CN115765121B - New energy storage battery distributed control system - Google Patents
New energy storage battery distributed control system Download PDFInfo
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- CN115765121B CN115765121B CN202310036267.5A CN202310036267A CN115765121B CN 115765121 B CN115765121 B CN 115765121B CN 202310036267 A CN202310036267 A CN 202310036267A CN 115765121 B CN115765121 B CN 115765121B
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Abstract
The invention provides a distributed control system of a new energy storage battery, which comprises a plurality of batteries, a top layer control unit, a plurality of middle layer control units and a plurality of bottom layer control units, wherein the batteries are divided into n groups of battery packs, the n groups of battery packs are connected with the middle layer control units in a one-to-one correspondence manner, the middle layer control units are controlled by the top layer control units, and the middle layer control units at least control one bottom layer control unit; the bottom layer control unit is used for acquiring a parameter set of the battery. The invention has the beneficial effects that: through obtaining the SOC capacity sets of a plurality of time points of each middle layer control unit, calculating conversion parameters, selecting corresponding target middle layer control units and equalization parameters through a neural network model, equalizing between battery packs, and transferring the electric energy in a part of batteries with more electric energy to other batteries with less electric energy, so that loss is reduced, discharge efficiency is improved, and the temperature of the batteries during working is reduced.
Description
Technical Field
The invention relates to the field of new energy storage batteries, in particular to a distributed control system of a new energy storage battery.
Background
With the increasing demand for products, the new energy storage battery may include a plurality of unit cells. The energy storage battery may cause a cell voltage inconsistency between each unit cell after some time of charge and discharge processes. When one or more single cells connected in series in the energy storage battery are excessively fast or excessively slow to discharge relative to other single cells, an unbalanced situation occurs, and if the unbalanced situation occurs, the aging process of the energy storage battery is accelerated, so that the service life of the energy storage battery is shortened.
In the prior art, for the process of balancing batteries, a resistor and a switch are connected to two ends of each battery, when the voltage of a certain battery is higher than a set threshold value, energy is released through the resistor, however, the energy loss is too high in this way, and the working environment temperature of the battery is increased.
Disclosure of Invention
The invention mainly aims to provide a new energy storage battery distributed control system, and aims to solve the problem that the existing battery balancing method can cause excessive energy loss.
The invention provides a new energy storage battery distributed control system, which comprises: the system comprises a plurality of batteries, a top layer control unit, a plurality of middle layer control units and a plurality of bottom layer control units, wherein the batteries are divided into n groups of battery packs, the n groups of battery packs are connected with the middle layer control units in a one-to-one correspondence manner, the middle layer control units are controlled by the top layer control units, and the middle layer control units at least control one bottom layer control unit; the bottom layer control unit comprises a voltage detection module, a current detection module, a temperature detection module and an internal resistance measurement module, wherein the bottom layer control unit is connected with the batteries in a one-to-one correspondence manner and is used for acquiring parameter sets of the batteries, a first equalization circuit is further arranged between the battery packs, the first equalization circuit is controlled by the top layer control unit, and the first equalization circuit is used for equalizing energy between the battery packs;
the bottom layer control unit is in data connection with the middle layer control unit, and the middle layer control unit is in data connection with the top layer control unit;
acquiring parameter sets of batteries at a plurality of time points through a bottom layer control unit, and uploading the parameter sets to the middle layer control unit;
calculating SOC capacity sets at a plurality of time points of each middle-layer control unit by a neural network method based on the parameter sets
The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
The presentation time is +.>
The ith middle layer control unitSOC capacity, and->
；
Inputting each element in each SOC capacity set into a formula
Obtaining conversion parameters corresponding to each SOC capacity set
The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
Conversion parameter +.>
；/>
Acquiring a real-time first SOC corresponding to each middle layer control unit at present, inputting the real-time first SOC and the conversion parameters of each middle layer control unit into a preset neural network model, obtaining a target middle layer control unit to be balanced and balanced parameters, and uploading the target middle layer control unit and balanced parameters to the top layer control unit; wherein the target middle layer control unit is the middle layer control unit; the target middle layer control unit to be balanced is used as output for training through different real-time SOCs and corresponding conversion parameters as input;
and controlling the target battery packs corresponding to the middle-layer control units to be communicated with the first equalization circuit through the top-layer control units until the difference value of the real-time first SOC between the target battery packs meets the equalization parameters.
Further, a second equalization circuit is further provided in the battery pack, and the second equalization circuit is controlled by the intermediate control unit, and further includes:
acquiring a real-time second SOC of each battery in the battery pack through an intermediate control unit;
and selecting a battery with the largest real-time second SOC and a battery with the smallest real-time second SOC to be communicated with the second equalization circuit according to the value of the real-time second SOC of each battery until the difference value of the real-time second SOCs of the two batteries is smaller than a second preset difference value.
Further, the first equalization circuit comprises n switch tubes, n inductors and n groups of battery packs, n equalization sub-circuits are formed, the anode of the battery pack of the first equalization sub-circuit is connected with the output end of the switch tube, the input end of the switch tube is connected with the first end of the inductor, and the second end of the inductor is connected with the cathode of the battery pack;
the positive pole of the battery pack of the equalization subcircuit in the next equalization subcircuit is connected with the negative pole of the battery pack of the last equalization subcircuit, the input end of the switching tube in the next equalization subcircuit is connected with the output end of the switching tube of the last equalization subcircuit, the input end of the switching tube in the next equalization subcircuit is also connected with the positive pole of the battery pack in the next equalization subcircuit, the output end of the switching tube in the next equalization subcircuit is connected with the negative pole of the battery pack in the next equalization subcircuit after being connected with an inductor in series, the control ends of the n switching tubes are connected with the top layer control unit, and the top layer control unit is used for controlling the on-off of each switching tube.
Further, the voltage detection module comprises a voltage sensor which is connected between the positive bus and the negative bus of the battery in parallel, and the current detection module comprises a current sensor which is connected on the positive bus of the battery in series; the temperature detection module comprises a temperature sensor, and the temperature sensor is attached to the surface of the battery; the internal resistance measuring module comprises an internal resistance measuring instrument which is connected in series on the positive and negative bus of the power battery pack.
Further, the equalization circuit further comprises an equalization duration recording unit;
the equalization duration recording unit is used for recording the duration of single equalization;
when the single equalization time length reaches a preset time length, acquiring the maximum difference value of the real-time first SOC between each battery pack;
and when the maximum difference value is larger than a preset value, judging that the battery pack needs to be repaired.
The invention also provides a battery balancing method of the new energy storage battery distributed control system, which is applied to the top layer control unit of any one of the above, wherein the new energy storage battery distributed control system comprises a plurality of batteries, a top layer control unit, a plurality of middle layer control units and a plurality of bottom layer control units, the batteries are divided into n groups of battery packs, the n groups of battery packs are connected with the middle layer control units in a one-to-one correspondence manner, the middle layer control units are controlled by the top layer control units, and the middle layer control units at least control one bottom layer control unit; the bottom layer control unit comprises a voltage detection module, a current detection module, a temperature detection module and an internal resistance measurement module, wherein the bottom layer control unit is connected with the batteries in a one-to-one correspondence manner and is used for acquiring parameter sets of the batteries, a first equalization circuit is further arranged between the battery packs, the first equalization circuit is controlled by the top layer control unit, and the first equalization circuit is used for equalizing energy between the battery packs; the bottom layer control unit is in data connection with the middle layer control unit, and the middle layer control unit is in data connection with the top layer control unit;
the battery equalization method comprises the following steps:
acquiring parameter sets of batteries at a plurality of time points through a bottom layer control unit, and uploading the parameter sets to the middle layer control unit;
calculating SOC capacity sets at a plurality of time points of each middle-layer control unit by a neural network method based on the parameter sets
The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
The presentation time is +.>
SOC capacity of the ith middle layer control unit, and +.>
；
Inputting each element in each SOC capacity set into a formula
Obtaining conversion parameters corresponding to each SOC capacity set
The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
Conversion parameter +.>
；
Acquiring a real-time first SOC corresponding to each middle layer control unit at present, inputting the real-time first SOC and the conversion parameters of each middle layer control unit into a preset neural network model, obtaining a target middle layer control unit to be balanced and balanced parameters, and uploading the target middle layer control unit and balanced parameters to the top layer control unit; wherein the target middle layer control unit is the middle layer control unit; the target middle layer control unit to be balanced is used as output for training through different real-time SOCs and corresponding conversion parameters as input;
and controlling the target battery packs corresponding to the middle-layer control unit to be communicated with the first equalization circuit until the difference value of the real-time first SOC between the target battery packs meets the equalization parameters.
The invention also provides a computer device comprising a memory storing a computer program and a processor implementing the steps of any of the methods described above when the processor executes the computer program.
The invention also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the method of any of the preceding claims.
The invention has the beneficial effects that: through obtaining the SOC capacity sets of a plurality of time points of each middle layer control unit, calculating conversion parameters, selecting corresponding target middle layer control units and equalization parameters through a neural network model, equalizing between battery packs, and transferring electric energy in a part of batteries with more electric energy to other batteries with less electric energy through an equalization circuit formed by inductors.
Drawings
FIG. 1 is a schematic block diagram of a distributed control system for a new energy storage battery according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a battery balancing method of a new energy storage battery distributed control system according to an embodiment of the present invention;
fig. 3 is a block diagram schematically illustrating a structure of a computer device according to an embodiment of the present application.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that, in the embodiments of the present invention, all directional indicators (such as up, down, left, right, front, and back) are merely used to explain the relative positional relationship, movement conditions, and the like between the components in a specific posture (as shown in the drawings), if the specific posture is changed, the directional indicators correspondingly change, and the connection may be a direct connection or an indirect connection.
The term "and/or" is herein merely an association relation describing an associated object, meaning that there may be three relations, e.g., a and B, may represent: a exists alone, A and B exist together, and B exists alone.
Furthermore, descriptions such as those referred to as "first," "second," and the like, are provided for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implying an order of magnitude of the indicated technical features in the present disclosure. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent and not within the scope of protection claimed in the present invention.
The invention provides a new energy storage battery distributed control system, which comprises: the system comprises a plurality of batteries, a top layer control unit, a plurality of middle layer control units and a plurality of bottom layer control units, wherein the batteries are divided into n groups of battery packs, the n groups of battery packs are connected with the middle layer control units in a one-to-one correspondence manner, the middle layer control units are controlled by the top layer control units, and the middle layer control units at least control one bottom layer control unit; the bottom layer control unit comprises a voltage detection module, a current detection module, a temperature detection module and an internal resistance measurement module, wherein the bottom layer control unit is connected with the batteries in a one-to-one correspondence manner and is used for acquiring parameter sets of the batteries, a first equalization circuit is further arranged between the battery packs, the first equalization circuit is controlled by the top layer control unit, and the first equalization circuit is used for equalizing energy between the battery packs;
the bottom layer control unit is in data connection with the middle layer control unit, and the middle layer control unit is in data connection with the top layer control unit;
acquiring parameter sets of batteries at a plurality of time points through a bottom layer control unit, and uploading the parameter sets to the middle layer control unit;
calculating SOC capacity sets at a plurality of time points of each middle-layer control unit by a neural network method based on the parameter sets
The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
The presentation time is +.>
SOC capacity of the ith middle layer control unit, and +.>
；
Inputting each element in each SOC capacity set into a formula
Obtaining conversion parameters corresponding to each SOC capacity set
The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
Conversion parameter +.>
；
Acquiring a real-time first SOC corresponding to each middle layer control unit at present, inputting the real-time first SOC and the conversion parameters of each middle layer control unit into a preset neural network model, obtaining a target middle layer control unit to be balanced and balanced parameters, and uploading the target middle layer control unit and balanced parameters to the top layer control unit; wherein the target middle layer control unit is the middle layer control unit; the target middle layer control unit to be balanced is used as output for training through different real-time SOCs and corresponding conversion parameters as input;
and controlling the target battery packs corresponding to the middle-layer control units to be communicated with the first equalization circuit through the top-layer control units until the difference value of the real-time first SOC between the target battery packs meets the equalization parameters.
The new energy storage battery can be any feasible battery, such as a lithium ion battery, a fuel cell, a photovoltaic cell and the like.
Specifically, the top layer control units, the middle layer control units and the bottom layer control units CAN communicate with each other through a controller area network (Controller Area Network, CAN), signals CAN be transmitted to a CAN bus through a CAN communication interface, and CAN be received by a CAN controller through the CAN bus, wherein each control unit (including the top layer control unit, the middle layer control unit and the bottom layer control unit) CAN be a microprocessor, a CPU and other processing devices. Calculating SOC capacity sets at a plurality of time points of each middle-layer control unit by a neural network method based on the parameter sets
. Here, the SOC capacity set refers to the SOC of the entire series-connected battery packs, and thus a corresponding capacity set is obtained, and the loss values of the respective batteries also differ after a plurality of discharges. Specifically, each element in each of the SOC capacity sets is input into the formula +.>
In (2) obtaining conversion parameters corresponding to each SOC capacity set>
The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
Represent the firstConversion parameter +.>
The method comprises the steps of carrying out a first treatment on the surface of the When the conversion parameters are larger, the speed of releasing the electric quantity of the battery is higher, and more energy supplement is considered to be carried out on the part of the battery, namely the application only calculates the real-time SOC of each battery, but balances the battery according to the discharging efficiency of each battery pack, and more energy is needed to be supplemented for the battery with faster discharging, namely each conversion parameter is calculated to serve as one parameter of balancing, so that the number of times of balancing can be effectively reduced, the balancing efficiency can be effectively improved, and the model is particularly input into a preset neural network model, and the model is trained by taking different real-time SOCs and corresponding conversion parameters as inputs and taking a target middle layer control unit to be balanced as output. The target battery pack corresponding to the middle-layer control unit is controlled by the top-layer control unit to be communicated with the first equalization circuit until the difference value of the real-time first SOC between the target battery packs meets the equalization parameters, wherein the specific form of the first equalization circuit is not limited, the first equalization circuit is an equalization circuit formed by inductors, and the equalization circuit formed by the inductors can transfer the electric energy in a part of batteries with more electric energy to other batteries with less electric energy.
In addition, the calculation mode of the SOC is calculated by the existing neural network method, and the principle of the neural network method applied to the lithium battery state of charge detection is as follows: and taking a large amount of corresponding external data such as voltage and current and the state of charge data of the battery as training samples, repeatedly training and modifying through forward propagation of input information and reverse propagation of error transmission in the self-learning process of the neural network, and obtaining a predicted state of charge value of the battery through inputting new data when the predicted state of charge reaches the error range of design requirements. Since the equalization of a part of the batteries is specific to individual batteries, the equalization of the battery energy is slow and is very unfavorable for devices with a plurality of single batteries, so that the batteries are grouped, and if the voltage difference between the batteries in a certain battery pack is too large, the equalization of the battery power in the battery pack can be performed.
In one embodiment, a second equalization circuit is further disposed in the battery pack, and the second equalization circuit is controlled by an intermediate control unit, and further includes:
acquiring a real-time second SOC of each battery in the battery pack through an intermediate control unit;
and selecting a battery with the largest real-time second SOC and a battery with the smallest real-time second SOC to be communicated with the second equalization circuit according to the value of the real-time second SOC of each battery until the difference value of the real-time second SOCs of the two batteries is smaller than a second preset difference value.
In this embodiment, equalization between the batteries in the battery pack may be performed, which is to obtain the real-time second SOC of each battery in the battery pack in a manner that the voltage difference between the batteries is too large, and the manner of obtaining the real-time second SOC is similar to that of obtaining the real-time first SOC described above, and the SOC is obtained by a neural network method, and then the battery with the largest real-time second SOC and the battery with the smallest real-time second SOC are selected to be connected to the second equalization circuit until the difference value of the real-time second SOCs of the two is smaller than the second preset difference value, where, of course, if the difference value is smaller than the second preset difference value, the second equalization circuit is not required to be connected.
Referring to fig. 1, in one embodiment, the first equalization circuit includes n switch tubes, n inductors and n groups of battery packs, n equalization sub-circuits are formed, wherein in the n equalization sub-circuits, the positive electrode of the battery pack of the first equalization sub-circuit is connected with the output end of the switch tube, the input end of the switch tube is connected with the first end of the inductor, and the second end of the inductor is connected with the negative electrode of the battery pack;
the positive pole of the battery pack of the equalization subcircuit in the next equalization subcircuit is connected with the negative pole of the battery pack of the last equalization subcircuit, the input end of the switching tube in the next equalization subcircuit is connected with the output end of the switching tube of the last equalization subcircuit, the input end of the switching tube in the next equalization subcircuit is also connected with the positive pole of the battery pack in the next equalization subcircuit, the output end of the switching tube in the next equalization subcircuit is connected with the negative pole of the battery pack in the next equalization subcircuit after being connected with an inductor in series, the control ends of the n switching tubes are connected with the top layer control unit, and the top layer control unit is used for controlling the on-off of each switching tube.
Through the target group battery of waiting to balance of selecting, communicate its corresponding switch tube, certainly, the here is not direct to switch on, but need set up the duty cycle, charge the target group battery that the electric energy is many to corresponding inductance, then the rethread inductance charges the target group battery that the electric energy is few, specifically can accomplish according to setting up the duty cycle, set up this application of duty cycle and do not limit, specifically need set up according to the capacity and the state of battery, guarantee that it can be through the electric energy transfer in the target group battery that the electric energy is few in the target group battery of electric energy in the target group battery that the electric energy is many. As shown in the figure, B1, B2, B3 and B4 are all different battery packs, the battery packs at least comprise a single cell, L1, L2, L3 and L4 are inductors, S1, S2, S3 and S4 are switching tubes, the switching tubes can be triodes, MOS tubes and the like, and the MOS tubes are preferably used in the application.
In one embodiment, the voltage detection module comprises a voltage sensor which is connected between the positive bus and the negative bus of the battery in parallel, and the current detection module comprises a current sensor which is connected on the positive bus of the battery in series; the temperature detection module comprises a temperature sensor, and the temperature sensor is attached to the surface of the battery; the internal resistance measuring module comprises an internal resistance measuring instrument which is connected in series on the positive and negative bus of the power battery pack.
When each voltage sensor collects related temperature, voltage, current and other parameter sets, the parameter sets are converted and processed into digital signals and are transmitted to a bottom layer control unit, the bottom layer control unit is used for receiving information uploaded by each module, and meanwhile, communication work between the bottom layer and the same layer is completed through a CAN communication module, so that data exchange between the bottom layer and the upper layer and sharing of information between the same layer are realized.
In one embodiment, the equalization circuit further includes an equalization duration recording unit;
the equalization duration recording unit is used for recording the duration of single equalization;
when the single equalization time length reaches a preset time length, acquiring the maximum difference value of the real-time first SOC between each battery pack;
and when the maximum difference value is larger than a preset value, judging that the battery pack needs to be repaired.
In this embodiment, when the single equalization time is too long, it is indicated that the equalization of the batteries has not satisfied the conversion efficiency between the electric energy, and at this time, it is indicated that the voltage difference between the battery packs has reached a certain value, and in this case, the conversion efficiency of the batteries is very slow, so that further repair is required, and the preset time period is a preset time period.
Referring to fig. 2, the invention further provides a battery balancing method of the new energy storage battery distributed control system, the battery balancing method is applied to the top layer control unit, the new energy storage battery distributed control system comprises a plurality of batteries, a top layer control unit, a plurality of middle layer control units and a plurality of bottom layer control units, wherein the batteries are divided into n groups of battery packs, the n groups of battery packs are connected with the middle layer control units in a one-to-one correspondence manner, the middle layer control units are controlled by the top layer control units, and the middle layer control units at least control one bottom layer control unit; the bottom layer control unit comprises a voltage detection module, a current detection module, a temperature detection module and an internal resistance measurement module, wherein the bottom layer control unit is connected with the batteries in a one-to-one correspondence manner and is used for acquiring parameter sets of the batteries, a first equalization circuit is further arranged between the battery packs, the first equalization circuit is controlled by the top layer control unit, and the first equalization circuit is used for equalizing energy between the battery packs; the bottom layer control unit is in data connection with the middle layer control unit, and the middle layer control unit is in data connection with the top layer control unit;
the battery equalization method comprises the following steps:
s1: acquiring parameter sets of batteries at a plurality of time points through a bottom layer control unit, and uploading the parameter sets to the middle layer control unit;
s2: calculating SOC capacity sets at a plurality of time points of each middle-layer control unit by a neural network method based on the parameter sets
The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
The presentation time is +.>
SOC capacity of the ith middle layer control unit, and +.>
；
S3: inputting each element in each SOC capacity set into a formula
Obtaining conversion parameters corresponding to each SOC capacity set
The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
Conversion parameter +.>
；
S4: acquiring a real-time first SOC corresponding to each middle layer control unit at present, inputting the real-time first SOC and the conversion parameters of each middle layer control unit into a preset neural network model, obtaining a target middle layer control unit to be balanced and balanced parameters, and uploading the target middle layer control unit and balanced parameters to the top layer control unit; wherein the target middle layer control unit is the middle layer control unit; the target middle layer control unit to be balanced is used as output for training through different real-time SOCs and corresponding conversion parameters as input;
s5: and controlling the target battery packs corresponding to the middle-layer control unit to be communicated with the first equalization circuit until the difference value of the real-time first SOC between the target battery packs meets the equalization parameters.
Calculating SOC capacity sets at a plurality of time points of each middle-layer control unit by a neural network method based on the parameter sets as described in the above steps
. Here, the SOC capacity set refers to the SOC of the entire series-connected battery pack, and thus a corresponding capacity set is obtained, in which the loss values of the respective batteries also differ after a plurality of discharges, and therefore, it is necessary to calculate the capacity set by acquiring the capacity sets at a plurality of time points. Specifically, each element in each of the SOC capacity sets is input into the formula +.>
Obtaining conversion parameters corresponding to each SOC capacity set
The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
Conversion parameter +.>
When the conversion parameter is larger, the speed of discharging the battery is higher, and more energy supplement is considered to be carried out on the battery, namely the application does not just calculate based on the real-time SOC of each battery, but needs to balance the discharging efficiency of each battery pack, and the discharging is fasterThe battery needs to be supplemented with more energy, namely, each conversion parameter is calculated to serve as one parameter of balancing, so that the number of times of balancing can be effectively reduced, and the balancing efficiency can be effectively improved. The target battery pack corresponding to the middle-layer control unit is controlled by the top-layer control unit to be communicated with the first equalization circuit until the difference value of the real-time first SOC between the target battery packs meets the equalization parameters, wherein the specific form of the first equalization circuit is not limited, the first equalization circuit is an equalization circuit formed by inductors, and the equalization circuit formed by the inductors can transfer the electric energy in a part of batteries with more electric energy to other batteries with less electric energy.
In addition, the calculation mode of the SOC is calculated by the existing neural network method, and the principle of the neural network method applied to the lithium battery state of charge detection is as follows: and taking a large amount of corresponding external data such as voltage and current and the state of charge data of the battery as training samples, repeatedly training and modifying through forward propagation of input information and reverse propagation of error transmission in the self-learning process of the neural network, and obtaining a predicted state of charge value of the battery through inputting new data when the predicted state of charge reaches the error range of design requirements. Since the equalization of a part of the batteries is specific to individual batteries, the equalization of the battery energy is slow and is very unfavorable for devices with a plurality of single batteries, so that the batteries are grouped, and if the voltage difference between the batteries in a certain battery pack is too large, the equalization of the battery power in the battery pack can be performed.
The invention has the beneficial effects that: through obtaining the SOC capacity sets of a plurality of time points of each middle layer control unit, calculating conversion parameters, selecting corresponding target middle layer control units and equalization parameters through a neural network model, equalizing between battery packs, and transferring electric energy in a part of batteries with more electric energy to other batteries with less electric energy through an equalization circuit formed by inductors.
Referring to fig. 3, a computer device is further provided in the embodiment of the present application, where the computer device may be a server, and the internal structure of the computer device may be as shown in fig. 3. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the computer is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used to store various capacity sets and the like. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by the processor, may implement the new energy storage battery distributed control method described in any of the foregoing embodiments.
Those skilled in the art will appreciate that the architecture shown in fig. 3 is merely a block diagram of a portion of the architecture in connection with the present application and is not intended to limit the computer device to which the present application is applied.
The embodiment of the application also provides a computer readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the distributed control system of the new energy storage battery described in any of the above embodiments can be implemented.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium provided herein and used in embodiments may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, apparatus, article or method that comprises the element.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application of simulating, extending and expanding human intelligence, sensing environment, acquiring knowledge and using knowledge to obtain optimal results using digital computers or digital computer-controlled machines.
Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims (8)
1. The utility model provides a new forms of energy storage battery distributed control system which characterized in that includes: the system comprises a plurality of batteries, a top layer control unit, a plurality of middle layer control units and a plurality of bottom layer control units, wherein the batteries are divided into n groups of battery packs, the n groups of battery packs are connected with the middle layer control units in a one-to-one correspondence manner, the middle layer control units are controlled by the top layer control units, and the middle layer control units at least control one bottom layer control unit; the bottom layer control unit comprises a voltage detection module, a current detection module, a temperature detection module and an internal resistance measurement module, wherein the bottom layer control unit is connected with the batteries in a one-to-one correspondence manner and is used for acquiring parameter sets of the batteries, a first equalization circuit is further arranged between the battery packs, the first equalization circuit is controlled by the top layer control unit, and the first equalization circuit is used for equalizing energy between the battery packs;
the bottom layer control unit is in data connection with the middle layer control unit, and the middle layer control unit is in data connection with the top layer control unit;
acquiring parameter sets of batteries at a plurality of time points through a bottom layer control unit, and uploading the parameter sets to the middle layer control unit;
calculating SOC capacity sets at a plurality of time points of each middle-layer control unit by a neural network method based on the parameter sets
The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
Time of presentationIs->
The SOC capacity of the ith middle layer control unit, and +.>
；
Inputting each element in each SOC capacity set into a formula
In (2) obtaining conversion parameters corresponding to each SOC capacity set>
The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
Conversion parameter +.>
；
Acquiring a real-time first SOC corresponding to each middle layer control unit at present, inputting the real-time first SOC and the conversion parameters of each middle layer control unit into a preset neural network model, obtaining a target middle layer control unit to be balanced and balanced parameters, and uploading the target middle layer control unit and balanced parameters to the top layer control unit; wherein the target middle layer control unit is the middle layer control unit; the target middle layer control unit to be balanced is used as output for training through different real-time SOCs and corresponding conversion parameters as input;
and controlling the target battery packs corresponding to the middle-layer control units to be communicated with the first equalization circuit through the top-layer control units until the difference value of the real-time first SOC between the target battery packs meets the equalization parameters.
2. The distributed control system of a new energy storage battery as set forth in claim 1, wherein a second equalization circuit is further provided in the battery pack, the second equalization circuit being controlled by an intermediate control unit, further comprising:
acquiring a real-time second SOC of each battery in the battery pack through an intermediate control unit;
and selecting a battery with the largest real-time second SOC and a battery with the smallest real-time second SOC to be communicated with the second equalization circuit according to the value of the real-time second SOC of each battery until the difference value of the real-time second SOCs of the two batteries is smaller than a second preset difference value.
3. The distributed control system of new energy storage cells of claim 1, wherein in one embodiment, the first equalization circuit comprises n switching tubes, n inductors and n groups of cells, and n equalization sub-circuits are formed, wherein in the n equalization sub-circuits, the positive electrode of the first equalization sub-circuit cell group is connected with the output end of the switching tube, the input end of the switching tube is connected with the first end of the inductor, and the second end of the inductor is connected with the negative electrode of the cell group;
the positive pole of the battery pack of the equalization subcircuit in the next equalization subcircuit is connected with the negative pole of the battery pack of the last equalization subcircuit, the input end of the switching tube in the next equalization subcircuit is connected with the output end of the switching tube of the last equalization subcircuit, the input end of the switching tube in the next equalization subcircuit is also connected with the positive pole of the battery pack in the next equalization subcircuit, the output end of the switching tube in the next equalization subcircuit is connected with the negative pole of the battery pack in the next equalization subcircuit after being connected with an inductor in series, the control ends of the n switching tubes are connected with the top layer control unit, and the top layer control unit is used for controlling the on-off of each switching tube.
4. The distributed control system of the new energy storage battery according to claim 1, wherein the voltage detection module comprises a voltage sensor connected in parallel between positive and negative buses of the battery, and the current detection module comprises a current sensor connected in series on the positive bus of the battery; the temperature detection module comprises a temperature sensor, and the temperature sensor is attached to the surface of the battery; the internal resistance measuring module comprises an internal resistance measuring instrument which is connected in series on the positive and negative bus of the power battery pack.
5. The new energy storage battery distributed control system according to claim 1, wherein the equalization circuit further comprises an equalization duration recording unit;
the equalization duration recording unit is used for recording the duration of single equalization;
when the single equalization time length reaches a preset time length, acquiring the maximum difference value of the real-time first SOC between each battery pack;
and when the maximum difference value is larger than a preset value, judging that the battery pack needs to be repaired.
6. A battery equalization method of a new energy storage battery distributed control system, characterized in that the battery equalization method is applied to the new energy storage battery distributed control system according to any one of claims 1 to 5;
the battery equalization method comprises the following steps:
acquiring parameter sets of batteries at a plurality of time points through a bottom layer control unit, and uploading the parameter sets to the middle layer control unit;
calculating SOC capacity sets at a plurality of time points of each middle-layer control unit by a neural network method based on the parameter sets
The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
The presentation time is +.>
The SOC capacity of the ith middle layer control unit, and +.>
；
Each of the SOC capacitiesEach element in the set is respectively input into a formula
In (2) obtaining conversion parameters corresponding to each SOC capacity set>
The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
Conversion parameter +.>
；
Acquiring a real-time first SOC corresponding to each middle layer control unit at present, inputting the real-time first SOC and the conversion parameters of each middle layer control unit into a preset neural network model, obtaining a target middle layer control unit to be balanced and balanced parameters, and uploading the target middle layer control unit and balanced parameters to the top layer control unit; wherein the target middle layer control unit is the middle layer control unit; the target middle layer control unit to be balanced is used as output for training through different real-time SOCs and corresponding conversion parameters as input;
and controlling the target battery packs corresponding to the middle-layer control unit to be communicated with the first equalization circuit until the difference value of the real-time first SOC between the target battery packs meets the equalization parameters.
7. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of claim 6 when executing the computer program.
8. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method as claimed in claim 6.
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| CN109617185B (en) * | 2018-12-31 | 2021-04-09 | 浙江高泰昊能科技有限公司 | Battery passive equalization method based on master-slave mode |
| JP2020145905A (en) * | 2019-03-08 | 2020-09-10 | 株式会社豊田自動織機 | Battery pack |
| KR20220141599A (en) * | 2021-04-13 | 2022-10-20 | 현대자동차주식회사 | Method for equalizing SOC of battery pack in electric vehicle |
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