CN113419113A - Method and system for online recognizing state of vehicle-mounted super-capacitor energy storage system of tramcar - Google Patents

Method and system for online recognizing state of vehicle-mounted super-capacitor energy storage system of tramcar Download PDF

Info

Publication number
CN113419113A
CN113419113A CN202110615359.XA CN202110615359A CN113419113A CN 113419113 A CN113419113 A CN 113419113A CN 202110615359 A CN202110615359 A CN 202110615359A CN 113419113 A CN113419113 A CN 113419113A
Authority
CN
China
Prior art keywords
real
time
value
expression
super
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202110615359.XA
Other languages
Chinese (zh)
Other versions
CN113419113B (en
Inventor
孟秋艳
杨阳
刘陆洲
赵丹
周道亮
张云贺
张亚鑫
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
CRRC Qingdao Sifang Rolling Stock Research Institute Co Ltd
Original Assignee
CRRC Qingdao Sifang Rolling Stock Research Institute Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by CRRC Qingdao Sifang Rolling Stock Research Institute Co Ltd filed Critical CRRC Qingdao Sifang Rolling Stock Research Institute Co Ltd
Priority to CN202110615359.XA priority Critical patent/CN113419113B/en
Publication of CN113419113A publication Critical patent/CN113419113A/en
Application granted granted Critical
Publication of CN113419113B publication Critical patent/CN113419113B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R27/00—Arrangements for measuring resistance, reactance, impedance, or electric characteristics derived therefrom
    • G01R27/02—Measuring real or complex resistance, reactance, impedance, or other two-pole characteristics derived therefrom, e.g. time constant
    • G01R27/26—Measuring inductance or capacitance; Measuring quality factor, e.g. by using the resonance method; Measuring loss factor; Measuring dielectric constants ; Measuring impedance or related variables
    • G01R27/2605—Measuring capacitance
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • Artificial Intelligence (AREA)
  • Biomedical Technology (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Health & Medical Sciences (AREA)
  • Power Engineering (AREA)
  • Measurement Of Resistance Or Impedance (AREA)

Abstract

The application relates to a method and a system for online recognizing the state of a vehicle-mounted super-capacitor energy storage system of a tramcar. Wherein, the method comprises the following steps: a real-time capacitance value obtaining step, namely obtaining real-time voltage and real-time current, and obtaining a real-time capacitance value according to a charge conservation law and an energy conservation law; a theoretical capacity value expression obtaining step, wherein an expression of the theoretical capacity value is obtained according to a particle swarm algorithm according to a plurality of groups of real-time voltages, real-time currents and real-time capacity values; a super capacitor state obtaining step, namely judging the current state of the super capacitor energy storage system according to the real-time capacitance value and the theoretical capacitance value; and a current adjusting step of adjusting the charging current according to the state and circulating the steps. The optimal parameter identification of the expression book of the theoretical capacitance value is carried out through the particle swarm algorithm, the state of the super capacitor in the whole charging process can be judged in real time, and the charging current of the energy storage system is adjusted in real time.

Description

Method and system for online recognizing state of vehicle-mounted super-capacitor energy storage system of tramcar
Technical Field
The application relates to the technical field of traffic energy storage and energy conservation, in particular to a method and a system for online state identification of a tramcar-mounted super-capacitor energy storage system.
Background
The development of the rail transit engineering in China is rapid, and the vehicle-mounted energy storage type tramcar (hereinafter referred to as tramcar) is generally concerned by the advantages of beauty, environmental protection, energy conservation and the like. At present, energy storage elements of the tramcar mainly adopt a lithium battery and a super capacitor, wherein the super capacitor has the advantages of long cycle service life, high power density, high heavy current charging speed, wide use temperature range and the like, and is suitable for the tramcar engineering in a station charging operation mode.
The tramcar adopts the full-line mode of not having the contact net, and super capacitor energy storage system utilizes the interior electric pile that charges in the station to charge in the passenger gets on or off the bus-hour, provides the electric quantity for vehicle traction operation and supplementary. The super capacitor energy storage system generally adopts a parallel technology of a plurality of groups of super capacitors, when one or more groups of super capacitors of the system have faults, the vehicle can be maintained to continue to operate, and the reliability of the system is improved. In the charging process of the super-capacitor energy storage system, the charging pile identifies the states of the multiple groups of super-capacitor systems and adjusts the charging current value so as to protect the safety of the super-capacitor energy storage system.
Because there is not communication between charging pile and the super capacitor energy storage system, the charging pile can only rely on the following method to obtain the state of the super capacitor energy storage system:
firstly, adding a trial charging link on the basis of a constant-current and constant-voltage charging process;
secondly, calculating the current capacitance value of the super capacitor according to the voltage increment, the current and the time of the trial charge stage;
and finally, comparing the current capacitance value with the theoretical capacitance value to determine the state of the super-capacitor energy storage system.
Chinese patent document No. CN104297578B discloses a method for estimating the load state of a supercapacitor based on a synovial observer, which comprises the following steps: the charging and discharging current value and the terminal voltage value of each super capacitor monomer when the super capacitor bank works are collected in real time, the charge state value of each super capacitor monomer is estimated, and the state of the super capacitor is obtained through comparison and judgment. In the prior art, the state of charge value is estimated by adopting a synovial observer estimation algorithm, the obtained state of charge values of all monomers are not accurate, and certain deviation exists in the judgment of the state of the super capacitor.
At present, no effective solution is provided for the problem that the state judgment in the charging process is inaccurate in the related art.
Disclosure of Invention
The embodiment of the application provides a method and a system for online identifying the state of a vehicle-mounted super-capacitor energy storage system of a tramcar, which aim to at least solve the problem of inaccurate state judgment in the charging process in the related technology.
In a first aspect, an embodiment of the present application provides an online state identification method for a tramcar-mounted super capacitor energy storage system, including the following steps:
a real-time capacitance value obtaining step, namely obtaining real-time voltage and real-time current, and obtaining a real-time capacitance value according to a charge conservation law and an energy conservation law;
a theoretical capacity value expression obtaining step, wherein an expression of the theoretical capacity value is obtained according to a particle swarm algorithm according to a plurality of groups of real-time voltages, real-time currents and real-time capacity values;
a super capacitor state obtaining step, namely judging the current state of the super capacitor energy storage system according to the real-time capacitance value and the theoretical capacitance value;
and a current adjusting step of adjusting the charging current according to the state and circulating the steps until the charging is finished.
In some embodiments, the obtaining of the theoretical capacity value expression specifically includes:
a data initialization step, wherein the particle number, the iteration times, the individual progression and the group progression are initialized;
and a fitness updating step, namely acquiring the current fitness according to the expression of the real-time capacity value and the theoretical capacity value, wherein the expression of the fitness is as follows:
Figure RE-GDA0003226262160000021
wherein C (t) is a real-time capacity value, V(t)For real-time voltage values, I(t)F (V (t), I (t), X) is an expression of a theoretical capacity value, and Y is an expression parameter variable Y (Y) of dimension D(1),Y(2),...,Y(D))(D≥1)；
A condition judging step of judging whether the preset iteration constraint condition is met or not according to the fitness,
the preset iteration constraint condition is specifically expressed as follows;
|Jg(X(k+1)-Jg(X(k))|≤ε
wherein X (k) and X (k +1) are the positions of the particles at the k and k +1 iterations respectively, and Jg(X(k+1))、Jg(X (k)) is the optimal fitness value of the particle at the k, k +1 iteration, and epsilon is the smallest positive number.
And an adjusting step, when the preset iteration constraint condition is not satisfied, updating the displacement and the speed of the particle until the corresponding fitness meets the preset iteration constraint condition, wherein the updating displacement of the particle is as follows:
Xi(k+1)＝Xi(k)+Vi(k+1)(i＝1，2，...，N)
the particle update rate is:
Vi(k+1)＝ωVi(k)+c1r1(Pi(k)-Xi(k))+c2r2(Pg(k)-Xi(k))
wherein, c1、c2Is a learning factor, r1、r2Is a random number between (0, 1), ω is an inertial weight, Pi(k)、Pg(k) The individual extremum and the population extremum of the particle at the kth iteration, respectively.
And a theoretical capacitance value expression output step, wherein when a preset iteration constraint condition is met, an optimal parameter result of the expression of the ideal capacitance is output.
In some embodiments, the step of acquiring the state of the super capacitor specifically includes the following steps:
a comparison reference value obtaining step, namely obtaining a plurality of groups of expressions of theoretical capacity values according to the theoretical capacity value expression output step, comparing the real-time capacity value with the theoretical capacity value to obtain a comparison reference value, wherein the specific expression of the comparison reference value is as follows:
min{[C(t)-f1(V(t)，I(t)，X)]2，[C(t)-f2(V(t)，I(t)，X)]2，...， [C(t)-fm(V(t)，I(t)，X)]2}
acquiring the number of groups of the super capacitors, namely acquiring the number m of the groups of the super capacitors which normally operate according to the comparison reference value;
in some embodiments, the current adjusting step specifically includes:
and according to the value of the group number m corresponding to the comparison reference value, referring to the group number current comparison table to obtain the charging current and adjusting the charging current to a corresponding size.
In a second aspect, an embodiment of the present application provides an online recognition system for a state of a vehicle-mounted super-capacitor energy storage system of a tram, where the online recognition method for a state of a vehicle-mounted super-capacitor energy storage system of a tram according to the first aspect is applied, and includes:
the real-time capacitance value acquisition module acquires real-time voltage and real-time current and acquires a real-time capacitance value according to a charge conservation law and an energy conservation law;
the theoretical capacity value expression obtaining module is used for obtaining an expression of a theoretical capacity value according to a particle swarm algorithm according to a plurality of groups of real-time voltages, real-time currents and real-time capacity values;
the super-capacitor state acquisition module is used for judging the current state of the super-capacitor energy storage system according to the real-time capacitance value and the theoretical capacitance value;
and the current adjusting module adjusts the charging current according to the state until the charging is finished.
In some embodiments, the theoretical capacity value expression obtaining module specifically includes:
the data initialization unit initializes the number of particles, the iteration times, the individual progression and the population progression;
and the fitness updating unit is used for obtaining the current fitness according to the expression of the real-time capacity value and the theoretical capacity value, wherein the expression of the fitness is as follows:
Figure RE-GDA0003226262160000041
wherein C (t) is a real-time capacity value, V(t)For real-time voltage values, I(t)F (V (t), I (t), X) is an expression of a theoretical capacity value, and Y is an expression parameter variable Y (Y) of dimension D(1)，Y(2)，...，Y(D))(D≥1)；
The condition judgment unit judges whether the preset iteration constraint condition is met or not according to the fitness, and the preset iteration constraint condition is specifically expressed as follows;
|Jg(X(k+1)-Jg(X(k))|≤ε
wherein X (k) and X (k +1) are the positions of the particles at the k and k +1 iterations respectively, and Jg(X(k+1))、Jg(X (k)) is the optimal fitness value of the particle at the k, k +1 iteration, and epsilon is the smallest positive number.
And the adjusting unit is used for updating the displacement and the speed of the particle until the corresponding fitness meets the preset iteration constraint condition when the preset iteration constraint condition is not met, wherein the updated displacement of the particle is as follows:
Xi(k+1)＝Xi(k)+Vi(k+1)(i＝1，2，...，N)
the particle update rate is:
Vi(k+1)＝ωVi(k)+c1r1(Pi(k)-Xi(k))+c2r2(Pg(k)-Xi(k))
wherein, c1、c2Is a learning factor, r1、r2Is between (0, 1)ω is the inertial weight, Pi(k)、Pg(k) The individual extremum and the population extremum of the particle at the kth iteration, respectively.
And the theoretical capacitance value expression output unit outputs the optimal parameter result of the expression of the ideal capacitance when the preset iteration constraint condition is met.
In some embodiments, the super capacitor state obtaining module specifically includes:
the comparison reference value obtaining unit obtains a plurality of groups of expressions of the theoretical capacity value according to the theoretical capacity value expression output step, compares the real-time capacity value with the theoretical capacity value, and obtains a comparison reference value, wherein the specific expression of the comparison reference value is as follows:
min{[C(t)-f1(V(t)，I(t)，X)]2，[C(t)-f2(V(t)，I(t)，X)]2，...， [C(t)-fm(V(t)，I(t)，X)]2}
and the super capacitor group number acquisition unit acquires the group number m of the super capacitors which normally operate according to the comparison reference value.
In some embodiments, the current adjustment module refers to the group number current comparison table according to the value of the group number m corresponding to the comparison reference value to obtain the charging current and adjusts the charging current to a corresponding magnitude.
In a third aspect, the present application provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement the online state identification method for the tram-mounted super capacitor energy storage system according to the first aspect.
In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the online status identification method for the tramcar-mounted super-capacitor energy storage system according to the first aspect.
Compared with the prior art, the method and the system for online recognizing the state of the vehicle-mounted super capacitor of the tramcar have the advantages that the optimal parameter recognition of the expression book of the theoretical capacitance value is carried out through the particle swarm optimization, the state of the super capacitor in the whole charging process can be judged in real time, and the charging current of an energy storage system can be adjusted in real time.
The details of one or more embodiments of the application are set forth in the accompanying drawings and the description below to provide a more thorough understanding of the application.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a schematic diagram illustrating a connection mode between a super capacitor energy storage system and a charging pile;
fig. 2 is a flowchart of a method for online identifying the state of a super capacitor energy storage system on a tramcar in an embodiment of the application;
FIG. 3 is an equivalent circuit schematic of a super capacitor;
FIG. 4 is a flowchart of a theoretical capacity expression obtaining step according to an embodiment of the present application;
FIG. 5 is a flowchart of a super capacitor state obtaining step according to an embodiment of the present application;
FIG. 6 is a flow chart of a method for online identification of the state of a super capacitor energy storage system on board a tram according to a preferred embodiment of the application;
FIG. 7 is a schematic flow chart of a particle swarm algorithm;
FIG. 8 is a schematic diagram of a charging process of the super capacitor energy storage system;
FIG. 9 is a block diagram of a state online identification system of a super capacitor energy storage system on board a tram according to an embodiment of the application;
fig. 10 is a hardware configuration diagram of a computer device according to an embodiment of the present application.
Description of the drawings:
a real-time capacity value acquisition module 1; a theoretical capacity value expression obtaining module 2;
a super capacitor state acquisition module 3; a current adjusting module 4;
a data initialization unit 21; a fitness updating unit 22; a condition judgment unit 23;
an adjustment unit 24; a theoretical capacity value expression output unit 2; :
a comparative reference value acquisition unit 31; a super capacitor bank number acquisition unit 32;
a processor 81; a memory 82; a communication interface 83; a bus 80.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments provided in the present application without any inventive step are within the scope of protection of the present application.
It is obvious that the drawings in the following description are only examples or embodiments of the present application, and that it is also possible for a person skilled in the art to apply the present application to other similar contexts on the basis of these drawings without inventive effort. Moreover, it should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the specification. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of ordinary skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments without conflict.
Unless defined otherwise, technical or scientific terms referred to herein shall have the ordinary meaning as understood by those of ordinary skill in the art to which this application belongs. Reference to "a," "an," "the," and similar words throughout this application are not to be construed as limiting in number, and may refer to the singular or the plural. The present application is directed to the use of the terms "including," "comprising," "having," and any variations thereof, which are intended to cover non-exclusive inclusions; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to the listed steps or elements, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. Reference to "connected," "coupled," and the like in this application is not intended to be limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The term "plurality" as referred to herein means two or more. "and/or" describes an association relationship of associated objects, meaning that three relationships may exist, for example, "A and/or B" may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. Reference herein to the terms "first," "second," "third," and the like, are merely to distinguish similar objects and do not denote a particular ordering for the objects.
Currently, the charging mode of the super capacitor energy storage system is as follows: when the charging pile detects that a vehicle enters a station, the vehicle energy storage system is charged through the pantograph, the charging process comprises trial charging, constant-current charging and constant-voltage charging, after the charging is completed, the super-capacitor energy storage system provides vehicle traction and auxiliary operation, and the schematic diagram of the connection of the plurality of groups of super-capacitor energy storage systems and the charging pile is shown in figure 1.
However, in the prior art, the state of the super capacitor energy storage system cannot be accurately identified due to the fact that the theoretical capacitance value cannot be accurately identified.
Based on the above, an embodiment of the present application provides an online recognition method for a state of a super capacitor energy storage system on a tramcar, and fig. 2 is a flowchart of the online recognition method for the state of the super capacitor energy storage system on the tramcar according to the embodiment of the present application, and as shown in fig. 2, the method includes the following steps:
a real-time capacitance value obtaining step S1 of obtaining a real-time voltage and a real-time current, and obtaining a real-time capacitance value according to a charge conservation law and an energy conservation law;
a theoretical capacity value expression obtaining step S2, obtaining an expression of a theoretical capacity value according to a particle swarm algorithm according to a plurality of groups of real-time voltages, real-time currents and real-time capacity values;
a super capacitor state obtaining step S3, judging the current state of the super capacitor energy storage system according to the real-time capacitance value and the theoretical capacitance value;
and a current adjusting step 84, adjusting the charging current according to the state, and circulating the steps until the charging is finished.
Through the steps, the state of the super-capacitor energy storage system is identified in real time in the whole charging process of the super-capacitor energy storage system, the fault rate of the super-capacitor energy storage system is reduced, and the safety of the system is protected.
The state of the super capacitor energy storage system in the prior art is only recognized once at the initial charging stage, and if the super capacitor breaks down in the charging process, the charging pile cannot adjust the charging current value, so that other super capacitors quit operation due to overcurrent. In the embodiment, a capacitance value real-time detection method of the super capacitor is adopted, theoretical capacitance value expressions of a plurality of groups of super capacitors are calculated, and in the whole charging process, the real-time capacitance value of the super capacitor is compared with the theoretical capacitance values of the plurality of groups, so that the state of the super capacitor energy storage system can be judged in real time, and the charging current value can be adjusted in real time.
In the real-time capacitance value obtaining step, in order to accurately calculate the capacitance value of the super capacitor in the charging process, a super capacitor equivalent model is established according to engineering requirements, and a classical RC equivalent circuit is adopted, as shown in figure 3.
According to the equivalent circuit, the voltage V between two ends of the super capacitor at any time tC(t)＝V(t)- I(t)R is, wherein I(t)For charging current, V(t)The voltage is the voltage at two ends of the super capacitor; r is the internal resistance of the super capacitor, reflects the energy loss generated by the super capacitor in the charging process, and can be approximately constant.
And on the basis of the equivalent circuit model, the capacitance value at any moment is calculated according to a charge conservation and energy conservation equation.
Calculating the charge q at the moment of the capacitance t(t)See formula (1); electric charge q at time T + n.DELTA.T(t+n·ΔT)See formula (2).
q(t)＝[V(t)-I(t)·R]·C(t) (1)
q(t+n·ΔT)＝[V(t+n·ΔT)-I(t+n·ΔT)·R]·C(t+n·ΔT) (2)
Wherein, C(t)Is the capacity value at the time t; c(t+n·ΔT)Is the capacity value at the moment T + n.DELTA T; Δ T is the sampling interval; n is the number of samples.
According to the charge q at two t moments(t)Electric charge q at time T + n.DELTA.T(t+n·ΔT)The difference Δ q between the electric charges at both times is obtained, see equation (3).
Δq＝q(t+n·ΔT)-q(t) (3)
The [ T, T + n · Δ T ] charge change amount Δ q can be calculated from the current integral similarly, see equation (4).
Figure RE-GDA0003226262160000091
Calculating electric energy w at moment t of capacitance(t)See formula (5); the electric energy w (T + n · Δ T) at time T + n · Δ T is shown in equation (6).
Figure RE-GDA0003226262160000092
Figure RE-GDA0003226262160000093
According to the electric energy w (T) at two T moments and the electric energy w at T + n & delta T moments(t+n·ΔT)The difference Δ w between the charges at both times is obtained, as shown in equation (7).
Δw＝w(t+n·ΔT)-w(t) (7)
According to the active power p(t)Integration can also calculate [ T, T + n.DELTA.T ]]The amount of change in electric energy Δ w is shown in equation (8).
Figure RE-GDA0003226262160000094
Wherein the power at the time of T + (n-1). DELTA.T is
p(t+(n-1)·ΔT)＝v(t+(n-1)·ΔT)·I(t+(n-1)·ΔT)-I(t+(n-1)·ΔT) 2R; power at time T + n.DELTA.T is p(t+n·ΔT)＝V(t+n·ΔT)·I(t+n·ΔT)-I(t+n·ΔT) 2·R。
According to the charge conservation equations (3) and (4) and the energy conservation equations (7) and (8), two equations related to C are obtained(t)And C(t+n·ΔT)Under the working condition that the voltage and the current of the super capacitor dynamically change, the equation of (1) is solved to obtain a real-time capacitance value C(t)And C(t+n·ΔT)。
In some embodiments, fig. 4 is a flowchart of a theoretical capacity-value expression obtaining step in the embodiments of the present application, and as shown in fig. 4, the theoretical capacity-value expression obtaining step S2 specifically includes:
a data initialization step S21, wherein the number of particles, the number of iterations, the individual progression and the population progression are initialized;
a fitness updating step S22, wherein the current fitness is obtained according to the expression of the real-time capacity value and the theoretical capacity value, wherein the expression of the fitness is as follows:
Figure RE-GDA0003226262160000101
wherein C (t) is a real-time capacity value, V(t)In real timeValue of voltage, I(t)F (V (t), I (t), X) is an expression of a theoretical capacity value, and Y is an expression parameter variable Y (Y) of dimension D(1)，Y(2)，...，Y(D))(D≥1)；
A condition judgment step S23 of judging whether a preset iteration constraint condition is met according to the fitness, the preset iteration constraint condition being specifically expressed as follows;
|Jg(X(k+1)-Jg(X(k))|≤ε
wherein X (k) and X (k +1) are the positions of the particles at the k and k +1 iterations respectively, and Jg(X(k+1))、Jg(X (k)) is the optimal fitness value of the particle at the k, k +1 iteration, and epsilon is the smallest positive number.
Adjusting step S24, when the preset iteration constraint condition is not satisfied, updating the displacement and speed of the particle until the corresponding fitness meets the preset iteration constraint condition, where the updated displacement of the particle is:
Xi(k+1)＝Xi(k)+Vi(k+1)(i＝1，2，...，N)
the particle update rate is:
Vi(k+1)＝ωVi(k)+c1r1(Pi(k)-Xi(k))+c2r2(Pg(k)-Xi(k))
wherein, c1、c2Is a learning factor, r1、r2Is a random number between (0, 1), ω is an inertial weight, Pi(k)、Pg(k) The individual extremum and the population extremum of the particle at the kth iteration, respectively.
And a theoretical capacitance value expression output step S25, wherein when a preset iteration constraint condition is met, an optimal parameter result of the expression of the ideal capacitance is output.
Through the steps, a particle swarm algorithm is adopted to replace a parameter estimation method in the prior art (a function of a theoretical capacitance value of a super capacitor and a single capacitor voltage (or current) is obtained), an accurate expression of the theoretical capacitance value of the super capacitor is obtained, optimal parameter identification is carried out on the theoretical capacitance value of the super capacitor, a function of the theoretical capacitance value of the super capacitor on the voltage and the current is obtained, the advantages of high precision and strong adaptability are achieved, a basis is provided for state identification of a super capacitor energy storage system, communication equipment does not need to be added between a charging pile and the energy storage system, and cost is saved.
In some embodiments, fig. 5 is a flowchart of a super capacitor state obtaining step in the embodiments of the present application, and as shown in fig. 5, the super capacitor state obtaining step S3 specifically includes the following steps:
a comparison reference value obtaining step S31, obtaining multiple groups of expressions of theoretical capacity values according to the theoretical capacity value expression output step, comparing the real-time capacity value with the theoretical capacity value, and obtaining a comparison reference value, where the specific expression of the comparison reference value is as follows:
min{[C(t)-f1(V(t)，I(t)，X)]2，[C(t)-f2(V(t)，I(t)，X)]2，...， [C(t)-fm(V(t)，I(t)，X)]2}
a super capacitor group number obtaining step S32, obtaining the group number of the super capacitor which normally operates according to the comparison reference value;
in some embodiments, the current adjusting step S4 specifically includes:
and according to the value of the group number m corresponding to the comparison reference value, referring to the group number current comparison table to obtain the charging current and adjusting the charging current to a corresponding size. The embodiments of the present application are described and illustrated below by means of preferred embodiments.
Fig. 6 is a flowchart of a method for online identifying the state of a super capacitor energy storage system on board a tram according to a preferred embodiment of the application.
S601, obtaining two related C according to the charge conservation equation and the energy conservation equation(t)And C(t+n·ΔT)Under the working condition that the voltage and the current of the super capacitor dynamically change, the equation of (1) is solved to obtain a real-time capacitance value C(t)And C(t+n·ΔT)。
S602, researches show that the theoretical capacitance value of the super capacitor is a function of voltage and current, and the optimal expression of the theoretical capacitance value of the super capacitor is solved by adopting a particle swarm algorithm.
Fig. 7 is a schematic flow chart of a particle swarm algorithm, and as shown in fig. 7, the number of particles, the number of iterations, the individual extremum value and the population extremum value are initialized, the fitness value is updated, whether the iteration condition is satisfied is judged, if so, the optimal parameter result of the theoretical capacitance value expression is output, and if not, the speed and the position of the particles are updated until the fitness satisfies the iteration condition.
The method comprises the following specific steps:
charging the super capacitor according to the real-time capacitance value detection method of the super capacitor, and obtaining N capacitance value measurement values of C ═ C (C)(t)，C(t+1)，...C(N)) (t ═ 1, 2.., N), corresponding to voltage V ═ V · (V ·, N)(t)，V(t+1)，...V(N)) (t ═ 1, 2.., N), corresponding to current I ═ I (I ═ I)(t)，I(t+1)，...I(N)) (t ═ 1, 2,. cndot, N); the capacity expression is set as c (Y) f (V, I, Y), where Y is an expression parameter variable Y ═ of D dimension (Y)(1)，Y(2)，...，Y(D))(D≥1)。
Particle X is a set of solution variables for the objective function, including displacement and velocity; velocity is the step size of the change of a particle, and is mainly determined by the inertia of the particle itself, the learning of the particle itself, and the learning to other particles, and each particle in the population updates its velocity and position according to the following formula.
Vi(k+1)＝ωVi(k)+c1r1(Pi(k)-Xi(k))+c2r2(Pg(k)-Xi(k)) (9)
Xi(k+1)＝Xi(k)+Vi(k+1)(i＝1，2，...，N) (10)
In formulae (9) and (10), Vi(k+1)、Vi(k) The speeds of the particle i in the k +1 th iteration and the k th iteration are respectively; pi(k)、Pg(k) Respectively an individual extreme value and a group extreme value of the kth iteration of the particle i; xi(k)、Xi(k +1) is the position of the particle i in the kth iteration and k +1 iteration respectively; omega is inertia weight, generally takes the value between 0.4 and 0.9The size determines the search capability; c. C1、c2For learning factors, take c1＝c2＝2；r1、r2Is a random number between (0, 1).
The fitness value is the objective function value corresponding to the particle:
Figure RE-GDA0003226262160000121
the constraints of the iteration are:
|Jg(X(k+1)-Jg(X(k))|≤ε (12)
wherein, Jg(X(k+1))、Jg(X (k)) is the optimal fitness value of the particle at the k, k +1 iteration, and epsilon is the smallest positive number.
S603, identifying the state of the super capacitor energy storage system
And calculating theoretical capacitance value expressions of a plurality of groups of super capacitors by adopting a super capacitor theoretical capacitance value expression parameter acquisition method, comparing the real-time capacitance value of the super capacitor with the plurality of groups of theoretical capacitance values, and judging the state of the super capacitor energy storage system. The charging process of the super capacitor energy storage system mainly comprises four stages, as shown in fig. 8.
Stage 1: in the climbing stage, the current is increased from 0 to the test current (the current change rate is a constant value);
and (2) stage: in the trial charging stage, the test current (the maximum current born by a single group of super capacitors in a short time) is charged;
and (3) stage: in the constant current stage, constant current charging is carried out until the highest voltage is reached;
and (4) stage: and in the constant voltage stage, maintaining the highest voltage for charging.
Setting the theoretical capacity value expression of m groups of super capacitors in normal operation as fm(V, I, X) (M ═ 1, 2.., M), where X is a constant, and M ═ 1 indicates that only 1 group of supercapacitors is operating normally, and the other groups are malfunctioning; m-2 indicates that two groups of super capacitors operate normally and other groups have faults; and so on, M-M means that all super capacitors operate normally. The super-capacitor energy storage system performs state identification in the charging stages 2, 3 and 4, such asThe following:
charging pile for acquiring voltage V of super capacitor energy storage system(t)Current I(t)Calculating the real-time capacity value C (t) and the theoretical capacity value fm(V(t),I(t)The minimum value of the square of the difference of X) is shown in formula (12).
min{[C(t)-f1(V(t),I(t),X)]2,[C(t)-f2(V(t),I(t),X)]2,...,[C(t)-fm(V(t),I(t),X)]2} (12)
And S604, obtaining the value of m when the minimum value is obtained according to the formula (12), referring to the group number current comparison table, adjusting the charging current value in the charging stage 3 according to the state of the super capacitor system, and circulating the steps until the charging is finished.
The group number current map table may be obtained in advance by study.
Taking the super capacitor energy storage system M of the tramcar in the city of Tianshui as an example of 3 groups, the corresponding relationship between the state of the super capacitor and the adjusted charging current is shown in the following table:
energy storage system status Charging current value/A
Three groups are normal (m is 3) 1500
Two groups are normal (m is 2) 1000
Only one set of boxes is normal (m ═ 1) 600
Therefore, when m is 3, the charging current value in the charging stage 3 is adjusted to 1500A.
It should be noted that the steps illustrated in the above-described flow diagrams or in the flow diagrams of the figures may be performed in a computer system, such as a set of computer-executable instructions, and that, although a logical order is illustrated in the flow diagrams, in some cases, the steps illustrated or described may be performed in an order different than here. For example, when the fitness directly satisfies the preset iteration constraint condition in the condition determining step S23, the adjusting step S24 may be omitted.
The embodiment also provides an online state identification system for the tramcar-mounted super-capacitor energy storage system, which is used for implementing the above embodiments and preferred embodiments, and the description of the system is omitted. As used hereinafter, the terms "module," "unit," "subunit," and the like may implement a combination of software and/or hardware for a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Fig. 9 is a block diagram of a state online identification system of a super capacitor energy storage system on a tram according to an embodiment of the application, and as shown in fig. 9, the system includes:
the real-time capacitance value acquisition module 1 acquires real-time voltage and real-time current, and acquires a real-time capacitance value according to a charge conservation law and an energy conservation law;
the theoretical capacity value expression obtaining module 2 obtains an expression of a theoretical capacity value according to a particle swarm algorithm according to a plurality of groups of real-time voltages, real-time currents and real-time capacity values;
the super-capacitor state acquisition module 3 is used for judging the current state of the super-capacitor energy storage system according to the real-time capacitance value and the theoretical capacitance value;
and the current adjusting module 4 adjusts the charging current according to the state until the charging is finished.
Through the arrangement, the state of the super-capacitor energy storage system is identified in real time in the whole charging process of the super-capacitor energy storage system, the fault rate of the super-capacitor energy storage system is reduced, and the safety of the system is protected.
In some embodiments, the theoretical capacity value expression obtaining module 2 specifically includes:
a data initialization unit 21 that initializes the number of particles, the number of iterations, the number of individual steps, and the number of population steps;
the fitness updating unit 22 obtains the current fitness according to the expression of the real-time capacity value and the theoretical capacity value, wherein the expression of the fitness is as follows:
Figure RE-GDA0003226262160000141
wherein C (t) is a real-time capacity value, V(t)For real-time voltage values, I(t)F (V (t), I (t), X) is an expression of a theoretical capacity value, and Y is an expression parameter variable Y (Y) of dimension D(1)，Y(2)，...，Y(D))(D≥1)；
The condition judgment unit 23 judges whether the preset iteration constraint condition is met according to the fitness, wherein the preset iteration constraint condition is specifically expressed as follows;
|Jg(X(k+1)-Jg(X(k))|≤ε
wherein X (k) and X (k +1) are the positions of the particles at the k and k +1 iterations respectively, and Jg(X(k+1))、Jg(X (k)) is the optimal fitness value of the particle at the k, k +1 iteration, and epsilon is the smallest positive number.
And the adjusting unit 24, when the preset iteration constraint condition is not satisfied, updating the displacement and the speed of the particle until the corresponding fitness meets the preset iteration constraint condition, wherein the updated displacement of the particle is:
Xi(k+1)＝Xi(k)+Vi(k+1)(i＝1，2，...，N)
the particle update rate is:
Vi(k+1)＝ωVi(k)+c1r1(Pi(k)-Xi(k))+c2r2(Pg(k)-Xi(k))
wherein, c1、c2Is a learning factor, r1、r2Is a random number between (0, 1), ω is an inertial weight, Pi(k)、Pg(k) The individual extremum and the population extremum of the particle at the kth iteration, respectively.
And the theoretical capacitance value expression output unit 25 outputs the optimal parameter result of the expression of the ideal capacitance when the preset iteration constraint condition is met.
In some embodiments, the super capacitor state obtaining module 3 specifically includes:
the comparison reference value obtaining unit 31 obtains a plurality of groups of expressions of the theoretical capacity values according to the theoretical capacity value expression output step, compares the real-time capacity value with the theoretical capacity value, and obtains a comparison reference value, wherein the specific expression of the comparison reference value is as follows:
min{[C(t)-f1(V(t)，I(t)，X)]2，[C(t)-f2(V(t)，I(t)，X)]2，...， [C(t)-fm(V(t)，I(t)，X)]2}
and the super capacitor group number acquiring unit 32 acquires the group number of the super capacitors which normally operate according to the comparison reference value.
In some embodiments, the current adjustment module 4 refers to the group number current comparison table according to the value of the group number m corresponding to the comparison reference value, obtains the charging current, and adjusts the charging current to a corresponding magnitude.
The above modules may be functional modules or program modules, and may be implemented by software or hardware. For a module implemented by hardware, the modules may be located in the same processor; or the modules can be respectively positioned in different processors in any combination.
In addition, the method for online identifying the state of the super capacitor energy storage system on board the tramcar according to the embodiment of the application described in conjunction with fig. 1 may be implemented by a computer device. Fig. 10 is a hardware configuration diagram of a computer device according to an embodiment of the present application.
The computer device may comprise a processor 81 and a memory 82 in which computer program instructions are stored.
Specifically, the processor 81 may include a Central Processing Unit (CPU), or A Specific Integrated Circuit (ASIC), or may be configured to implement one or more Integrated circuits of the embodiments of the present Application.
Memory 82 may include, among other things, mass storage for data or instructions. By way of example, and not limitation, memory 82 may include a Hard Disk Drive (Hard Disk Drive, abbreviated to HDD), a floppy Disk Drive, a Solid State Drive (SSD), flash memory, an optical Disk, a magneto-optical Disk, tape, or a Universal Serial Bus (USB) Drive or a combination of two or more of these. Memory 82 may include removable or non-removable (or fixed) media, where appropriate. The memory 82 may be internal or external to the data processing apparatus, where appropriate. In a particular embodiment, the memory 82 is a Non-Volatile (Non-Volatile) memory. In particular embodiments, Memory 82 includes Read-Only Memory (ROM) and Random Access Memory (RAM). The ROM may be mask-programmed ROM, Programmable ROM (PROM), Erasable PROM (EPROM), Electrically Erasable PROM (EEPROM), Electrically rewritable ROM (EAROM), or FLASH Memory (FLASH), or a combination of two or more of these, where appropriate. The RAM may be a Static Random-Access Memory (SRAM) or a Dynamic Random-Access Memory (DRAM), where the DRAM may be a Fast Page Mode Dynamic Random-Access Memory (FPMDRAM), an Extended data output Dynamic Random-Access Memory (EDODRAM), a Synchronous Dynamic Random-Access Memory (SDRAM), and the like.
The memory 82 may be used to store or cache various data files for processing and/or communication use, as well as possible computer program instructions executed by the processor 81.
The processor 81 reads and executes the computer program instructions stored in the memory 82 to realize the online state identification method of the tram vehicle-mounted super capacitor energy storage system in any one of the above embodiments.
In some of these embodiments, the computer device may also include a communication interface 83 and a bus 80. As shown in fig. 10, the processor 81, the memory 82, and the communication interface 83 are connected via the bus 80 to complete mutual communication.
The communication interface 83 is used for implementing communication between modules, devices, units and/or equipment in the embodiment of the present application. The communication port 83 may also be implemented with other components such as: the data communication is carried out among external equipment, image/data acquisition equipment, a database, external storage, an image/data processing workstation and the like.
The bus 80 includes hardware, software, or both. . . The components of the device are coupled to each other. Bus 80 includes, but is not limited to, at least one of the following: data Bus (Data Bus), Address Bus (Address Bus), Control Bus (Control Bus), Expansion Bus (Expansion Bus), and Local Bus (Local Bus). By way of example, and not limitation, Bus 80 may include an Accelerated Graphics Port (AGP) or other Graphics Bus, an Enhanced Industry Standard Architecture (EISA) Bus, a Front-Side Bus (FSB), a Hyper Transport (HT) Interconnect, an ISA (ISA) Bus, an InfiniBand (InfiniBand) Interconnect, a Low Pin Count (LPC) Bus, a memory Bus, a microchannel Architecture (MCA) Bus, a PCI (Peripheral Component Interconnect) Bus, a PCI-Express (PCI-X) Bus, a Serial Advanced Technology Attachment (SATA) Bus, a Video Electronics Bus (audio Electronics Association), abbreviated VLB) bus or other suitable bus or a combination of two or more of these. Bus 80 may include one or more buses, where appropriate. Although specific buses are described and shown in the embodiments of the application, any suitable buses or interconnects are contemplated by the application.
The computer device can execute the real-time capacitance value obtaining step in the embodiment of the application based on the obtained real-time voltage value and real-time current value, and obtain a theoretical capacitance value expression by combining with the theoretical capacitance value expression obtaining method described in fig. 4, so as to realize state identification of the super-capacitor energy storage system.
In addition, by combining the online state identification method for the tramcar-mounted super-capacitor energy storage system in the above embodiment, the embodiment of the application can be implemented by providing a computer-readable storage medium. The computer readable storage medium having stored thereon computer program instructions; when executed by a processor, the computer program instructions implement any one of the above embodiments of the method for online identifying the state of the super capacitor energy storage system on board a tram.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (10)

1. A method for online recognizing the state of a vehicle-mounted super-capacitor energy storage system of a tramcar is characterized by comprising the following steps:
a real-time capacitance value obtaining step, namely obtaining real-time voltage and real-time current, and obtaining a real-time capacitance value according to a charge conservation law and an energy conservation law;
a theoretical capacity value expression obtaining step, wherein an expression of a theoretical capacity value is obtained according to a particle swarm algorithm according to the real-time voltages, the real-time currents and the real-time capacity values;
a super-capacitor state obtaining step, namely judging the current state of the super-capacitor energy storage system according to the real-time capacitance value and the theoretical capacitance value;
and a current adjusting step, adjusting the charging current according to the state, and circulating the steps until the charging is finished.
2. The method for on-line identification of the state of the tram vehicle-mounted super-capacitor energy storage system according to claim 1, wherein the theoretical capacitance value expression obtaining step specifically comprises:
a data initialization step, wherein the particle number, the iteration times, the individual progression and the group progression are initialized;
and a fitness updating step, namely acquiring the current fitness according to the expression of the real-time capacity value and the theoretical capacity value, wherein the expression of the fitness is as follows:
Figure FDA0003097181610000011
wherein C (t) is a real-time capacity value, V(t)For real-time voltage values, I(t)F (V (t), I (t), X) is an expression of a theoretical capacity value, and Y is an expression parameter variable Y (Y) of dimension D(1)，Y(2)，...，Y(D))(D≥1)；
A condition judgment step, judging whether a preset iteration constraint condition is met or not according to the fitness, wherein the preset iteration constraint condition is specifically expressed as follows;
|Jg(X(k+1)-Jg(X(k))|≤ε
wherein X (k) and X (k +1) are the positions of the particles at the k and k +1 iterations respectively, and Jg(X(k+1))、Jg(X (k)) is the optimal fitness value of the particle at the k, k +1 iteration, and epsilon is the smallest positive number.
And an adjusting step, when the preset iteration constraint condition is not satisfied, updating the displacement and the speed of the particle until the corresponding fitness meets the preset iteration constraint condition, wherein the updated displacement of the particle is as follows:
Xi(k+1)＝Xi(k)+Vi(k+1)(i＝1，2，...，N)
the particle update speed is as follows:
Vi(k+1)＝ωVi(k)+c1r1(Pi(k)-Xi(k))+c2r2(Pg(k)-Xi(k))
wherein, c1、c2Is a learning factor, r1、r2Is a random number between (0, 1), ω is an inertial weight, Pi(k)、Pg(k) The individual extremum and the population extremum of the particle at the kth iteration, respectively.
And a theoretical capacitance value expression output step, wherein when the preset iteration constraint condition is met, the optimal parameter result of the expression of the ideal capacitance is output.
3. The method for on-line identification of the state of the super-capacitor energy storage system on the tram according to claim 1, wherein the super-capacitor state obtaining step specifically comprises the following steps:
a comparison reference value obtaining step, namely obtaining a plurality of groups of expressions of the theoretical capacity values according to the theoretical capacity value expression output step, and comparing the real-time capacity value with the theoretical capacity value to obtain a comparison reference value, wherein the specific expression of the comparison reference value is as follows:
min{[C(t)-f1(V(t)，I(t)，X)]2，[C(t)-f2(V(t)，I(t)，X)]2，...，[C(t)-fm(V(t)，I(t)，X)]2}
acquiring the number of groups of the super capacitors, namely acquiring the number m of the groups of the super capacitors which normally operate according to the comparison reference value;
4. the method for online identifying the state of the on-board super-capacitor energy storage system of the tram according to claim 3, wherein the current adjusting step specifically comprises:
and according to the value of the group number m corresponding to the comparison reference value, referring to a group number current comparison table to obtain the charging current and adjusting the charging current to a corresponding size.
5. An online recognition system for the state of a super capacitor energy storage system on board a tram, which applies the online recognition method for the state of the super capacitor energy storage system on board the tram according to any one of claims 1 to 4, and is characterized by comprising the following steps:
the real-time capacitance value acquisition module acquires real-time voltage and real-time current and acquires a real-time capacitance value according to a charge conservation law and an energy conservation law;
the theoretical capacity value expression obtaining module is used for obtaining an expression of a theoretical capacity value according to a particle swarm algorithm according to the real-time voltages, the real-time currents and the real-time capacity values;
the super-capacitor state acquisition module is used for judging the current state of the super-capacitor energy storage system according to the real-time capacitance value and the theoretical capacitance value;
and the current adjusting module adjusts the charging current according to the state until the charging is finished.
6. The system for on-line recognition of the state of the tram vehicle-mounted super-capacitor energy storage system according to claim 5, wherein the theoretical capacitance value expression obtaining module specifically comprises:
the data initialization unit initializes the number of particles, the iteration times, the individual progression and the population progression;
and the fitness updating unit is used for obtaining the current fitness according to the expression of the real-time capacity value and the theoretical capacity value, wherein the expression of the fitness is as follows:
Figure FDA0003097181610000031
wherein C (t) is a real-time capacity value, V(t)For real-time voltage values, I(t)F (V (t), I (t), X) is an expression of a theoretical capacity value, and Y is an expression parameter variable Y (Y) of dimension D(1)，Y(2)，...，Y(D))(D≥1)；
The condition judgment unit judges whether a preset iteration constraint condition is met or not according to the fitness, wherein the preset iteration constraint condition is specifically expressed as follows;
|Jg(X(k+1)-Jg(X(k))|≤ε
wherein X (k) and X (k +1) are the positions of the particles at the k and k +1 iterations respectively, and Jg(X(k+1))、Jg(X (k)) is the optimal fitness value of the particle at the k, k +1 iteration, and epsilon is the smallest positive number.
And the adjusting unit is used for updating the displacement and the speed of the particle until the corresponding fitness meets the preset iteration constraint condition when the preset iteration constraint condition is not met, wherein the updated displacement of the particle is as follows:
Xi(k+1)＝Xi(k)+Vi(k+1)(i＝1，2，...，N)
the particle update speed is as follows:
Vi(k+1)＝ωVi(k)+c1r1(Pi(k)-Xi(k))+c2r2(Pg(k)-Xi(k))
wherein, c1、c2Is a learning factor, r1、r2Is a random number between (0, 1), ω is an inertial weight, Pi(k)、Pg(k) Individual extrema and populations of particles at the kth iteration, respectivelyAnd (4) an extreme value.
And the theoretical capacitance value expression output unit outputs the optimal parameter result of the expression of the ideal capacitance when the preset iteration constraint condition is met.
7. The system for online identification of the state of the on-board super-capacitor energy storage system of the tram according to claim 5, wherein the super-capacitor state acquisition module specifically comprises:
a comparison reference value obtaining unit, configured to obtain a plurality of groups of expressions of the theoretical capacity value according to the theoretical capacity value expression output step, and compare the real-time capacity value with the theoretical capacity value to obtain a comparison reference value, where a specific expression of the comparison reference value is as follows:
min{[C(t)-f1(V(t)，I(t)，X)]2，[C(t)-f2(V(t)，I(t)，X)]2，...，[C(t)-fm(V(t)，I(t)，X)]2}
and the super capacitor group number acquisition unit is used for acquiring the group number m of the super capacitors which normally operate according to the comparison reference value.
8. The system for on-line identification of the state of the super capacitor energy storage system on the tramcar according to claim 7, wherein the current adjustment module refers to a group number current comparison table according to a value of a group number m corresponding to the comparison reference value to obtain a charging current and adjusts the charging current to a corresponding value.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the on-board supercapacitor status online identification method according to any one of claims 1 to 4 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, is characterized in that it implements the on-board supercapacitor status online identification method of a tram according to any one of claims 1 to 4.
CN202110615359.XA 2021-06-02 2021-06-02 Method and system for online recognizing state of vehicle-mounted super-capacitor energy storage system of tramcar Active CN113419113B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110615359.XA CN113419113B (en) 2021-06-02 2021-06-02 Method and system for online recognizing state of vehicle-mounted super-capacitor energy storage system of tramcar

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110615359.XA CN113419113B (en) 2021-06-02 2021-06-02 Method and system for online recognizing state of vehicle-mounted super-capacitor energy storage system of tramcar

Publications (2)

Publication Number Publication Date
CN113419113A true CN113419113A (en) 2021-09-21
CN113419113B CN113419113B (en) 2022-08-02

Family

ID=77713689

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110615359.XA Active CN113419113B (en) 2021-06-02 2021-06-02 Method and system for online recognizing state of vehicle-mounted super-capacitor energy storage system of tramcar

Country Status (1)

Country Link
CN (1) CN113419113B (en)

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104300685A (en) * 2013-07-15 2015-01-21 同济大学 Monitoring system for large scale super capacitor energy storage module
CN104297578A (en) * 2013-07-15 2015-01-21 同济大学 Sliding mode observer-based based super capacitor bank state-of-charge estimation method
CN104730386A (en) * 2015-03-23 2015-06-24 大连理工大学 Supercapacitor charge state estimating method based on Kalman filtering algorithm
CN106230012A (en) * 2016-09-19 2016-12-14 华北电力大学（保定） Ultracapacitor and the Optimal Configuration Method of accumulator capacity in grid-connected photovoltaic system
CN106786696A (en) * 2016-12-29 2017-05-31 东南大学 A kind of mixed energy storage system control method based on fuzzy logic controller
CN109245141A (en) * 2018-11-12 2019-01-18 浙江大学 The capacity configuration optimizing method of composite energy storing device in a kind of distribution automation system
CN110687356A (en) * 2019-07-19 2020-01-14 国网辽宁省电力有限公司大连供电公司 Detection method of super-capacitor capacity and internal resistance rapid detection device

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104300685A (en) * 2013-07-15 2015-01-21 同济大学 Monitoring system for large scale super capacitor energy storage module
CN104297578A (en) * 2013-07-15 2015-01-21 同济大学 Sliding mode observer-based based super capacitor bank state-of-charge estimation method
CN104730386A (en) * 2015-03-23 2015-06-24 大连理工大学 Supercapacitor charge state estimating method based on Kalman filtering algorithm
CN106230012A (en) * 2016-09-19 2016-12-14 华北电力大学（保定） Ultracapacitor and the Optimal Configuration Method of accumulator capacity in grid-connected photovoltaic system
CN106786696A (en) * 2016-12-29 2017-05-31 东南大学 A kind of mixed energy storage system control method based on fuzzy logic controller
CN109245141A (en) * 2018-11-12 2019-01-18 浙江大学 The capacity configuration optimizing method of composite energy storing device in a kind of distribution automation system
CN110687356A (en) * 2019-07-19 2020-01-14 国网辽宁省电力有限公司大连供电公司 Detection method of super-capacitor capacity and internal resistance rapid detection device

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
JIANGHONG GUO: "Energy-Saving Method for Super Capacitor-Powered Trams Based on Particle Swarm Algorithm", 《2020 10TH INTERNATIONAL CONFERENCE ON POWER AND ENERGY SYSTEMS (ICPES)》 *
TEDJANI MESBAHI: "Advanced Model of Hybrid Energy Storage System Integrating Lithium-Ion Battery and Supercapacitor for Electric Vehicle Applications", 《IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS》 *
刘红兵: "基于改进粒子群算法的城轨交...容储能系统容量优化配置研究", 《电子制作》 *
周莹: "基于粒子群的有轨电车混合储能参数匹配研究", 《铁路计算机应用》 *

Also Published As

Publication number Publication date
CN113419113B (en) 2022-08-02

Similar Documents

Publication Publication Date Title
CN108445406B (en) Power battery state of health estimation method
CN107238758B (en) New energy automobile high-voltage system Y capacitance detection system and method and new energy automobile
US10027134B2 (en) Active equalization method and system of lithium iron phosphate battery pack
CN104773083B (en) hybrid power transmission system and vehicle
CN104237795B (en) The unbalance detection of multiple battery units is measured by identical voltage sensor
CN104181470B (en) Battery state-of-charge (SOC) estimation method based on nonlinear prediction extended Kalman filtering
US20170072803A1 (en) Failure determination apparatus and method for determining failure
CN106915270B (en) Method and device for detecting driving range of automobile and automobile
US20180316206A1 (en) Method, apparatus and device for charging a battery
CN103003710A (en) Method and arrangement for estimating the efficiency of at least one battery unit of a rechargeable battery
CN103311979A (en) Battery system
EP3929606A1 (en) Battery management system, battery pack, electric vehicle, and battery management method
US10794961B2 (en) Internal state estimating device
CN109901004A (en) A kind of vehicle mounted dynamic battery internal short-circuit detection method and system
CN106712182A (en) Power battery pack active equalization control strategy based on depth-first search algorithm
WO2019176395A1 (en) Management device, power storage system
CN113728242A (en) Characterization of lithium evolution in rechargeable batteries
Zhou et al. High accuracy state-of-charge online estimation of EV/HEV lithium batteries based on Adaptive Wavelet Neural Network
CN107255757A (en) A kind of ultracapacitor state-of-charge method of estimation based on dynamic capacitance amendment
KR101835373B1 (en) Apparatus for estimating imbalance of battery pack and method thereof
JP2020136247A (en) Abnormality detection device of parallel unit
CN113419113B (en) Method and system for online recognizing state of vehicle-mounted super-capacitor energy storage system of tramcar
CN103904740B (en) A kind of method and system of the energy storage device balance of voltage
CN112104057B (en) Satellite platform with power architecture
Luzi et al. A PSO algorithm for transient dynamic modeling of lithium cells through a nonlinear RC filter

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant