CN114400387A - Battery equalization management method and system based on multi-agent game - Google Patents
Battery equalization management method and system based on multi-agent game Download PDFInfo
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- 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
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0013—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries acting upon several batteries simultaneously or sequentially
- H02J7/0014—Circuits for equalisation of charge between batteries
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
- H01M10/4207—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells for several batteries or cells simultaneously or sequentially
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
- H01M10/44—Methods for charging or discharging
- H01M10/441—Methods for charging or discharging for several batteries or cells simultaneously or sequentially
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
- H01M10/425—Structural combination with electronic components, e.g. electronic circuits integrated to the outside of the casing
- H01M2010/4271—Battery management systems including electronic circuits, e.g. control of current or voltage to keep battery in healthy state, cell balancing
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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
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Abstract
The invention provides a battery equalization management method and a system based on a multi-agent game, which comprises the following steps: determining the charge state data of each single battery according to the attenuation data and the current electric quantity data of each single battery in the battery pack; obtaining a charge-discharge strategy of each single battery in the battery pack through a Nash equilibrium method according to the difference value of the charge state data of each single battery and the charge state data of the other single batteries in the battery pack; and carrying out battery balance management according to the charge and discharge strategy. Compared with the prior art, the charge-discharge strategy of each single battery in the battery pack is obtained by the difference value of the charge state data of each single battery and the charge state data of other single batteries through a Nash equilibrium method, the influence of the inconsistency of the single batteries on the available capacity of the whole battery pack is reduced, the service life of the battery is prolonged, and the energy utilization rate of the battery is improved.
Description
Technical Field
The invention relates to the technical field of optimization algorithms, in particular to a battery balance management method and system based on a multi-agent game.
Background
The energy storage battery and the electric automobile power battery are key equipment for energy structure transformation and promotion of green traffic development. The energy storage battery and the electric automobile power battery meet larger capacity requirements and higher voltage requirements through series-parallel connection grouping of large-scale single batteries. Due to the limitations of the "tub principle", the performance of the entire battery pack is limited by each cell. The single batteries inevitably have certain difference in manufacturing process, working temperature, discharge depth and the like, and the inconsistency problem of voltage and charge state generated in the charging and discharging process may cause the consequences of battery fire, aging acceleration and the like, thereby seriously affecting the battery performance. Therefore, the state of a single Battery or a Battery pack needs to be balanced and controlled by a Battery Balancing manager (BBS) in a Battery Management System (BMS), so that the end voltages or the states of charge of the batteries are consistent, the working performance of the batteries is improved, and the service life of the batteries is prolonged.
From the point of view of energy dissipation and transfer, the various equalization methods can be divided into two main categories: active equalization and passive equalization. In the passive equalization, the voltage of the single battery is restrained from rising through the shunt of a parallel resistor or a switching device of the single battery in the charging process of the battery. The energy consumption balancing method is a passive energy consumption balancing scheme, the balancing current is small, the balancing time is long, and heat is generated due to energy consumption in the balancing process. However, the equalization circuit is simple and easy to implement, and the equalization scheme can play a certain equalization effect when the single battery is light in inconsistent degree. The active equalization mainly utilizes a capacitor, an inductor or a transformer as an energy storage or energy transmission element, and energy is transferred between a single battery and a single battery or between the single battery and a battery pack through a switching device. Ideally, the equalizing circuit is a non-energy-consumption equalizing scheme, but in practical application, due to the switching loss of the switching device, a small amount of energy is consumed in the equalizing circuit during the equalizing process.
Patent document CN113394840A discloses an energy storage battery electric quantity intelligent balance control method and system, including: calculating the residual capacity of each energy storage battery to obtain the average expected residual capacity of all the energy storage batteries; comparing the residual electric quantity of each energy storage battery with the average expected residual electric quantity, and obtaining the optimal charge-discharge strategy adopted by each energy storage battery through a Nash equilibrium method; and balancing the energy of the battery based on the optimal charge and discharge strategy. However, this method compares the remaining capacity of each energy storage battery with the average expected remaining capacity, and does not solve the problem of the influence of the inconsistency of the single batteries on the available capacity of the whole battery pack.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a battery equalization management method and system based on multi-agent game.
The invention provides a multi-agent game-based battery equalization management method, which comprises the following steps:
step 1: determining the charge state data of each single battery according to the attenuation data and the current electric quantity data of each single battery in the battery pack;
step 2: obtaining a charge-discharge strategy of each single battery in the battery pack through a Nash equilibrium method according to the difference value of the charge state data of each single battery and the charge state data of the other single batteries in the battery pack;
and step 3: and carrying out battery balance management according to the charge and discharge strategy.
Preferably, step 1, comprises:
step 101: obtaining attenuation data and current electric quantity data through the current state data and the historical state data of each single battery;
step 102: and determining the charge state data of each single battery according to the attenuation data and the current electric quantity data.
Preferably, step 101, further comprises:
step 1011: and inputting the current state data and the historical state data into the artificial neural network to obtain attenuation data and current electric quantity data output by the artificial neural network.
Preferably, step 2, comprises:
step 201: obtaining a utility function of each single battery according to the difference value of the charge state data of each single battery and the charge state data of the other single batteries in the battery pack;
step 202: and obtaining the charge-discharge strategy of each single battery in the battery pack through a Nash equilibrium method and a utility function.
Preferably, step 202 comprises:
step 2021: obtaining a Nash equilibrium solution of the utility function according to a preset constraint condition pair by a Nash equilibrium method;
step 2022: and obtaining the charge-discharge strategy of each single battery in the battery pack according to the Nash equilibrium solution.
The invention provides a multi-agent game-based battery equalization management system, which comprises:
module M1: determining the charge state data of each single battery according to the attenuation data and the current electric quantity data of each single battery in the battery pack;
module M2: obtaining a charge-discharge strategy of each single battery in the battery pack through a Nash equilibrium method according to the difference value of the charge state data of each single battery and the charge state data of the other single batteries in the battery pack;
module M3: and carrying out battery balance management according to the charge and discharge strategy.
Preferably, the module M1, comprises:
submodule M101: obtaining attenuation data and current electric quantity data through the current state data and the historical state data of each single battery;
submodule M102: and determining the charge state data of each single battery according to the attenuation data and the current electric quantity data.
Preferably, the sub-module M101 further includes:
unit D1011: and inputting the current state data and the historical state data into the artificial neural network to obtain attenuation data and current electric quantity data output by the artificial neural network.
Preferably, the module M2, comprises:
submodule M201: obtaining a utility function of each single battery according to the difference value of the charge state data of each single battery and the charge state data of the other single batteries in the battery pack;
submodule M202: and obtaining the charge-discharge strategy of each single battery in the battery pack through a Nash equilibrium method and a utility function.
Preferably, the submodule M202 includes:
unit D2021: obtaining a Nash equilibrium solution of the utility function according to a preset constraint condition pair by a Nash equilibrium method;
unit D2022: and obtaining the charge-discharge strategy of each single battery in the battery pack according to the Nash equilibrium solution.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the method, the utility function is solved through a Nash equilibrium method, so that the charge-discharge strategy of each single battery is obtained, the influence of the inconsistency of the single batteries on the available capacity of the whole battery pack is reduced, and the energy utilization rate is improved.
2. The invention realizes effective equalization of the battery by a Nash equalization method, and reduces the cycle times of the battery, thereby prolonging the service life of the battery.
3. The invention can realize the balance of the battery electric quantity and can carry out on-line detection on the battery electric quantity and the battery health state at the same time without adding extra power devices and circuits, and assists the battery management system to carry out battery balance operation.
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Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a schematic diagram of a battery state estimation according to the present invention;
FIG. 3a is a schematic diagram of a first battery equalization management of the present invention;
FIG. 3b is a diagram illustrating a second battery equalization management of the present invention;
fig. 4 is a schematic structural diagram of a battery equalization management system according to the present invention.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
Fig. 1 is a schematic flow chart of the present invention, and as shown in fig. 1, the present invention provides a battery equalization management method based on multi-agent gaming, which includes the following steps:
step 1: and determining the charge state data of each single battery according to the attenuation data and the current electric quantity data of each single battery in the battery pack.
The State of Charge (SOC) is used to reflect the remaining capacity of the battery, and is numerically defined as the ratio of the remaining capacity to the battery capacity, and is usually expressed as a percentage. The value range is 0-1.
According to the attenuation data and the current electric quantity data of each single battery, the charge state data of each single battery is determined.
Wherein the attenuation data represents the total capacity attenuation condition of the current battery; the current charge data represents the actual charge of the battery.
Preferably, step 1, comprises: step 101: obtaining attenuation data and current electric quantity data through the current state data and the historical state data of each single battery; step 102: and determining the charge state data of each single battery according to the attenuation data and the current electric quantity data.
The current state data comprises data such as voltage, current, temperature and discharge mode of the battery; the historical state data comprises data such as charging and discharging electricity quantity, cycle times, working conditions and the like.
Preferably, step 101, further comprises: step 1011: and inputting the current state data and the historical state data into the artificial neural network to obtain attenuation data and current electric quantity data output by the artificial neural network.
Fig. 2 is a schematic structural diagram of the battery state estimation according to the present invention, and as shown in fig. 2, current state data of the battery, including voltage data, current data and temperature data, are obtained through measurement; historical state data measured by the battery comprise charge and discharge electric quantity, cycle times and working conditions; the historical state data is input into a cache unit to serve as battery historical parameters, the current state data and the battery historical parameters are input into an artificial neural network, so that attenuation data and current electric quantity data of the battery are obtained, and meanwhile, the current electric quantity data reflecting the current electric quantity of the battery and training parameters of the artificial neural network are sent to the cache unit to be stored for obtaining the subsequent attenuation data and the current electric quantity data.
It can be known that accurately obtaining the battery state is a prerequisite for battery equalization management. The method adopts an algorithm based on artificial intelligence, and comprehensively evaluates the total capacity attenuation condition and the actual electric quantity of the current battery by measuring parameters such as the voltage, the current, the temperature, the discharge mode and the like of the battery. Compared with the prior art, the current state data of the battery can be obtained more accurately.
Step 2: and obtaining the charge-discharge strategy of each single battery in the battery pack through a Nash equilibrium method according to the difference value of the charge state data of each single battery and the charge state data of the other single batteries in the battery pack.
Specifically, the difference between each single battery and the rest of the single batteries is determined according to the difference of the state of charge data of each single battery and the rest of the single batteries, and the battery balance management is realized by minimizing the difference.
Preferably, step 2, comprises: step 201: obtaining a utility function of each single battery according to the difference value of the charge state data of each single battery and the charge state data of the other single batteries in the battery pack; step 202: and obtaining the charge-discharge strategy of each single battery in the battery pack through a Nash equilibrium method and a utility function.
Specifically, based on the multi-agent game theory, the battery utility evaluation is carried out on each single battery. The utility function of each cell is defined to minimize the state difference from the neighboring cells. And analyzing a Nash equilibrium point of the battery utility evaluation problem, when the states of all the single batteries are closest, the battery pack reaches an equilibrium state, and each single battery also achieves the aim of the minimum utility function.
Preferably, step 202 comprises: step 2021: obtaining a Nash equilibrium solution of the utility function according to a preset constraint condition pair by a Nash equilibrium method; step 2022: and obtaining the charge-discharge strategy of each single battery in the battery pack according to the Nash equilibrium solution.
Fig. 3a is a schematic diagram of a first battery equalization management of the present invention, and fig. 3b is a schematic diagram of a second battery equalization management of the present invention, as shown in fig. 3a and fig. 3b, including a battery cell # 1, a battery cell # 2, a battery cell # 3, a battery cell # 4, a battery cell # 5, …, and a battery cell # N, where N is the total number of battery cells in the battery pack. Setting the initial SoC of each single battery to
The power required to be provided by the current battery pack is P, and the capacity of each single battery is CiThen, for each cell, the utility function can be expressed as formula (1):
wherein i and k are integers; i represents an integer; alpha is a weight coefficient, t is a positive number and represents time, piIndicating the power provided by the ith elevator battery.
Specifically, the first part in the formula (1) represents the opposite number of the difference between the state of charge data (SoC) of the ith unit cell and the rest unit cells, and the second part represents the opposite number of the maximized charge and discharge cost of the ith unit cell.
Further, the utility function needs to satisfy a preset constraint condition, and the preset constraint condition can be expressed by formula (2) and formula (3):
∑i∈Ipi＝P； (2)
that is, the total power provided by all the single batteries in the battery pack is P, and the SoC change of each battery is related to the power.
It can be known that the above formula (1), formula (2) and formula (3) constitute a generalized starberg game problem, which has nash equilibrium solution, that is, the cells reach the equilibrium state with the same electric quantity. And solving in an iterative mode to obtain the optimal charge and discharge power of each single battery, and regulating and controlling through an active equalization system. And at the next moment, measuring the battery SoC, and obtaining a new Nash equilibrium point according to the new required power, namely sequentially obtaining the charge and discharge power of the battery at each subsequent moment to form stable battery electric quantity equilibrium. The final result is shown in fig. 3 b.
As can be seen from fig. 3a and fig. 3b, the voltage data of the battery can be divided into 1.5V, 3.0V, 3.6V and 4.4V, wherein the voltage range of the normal operation of the battery is 3.0V-3.6V, the total capacity of each single battery in fig. 3a is very different, which accelerates the aging of the single battery and even causes explosion and fire, fig. 3b is the operation state of each single battery after the equalization is achieved by the multi-agent game-based battery equalization management method of the present invention, each single battery operates in the voltage range of the normal operation of the battery, and the difference of the state of charge data of each single battery is small.
And step 3: and carrying out battery balance management according to the charge and discharge strategy.
Specifically, battery charging and discharging management is carried out according to a charging and discharging strategy to realize active equalization of the battery. By optimizing the charging and discharging time sequence of the battery, the loop loss in the battery equalization is reduced.
The invention provides a battery equalization management system based on a multi-agent game, which comprises:
module M1: and determining the charge state data of each single battery according to the attenuation data and the current electric quantity data of each single battery in the battery pack.
Preferably, the module M1, comprises: submodule M101: obtaining attenuation data and current electric quantity data through the current state data and the historical state data of each single battery; submodule M102: and determining the charge state data of each single battery according to the attenuation data and the current electric quantity data.
Preferably, the sub-module M101 further includes: unit D1011: and inputting the current state data and the historical state data into the artificial neural network to obtain attenuation data and current electric quantity data output by the artificial neural network.
Module M2: and obtaining the charge-discharge strategy of each single battery in the battery pack through a Nash equilibrium method according to the difference value of the charge state data of each single battery and the charge state data of the other single batteries in the battery pack.
Preferably, the module M2, comprises: submodule M201: obtaining a utility function of each single battery according to the difference value of the charge state data of each single battery and the charge state data of the other single batteries in the battery pack; submodule M202: and obtaining the charge-discharge strategy of each single battery in the battery pack through a Nash equilibrium method and a utility function.
Preferably, the submodule M202 includes: unit D2021: obtaining a Nash equilibrium solution of the utility function according to a preset constraint condition pair by a Nash equilibrium method; unit D2022: and obtaining the charge-discharge strategy of each single battery in the battery pack according to the Nash equilibrium solution.
Module M3: and carrying out battery balance management according to the charge and discharge strategy.
Fig. 4 is a schematic structural diagram of a battery balance management system of the present invention, and as shown in fig. 4, the battery balance management system for multi-agent gaming includes: estimating the state of the battery, evaluating the utility function of the battery and managing the charge and discharge of the battery. The battery state estimation is used for obtaining the state of charge data of the battery, and the accurate obtaining of the state of charge data of the battery is the premise for battery balance management. The method adopts an algorithm based on artificial intelligence, and comprehensively evaluates the total capacity attenuation condition and the actual electric quantity of the current single battery by measuring parameters such as voltage, current, temperature, discharge mode and the like of the single battery. Historical state data measured from the single battery, including charging and discharging electric quantity, cycle times and working conditions, are input into the cache unit as historical parameter information, and meanwhile, current state data of the battery, including voltage, current and temperature, are input into the artificial neural network, so that attenuation data and current electric quantity data of the single battery are obtained. And (4) evaluating the utility function of the battery, namely performing modeling evaluation on the utility function of each single battery based on a multi-agent game theory framework. The utility function definition of each cell is minimized to the state difference from the neighboring cells. Analyzing the Nash equilibrium point of the problem, when the states of all the single batteries are the closest, the system reaches the equilibrium state, and each single battery also achieves the aim of the minimum utility function. And managing battery charging and discharging, namely managing the battery charging and discharging according to the evaluation result of the battery utility function to realize active equalization of the battery. By optimizing the charging and discharging time sequence of the battery, the loop loss in the battery equalization is reduced.
The technical problem solved by the invention is as follows:
1. the inconsistency of the individual cells in the battery pack can have a significant impact on the available capacity of the battery pack. Also, as the battery pack usage time accumulates, the effects of the inconsistencies on the battery pack become greater.
2. Imbalance in the cells in the battery pack increases the number of battery cycles and decreases the battery life.
3. In order to realize battery equalization management, additional power devices and circuits are required.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention can improve the energy utilization rate and reduce the influence of the inconsistency of the single batteries on the available capacity of the whole battery pack.
2. The invention can effectively improve the energy utilization rate of the battery, prolong the service life of the battery, and realize less components, reliable structure, easy control, high circuit efficiency and high robustness.
3. The invention can realize the balance of the battery electric quantity and can carry out on-line detection on the battery electric quantity and the battery health state at the same time without adding extra power devices and circuits, and assists the battery management system to carry out battery balance operation.
It is known to those skilled in the art that, except for implementing the system, the apparatus and the respective modules thereof provided by the present invention in a pure computer readable program code manner, the system, the apparatus and the respective modules thereof provided by the present invention can be implemented with the same program in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like by logically programming the method submodule M. Therefore, the system, the device and the modules thereof provided by the present invention can be considered as a hardware component, and the modules included in the system, the device and the modules thereof for implementing various programs can also be considered as structures in the hardware component; modules for performing various functions may also be considered to be both software programs for performing the methods and structures within hardware components.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.
Claims (10)
1. A battery equalization management method based on multi-agent game is characterized by comprising the following steps:
step 1: determining the charge state data of each single battery according to the attenuation data and the current electric quantity data of each single battery in the battery pack;
step 2: obtaining a charge-discharge strategy of each single battery in the battery pack by a Nash equilibrium method according to the difference value of the charge state data of each single battery and the charge state data of the other single batteries in the battery pack;
and step 3: and carrying out battery balance management according to the charge and discharge strategy.
2. The multi-agent gaming-based battery equalization management method of claim 1, wherein said step 1 comprises:
step 101: obtaining the attenuation data and the current electric quantity data through the current state data and the historical state data of each single battery;
step 102: and determining the state of charge data of each single battery according to the attenuation data and the current electric quantity data.
3. The multi-agent gaming based battery equalization management method of claim 2, wherein said step 101 further comprises:
step 1011: and inputting the current state data and the historical state data into an artificial neural network to obtain the attenuation data and the current electric quantity data output by the artificial neural network.
4. The multi-agent gaming-based battery equalization management method of claim 1, wherein said step 2 comprises:
step 201: obtaining a utility function of each single battery according to the difference value of the charge state data of each single battery and the charge state data of other single batteries in the battery pack;
step 202: and obtaining the charge-discharge strategy of each single battery in the battery pack through the Nash equilibrium method and the utility function.
5. The multi-agent gaming based battery equalization management method of claim 1 or 4, wherein said step 202 comprises:
step 2021: obtaining a Nash equilibrium solution of the utility function according to a preset constraint condition pair by the Nash equilibrium method;
step 2022: and obtaining the charge-discharge strategy of each single battery in the battery pack according to the Nash equilibrium solution.
6. A multi-agent gaming-based battery equalization management system, comprising:
module M1: determining the charge state data of each single battery according to the attenuation data and the current electric quantity data of each single battery in the battery pack;
module M2: obtaining a charge-discharge strategy of each single battery in the battery pack by a Nash equilibrium method according to the difference value of the charge state data of each single battery and the charge state data of the other single batteries in the battery pack;
module M3: and carrying out battery balance management according to the charge and discharge strategy.
7. The multi-agent gaming based battery equalization management system of claim 6, wherein said module M1, comprises:
submodule M101: obtaining the attenuation data and the current electric quantity data through the current state data and the historical state data of each single battery;
submodule M102: and determining the state of charge data of each single battery according to the attenuation data and the current electric quantity data.
8. The multi-agent gaming-based battery equalization management system of claim 7, wherein said sub-module M101 further comprises:
unit D1011: and inputting the current state data and the historical state data into an artificial neural network to obtain the attenuation data and the current electric quantity data output by the artificial neural network.
9. The multi-agent gaming based battery equalization management system of claim 6, wherein said module M2, comprises:
submodule M201: obtaining a utility function of each single battery according to the difference value of the charge state data of each single battery and the charge state data of other single batteries in the battery pack;
submodule M202: and obtaining the charge-discharge strategy of each single battery in the battery pack through the Nash equilibrium method and the utility function.
10. The multi-agent gaming-based battery equalization management system according to claim 6 or 9, wherein the sub-module M202 comprises:
unit D2021: obtaining a Nash equilibrium solution of the utility function according to a preset constraint condition pair by the Nash equilibrium method;
unit D2022: and obtaining the charge-discharge strategy of each single battery in the battery pack according to the Nash equilibrium solution.
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