for various application environments, and effectively avoids a series of accidents caused by out-of-control temperature.
In one embodiment, the method for pre-adjusting and controlling the battery temperature of the multi-factor automobile based on the big data of the internet of vehicles further includes:
and S60, when the liquid cooling system receives the system cooling liquid flow rate V _ sys of the flow rate adjusting device, the liquid cooling system operates to cool or keep the temperature of the system, and the cooled system temperature T _ sys is output.
In this embodiment, the liquid cooling system receives the V _ sys signal from the flow rate adjusting device, operates to cool or keep the temperature of the system, and then outputs the cooled temperature T _ sys to output the cooled temperature T _ sys in a form of corresponding display, so that a user can know the cooled temperature T _ sys in time.
In one embodiment, before step S20, the method further includes:
and S10, setting a target temperature T _ ref for system operation, transmitting a target temperature signal of the system to the comparison link 1, putting the signal in each sampling period into a register for storage, and calculating and predicting the sampling period of the next cycle.
The target temperature T _ ref is connected with a signal register and an input interface, and the working temperature of the system can be set by a manufacturer.
In one embodiment, the flow controller is provided with a PID control module, and flow control is carried out through a PID control algorithm.
Specifically, the flow controller is arranged in the control cabinet and is connected with a flow supply system; the output signal of the flow controller is used for controlling the rotating speed of a water pump in the flow supply system or the opening of a proportional valve, so that the control of the cooling liquid flow of the liquid cooling system is realized.
In this embodiment, the flow controller is provided with a PID control module, and the flow control is performed by a PID control algorithm, and parameters of the PID controller can be adjusted according to different vehicle types and driving habits of drivers. The flow controller is provided with a PID control module, flow control is carried out through a PID control algorithm, and parameters of the PID controller can be adjusted according to different vehicle types and driving habits of drivers.
In one embodiment, a frequency converter is arranged in the liquid cooling system, and the frequency converter is connected with the booster pump and used for controlling the rotating speed of the booster pump so as to adjust the flow rate of the liquid cooling system.
In one embodiment, the method for pre-regulating and controlling the temperature of the multi-factor automobile battery based on the internet of vehicles big data can be operated by a temperature control system, the temperature control system is provided with a plurality of data sampling points in a battery pack to ensure the accuracy of data, a series of temperature points and the corresponding opening of a self-regulating valve are established to realize an automatic temperature regulation control system of a liquid cooling system, and the method has the characteristics that the larger the deviation between the real-time temperature and the target temperature is, the larger the valve amplitude modulation is (the two-way opening and closing is), and the regulation to the set target temperature is quickly realized. The flow of the liquid cooling system is adjusted according to the temperature change, unnecessary power loss of the liquid cooling system is avoided, and therefore the system is in an optimal running state. The temperature control system is internally provided with a communication module, and can update the database in real time through the Internet and adjust the accuracy and effectiveness of the temperature control system.
In one example, an electrical schematic diagram of the temperature control system may be shown in fig. 2, a schematic diagram of an environment in which the temperature control system shown in fig. 2 is used may be shown in fig. 3, and a corresponding schematic diagram of an operation process may be shown in fig. 4. In fig. 2, S001 represents the set target temperature; s002 represents comparison link 1; s003 represents an interpolation table look-up link; s004 represents a comparison link 2; s005 denotes a flow controller; s006 denotes a flow rate adjusting device; s007 a liquid cooling system; s008 indicates the control system output temperature; s009 denotes a flow sensor; s010 denotes a temperature sensor. In fig. 4, S010 denotes vehicle start; s020 represents system self-checking; s021 represents a vehicle battery state check; s022, checking the state of the micro-channel and the state of the water pump; s030 represents temperature control parameter setting through the Internet of vehicles cloud platform; s040 represents the collection of the temperature signal of the vehicle; s050 represents that whether the weighted average temperature in the battery pack is larger than a set lower temperature threshold value or not is judged; s060 represents the duration of the high temperature if the weighted average temperature in the battery pack is greater than the lower temperature threshold; s120 represents determining whether or not the duration of the high temperature duration t _ kp is greater than the set temperature t _ st; s130, preheating the water pump; s110, closing the water cooling system, and radiating by using air cooling and a heat pipe; s070 shows that whether the weighted average temperature inside the battery pack is larger than the set upper limit of the working temperature of the battery pack is judged; s080, the water cooling system works; s090 represents collecting the temperature of a cooling water outlet of the micro-channel; s100 represents the determination of the cooling flow rate by interpolation on the temperature-flow rate graph.
As shown in fig. 4, the operation process of the temperature control system includes:
step one, S010 starts the whole vehicle, and the vehicle enters a starting state; then, entering a step S020 vehicle state checking step, and judging the validity of the battery pack BMS system through the step S021 battery working state checking and the step S022 microchannel water pump state checking;
step two, S030 temperature control system parameter setting mainly relates to the setting of a battery working temperature interval and the setting of temperature signal duration, and can be set through calibration parameters when the battery leaves a factory;
thirdly, collecting temperature signals S040, collecting battery working temperature signals through a temperature signal collecting device in the battery pack fixing device 5, and simultaneously transmitting the temperature signals to a BMS (battery management system);
fourthly, judging by an S050 statement after the temperature signal is transmitted to the BMS, wherein the temperature weight of each battery pack in the battery pack is different, the battery temperature weight of the battery pack close to the edge part is small, and vice versa, obtaining a battery pack temperature weighted average temperature T _ ave through calculation, comparing and judging through the weighted average temperature T _ ave and a battery work lower threshold T _ lo set in the S030 temperature threshold setting, and entering the next step S060 if the weighted average temperature T _ ave of the battery pack is greater than the set battery work lower threshold T _ lo, otherwise, entering S110;
fifthly, when the weighted average temperature T _ ave of the battery pack is larger than the lower battery working threshold value T _ lo, collecting signals of S060 duration T _ kp, wherein the step is used for eliminating BMS misjudgment caused by transient heat generation and increase of the battery;
sixthly, S120 compares the t _ kp with the set temperature duration t _ st, and if the temperature duration t _ kp is less than the set temperature duration t _ st, the step returns to the S050 judgment step in the fifth step; if the duration t _ kp is greater than the set threshold t _ st, the next step S130 is performed;
and seventhly, preheating by using a water pump S130, namely, in order to prevent heat accumulation of the battery caused by overlong temperature duration, at the moment, preheating by using the water pump at the lowest working rotating speed, and cooling by using a part of cooling liquid in a microchannel. Meanwhile, whether the weighted average temperature T _ ave of the battery pack at the moment is larger than a set upper threshold value T _ hi of the working temperature of the battery is judged, and if the T _ ave is larger than the set upper threshold value T _ hi, the S080 water pump starts to work; if the T _ ave is smaller than the set upper threshold value T _ hi, closing the S110 water cooling system, and adopting air cooling and a heat pipe for heat dissipation;
and eighthly, after the S080 water cooling system works, S090 is needed to collect outlet temperature values of the cooling liquid of the snake-shaped micro-channel, and two-dimensional interpolation is carried out in an S100 temperature-flow diagram by utilizing the outlet temperature values of the cooling water at the moment to obtain the flow required to be supplied by the current water pump, so that the pump consumption is reduced, and the purpose of global optimization is achieved.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
It should be noted that the terms "first \ second \ third" referred to in the embodiments of the present application merely distinguish similar objects, and do not represent a specific ordering for the objects, and it should be understood that "first \ second \ third" may exchange a specific order or sequence when allowed. It should be understood that "first \ second \ third" distinct objects may be interchanged under appropriate circumstances such that the embodiments of the application described herein may be implemented in an order other than those illustrated or described herein.
The terms "comprising" and "having" and any variations thereof in the embodiments of the present application are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product, or device that comprises a list of steps or modules is not limited to the listed steps or modules but may alternatively include other steps or modules not listed or inherent to such process, method, product, or device.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (6)

1. A multi-factor automobile battery temperature pre-regulation and control method based on Internet of vehicles big data is characterized by comprising the following steps:
s20, in the current sampling period, obtaining a system temperature T _ sys measured by a temperature sensor and a system cooling liquid flow V _ sys measured by a flow sensor, transmitting the system temperature T _ sys to a comparison link 1 through a signal transmission line, and comparing the system temperature T _ sys with a target temperature T _ ref to obtain a first difference T _ dif between the target temperature T _ ref and the system temperature T _ sys;
s30, transmitting the difference value T _ dif of the signal transmission line to an interpolation table look-up link, obtaining a corresponding relation between a first difference value T _ dif of the system temperature and the flow V _ ref required by reaching the target temperature T _ ref in an online training mode, obtaining a fitting relation curve of the first difference value T _ dif and the flow V _ ref in a fitting mode, and obtaining the required flow V _ ref by adopting an interpolation method after receiving the first difference value T _ dif;
s40, the required flow V _ ref is transmitted to a comparison link 2 through a signal transmission line, and in the comparison link 2, the system cooling liquid flow V _ sys and the required flow V _ ref are compared to obtain a second difference value V _ dif between the system cooling liquid flow V _ sys and the required flow V _ ref;
and S50, sending the second difference V _ dif to the flow controller, and enabling the flow controller to control the flow adjusting device to output the current cooling liquid flow of the system according to the second difference V _ dif.
2. The multi-factor automobile battery temperature pre-regulation and control method based on Internet of vehicles big data according to claim 1, further comprising:
and S60, when the liquid cooling system receives the system cooling liquid flow rate V _ sys of the flow rate adjusting device, the liquid cooling system operates to cool or keep the temperature of the system, and the cooled system temperature T _ sys is output.
3. The method for pre-regulating the battery temperature of the multi-factor automobile based on the big data of the Internet of vehicles according to claim 1, further comprising, before step S20:
and S10, setting a target temperature T _ ref for system operation, transmitting a target temperature signal of the system to the comparison link 1, putting the signal in each sampling period into a register for storage, and calculating and predicting the sampling period of the next cycle.
4. The multi-factor automobile battery temperature pre-regulation and control method based on the Internet of vehicles big data as claimed in claim 1, wherein the flow controller is provided with a PID control module, and the flow is controlled through a PID control algorithm.
5. The multi-factor automobile battery temperature pre-regulation and control method based on the internet of vehicles big data is characterized in that the flow controller is installed in a control cabinet and is connected with a flow supply system; the output signal of the flow controller is used for controlling the rotating speed of a water pump in the flow supply system or the opening of a proportional valve, so that the control of the cooling liquid flow of the liquid cooling system is realized.
6. The multi-factor automobile battery temperature pre-regulation and control method based on the Internet of vehicles big data according to any one of claims 1 to 5, wherein a frequency converter is arranged in the liquid cooling system, and the frequency converter is connected with the booster pump and used for controlling the rotating speed of the booster pump so as to regulate the flow of the liquid cooling system.
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