CN114330226A - Battery management and control method, device, equipment and medium based on graph theory - Google Patents
Battery management and control method, device, equipment and medium based on graph theory Download PDFInfo
- Publication number
- CN114330226A CN114330226A CN202111679217.6A CN202111679217A CN114330226A CN 114330226 A CN114330226 A CN 114330226A CN 202111679217 A CN202111679217 A CN 202111679217A CN 114330226 A CN114330226 A CN 114330226A
- Authority
- CN
- China
- Prior art keywords
- battery
- energy
- energy path
- graph theory
- epsilon
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 43
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 14
- 238000005457 optimization Methods 0.000 claims abstract description 12
- 238000007726 management method Methods 0.000 claims description 35
- 238000004590 computer program Methods 0.000 claims description 18
- 238000004364 calculation method Methods 0.000 claims description 8
- 230000006870 function Effects 0.000 claims description 8
- 238000003860 storage Methods 0.000 claims description 7
- 238000007599 discharging Methods 0.000 claims description 6
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 238000012163 sequencing technique Methods 0.000 claims description 3
- 238000012216 screening Methods 0.000 claims description 2
- 238000010586 diagram Methods 0.000 description 10
- 238000004146 energy storage Methods 0.000 description 6
- 239000000178 monomer Substances 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 2
- 230000001052 transient effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000000872 buffer Substances 0.000 description 1
- 239000003990 capacitor Substances 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 239000008358 core component Substances 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000007667 floating Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000002035 prolonged effect Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
Images
Landscapes
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The invention belongs to the technical field of battery management, and particularly discloses a battery management and control method, device, equipment and medium based on graph theory. The method comprises the following steps: s1, mathematically abstracting the connection relation of each battery unit in the target battery system and the energy path characteristics thereof based on a graph theory method, and establishing a battery model; s2, acquiring a vertex set and an edge set according to the battery model; the vertex set is a battery unit with independent control function; the edge set is the connection relation of the battery units formed under the independent control logic, namely the energy path from the initial battery unit to the terminal battery unit; s3, calculating the weight of each energy path in the edge set by adopting a battery energy path optimization algorithm according to the vertex set and the edge set to obtain the weight; and S4, sorting the weight values in the weights so as to obtain an optimal energy path. The invention realizes the safe and efficient use of the battery by using the graph theory method to regard the battery system as an energy path network.
Description
Technical Field
The invention belongs to the technical field of battery management, and particularly relates to a battery management and control method, device, equipment and medium based on graph theory.
Background
With the breakthrough of the energy storage battery material technology, new energy application is greatly developed, an energy storage battery system is an important part of new energy application and is used as a core component of the energy storage system, and a battery is a main detection object and a control target of the whole energy storage system. The main electrical performance indexes and control bases of the battery are voltage, current and residual energy, and the sizes of the three are in floating change along with the charge and discharge state and have correlation. During charging, the voltage and the residual energy can be continuously increased in the whole process, and the voltage at a certain moment is in direct proportion to the current; during discharging, the voltage and the residual energy can be continuously reduced in the whole process, and the voltage and the current at a certain moment are in inverse proportion; there is no direct mathematical relationship between the charge and discharge voltage or current and the battery residual energy.
In the conventional battery management system, each battery cell or module is fixedly connected, and assuming that the remaining energy of each battery cell or module is consistent or similar, the conventional battery management system detects and controls the current of the dc voltage to realize charging or discharging. However, in actual use, there is inconsistency in the residual energy due to the electrochemical difference between the individual battery cells or modules. Especially after several cycles of charge and discharge use, the difference in residual energy will be primarily enlarged. On the other hand, the conventional battery energy algorithm performs logic judgment under the condition of voltage, so that in a system in which a plurality of battery cells or modules are fixedly connected in series and in parallel, the protection of the whole battery system caused by overvoltage or undervoltage of partial battery cells or modules inevitably occurs, and at the moment, a large amount of residual energy of other battery cells or modules cannot be used. This is a particular problem of inflexibility and inefficiency of energy management in conventional battery management systems and energy algorithms.
Disclosure of Invention
The invention aims to provide a battery management and control method, device, equipment and medium based on graph theory, so as to solve the technical problem that all battery cells/modules are difficult to be fully charged when the battery cells/modules have large differences in the traditional battery management method.
In a first aspect, a battery management and control method based on graph theory includes the following steps:
s1, mathematically abstracting the connection relation and the energy path characteristic of each battery unit in the target battery system based on a graph theory method, and establishing a battery model G (P, epsilon, omega);
s2, obtaining a vertex set P ═ { n ═ from the battery model G ═ (P, epsilon, ω)1,n2,n3…nnAnd the set of edges ε ═ ε1,ε2,ε3…εm}；
Set of vertices P ═ n1,n2,n3…nnThe battery units with independent control functions are provided;
edge set epsilon ═ epsilon1,ε2,ε3…εmThe connection relation of the battery units formed under the independent control logic, namely the energy path from the initial battery unit to the terminal battery unit;
s3, according to the vertex set P ═ { n ═ n1,n2,n3…nnAnd the set of edges ε ═ ε1,ε2,ε3…εmAnd adopting a battery energy path optimization algorithm to set the edge set epsilon as { epsilon }1,ε2,ε3…εmCalculating the weight of each energy path in the energy path to obtain the weight omega ═ epsilon1,ε2,ε3…εm}；
S4, pairWeight ω ═ ε1,ε2,ε3…εmAnd sequencing the weighted values in the energy path to obtain the optimal energy path.
The invention is further improved in that: the battery unit is a battery monomer or a module on an energy path node, and the performance characteristics of the battery unit comprise residual capacity, maximum capacity, charge-discharge cut-off voltage and charge-discharge limiting current.
The invention is further improved in that: the edge set epsilon ═ epsilon1,ε2,ε3…εmAnd weight ω ═ ε1,ε2,ε3…εmM in denotes the total number of feasible remaining energy conversion paths searched exhaustively.
The invention is further improved in that: the battery energy path optimization algorithm comprises a residual capacity control boundary condition and an electrical condition based on kirchhoff's law.
The invention is further improved in that: the residual capacity control boundary condition refers to the total energy expectation and the consistency expectation of the residual capacity of each battery unit, and comprises the following steps:
selecting all energy paths with total energy larger than energy required by the load of the electric system;
and judging the remaining capacity control relation of all the battery units on each energy path with the total energy larger than the energy required by the load of the power utilization system, and screening the energy paths with the remaining energy of each battery unit meeting the remaining capacity control relation through electrical conditions based on kirchhoff law.
The invention is further improved in that: the safe working range of the electrical conditions based on kirchhoff's law, namely the voltage and the current of the battery unit, is limited by the charging and discharging cut-off conditions;
and the energy paths which meet the control relation of the residual capacity and in which the sum of the currents of all nodes is equal to zero and the sum of the voltages of the loops is equal to zero meet the electrical condition based on kirchhoff's law.
The invention is further improved in that: according to the following steps:
and judging the remaining capacity control relation of all the battery units on each energy path with the total energy larger than the energy required by the load of the power utilization system.
In a second aspect, a battery management and control device based on graph theory includes:
a battery model establishing module: the method is used for carrying out mathematical abstraction on the connection relation and the energy path characteristic of each battery unit in the target battery system based on a graph theory method, and establishing a battery model;
a vertex set and edge set acquisition module: the method comprises the steps of obtaining a vertex set and an edge set according to a battery model;
a weight calculation module: the weight calculation module is used for calculating the weight of each energy path in the edge set by adopting a battery energy path optimization algorithm according to the vertex set and the edge set to obtain the weight;
an optimal energy path acquisition module: for ordering the weights to obtain an optimal energy path.
In a third aspect, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements a graph-theory-based battery management and control method when executing the computer program.
In a fourth aspect, a computer-readable storage medium stores a computer program, and the computer program is used for implementing a graph-theory-based battery management and control method when executed by a processor.
Compared with the prior art, the invention at least comprises the following beneficial effects:
1. the battery pack is characterized in that the battery pack is composed of a plurality of battery units, and the battery units are arranged in the battery pack. The electrical connection of the positive electrode, the energy points and the negative electrode is regarded as a changeable path, and the summation of the battery energy and the voltage on different paths is taken as a graph result, and compared with the traditional technical scheme, the energy path is fixed, and the number of the energy points of the battery passed by the path is also fixed, so that the in-group optimization of the battery cannot be carried out. Each battery unit in the battery system is flexibly controlled, and the energy management efficiency is improved;
2. according to the invention, the energy paths are controlled to change through the remaining capacity control boundary conditions, the necessary energy paths with the shortest path, the minimum sum of path energy points and the minimum difference among the energy points are calculated according to the external energy requirements (voltage, current, power, duration) and the like serving as the boundary conditions through the change and selection of the paths, namely, the internal optimization of starting or stopping is carried out according to the difference of the states of each battery monomer, and finally, the effects of using the battery with strong capacity firstly and then using the battery with weak capacity secondly are achieved, and the influence of the battery difference on the operation of the battery pack is eliminated.
3. According to the invention, the rough judgment of the battery state through charging and discharging voltage and current in the traditional battery management is abandoned, the battery system is regarded as an energy path network, the energy path formed by the residual energy of each battery is subjected to exhaustive calculation and analysis from the perspective of a transient system, and feasibility and safety constraints are carried out on the energy path calculation result through the control limiting conditions, so that the function of dynamic energy balance on each battery unit is realized while the energy requirement of the system is safely and stably met, and the maximum utilization of the energy of the target battery system is finally realized. The energy supply stability and the continuity of the battery energy storage system are decoupled from the consistency of the battery, and the service life of the battery system is prolonged.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a model construction diagram of a battery management and control method based on graph theory according to the invention;
FIG. 2 is a flow chart of a battery management and control method based on graph theory according to the present invention;
fig. 3 is a system block diagram of a battery management and control apparatus based on graph theory according to the present invention.
Detailed Description
The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings. It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
The following detailed description is exemplary in nature and is intended to provide further details of the invention. Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention.
Example 1
As shown in fig. 1-2, a method for managing and controlling a battery based on graph theory includes the following steps:
and S1, mathematically abstracting the connection relation and the energy path characteristic of each battery unit in the target battery system based on a graph theory method, and establishing a battery model G (P, epsilon, omega), wherein the algorithm graph theory algorithm is composed of a vertex, an edge set and weights. The vertex is a battery monomer or a battery module with each independent control function; the edge set represents the connection relation of the battery units formed under different independent control logics; the weight is the feasibility of the battery system under each edge set scheme;
the purpose of establishing a battery model is to obtain information with higher frequency and resolution and simultaneously represent energy tidal flow;
s2, obtaining a vertex set P ═ { n ═ from the battery model G ═ (P, epsilon, ω)1,n2,n3…nnAnd the set of edges ε ═ ε1,ε2,ε3…εm}；
Set of vertices P ═ n1,n2,n3…nnThe residual energy of each battery unit is the battery monomer or module on the energy path node, and the performance characteristics of the battery units, such as residual capacity, maximum capacity, charge-discharge cutoff voltage, charge-discharge limiting current and the like, are known parameters;
edge set epsilon ═ epsilon1,ε2,ε3…εmThe battery units on the connection path form a system for supporting an energy output battery and used for representing the flexibility of residual energy configuration, and m represents the total number of feasible residual energy conversion paths for exhaustive search;
according to vertex set P ═ n1,n2,n3…nnCalculating the set of edges ═ epsilon } ═ epsilon1,ε2,ε3…εmThe feasible weight of each energy path in the system, and the weight set of m energy paths is the weight omega ═ epsilon1,ε2,ε3…εmThe weight ω ═ ε1,ε2,ε3…εmRepresenting the safe operation of the control values of the residual energy, the voltage and the current of the corresponding edge set;
s3, calculating each energy path through a battery energy path optimization algorithm to obtain a weight value corresponding to each energy path;
the battery energy path optimization algorithm comprises a residual capacity control boundary condition and an electrical condition based on kirchhoff's law;
the remaining capacity control boundary condition refers to the total energy expectation and consistency expectation of the remaining capacity of each battery unit;
the electrical condition based on kirchhoff's law refers to the condition that the working range of the voltage of each battery unit is limited by cut-off voltage and full voltage;
the remaining capacity control boundary condition comprises the consistency of total energy and battery unit energy; according to the charge and discharge energy requirements of the target battery system, the energy sum of all battery units on the paths under different energy path schemes can meet the transient requirement of external energy. For example, the power system load requires 1000W of energy, and the target battery system has 4 battery units in a possible energy path, so the total energy Σ PnShould be greater than or equal to 1000W, where (n ═ 1,2,3, 4). Cell energy uniformity is used for battery energy selection issues between multiple possible energy paths. Possible storage of target battery systemAt a plurality of satisfied total energies Sigma PmnEnergy paths such as (m-1, 2,3, 4; n-1, 2,3,4) of not less than 1000W are determined for different P1n、P2n、P3nAccording to the remaining capacity control relation of the four battery units on the equal-energy path, the remaining energy of each battery unit needs to meet the requirement of the mathematical average value of the remaining capacities of all the four battery units +/-the energy consistency correction value. Through the management and control boundary analysis of the remaining capacity of the battery unit, the optimal battery equalization combination possible path meeting the external power utilization condition can be obtained.
The safe working ranges of the voltage and the current of the battery unit based on the electrical conditions of kirchhoff's law are limited by the charging and discharging cut-off conditions; under the electrical topology of lower frequency, the static supply voltage and current related in the circuit need to satisfy kirchhoff's law, that is, the sum of node currents is equal to zero, and the sum of loop voltages is equal to zero; in addition, when the individual switches in the vertices are dynamically operated at the appropriate duty cycle, the high frequency range that these switches can withstand, and the maximum energy storage limit of the inductor and the super capacitor as energy buffers need to be considered;
s4, according to the weight ω ═ ε1,ε2,ε3…εmAnd sequencing the energy paths corresponding to each weight value according to the weight value of each weight value to obtain the optimal energy path.
Example 2
As shown in fig. 3, a battery management and control apparatus based on graph theory, based on the battery management and control method based on graph theory in embodiment 1, includes:
a battery model establishing module: the method is used for carrying out mathematical abstraction on the connection relation and the energy path characteristic of each battery unit in the target battery system based on a graph theory method, and establishing a battery model;
a vertex set and edge set acquisition module: the method comprises the steps of obtaining a vertex set and an edge set according to a battery model;
a weight calculation module: the weight calculation module is used for calculating the weight of each energy path in the edge set by adopting a battery energy path optimization algorithm according to the vertex set and the edge set to obtain the weight;
an optimal energy path acquisition module: for ordering the weights to obtain an optimal energy path.
Example 3
The invention also provides a computer device, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor implements the graph theory-based battery management and control method in embodiment 1 when executing the computer program.
Example 4
The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the graph-theory-based battery management and control method of embodiment 1.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. A battery management and control method based on graph theory is characterized by comprising the following steps:
s1, mathematically abstracting the connection relation and the energy path characteristic of each battery unit in the target battery system based on a graph theory method, and establishing a battery model G (P, epsilon, omega);
s2, obtaining a vertex set P ═ { n ═ from the battery model G ═ (P, epsilon, ω)1,n2,n3…nnAnd the set of edges ε ═ ε1,ε2,ε3…εm}；
Set of vertices P ═ n1,n2,n3…nnThe battery units with independent control functions are provided;
edge set epsilon ═ epsilon1,ε2,ε3…εmThe connection relation of the battery units formed under the independent control logic, namely the energy path from the initial battery unit to the terminal battery unit;
s3, according to the vertex set P ═ { n ═ n1,n2,n3…nnAnd the set of edges ε ═ ε1,ε2,ε3…εmAnd adopting a battery energy path optimization algorithm to set the edge set epsilon as { epsilon }1,ε2,ε3…εmCalculating the weight of each energy path in the energy path to obtain the weight omega ═ epsilon1,ε2,ε3…εm}；
S4, pair weight ω ═ ε1,ε2,ε3…εmAnd sequencing the weighted values in the energy path to obtain the optimal energy path.
2. The method as claimed in claim 1, wherein the battery unit is a battery cell or a battery module on an energy path node, and the performance characteristics include a remaining capacity, a maximum capacity, a charge-discharge cutoff voltage, and a charge-discharge limiting current.
3. The method for battery management and control based on graph theory according to claim 1, wherein the edge set ∈ ═ epsilon { [ epsilon ] } is set1,ε2,ε3…εmAnd weight ω ═ ε1,ε2,ε3…εmM in denotes the total number of feasible remaining energy conversion paths searched exhaustively.
4. The graph theory-based battery management and control method according to claim 1, wherein the battery energy path optimization algorithm comprises a remaining capacity management boundary condition and an electrical condition based on kirchhoff's law.
5. The battery management and control method based on graph theory according to claim 4, wherein the remaining capacity management and control boundary condition refers to a total energy expectation and a consistency expectation of the remaining capacity of each battery unit, and comprises the following steps:
selecting all energy paths with total energy larger than energy required by the load of the electric system;
and judging the remaining capacity control relation of all the battery units on each energy path with the total energy larger than the energy required by the load of the power utilization system, and screening the energy paths with the remaining energy of each battery unit meeting the remaining capacity control relation through electrical conditions based on kirchhoff law.
6. The battery management and control method based on the graph theory as claimed in claim 5, wherein the safe working range of the electrical conditions based on kirchhoff's law, namely, the voltage and the current of the battery unit, is limited by the cut-off condition of charging and discharging;
and the energy paths which meet the control relation of the residual capacity and in which the sum of the currents of all nodes is equal to zero and the sum of the voltages of the loops is equal to zero meet the electrical condition based on kirchhoff's law.
7. The graph theory-based battery management and control method according to claim 5, wherein the graph theory-based battery management and control method is characterized in that according to the following steps:
and judging the remaining capacity control relation of all the battery units on each energy path with the total energy larger than the energy required by the load of the power utilization system.
8. A battery management and control device based on graph theory is characterized by comprising:
a battery model establishing module: the method is used for carrying out mathematical abstraction on the connection relation and the energy path characteristic of each battery unit in the target battery system based on a graph theory method, and establishing a battery model;
a vertex set and edge set acquisition module: the method comprises the steps of obtaining a vertex set and an edge set according to a battery model;
a weight calculation module: the weight calculation module is used for calculating the weight of each energy path in the edge set by adopting a battery energy path optimization algorithm according to the vertex set and the edge set to obtain the weight;
an optimal energy path acquisition module: for ordering the weights to obtain an optimal energy path.
9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the graph-theory based battery management method according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium storing a computer program, wherein the computer program is configured to implement the graph-theory-based battery management and control method according to any one of claims 1 to 7 when executed by a processor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111679217.6A CN114330226A (en) | 2021-12-31 | 2021-12-31 | Battery management and control method, device, equipment and medium based on graph theory |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111679217.6A CN114330226A (en) | 2021-12-31 | 2021-12-31 | Battery management and control method, device, equipment and medium based on graph theory |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114330226A true CN114330226A (en) | 2022-04-12 |
Family
ID=81023670
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111679217.6A Pending CN114330226A (en) | 2021-12-31 | 2021-12-31 | Battery management and control method, device, equipment and medium based on graph theory |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114330226A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116667475A (en) * | 2023-03-13 | 2023-08-29 | 深圳库博能源科技有限公司 | Energy storage management system and method based on cloud computing |
-
2021
- 2021-12-31 CN CN202111679217.6A patent/CN114330226A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116667475A (en) * | 2023-03-13 | 2023-08-29 | 深圳库博能源科技有限公司 | Energy storage management system and method based on cloud computing |
| CN116667475B (en) * | 2023-03-13 | 2024-05-07 | 深圳库博能源科技有限公司 | Energy storage management system and method based on cloud computing |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110048487B (en) | Control method and system for battery charging and discharging | |
| Li et al. | Voltage scheduling droop control for State-of-Charge balance of distributed energy storage in DC microgrids | |
| JP6936578B2 (en) | Battery pack charging device and method | |
| Li et al. | Multiagent based distributed control for state-of-charge balance of distributed energy storage in DC microgrids | |
| CN103647310B (en) | Swap modules realize MMC battery energy storage system mutually in the method for SOC balance | |
| CN105337297A (en) | Balancing method and the device for state of charge (SOC) of energy storage system | |
| Ma et al. | Enhanced SOH balancing method of MMC battery energy storage system with cell equalization capability | |
| CN110994050A (en) | Management control method and device for direct-current power supply storage battery pack | |
| CN110518159B (en) | Reconfigurable multi-monomer battery topology and reconfiguration algorithm for large-scale battery pack | |
| Chatzinikolaou et al. | Performance evaluation of duty cycle balancing in power electronics enhanced battery packs compared to conventional energy redistribution balancing | |
| CN108233464A (en) | A kind of battery pack active equalization method and system | |
| CN114709497A (en) | Parallel battery cluster state control system and circulating current restraining method and state of charge balancing method thereof | |
| CN114330226A (en) | Battery management and control method, device, equipment and medium based on graph theory | |
| KR101689017B1 (en) | System and method for fuzzy droop control of multi-bess in microgrid | |
| CN114649854A (en) | Battery energy storage network, method, system and device for balancing residual electric quantity among batteries | |
| Ekanayake et al. | Active and passive based hybrid cell balancing approach to series connected lithium-ion battery pack | |
| Smith et al. | Architecture for utility-scale multi-chemistry battery energy storage | |
| CN113489080A (en) | Battery electric quantity balancing method, device, equipment and storage medium | |
| Du et al. | An any‐unit‐to‐any‐unit method for hybrid‐structured voltage equalizer in series‐connected battery/super‐capacitor strings | |
| CN115800418B (en) | Battery control method, energy storage system, device, computer equipment and storage medium | |
| CN111146794B (en) | Ultra-large scale hybrid energy storage power balance control system and method | |
| CN114188936A (en) | Optimal scheduling method for black-start mobile energy storage system of traditional thermal power generating unit | |
| Wang et al. | State of charge balancing control of a multi-functional battery energy storage system based on a 11-level cascaded multilevel PWM converter | |
| Li et al. | Linearized operation of MMC battery energy storage system | |
| CN113612277A (en) | Battery unit and control method thereof |
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 |