CN116653645B - Self-adaptive charging method, system and medium under monitoring of self-networking battery state of heavy-load freight train - Google Patents
Self-adaptive charging method, system and medium under monitoring of self-networking battery state of heavy-load freight train Download PDFInfo
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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
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
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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
-
- 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
- B60L2200/00—Type of vehicles
- B60L2200/26—Rail vehicles
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- 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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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2240/00—Control parameters of input or output; Target parameters
- B60L2240/40—Drive Train control parameters
- B60L2240/54—Drive Train control parameters related to batteries
- B60L2240/547—Voltage
Abstract
The invention discloses a self-adaptive charging method, a system and a medium under the condition of monitoring the state of an ad hoc network battery of a heavy-duty freight train, wherein the method comprises the following steps: constructing a battery coupling model, and testing to obtain a battery coupling model parameter database; the initial temperature and the open-circuit voltage of each battery pack in the self-networking battery are obtained, the initial charge state of each battery pack is calibrated, the change track of the battery pack state in the charging process is predicted, and the number of charging stages is further obtained; taking the charging speed and the service life loss as optimization targets, and solving an optimal charging current sequence by adopting a multi-target optimization algorithm; applying the solving result to the battery pack, monitoring the change of the battery temperature and the state of charge of the battery pack in the charging process, and updating the parameters of the battery coupling model in real time; and adjusting the charging current according to whether the charge states of the battery packs reach the target charge states or not until the charging is completed. The battery pack charging device is efficient, quick and uniform in charging and capable of reducing service life loss of the battery pack.
Description
Technical Field
The invention relates to the technical field of battery management, in particular to a self-adaptive charging method, a self-adaptive charging system and a self-adaptive charging medium for monitoring the state of an ad hoc network battery of a heavy-duty freight train.
Background
As a main power source of a heavy-duty freight train, a battery energy storage system needs to have high-efficiency charging performance, and the existing charging technology of the vehicle-mounted energy storage system faces the problems of long charging time, high battery life loss and the like, and particularly when the heavy-duty freight train works in a low-temperature environment, the charging rate is reduced by half, and the battery capacity is reduced along with a plurality of times of increase. In addition, the battery energy storage system is formed by connecting multiple battery packs in series and parallel, different battery packs have different initial states of charge and process states due to factory differences and aging differences, for example, the battery packs with serious aging have lower initial states of charge and generate large heat in the charging process. Conventional charging methods generally employ a predefined current profile for charging a battery, such as constant current charging, constant current constant voltage charging, etc., although the battery capacity fade can be alleviated by reducing the charging current to sacrifice the charging rate, its direct application still faces serious charging performance losses in low temperature environments.
Providing an adjustable charging current to a battery in a low temperature environment is a fundamental requirement to ensure efficient charging, and thus adaptive charging techniques are widely studied in the existing patents and literature. Publication number CN107431369a discloses a system, method and apparatus for adaptive battery charging, wherein the method considers the effects of battery aging, and adaptively charges a cutoff voltage threshold and a cutoff current threshold according to the aging resistance of the battery to increase the charging speed and charging capacity of the battery. Publication number CN112349988A discloses an on-line monitoring device for a vehicle storage battery and an adaptive charging method, wherein the method judges the battery state by collecting the voltage and current of the storage battery, and generates an adaptive charging instruction and an adaptive discharging instruction according to the battery state. The adaptive charging technique described above does not take into account the effect of the real-time temperature of the battery on the charging process. Publication number CN106571656a discloses a battery state monitoring and charging management system for an electric vehicle charging device, which calculates the current optimal battery charging current voltage value according to the conversion rate of the direct current voltage of the battery in the charging process, in combination with the real-time temperature and the allowable maximum temperature of the battery. However, the system only takes the battery temperature as an index of fault alarm of charging, and neglects the influence of the battery temperature and the state of charge on the internal parameters of the battery.
The existing academic research analysis, the multi-stage constant current charging technology becomes a research hot spot in the field of self-adaptive charging in recent years due to the characteristics of flexibility and easy realization, wherein the number of charging stages and the stage jump condition are the most important two factors in the multi-stage constant current charging technology. In the existing research, the number of stages is generally fixed to five stages, and the jump condition is generally determined by referring to a single parameter of the battery, for example, whether one of the cut-off voltage, the terminal voltage, the state of charge and the charging time of the battery meets a preset condition is determined, so that the stage jump is started, and the influence of the temperature change of the battery on the number of charging stages and the jump time is often ignored. The battery temperature change is obvious in the charging process, the battery parameters are greatly influenced by the temperature, the change of the battery model parameters under the comprehensive influence of different temperatures and different states of charge is not considered in the conventional multi-stage constant current charging technology, so that the charging current cannot adapt to the change of the battery state, faster and more efficient charging cannot be realized, and meanwhile, the service life loss of the battery is increased. In addition, the predefined current profile does not solve the charge imbalance problem caused by the battery pack difference.
Disclosure of Invention
The invention provides a self-adaptive charging method, a system and a medium under the condition of monitoring the battery state of an ad hoc network of a heavy-load freight train, wherein the method is used for solving the technical problems that the charging current caused by the fact that the change of the battery model parameters under the comprehensive influence of different temperatures and different states of charge is not considered in the related art, the change of the battery state cannot be adapted, and the high-efficiency quick charging, the service life loss of a battery and the charging non-uniformity caused by the difference of battery packs cannot be solved.
In a first aspect, the present invention provides an adaptive charging method under monitoring of a state of an ad hoc network battery of a heavy-duty freight train, including:
s1: constructing a battery coupling model, and testing the battery coupling model to obtain a battery coupling model parameter database;
s2: setting a target state of charge of each battery pack in the ad hoc network battery;
s3: acquiring initial temperature and open-circuit voltage of each battery pack before charging, and calibrating initial charge states of each battery pack;
s4: predicting the change track of the battery pack state in the charging process according to the initial temperature and the initial charge state of each battery pack to obtain the number of charging stages in a self-adaptive manner;
s5: taking the charging speed and the service life loss as optimization targets, and solving an optimal charging current sequence by adopting a multi-target optimization algorithm;
s6: applying the solved optimal charging current sequence to the battery pack, and monitoring the temperature and the change of the charge state of the battery pack in the charging process, so as to update the parameters of the battery coupling model in real time;
s7: judging whether the charge states of the battery packs reach the target charge states or not:
if not, calculating the maximum charging current allowed by the battery pack under the current state of the battery pack not reaching the standard, and judging whether the maximum charging current meets the preset jump condition or not: if so, charging the battery pack by applying a charging current of the next charging stage in the optimal charging current sequence; if not, charging is carried out by adopting the charging current of the current charging stage;
if yes, the charging is ended.
Further, the construction process of the battery coupling model in S1 specifically includes:
taking the temperature and the state of charge of the battery pack as intermediate parameters, and coupling a battery electric model, a battery thermal model and a battery aging model to obtain a battery coupling model; the input of the battery coupling model is the charging current and the ambient temperature of the battery pack, and the output of the battery coupling model is the temperature, the state of charge, the terminal voltage and the capacity attenuation.
Furthermore, the battery coupling model parameter database is constructed by obtaining values of open-circuit voltage, series resistance, polarization resistance and polarization capacitance parameters of each battery pack under different temperatures and states of charge through a mixed pulse power characteristic test, and obtaining battery surface heat dissipation coefficients through an air convection cooling test under different ambient temperatures.
Further, the process of obtaining the number of charging stages in S4 is:
taking the maximum allowable voltage of the battery pack as a limit, and acquiring the maximum allowable charging current curved surface of the battery pack under different temperatures and charge states based on a battery coupling model parameter database;
acquiring a contour plane of a curved surface of the maximum allowable charging current at preset current size intervals, wherein the contour plane is divided into different areas by the contour;
and taking the number of areas experienced in the process of charging the battery pack from the current initial temperature to the target state of charge under the state of charge as the number of charging stages.
Further, the expression of the charge optimization target in S5The method comprises the following steps:
wherein:for the total charging time, +.>For maximum charging time, +.>For the life loss of the battery during charging, < >>Penalty factors for charge time;
the multi-objective optimization algorithm is a particle swarm optimization algorithm, and the obtained optimal charging current sequence of the battery pack is。
Further, in S6, the temperature of each battery pack is acquired by a temperature sensor; the state of charge of each battery pack is calculated as follows:
wherein:for the current state of charge of the battery, < > and->For a nominal initial state of charge of the battery, +.>For coulombic efficiency, +.>For the rated capacity of the battery, ">For the sampling interval +.>To obtain an optimal charging current sequence.
Further, the maximum charge current allowed in the current state of the battery pack in S7Is calculated as follows:
wherein:maximum operating voltage allowed for the battery; />Open circuit voltage of the battery pack updated in real time; />Is a series resistance updated in real time.
Further, the preset jump condition in S7 is:
wherein:for the maximum charge current allowed in the current state of the battery, +.>Representing the maximum charge current allowed by the battery pack calculated at the previous sampling period, +.>Representing the +.>Current amplitude corresponding to contour line, +.>Representing a set of all contours.
In a second aspect, the present invention provides an adaptive charging system under monitoring of a battery status of an ad hoc network of a heavy-duty freight train, including:
a database module: the method comprises the steps of constructing a battery coupling model, and testing the battery coupling model to obtain a battery coupling model parameter database;
a battery pack state acquisition module: the method comprises the steps of setting a target state of charge of each battery pack in the ad hoc network battery; acquiring initial temperature and open-circuit voltage of the battery packs before charging, and calibrating initial charge states of the battery packs; predicting the change track of the battery pack state in the charging process according to the initial temperature and the initial charge state of each battery pack to obtain the number of charging stages in a self-adaptive manner;
and a charging control module: the method is used for taking the charging speed and the service life loss as optimization targets and solving an optimal charging current sequence by adopting a multi-target optimization algorithm; applying the solved optimal rechargeable battery sequence to the battery pack, and monitoring the change of the battery temperature and the state of charge of the battery pack in the charging process, so as to update the parameters of the battery coupling model in real time; judging whether the charge states of the battery packs reach the target charge states or not: if not, calculating the maximum charging current allowed by the battery pack under the current state of the battery pack not reaching the standard, and judging whether the maximum charging current meets the preset jump condition or not: if so, charging the battery pack by applying a charging current of the next charging stage in the optimal charging current sequence; if not, charging is carried out by adopting the charging current of the current charging stage; if yes, the charging is ended.
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 adaptive charging method under heavy haul train ad hoc network battery state monitoring as described above.
Advantageous effects
The invention provides a self-adaptive charging method, a self-adaptive charging system and a self-adaptive charging medium under the condition of a battery state of an ad hoc network of a heavy-load freight train, wherein the method constructs a battery coupling model, and the mutual action among the electric, thermal and aging characteristics of the battery is well known by measuring and identifying parameters of the battery coupling model. And a database of battery coupling model parameters, battery temperature and state of charge is constructed, so that more accurate charging strategies can be designed, and a model and a data base are provided for charging optimization. The design of the self-adaptive multi-stage charging can enable the charging process to adjust the number of charging stages and the stage jump condition according to the initial state and the real-time state of the battery, and the problems of low charging efficiency, relatively long charging time and the like caused by the fixed stage number and the single stage jump condition are avoided. Aiming at the established nonlinear multi-objective charging optimization problem, a particle swarm optimization algorithm is introduced to provide rapid solving efficiency and timely charging decision. The battery coupling model parameter self-adaptive charging process is updated in real time in the charging process, so that quick and safe charging in a wide environment temperature range is realized, the charging performance of the battery in a low-temperature environment is improved, the service life of the battery is prolonged, and the reliable vehicle-mounted energy storage energy supply of the heavy-load freight train in different temperature environments is ensured.
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 an adaptive charging method under monitoring of a battery status of an ad hoc network of a heavy-duty freight train according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a battery coupling model provided by an embodiment of the present invention;
fig. 3 is a schematic view of a curved surface of a maximum charge current allowed by a battery according to an embodiment of the present invention;
fig. 4 is a schematic diagram of an adaptive charging process according to 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, the embodiment provides an adaptive charging method under monitoring of a battery state of an ad hoc network of a heavy-duty freight train, which includes:
s1: and constructing a battery coupling model, and testing the battery coupling model to obtain a battery coupling model parameter database.
Specifically, the construction process of the battery coupling model is as follows:
and taking the temperature and the charge state of the battery pack as intermediate parameters, and coupling the battery electric model, the battery thermal model and the battery aging model to obtain a battery coupling model, as shown in figure 2.
Wherein, the battery electric model is a Thevenin model, and the expression is:
wherein OCV is the open circuit voltage of the battery;is the terminal voltage of the battery; />For ohmic resistance of battery->The voltage across the terminals; />Is the polarization voltage of the battery; />Is the rate of change of the polarization voltage; />，/>Respectively a polarization resistance and a polarization capacitance of the battery; />Is the battery current.
The battery thermal model is an equivalent network model, and the expression is:
in the method, in the process of the invention,for the internal heating value of the battery, < > is->For the heat dissipation of the battery surface->Is the temperature of the battery, ">Represents the surface area of the battery, < >>Representing the heat transfer coefficient>Is ambient temperature, ++>For battery quality->Is the specific heat capacity of the cell.
The battery aging model is a semi-empirical model; the expression is:
in the method, in the process of the invention,for battery capacity loss ratio, +.>Is a current-dependent pre-exponential factor, +.>Is the activation energy constant of cyclic aging, +.>Is a gas constant->Is amp hour throughput, +.>Is the compensation factor of the current,/->Is a time factor.
The input of the battery coupling model is the charging current and the ambient temperature of the battery pack, and the output of the battery coupling model is the battery temperature, the state of charge, the terminal voltage and the capacity fade. Obtaining the electric parameters of each battery pack, namely the values of open-circuit voltage, series resistance, polarization resistance and polarization capacitance parameters at different temperatures and states of charge through a mixed pulse power characteristic test, wherein in the embodiment, the temperature of the battery is changed between-20 ℃ and 25 ℃, and the state of charge is changed between 0% and 100%; and obtaining the surface heat dissipation coefficient of the battery pack through an air convection cooling test at different environment temperatures (-20 ℃ to 25 ℃), and constructing a battery coupling model parameter database. The battery thermal model outputs battery temperature feedback to the electric model to update electric model parameters including series resistance, polarization capacitance, open circuit voltage. The acquired battery pack electrical parameters and thermal parameters form a battery coupling model parameter database, and data support is provided for subsequent construction of a charging current allowed curved surface and real-time updating of battery pack parameters.
S2: and setting a target charge state of each battery pack in the ad hoc network battery.
S3: and acquiring the initial temperature and the open-circuit voltage of each battery pack before charging, and calibrating the initial charge state of each battery pack.
S4: and predicting the change track of the battery pack state (namely the battery pack temperature and the charge state) in the charging process according to the initial temperature and the initial charge state of each battery pack, and obtaining the number of charging stages in a self-adaptive manner.
Specifically, taking the maximum allowable voltage of the battery pack during operation as a limit, and acquiring the maximum allowable charging current curved surface of the battery pack at different temperatures and charge states based on a battery coupling model parameter database, wherein the battery model is NCR18650 in the embodiment, and the maximum allowable charging current curved surface is shown in fig. 3;
the method comprises the steps of obtaining a contour plane of a curved surface of a maximum allowable charging current at intervals of a preset current, calculating that the maximum allowable charging current reaches a contour value in a current battery state based on a contour plane of the curved surface, and dividing the plane into different areas by contour lines; in this embodiment, the preset current is 1A.
And taking the number of areas passing through in the process of charging the battery pack from the current initial temperature to the target state of charge under the state of charge as the number of charging stages, namely the number of charging stages is determined by the initial state of the battery and the contour plane together.
S5: and taking the charging speed and the service life loss as optimization targets, and solving an optimal charging current sequence by adopting a multi-target optimization algorithm.
Specifically, the optimization target expression in S5The method comprises the following steps:
wherein:for the total charging time, +.>For maximum charging time, +.>For the life loss of the battery during charging, < >>Is a penalty factor for the charge time.
The multi-objective optimization algorithm is a particle swarm optimization algorithm, and the obtained optimal charging current sequence of the battery pack isThe particle swarm optimization algorithm comprises the following specific steps:
s51: initializing a particle group (n particles in the particle group), and giving random initial positions and speeds to each particle; wherein the position of each particle is a multidimensional array containing a sequence of charging currents, the dimension of which is the same as the number of charging phases, and the velocity of each particle is the rate of change of the sequence of currents in the particle.
S52: and calculating the fitness value of each particle (namely each group of charging current sequences) according to a fitness function, wherein the formula of the fitness function J is as follows:
wherein, the first term on the right of the equation is a charging speed adaptive value, and the second term is a life loss adaptive value;penalty factor for charging time, +.>For the total charging time, +.>For maximum charging time, +.>Is the life loss of the battery pack during charging.
S53: for each particle i, its current position is determinedAdaptation value (k-th iteration) and historical optimal positionComparing the corresponding adaptation values (the current sequence with the optimal adaptation value of the particle in the k generation and before), and if the adaptation value of the current position is higher, updating the historical optimal position of the particle with the current position;
s54: for each particle i, its current position is determinedAdaptive value (k-th iteration) and global optimal positionComparing the corresponding adaptation values (current sequences with optimal adaptation values of all particles in the generation k and before), and if the adaptation value of the current position is higher, updating the global optimal position by using the current position;
s55: the velocity and position of each particle are updated according to the following formula:
wherein:，/>respectively represent grainsSpeed of sub-i at k and k+1 generation,/->Representing inertial weights, ++>，/>Indicating acceleration factor, ++>，/>Random number 0 to 1, +.>，/>The current sequences of particle i at the k and k+1 generations, respectively;
s56: judging whether the algorithm is finished, if the finishing condition is not met, returning to S52, and if the finishing condition is met, finishing the algorithm, wherein the global optimal position is the global optimal solution; the end condition is that one of the following conditions is satisfied: (1) The global optimum position for the first 10 iterations varies by less than 10 -4 The method comprises the steps of carrying out a first treatment on the surface of the (2) 200 iterations are completed. Aiming at the established nonlinear charging optimization problem, a particle swarm optimization algorithm is adopted to quickly search a solution space, and an optimal charging current sequence for balancing charging speed and battery capacity attenuation is obtained.
S6: and applying the solved optimal rechargeable battery sequence to the battery pack, and monitoring the change of the battery temperature and the state of charge of the battery pack in the charging process, so as to update the parameters of the battery coupling model in real time.
Specifically, the obtained optimal charging current sequence is applied to the battery pack, so that the temperature and the state of charge of the battery pack are gradually increased, and the temperature change of the battery is monitored in real time through a temperature sensor; the state of charge of the battery is calculated in real time by adopting an ampere-hour integration method, and the method is specifically as follows:
wherein:for the current state of charge of the battery, +.>For a nominal initial state of charge of the battery, < >>For coulombic efficiency, +.>For the rated capacity of the battery pack>For the sampling interval +.>To obtain an optimal charging current sequence.
Updating parameters of a battery coupling model in real time according to the real-time temperature and the state of charge of the battery pack, wherein the parameters comprise: open circuit voltage, series resistance, polarization capacitance, and heat dissipation factor.
S7: judging whether the charge states of the battery packs reach the target charge states or not: if not, calculating the maximum charging current allowed by the battery pack under the current state of the battery pack not reaching the standard, and judging whether the maximum charging current meets the preset jump condition or not: if so, charging the battery pack by applying a charging current of the next charging stage in the optimal charging current sequence; if not, charging is carried out by adopting the charging current of the current charging stage; if yes, the charging is ended.
Wherein, the maximum charge current allowed under the current state of the battery packIs calculated as follows:
wherein:maximum operating voltage allowed for the battery; />Open circuit voltage of the battery pack updated in real time; />Is a series resistance updated in real time.
As shown in fig. 4, during the charging process, when the maximum charging current determined by the real-time state (temperature and state of charge) of the battery is transferred from one region of the contour plane to another region, that is, the battery pack state pair passes through the contour, it is indicated that the preset skip condition is satisfied, and the charging current of the next stage in the optimal charging current sequence is applied to the battery pack. The preset jump condition specifically comprises the following steps:
wherein:for the maximum charge current allowed in the current state of the battery, +.>Representing the maximum charge current allowed by the battery pack calculated at the previous sampling period, +.>Representing the +.>Current amplitude corresponding to contour line, +.>Representing a set of all contours.
In this embodiment, based on a battery electric model, a battery thermal model, and a battery aging model, a battery coupling model and a maximum charging current curve allowed by the battery are constructed, and an adaptive multi-stage constant current charging technique is designed, wherein the number of stages is determined by the initial state of the battery, and the stage transition condition is determined by the real-time state of the battery. And running the battery coupling model, reading a battery coupling model database, running an optimization algorithm and outputting an optimal charging current sequence. And further, optimal charging current setting is executed, quick and efficient charging is realized, the charging performance of the battery in a low-temperature environment is improved, the service life of the battery is prolonged, and reliable vehicle-mounted energy storage energy supply of the heavy-load freight train in different temperature environments is ensured. By utilizing the ad hoc network battery architecture, real-time state information can be shared among battery packs. After 20 charge cycle tests, compared with the existing constant-current constant-voltage charging method, the self-adaptive charging method and device can shorten the average charging time by 757 seconds, slow down the accumulated capacity loss of the battery by 143 milliamperes (performance improvement by 48.2%), and achieve voltage balance (deviation is smaller than 0.5V) after the three battery modules are charged.
Example 2
The embodiment provides an adaptive charging system under the monitoring of the state of an ad hoc network battery of a heavy-duty freight train, which comprises the following components:
a database module: the method comprises the steps of constructing a battery coupling model, and testing the battery coupling model to obtain a battery coupling model parameter database;
a battery pack state acquisition module: the method comprises the steps of setting a target state of charge of each battery pack in the ad hoc network battery; acquiring initial temperature and open-circuit voltage of the battery packs before charging, and calibrating initial charge states of the battery packs; predicting the change track of the battery pack state in the charging process according to the initial temperature and the initial charge state of each battery pack to obtain the number of charging stages in a self-adaptive manner;
and a charging control module: the method is used for taking the charging speed and the service life loss as optimization targets and solving an optimal charging current sequence by adopting a multi-target optimization algorithm; applying the solved optimal rechargeable battery sequence to the battery pack, and monitoring the change of the battery temperature and the state of charge of the battery pack in the charging process, so as to update the parameters of the battery coupling model in real time; judging whether the charge states of the battery packs reach the target charge states or not: if not, calculating the maximum charging current allowed by the battery pack under the current state of the battery pack not reaching the standard, and judging whether the maximum charging current meets the preset jump condition or not: if so, charging the battery pack by applying a charging current of the next charging stage in the optimal charging current sequence; if not, charging is carried out by adopting the charging current of the current charging stage; if yes, the charging is ended.
Example 3
The present embodiment provides a computer-readable storage medium: a computer program is stored which, when invoked by a processor, performs the steps of the adaptive charging method under monitoring of the ad hoc battery status of a heavy haul train as described above.
It should 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 (9)
1. An adaptive charging method under the condition of monitoring the state of an ad hoc network battery of a heavy-duty freight train is characterized by comprising the following steps:
s1: constructing a battery coupling model, and testing the battery coupling model to obtain a battery coupling model parameter database;
s2: setting a target state of charge of each battery pack in the ad hoc network battery;
s3: acquiring initial temperature and open-circuit voltage of each battery pack before charging, and calibrating initial charge states of each battery pack;
s4: predicting the change track of the battery pack state in the charging process according to the initial temperature and the initial charge state of each battery pack to obtain the number of charging stages in a self-adaptive manner;
the process of obtaining the number of charging stages in S4 is as follows:
taking the maximum allowable voltage of the battery pack as a limit, and acquiring the maximum allowable charging current curved surface of the battery pack under different temperatures and charge states based on a battery coupling model parameter database;
the method comprises the steps of obtaining a contour plane of a curved surface of a maximum allowable charging current at intervals of a preset current, wherein the contour plane is divided into different areas by the contour;
the number of areas passing through in the process of charging the battery pack from the current initial temperature to the target state of charge under the state of charge is used as the number of charging stages;
s5: taking the charging speed and the service life loss as optimization targets, and solving an optimal charging current sequence by adopting a multi-target optimization algorithm;
s6: applying the solved optimal charging current sequence to the battery pack, and monitoring the temperature and the change of the charge state of the battery pack in the charging process, so as to update the parameters of the battery coupling model in real time;
s7: judging whether the charge states of the battery packs reach the target charge states or not: if not, calculating the maximum charging current allowed by the battery pack under the current state of the battery pack not reaching the standard, and judging whether the maximum charging current meets the preset jump condition or not: if so, charging the battery pack by applying a charging current of the next charging stage in the optimal charging current sequence; if not, charging is carried out by adopting the charging current of the current charging stage; if yes, the charging is ended.
2. The self-adaptive charging method under the monitoring of the state of the battery of the ad hoc network of the heavy-duty freight train according to claim 1, wherein the construction process of the battery coupling model in S1 is specifically as follows: taking the temperature and the state of charge of the battery pack as intermediate parameters, and coupling a battery electric model, a battery thermal model and a battery aging model to obtain a battery coupling model; the input of the battery coupling model is the charging current of the battery pack and the ambient temperature, and the output of the battery coupling model is the temperature, the state of charge, the terminal voltage and the capacity fade.
3. The method for adaptively charging the battery state monitoring of the ad hoc network of the heavy-duty freight train according to claim 1, wherein the battery coupling model parameter database is constructed by obtaining values of open-circuit voltage, series resistance, polarization resistance and polarization capacitance parameters of each battery pack at different temperatures and states of charge through a mixed pulse power characteristic test and obtaining battery surface heat dissipation coefficients through an air convection cooling test at different ambient temperatures.
4. The adaptive charging method under monitoring of the status of an ad hoc network battery of a heavy haul train according to claim 1, wherein the expression of the charge optimization objective in S5JThe method comprises the following steps:
；
wherein:for the total charging time, +.>For maximum charging time, +.>For the life loss of the battery during charging, < >>Penalty factors for charge time;
the multi-objective optimization algorithm is a particle swarm optimization algorithm, and the obtained optimal charging current sequence of the battery pack is。
5. The method for adaptive charging under monitoring of the state of an ad hoc network battery of a heavy haul train according to claim 1, wherein in S6, the state of charge of each battery pack is calculated as follows:
；
wherein:for the current state of charge of the battery, +.>For a nominal initial state of charge of the battery, < >>For coulombic efficiency, +.>For the rated capacity of the battery pack>For the sampling interval +.>To obtain an optimal charging current sequence.
6. The adaptive charging method for monitoring the status of an ad hoc network battery of a heavy haul train according to claim 1, wherein the maximum charging current allowed in the current status of the battery in S7Is calculated as follows:
；
wherein:maximum operating voltage allowed for the battery; />The battery pack open circuit voltage updated in real time;is a series resistance updated in real time.
7. The adaptive charging method under the condition of monitoring the battery state of the ad hoc network of the heavy haul train according to claim 1, wherein the preset jump condition in S7 is:
；
wherein:for the maximum charge current allowed in the current state of the battery, +.>Representing the maximum charge current allowed by the battery pack calculated at the previous sampling period, +.>Representing the first in the contour planemCurrent amplitude corresponding to contour line, +.>Representing all the contoursA collection of lines.
8. An adaptive charging system for monitoring the status of an ad hoc network battery of a heavy-duty freight train, comprising:
a database module: the method comprises the steps of constructing a battery coupling model, and testing the battery coupling model to obtain a battery coupling model parameter database;
a battery pack state acquisition module: the method comprises the steps of setting a target state of charge of each battery pack in the ad hoc network battery; acquiring initial temperature and open-circuit voltage of the battery packs before charging, and calibrating initial charge states of the battery packs; predicting the change track of the battery pack state in the charging process according to the initial temperature and the initial charge state of each battery pack to obtain the number of charging stages in a self-adaptive manner; the acquisition process of the charging stage number is as follows:
taking the maximum allowable voltage of the battery pack as a limit, and acquiring the maximum allowable charging current curved surface of the battery pack under different temperatures and charge states based on a battery coupling model parameter database;
the method comprises the steps of obtaining a contour plane of a curved surface of a maximum allowable charging current at intervals of a preset current, wherein the contour plane is divided into different areas by the contour;
the number of areas passing through in the process of charging the battery pack from the current initial temperature to the target state of charge under the state of charge is used as the number of charging stages;
and a charging control module: the method is used for taking the charging speed and the service life loss as optimization targets and solving an optimal charging current sequence by adopting a multi-target optimization algorithm; applying the solved optimal rechargeable battery sequence to the battery pack, and monitoring the temperature and the change of the charge state of the battery pack in the charging process, so as to update the parameters of the battery coupling model in real time; judging whether the charge states of the battery packs reach the target charge states or not:
if not, calculating the maximum charging current allowed by the battery pack under the current state of the battery pack not reaching the standard, and judging whether the maximum charging current meets the preset jump condition or not: if so, charging the battery pack by applying a charging current of the next charging stage in the optimal charging current sequence; if not, charging is carried out by adopting the charging current of the current charging stage;
if yes, the charging is ended.
9. A computer-readable storage medium, characterized by: a computer program is stored which, when called by a processor, performs: the method for adaptively charging a heavy haul train under ad hoc network battery condition monitoring of any one of claims 1-7.
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