CN116826254A - Battery direct-current self-heating method, system, medium and terminal under low temperature of Ad hoc network of heavy-duty freight train - Google Patents
Battery direct-current self-heating method, system, medium and terminal under low temperature of Ad hoc network of heavy-duty freight train Download PDFInfo
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- 238000010438 heat treatment Methods 0.000 title claims abstract description 123
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- 238000009826 distribution Methods 0.000 claims description 3
- 238000005485 electric heating Methods 0.000 abstract 1
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- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 6
- 229910001416 lithium ion Inorganic materials 0.000 description 6
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- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 2
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- 238000001208 nuclear magnetic resonance pulse sequence Methods 0.000 description 1
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- 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
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- 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
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- 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
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- 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
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- 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
- H01M10/637—Control systems characterised by the use of reversible temperature-sensitive devices, e.g. NTC, PTC or bimetal devices; characterised by control of the internal current flowing through the cells, e.g. by switching
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Abstract
The invention discloses a method, a system, a medium and a terminal for self-heating battery direct current at low temperature of a heavy-load freight train ad hoc network, wherein the method comprises the steps of S1 obtaining an electric heating aging coupling model; testing the coupling model to obtain an offline parameter database of the coupling model under different temperatures and SOCs; s2, setting a target temperature for heating the battery; s3, acquiring the current temperature and the current SOC of the battery, and determining the parameter value of the current coupling model of the battery based on an offline parameter database of the coupling model; s4, taking the heating time and the battery capacity loss in the self-heating process of the battery as optimization targets, and solving an optimal self-heating multi-stage constant current sequence of the battery by adopting a multi-target optimization algorithm; s5, judging whether the current battery temperature reaches the target temperature or not: if not, returning to the step S3; if yes, the self-heating of the battery is finished. The internal heating battery can be quickly and efficiently carried out, and the cruising ability of the heavy-duty freight train in a low-temperature environment is greatly improved.
Description
Technical Field
The invention relates to the technical field of power battery management, in particular to a battery direct-current self-heating method, system, medium and terminal under the low temperature of an ad hoc network of a heavy-duty freight train.
Background
Lithium ion batteries have been widely used as a new generation of environmentally friendly energy storage devices due to their low self-discharge rate and high energy power density. The design goal of the vehicle-mounted ad hoc network is to establish a platform for communication between vehicles, so that the traffic efficiency is improved, the safety in the running process of a train is improved, and the electronic elements for real-time communication all need to maintain the endurance of the battery. However, the low-temperature environment causes the lithium ion battery to have greatly reduced performance, and the lithium ion battery is seriously lost in available capacity, so that the running mileage of the heavy-load freight train is lost, the ad hoc network used for communication between vehicles is invalid, the running safety of the heavy-load freight train is reduced, and the running cost and the maintenance cost of the heavy-load freight train are increased. Because how to adopt the real-time high-efficiency, safe and reliable heating mode before starting the heavy-load freight train, the temperature of the lithium ion battery is in an ideal working temperature range, and the method is a heavy problem of insufficient endurance of the heavy-load freight train in an extremely cold environment. And the key problem of the popularization of the heavy-duty freight train in cold areas is that the preheating time and the service life loss of the power battery are considered in consideration of the lower temperature environment, the battery is preheated in the low temperature environment, and the performance of the heavy-duty freight train is improved.
In the prior art, a specific thermal management system is generally used for external preheating of the lithium ion battery, heat generated by an external heat source is transferred to the battery pack by means of a unified medium, the heat transfer efficiency is low due to heat dissipation in the heat transfer process, and the problems of generally longer heating time, larger temperature gradient in the battery pack and the like exist in an external heating scheme. The internal heating scheme can well solve the problems, and by means of the characteristic that the internal impedance of the lithium ion battery is large in a low-temperature environment, the internal of the battery generates a large amount of chemical heat by releasing current through self energy, so that the self-heating of the battery is realized. The existing internal heating technology generally needs to design complicated heating current or extra external power supply to provide energy support, meanwhile, the influence of temperature change and SOC change on battery model parameters in the self-heating process of the battery is not considered, and the preheating time and capacity loss in the preheating process are long.
Disclosure of Invention
In order to solve the technical problems in the background technology, the invention provides a method, a system, a medium and a terminal for self-heating a battery direct current at a low temperature of an ad hoc network of a heavy-duty freight train, wherein the method constructs a parameter offline parameter database of an electrothermal-ageing coupling model of a lithium battery through basic experiments, calculates the change of the state of charge (SOC) of the battery in real time in the self-heating process of the lithium battery, acquires parameter values corresponding to a current model from the parameter offline parameter database of the electrothermal-ageing coupling model according to the current SOC and the current temperature of the battery, and updates model parameters related to heat production of the battery; the multi-objective optimization problem of the required preheating time and the battery capacity loss in the battery preheating process is constructed, and the conditions of the required preheating time and the power battery capacity loss in the battery preheating process are considered in a balanced manner; the internal heating battery can be quickly and efficiently carried out, the cost of a thermal management system of the heavy-duty freight train is reduced, the endurance of the heavy-duty freight train in a low-temperature environment is greatly improved, and the service life of the battery is prolonged.
In a first aspect, the invention provides a method for self-heating a battery at a low temperature in an ad hoc network of a heavy-duty freight train, comprising the following steps:
s1: coupling the battery electric model, the battery thermal model and the battery aging model to obtain an electrothermal-aging coupling model; testing the electric heating-ageing coupling model to obtain an offline parameter database of the electric heating-ageing coupling model under different temperatures and battery charge states;
s2: setting a target temperature for heating the battery;
s3: acquiring the current temperature and the current state of charge of the battery, and determining the parameter value of the current electrothermal-ageing coupling model of the battery based on an offline parameter database of the electrothermal-ageing coupling model;
s4: taking the heating time and the battery capacity loss in the self-heating process of the battery as optimization targets, and solving the optimal self-heating multi-stage constant current sequence of the battery under the current temperature and the current charge state of the battery by adopting a multi-target optimization algorithm; the weight of the battery heating time and the battery capacity loss is obtained by distributing according to the user demand;
s5: judging whether the current battery temperature reaches the target temperature or not: if not, returning to the step S3; if yes, the self-heating of the battery is finished.
Further, in the step S1, the specific acquisition process of the battery electric model is as follows: and (3) testing the hybrid power pulse characteristics of the battery, and determining the parameters of the battery electric model according to the voltage dynamic response conditions of the battery at different temperatures and charge states.
Further, in the step S1, the specific process of obtaining the thermal model of the battery is as follows: measuring a curve of the open-circuit voltage of the battery along with the temperature change in the same temperature and charge state interval as the battery electric model, and calculating the entropy change coefficient of the battery; and cooling the battery at constant temperature to obtain a curve of the change of the battery temperature along with time, and determining a coefficient of heat exchange between the battery and the environment to obtain the battery thermal model parameters.
Further, in the step S1, the specific acquisition process of the battery aging model is as follows: and performing aging test on the battery, and obtaining battery aging model parameters by using a semi-empirical aging model and combining the aging test result.
Further, the optimization objective in S4 is:
wherein f is an objective function; omega is a weight factor; q (Q) loss The battery capacity loss in the self-heating process of the battery is reduced; t is the heating time in the self-heating process of the battery; n is the nth constant current discharge stage; n is the constant current discharge stage number; i t Representing the discharge current; v (V) t Representing battery terminal voltage, SOC init Indicating the initial state of charge of the battery.
Further, the state of charge of the battery is calculated as follows:
wherein SOC (t) 0 ) The state of charge at the initial time of the battery; c (C) b The rated capacity of the battery is represented, eta represents the charge and discharge efficiency of the battery, and I represents the charge and discharge current.
Further, the battery self-heating multi-stage constant current sequence obtaining process comprises the following steps:
in the process of heating and heating the battery from the initial temperature to the target temperature, discretizing the battery temperature, taking a preset temperature interval as a gradient, taking the heating time and the battery capacity loss in the current gradient self-heating process of the battery as optimization targets when the temperature rises by one gradient, and solving the optimal self-heating multi-stage constant current sequence of the battery under the current gradient temperature and the charge state of the battery by adopting a multi-target optimization algorithm.
In a second aspect, the invention provides a battery direct current self-heating system under the low temperature of a heavy-duty freight train ad hoc network, comprising:
an offline parameter database module: the method comprises the steps of coupling a battery electric model, a battery thermal model and a battery aging model to obtain an electrothermal-aging coupling model; testing the electric heating-ageing coupling model to obtain an offline parameter database of the electric heating-ageing coupling model under different temperatures and battery charge states;
a target temperature setting module: for setting a target temperature for battery heating;
model parameter acquisition module: the parameter value of the current electrothermal-ageing coupling model of the battery is determined based on an offline parameter database of the electrothermal-ageing coupling model;
the battery heating current control module: the method is used for taking the heating time and the battery capacity loss in the self-heating process of the battery as optimization targets, and solving the optimal self-heating multi-stage constant current sequence of the battery under the current temperature and the current charge state of the battery by adopting a multi-target optimization algorithm; judging whether the current battery temperature reaches the target temperature or not: if yes, ending self-heating of the battery; if not, acquiring parameters of the coupling model from a model parameter acquisition module according to the current battery temperature and the state of charge, further solving a multi-stage constant current sequence of self-heating of the battery by adopting a multi-objective optimization algorithm, and updating the discharge current until the target temperature is reached to finish self-heating of the battery.
In a third aspect, the present invention provides a computer readable storage medium storing a computer program which when invoked by a processor performs the steps of the method for dc self-heating of a battery at low temperatures in an ad hoc network of heavy haul trains as described above.
In a fourth aspect, the present invention provides an electronic terminal, comprising a processor and a memory, the memory storing a computer program, the processor invoking the computer program to perform the steps of the battery direct current self-heating method at low temperature of the ad hoc network of a heavy haul train as described above.
Advantageous effects
The invention provides a method, a system, a medium and a terminal for self-heating a battery direct current at a low temperature of a heavy-load freight train ad hoc network, wherein the method couples the battery from three aspects of electricity, heat and aging, analyzes the influence of a charge state on the battery heat production process, and establishes an offline parameter database to update model parameters changed due to SOC change in real time; and (3) jointly optimizing the heating time and the battery capacity loss in the self-heating process of the battery, distributing weights for two mutually conflicting optimization targets according to different preheating preferences proposed by a user, and determining an optimal battery self-heating multi-stage constant current sequence under the multi-target optimization problem to realize balanced optimization by considering the change of the battery charge state in the self-heating process of the battery.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a method for self-heating a battery at low temperature in an ad hoc network of a heavy-duty freight train according to an embodiment of the present invention;
fig. 2 is a block diagram of a method for self-heating a battery at low temperature in an ad hoc network of a heavy-duty freight train according to an embodiment of the present invention;
FIG. 3 is a block diagram of an electro-thermal-aging coupling model provided by an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, based on the examples herein, which are within the scope of the invention as defined by the claims, will be within the scope of the invention as defined by the claims.
Example 1
As shown in fig. 1-2, the embodiment provides a method for self-heating a battery at a low temperature in an ad hoc network of a heavy-duty freight train, which includes:
s1: coupling the battery electric model, the battery thermal model and the battery aging model to obtain an electrothermal-aging coupling model; and testing the electric heating-ageing coupling model based on different temperatures and battery charge states to obtain an offline parameter database of the electric heating-ageing coupling model.
Specifically, the specific acquisition process of the battery electric model is as follows: and (3) testing the hybrid power pulse characteristics of the battery, and determining the parameters of the battery electric model according to the voltage dynamic response conditions of the battery at different temperatures and charge states. In this embodiment, according to the voltage dynamic response condition, the parameters of the second-order equivalent circuit model of the battery are determined by using the fitting parameters of the small square method. In specific implementation, 1C current multiplying power is used for discharging for 10 seconds, 0.75C multiplying power is used for charging for 10 seconds, the SOC of the power battery is released from 100% to 10% through a charging and discharging pulse sequence, a voltage-time change curve of the process end is fitted, and ohmic internal resistance, capacitance, polarization resistance and polarization capacitance parameter values in a battery equivalent circuit model are identified.
The specific acquisition process of the battery thermal model comprises the following steps: measuring a curve of the open-circuit voltage of the battery along with the temperature change in the same temperature and charge state interval as the battery electric model, and calculating the entropy change coefficient of the battery; and cooling the battery at constant temperature to obtain a curve of the change of the battery temperature along with time, and determining a coefficient of heat exchange between the battery and the environment to obtain the battery thermal model parameters. In this embodiment, the test of the entropy coefficient of the battery is to calculate the open-circuit voltage-temperature variation curve of the battery under different battery states of charge in the range of-25 ℃ to 10 ℃, thereby determining the entropy coefficient. And (3) placing the battery in a preset low-temperature incubator from the room temperature environment in a cooling experiment, and fitting a temperature-time change curve to calculate the heat exchange coefficient between the power battery and the environment.
The specific acquisition process of the battery aging model comprises the following steps: and performing aging test on the battery, and obtaining battery aging model parameters by using a semi-empirical aging model and combining the aging test result. In this embodiment, the discharge current with different multiplying power is implemented on the battery at different ambient temperatures, the relationship between the ambient temperature, the discharge multiplying power and the battery capacity loss is fitted, and the exponential factor, the compensation coefficient, the reference temperature and the compensation temperature in the semi-empirical aging model of the battery are determined.
The electrothermal-ageing coupling model is obtained by taking the battery current, the battery temperature and the battery discharge time as bridges and coupling the battery electric model, the battery thermal model and the battery ageing model, as shown in figure 3. Testing the electric heating-ageing coupling model based on different temperatures and battery charge states to obtain an offline parameter database of the electric heating-ageing coupling model under different temperatures and battery charge states
S2: and setting a target temperature for heating the battery, wherein the target temperature is a temperature value capable of meeting the output power requirement of the battery.
S3: and acquiring the current temperature and the current state of charge of the battery, and determining the parameter value of the current electrothermal-ageing coupling model of the battery based on an offline parameter database of the electrothermal-ageing coupling model.
Specifically, the current battery surface temperature (namely the temperature of the battery), terminal voltage and flowing heating current information are collected, and the state of charge of the battery is calculated by adopting a time integration method. And obtaining corresponding parameters of the electric heating-ageing coupling model battery heat generation related model from an offline parameter database of the electric heating-ageing coupling model according to the temperature of the battery and the calculated state of charge of the battery. In the embodiment of the battery, the surface temperature of the battery is collected in real time through a temperature sensor closely attached to the battery; the voltage sensor collects the terminal voltage and the open-circuit voltage of the battery in real time; the current sensor collects a pulse current flowing through the battery in real time.
S4: taking the heating time and the battery capacity loss in the self-heating process of the battery as optimization targets, and solving the optimal self-heating multi-stage constant current sequence of the battery under the current temperature and the charge state of the battery by adopting a multi-target optimization algorithm; the weight of the battery heating time and the battery capacity loss is obtained by distribution according to the user demand.
Specifically, the battery preheating is a complex electrothermal aging coupling process, the battery equivalent circuit model parameters influence the battery heat generation rate, the temperature rise can act on the battery equivalent circuit model to update the parameters, the current magnitude and the preheating time jointly determine the battery aging degree, and the battery aging degree is externally expressed as the power battery capacity Q loss Loss. According to the requirements of users on the heating time and the battery capacity loss in the self-heating process of the battery, the weights corresponding to the heating time and the battery capacity loss are distributed, the heating time and the battery capacity loss in the self-heating process of the battery are used as optimization targets, and the objective function expression of the optimization targets is specifically as follows:
wherein f is an objective function; ω is a weight factor, when ω=0, the objective function only considers the minimum value of the heating time, namely, at the expense of the battery capacity loss, realizes the fastest temperature rise, makes the battery reach a temperature interval with better performance, when ω=1, the objective function only considers the minimum value of the battery capacity loss, and needs to realize the minimum capacity loss in the battery preheating process (applicable to the scene of not being urgent to the use of the battery) at the expense of longer battery heating time; the other weight factor omega takes the value of [0-1 ] according to the consideration of the difference of the battery preheating time and the service life loss of the user]Taking a value in a range; q (Q) loss The battery capacity loss in the self-heating process of the battery is reduced; t is a batteryHeating time in the self-heating process; n is the nth constant current discharge stage; n is the constant current discharge stage number; i t Representing the discharge current; v (V) t Representing battery terminal voltage, SOC init Indicating the initial state of charge of the battery.
In the process of heating and heating the battery from the initial temperature to the target temperature, discretizing the battery temperature, taking a preset temperature interval as a gradient, taking the heating time and the battery capacity loss in the current gradient self-heating process of the battery as optimization targets when the temperature rises by one gradient, and solving the optimal self-heating multi-stage constant current sequence of the battery under the current gradient temperature and the charge state of the battery by adopting a multi-target optimization algorithm. In this embodiment, the preset temperature interval is 1 ℃, the battery temperature is updated every time the temperature is raised by 1 ℃, and then the change of the battery state of charge SOC in the process is calculated according to the ampere-hour integration method, so that the current temperature and the battery state of charge of the battery are input into the offline parameter database of the electric heating-ageing coupling model, and the parameter value of the current electric heating-ageing coupling model is correspondingly updated. The calculation formula of the SOC value SOC (t) at the time t is as follows:
wherein SOC (t) 0 ) SOC value representing initial time, C b The rated capacity of the power battery is represented, eta represents the charge and discharge efficiency of the battery, and I represents the charge and discharge current.
More specifically, the multi-objective algorithm in this embodiment adopts a particle swarm algorithm to solve an optimal self-heating multi-stage constant current sequence of the battery under the current temperature and the state of charge of the battery, and the specific process of the solution is as follows:
a: initializing a battery discharge current value, and initializing particle population number and algebra;
b: calculating the fitness of each initialized particle according to the objective function;
c: comparing, for each particle, its fitness value with the optimal position it has undergone, if better, taking it as the current optimal position;
d: comparing the adaptation value of each particle with the global optimal position, and resetting the global optimal position if the adaptation value of each particle is better;
e: updating the best particles to obtain the best solution.
In the method in this embodiment, in the case where the initial SOC of the battery is low (for example, the initial soc=10%), the temperature of the power battery can still be raised by consuming 3.83% of the SOC energy, so as to restore the ideal working performance of the battery.
Example 2
The embodiment provides a battery direct current self-heating system under a heavy load freight train ad hoc network low temperature, which comprises:
an offline parameter database module: the method comprises the steps of coupling a battery electric model, a battery thermal model and a battery aging model to obtain an electrothermal-aging coupling model; testing the electric heating-ageing coupling model to obtain an offline parameter database of the electric heating-ageing coupling model under different temperatures and battery charge states;
a target temperature setting module: for setting a target temperature for battery heating;
model parameter acquisition module: the parameter value of the current electrothermal-ageing coupling model of the battery is determined based on an offline parameter database of the electrothermal-ageing coupling model;
the battery heating current control module: the method is used for taking the heating time and the battery capacity loss in the self-heating process of the battery as optimization targets, and solving the optimal self-heating multi-stage constant current sequence of the battery under the current temperature and the current charge state of the battery by adopting a multi-target optimization algorithm; judging whether the current battery temperature reaches the target temperature or not: if yes, ending self-heating of the battery; if not, acquiring parameters of a coupling model from a model parameter acquisition module according to the current battery temperature and the state of charge, further solving a multi-stage constant current sequence of self-heating of the battery by adopting a multi-objective optimization algorithm, and updating the discharge current until the target temperature is reached to finish self-heating of the battery; the weight of the battery heating time and the battery capacity loss is obtained by distribution according to the user demand.
Example 3
The present embodiment provides a computer readable storage medium storing a computer program which when invoked by a processor performs the steps of the battery direct current self-heating method at low temperatures of a heavy haul train ad hoc network as described above.
Example 4
The embodiment provides an electronic terminal, which comprises a processor and a memory, wherein the memory stores a computer program, and the processor calls the computer program to execute the steps of the battery direct current self-heating method under the low temperature of the self-networking of the heavy-duty freight train.
It is to be appreciated that in embodiments of the present invention, the processor may be a central processing unit (Central Processing Unit, CPU), which may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf Programmable gate arrays (FPGAs) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The memory may include read only memory and random access memory and provide instructions and data to the processor. A portion of the memory may also include non-volatile random access memory. For example, the memory may also store information of the device type.
The readable storage medium is a computer readable storage medium, which may be an internal storage unit of the controller according to any one of the foregoing embodiments, for example, a hard disk or a memory of the controller. The readable storage medium may also be an external storage device of the controller, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the controller. Further, the readable storage medium may also include both an internal storage unit and an external storage device of the controller. The readable storage medium is used to store the computer program and other programs and data required by the controller. The readable storage medium may also be used to temporarily store data that has been output or is to be output.
Based on such understanding, the technical solution of the present invention is essentially or a part contributing to the prior art, or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned readable storage medium includes: a U-disk, a removable hard disk, a Read-only Memory (ROM), a random access Memory (RAM, randomAccess Memory), a magnetic disk, an optical disk, or other various media capable of storing program codes.
It is to be understood that the same or similar parts in the above embodiments may be referred to each other, and that in some embodiments, the same or similar parts in other embodiments may be referred to.
While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (10)
1. The battery direct current self-heating method under the low temperature of the self-networking of the heavy-duty freight train is characterized by comprising the following steps:
s1: coupling the battery electric model, the battery thermal model and the battery aging model to obtain an electrothermal-aging coupling model; testing the electric heating-ageing coupling model to obtain an offline parameter database of the electric heating-ageing coupling model under different temperatures and battery charge states;
s2: setting a target temperature for heating the battery;
s3: acquiring the current temperature and the current state of charge of the battery, and determining the parameter value of the current electrothermal-ageing coupling model of the battery based on an offline parameter database of the electrothermal-ageing coupling model;
s4: taking the heating time and the battery capacity loss in the self-heating process of the battery as optimization targets, and solving the optimal self-heating multi-stage constant current sequence of the battery under the current temperature and the current charge state of the battery by adopting a multi-target optimization algorithm; the weight of the battery heating time and the battery capacity loss is obtained by distributing according to the user demand;
s5: judging whether the current battery temperature reaches the target temperature or not: if not, returning to the step S3; if yes, the self-heating of the battery is finished.
2. The method for self-heating a battery at low temperature in an ad hoc network of a heavy-duty freight train according to claim 1, wherein in S1, the specific acquisition process of the battery power model is as follows: and (3) testing the hybrid power pulse characteristics of the battery, and determining the parameters of the battery electric model according to the voltage dynamic response conditions of the battery at different temperatures and charge states.
3. The method for self-heating the battery direct current at the low temperature of the ad hoc network of the heavy-duty freight train according to claim 1, wherein in the step S1, the specific obtaining process of the battery thermal model is as follows: measuring a curve of the open-circuit voltage of the battery along with the temperature change in the same temperature and charge state interval as the battery electric model, and calculating the entropy change coefficient of the battery; and cooling the battery at constant temperature to obtain a curve of the change of the battery temperature along with time, and determining a coefficient of heat exchange between the battery and the environment to obtain the battery thermal model parameters.
4. The method for self-heating a battery at low temperature in an ad hoc network of a heavy-duty freight train according to claim 1, wherein in S1, the specific acquisition process of the battery aging model is as follows: and performing aging test on the battery, and obtaining battery aging model parameters by using a semi-empirical aging model and combining the aging test result.
5. The method for self-heating a battery direct current at a low temperature of a heavy-duty freight train ad hoc network according to claim 1, wherein the optimization objective in S4 is:
wherein f is an objective function; omega is a weight factor; q (Q) loss The battery capacity loss in the self-heating process of the battery is reduced; t is the heating time in the self-heating process of the battery; n is the nth constant current discharge stage; n is the constant current discharge stage number; i t Representing the discharge current; v (V) t Representing battery terminal voltage, SOC init Indicating the initial state of charge of the battery.
6. The method for self-heating a battery at low temperature in an ad hoc network of a heavy haul train according to claim 1, wherein the state of charge of the battery is calculated as follows:
wherein SOC (t) 0 ) The state of charge at the initial time of the battery; c (C) b The rated capacity of the battery is represented, eta represents the charge and discharge efficiency of the battery, and I represents the charge and discharge current.
7. The method for self-heating a battery direct current at a low temperature in a heavy-duty freight train ad hoc network according to claim 1, wherein the battery self-heating multi-stage constant current sequence obtaining process comprises the following steps:
in the process of heating and heating the battery from the initial temperature to the target temperature, discretizing the battery temperature, taking a preset temperature interval as a gradient, taking the heating time and the battery capacity loss in the current gradient self-heating process of the battery as optimization targets when the temperature rises by one gradient, and solving the optimal self-heating multi-stage constant current sequence of the battery under the current gradient temperature and the charge state of the battery by adopting a multi-target optimization algorithm.
8. A battery direct current self-heating system under heavy load freight train ad hoc network low temperature, characterized by comprising:
an offline parameter database module: the method comprises the steps of coupling a battery electric model, a battery thermal model and a battery aging model to obtain an electrothermal-aging coupling model; testing the electric heating-ageing coupling model to obtain an offline parameter database of the electric heating-ageing coupling model under different temperatures and battery charge states;
a target temperature setting module: for setting a target temperature for battery heating;
model parameter acquisition module: the parameter value of the current electrothermal-ageing coupling model of the battery is determined based on an offline parameter database of the electrothermal-ageing coupling model;
the battery heating current control module: the method is used for taking the heating time and the battery capacity loss in the self-heating process of the battery as optimization targets, and solving the optimal self-heating multi-stage constant current sequence of the battery under the current temperature and the current charge state of the battery by adopting a multi-target optimization algorithm; judging whether the current battery temperature reaches the target temperature or not: if yes, ending self-heating of the battery; if not, acquiring parameters of a coupling model from a model parameter acquisition module according to the current battery temperature and the state of charge, further solving a multi-stage constant current sequence of self-heating of the battery by adopting a multi-objective optimization algorithm, and updating the discharge current until the target temperature is reached to finish self-heating of the battery; the weight of the battery heating time and the battery capacity loss is obtained by distribution according to the user demand.
9. A computer-readable storage medium, characterized by: a computer program is stored which, when called by a processor, performs: a method of dc self-heating a battery at low temperature for the ad hoc network of a heavy haul train according to any one of claims 1-7.
10. An electronic terminal, characterized in that: comprising a processor and a memory, the memory storing a computer program, the processor invoking the computer program to perform: a method of dc self-heating a battery at low temperature for the ad hoc network of a heavy haul train according to any one of claims 1-7.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117996293A (en) * | 2024-04-03 | 2024-05-07 | 浙江兴创新能源有限公司 | Temperature control and optimization method and system for immersed energy storage battery |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108333528A (en) * | 2018-02-07 | 2018-07-27 | 重庆大学 | SOC and SOT united state methods of estimation based on power battery electric-thermal coupling model |
| CN110083871A (en) * | 2019-03-27 | 2019-08-02 | 北京航空航天大学 | A kind of thermal runaway analogy method and device based on lithium ion battery thermal runaway prediction model |
| CN113281655A (en) * | 2021-05-20 | 2021-08-20 | 中南大学 | Predictive control method and device for internal heating of power battery in low-temperature environment |
-
2023
- 2023-08-17 CN CN202311040175.0A patent/CN116826254A/en not_active Withdrawn
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108333528A (en) * | 2018-02-07 | 2018-07-27 | 重庆大学 | SOC and SOT united state methods of estimation based on power battery electric-thermal coupling model |
| CN110083871A (en) * | 2019-03-27 | 2019-08-02 | 北京航空航天大学 | A kind of thermal runaway analogy method and device based on lithium ion battery thermal runaway prediction model |
| CN113281655A (en) * | 2021-05-20 | 2021-08-20 | 中南大学 | Predictive control method and device for internal heating of power battery in low-temperature environment |
Non-Patent Citations (2)
| Title |
|---|
| KAIFU GUAN 等: "A state of charge-aware internal preheating strategy for Li-ion batteries at low temperatures"", JOURNAL OF ENERGY STORAGE, 11 August 2023 (2023-08-11), pages 1 - 13 * |
| 徐智慧 等: "温度自适应的锂离子电池低温自加热方法", 电源技术, vol. 43, no. 12, 20 December 2019 (2019-12-20), pages 1989 - 1992 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117996293A (en) * | 2024-04-03 | 2024-05-07 | 浙江兴创新能源有限公司 | Temperature control and optimization method and system for immersed energy storage battery |
| CN117996293B (en) * | 2024-04-03 | 2024-06-07 | 浙江兴创新能源有限公司 | Temperature control and optimization method and system for immersed energy storage battery |
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