CN115765121A - New forms of energy storage battery distributed control system - Google Patents
New forms of energy storage battery distributed control system Download PDFInfo
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- CN115765121A CN115765121A CN202310036267.5A CN202310036267A CN115765121A CN 115765121 A CN115765121 A CN 115765121A CN 202310036267 A CN202310036267 A CN 202310036267A CN 115765121 A CN115765121 A CN 115765121A
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Abstract
The invention provides a new energy storage battery distributed control system 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 plurality of 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 unit is controlled by the top layer control unit, and the middle layer control unit controls at least 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: the SOC capacity sets of the middle-layer control units at multiple time points are obtained, the conversion parameters are calculated, the corresponding target middle-layer control units and the balance parameters are selected through the neural network model, the battery packs are balanced, electric energy in a part of batteries with high electric energy can be transferred to other batteries with low electric energy, the loss is reduced, the 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
As the demand for products is continuously expanding, the new energy storage battery may include a plurality of cells. After some time of charging and discharging processes of the energy storage battery, the battery voltage of each single battery may be inconsistent. When one or more single cells among the single cells connected in series in the energy storage battery discharge too fast or too slow relative to other single cells, imbalance occurs, and if imbalance 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, in the process of battery equalization, a resistor and a switch are mainly connected to two ends of each battery, and 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 due to the mode, and the working environment temperature of the battery is increased.
Disclosure of Invention
The invention mainly aims to provide a distributed control system for a new energy storage battery, and aims to solve the problem that the existing battery balancing method causes too high energy loss.
The invention provides a new energy storage battery distributed control system, which comprises: the battery pack 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 unit is controlled by the top layer control unit, and the middle layer control unit controls at least 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, the bottom layer control unit is connected with the batteries in a one-to-one correspondence mode and used for acquiring parameter sets of the batteries, a first equalization circuit is further arranged between the battery packs and controlled by the top layer control unit, and the first equalization circuit is used for equalizing energy among 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 the battery at a plurality of time points through a bottom layer control unit, and uploading the parameter sets to a middle layer control unit;
calculating SOC capacity sets of a plurality of time points of each middle-layer control unit by a neural network method based on the parameter sets
(ii) a Wherein, the first and the second end of the pipe are connected with each other,
represents time of
The ith middle layer controls the SOC capacity of the unit, and
；
inputting each element in each SOC capacity set into a formula
In the method, conversion parameters corresponding to each SOC capacity set are obtained
(ii) a Wherein, the first and the second end of the pipe are connected with each other,
conversion parameter representing SOC capacity set corresponding to ith middle layer control unit
；
Acquiring real-time first SOC (state of charge) corresponding to each middle-layer control unit, inputting the real-time first SOC and the conversion parameter of each middle-layer control unit into a preset neural network model to obtain a target middle-layer control unit to be balanced and a balance parameter, and uploading the target middle-layer control unit and the balance parameter to the top-layer control unit; wherein the target middle layer control unit is the middle layer control unit; the preset neural network model is formed by taking different real-time SOCs and corresponding conversion parameters as input and taking a target middle-layer control unit to be balanced as output training;
and controlling the target battery packs corresponding to the middle-layer control unit to be communicated with the first equalization circuit through the top-layer control unit until the real-time first SOC difference between the target battery packs meets the equalization parameters.
Further, still be provided with the second equalizer circuit in the group battery, the second equalizer circuit is controlled by middle the control unit, still includes:
acquiring real-time second SOC of each battery in the battery pack through an intermediate control unit;
and selecting the battery with the maximum real-time second SOC and the battery with the minimum real-time second SOC to be communicated with the second equalizing circuit according to the real-time second SOC value of each battery until the real-time second SOC difference between the battery with the maximum real-time second SOC and the battery with the minimum real-time second SOC is smaller than a second preset difference.
Furthermore, the first equalization circuit comprises n switching tubes, n inductors and n groups of battery packs to form n equalization sub-circuits, 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 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 battery pack;
the positive electrode of the battery pack of the equalization sub-circuit in the next equalization sub-circuit is connected with the negative electrode of the battery pack of the previous equalization sub-circuit, the input end of the switching tube in the next equalization sub-circuit is connected with the output end of the switching tube of the previous equalization sub-circuit, the input end of the switching tube in the next equalization sub-circuit is further connected with the positive electrode of the battery pack in the next equalization sub-circuit, the output end of the switching tube in the next equalization sub-circuit is connected with the negative electrode of the battery pack in the next equalization sub-circuit 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.
Furthermore, the voltage detection module comprises a voltage sensor which is connected in parallel between a positive bus and a negative bus of the battery, and the current detection module comprises a current sensor which is 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 with a positive bus and a negative bus of the power battery pack.
Further, the equalization circuit further comprises an equalization duration recording unit;
the balance time length recording unit is used for recording the time length of single balance;
when the single equalization time length reaches a preset time length, acquiring the maximum difference value of the real-time first SOC among the battery packs;
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 any one of the top layer control units, the new energy storage battery distributed control system comprises a plurality of batteries, one 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 control at least 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, the bottom layer control units are connected with the batteries in a one-to-one correspondence mode and used for acquiring parameter sets of the batteries, a first equalization circuit is further arranged between the battery packs and 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 the battery at a plurality of time points through a bottom layer control unit, and uploading the parameter sets to a middle layer control unit;
calculating SOC capacity sets of a plurality of time points of each middle layer control unit by a neural network method based on the parameter sets
(ii) a Wherein the content of the first and second substances,
represents time as
The ith middle layer controls the SOC capacity of the unit, and
；
inputting each element in each SOC capacity set into a formula
In the method, conversion parameters corresponding to each SOC capacity set are obtained
(ii) a Wherein the content of the first and second substances,
conversion parameter representing SOC capacity set corresponding to ith middle layer control unit
；
Acquiring a real-time first SOC corresponding to each middle-layer control unit, inputting the real-time first SOC and the conversion parameter of each middle-layer control unit into a preset neural network model to obtain a target middle-layer control unit to be balanced and a balance parameter, and uploading the target middle-layer control unit and the balance parameter to the top-layer control unit; wherein the target middle layer control unit is the middle layer control unit; the preset neural network model is formed by taking different real-time SOCs and corresponding conversion parameters as input and taking a target middle-layer control unit to be balanced as output training;
and controlling a target battery pack 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 parameter.
The invention also provides a computer device comprising a memory storing a computer program and a processor implementing the steps of any of the above methods when the processor executes the computer program.
The invention also provides a computer-readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the method of any of the above.
The invention has the beneficial effects that: the SOC capacity sets of a plurality of time points of each middle-layer control unit are obtained, the conversion parameters are calculated, the corresponding target middle-layer control units and the balance parameters are selected through the neural network model, the battery packs are balanced, the balance circuit formed by the inductors can transfer electric energy in a part of batteries with large electric energy to other batteries with small electric energy, compared with the prior art, the loss can be effectively reduced, the discharging efficiency is improved, and the temperature of the batteries during working is reduced.
Drawings
Fig. 1 is a schematic block diagram of a partial structure of a distributed control system for new energy storage batteries according to an embodiment of the present invention;
fig. 2 is a schematic flowchart of a battery balancing method of a distributed control system of new energy storage batteries according to an embodiment of the present invention;
fig. 3 is a block diagram illustrating a structure of a computer device according to an embodiment of the present application.
The implementation, functional features and advantages of the present invention will be further described with reference to the accompanying drawings.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that all directional indicators (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative position relationship between the components, the motion situation, etc. in a specific posture (as shown in the drawings), and if the specific posture is changed, the directional indicator is changed accordingly, and the connection may be a direct connection or an indirect connection.
The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and B, may mean: a exists alone, A and B exist simultaneously, and B exists alone.
In addition, descriptions such as "first", "second", etc. in the present invention are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In addition, technical solutions between various embodiments may be combined with each other, but must be realized by a person skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination should not be considered to exist, and is not within the protection scope of the present invention.
The invention provides a new energy storage battery distributed control system, which comprises: the battery pack 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 unit is controlled by the top layer control unit, and the middle layer control unit controls at least 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, the bottom layer control unit is connected with the batteries in a one-to-one correspondence mode and used for acquiring parameter sets of the batteries, a first equalization circuit is further arranged between the battery packs and controlled by the top layer control unit, and the first equalization circuit is used for equalizing energy among 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 the battery at a plurality of time points through a bottom layer control unit, and uploading the parameter sets to a middle layer control unit;
calculating SOC capacity sets of a plurality of time points of each middle layer control unit by a neural network method based on the parameter sets
(ii) a Wherein the content of the first and second substances,
represents time of
The ith middle layer controls the SOC capacity of the cell, an
；
Respectively inputting each element in each SOC capacity set into a formula
In the method, conversion parameters corresponding to each SOC capacity set are obtained
(ii) a Wherein, the first and the second end of the pipe are connected with each other,
conversion parameter representing SOC capacity set corresponding to ith middle layer control unit
；
Acquiring a real-time first SOC corresponding to each middle-layer control unit, inputting the real-time first SOC and the conversion parameter of each middle-layer control unit into a preset neural network model to obtain a target middle-layer control unit to be balanced and a balance parameter, and uploading the target middle-layer control unit and the balance parameter to the top-layer control unit; wherein the target middle layer control unit is the middle layer control unit; the preset neural network model is formed by taking different real-time SOCs and corresponding conversion parameters as input and taking a target middle-layer control unit to be balanced as output training;
and controlling the target battery packs corresponding to the middle-layer control unit to be communicated with the first equalization circuit through the top-layer control unit until the real-time first SOC difference 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 unit, the middle layer control units, and the bottom layer control units may communicate with each other through a Controller Area Network (CAN), and the signals may be transmitted to a CAN bus through a CAN communication interface and received by the CAN Controller through the CAN bus, where each of the control units (including the top layer control unit, the middle layer control unit, and the bottom layer control unit) may be a processing device such as a microprocessor, a CPU, or the like. Calculating SOC capacity sets of a plurality of time points of each middle-layer control unit by a neural network method based on the parameter sets
. The SOC capacity set herein refers to the SOC of the entire series-connected battery pack, so as to obtain a corresponding capacity set, wherein each battery is placed many timesSince the loss value varies after power consumption, it is necessary to calculate the capacity set by acquiring capacity sets at a plurality of time points, where the capacity set at each time point is actually the capacity set corresponding to each battery pack at each time point. Specifically, each element in each SOC capacity set is input into a formula
In the method, conversion parameters corresponding to each SOC capacity set are obtained
(ii) a Wherein the content of the first and second substances,
conversion parameter representing SOC capacity set corresponding to ith middle level control unit
(ii) a When the conversion parameter is larger, it is indicated that the speed of releasing electric quantity of the battery is higher, and at this time, more energy supplement should be considered for the part of batteries, that is, the present application does not only perform calculation based on the real-time SOC of each battery, but needs to balance the batteries in combination with the discharge efficiency of each battery pack, and needs to supplement more energy for the battery discharging faster, that is, each conversion parameter is calculated to serve as one parameter of the balancing, so that the number of times of balancing can be effectively reduced, and the balancing efficiency can be effectively improved. The top-level control unit controls the target battery packs corresponding to the middle-level control unit to be communicated with the first equalization circuit until the real-time first SOC difference between the target battery packs meets the equalization parameter, wherein the specific form of the first equalization circuit is not limited, the first equalization circuit is generally 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 large electric energy to other electric energyCompared with the prior art, the battery with less batteries can effectively reduce the loss, improve the discharge efficiency and reduce the temperature of the battery during operation.
In addition, the SOC is calculated by the existing neural network method, and the principle that the neural network method is applied to the lithium battery state of charge detection is as follows: a large amount of corresponding external data such as voltage, current and the like and the charge state data of the battery are used as training samples, training and modification are repeatedly carried out through forward propagation of input information and backward propagation of error transmission in the learning process of the neural network, and when the predicted charge state reaches the error range of the design requirement, a new data is input to obtain the charge state predicted value of the battery. Since the balance of some batteries is specific to individual batteries, but such a balance method causes the battery energy to be balanced slowly, which is very disadvantageous for a device with many single batteries, it is grouped, and of course, if the voltage difference between the batteries in a certain battery pack is too large, the battery power can be balanced in the battery pack.
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 the battery pack further includes:
acquiring real-time second SOC of each battery in the battery pack through the intermediate control unit;
and selecting the battery with the maximum real-time second SOC and the battery with the minimum real-time second SOC to be communicated with the second equalizing circuit according to the real-time second SOC value of each battery until the real-time second SOC difference between the battery with the maximum real-time second SOC and the battery with the minimum real-time second SOC is smaller than a second preset difference.
In this embodiment, balancing between the batteries in the battery pack may also be performed, so as to obtain an excessively large voltage difference between the battery packs, specifically, a real-time second SOC of each battery in the battery pack is obtained, the obtaining is performed in a manner similar to the manner of obtaining the real-time first SOC, where SOC is obtained through 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 balancing circuit until a difference between the real-time second SOCs of the battery and the battery with the smallest real-time second SOC is smaller than a second preset difference, and certainly, if the difference is smaller than the second preset difference, the second balancing circuit does not need to be connected.
Referring to fig. 1, in one embodiment, the first equalization circuit includes n switching tubes, n inductors, and n groups of battery packs, and constitutes n equalization sub-circuits, in the n equalization sub-circuits, an anode of the battery pack of the first equalization sub-circuit is connected to an output end of the switching tube, an input end of the switching tube is connected to a first end of the inductor, and a second end of the inductor is connected to a cathode of the battery pack;
the positive electrode of the battery pack of the equalization sub-circuit in the next equalization sub-circuit is connected with the negative electrode of the battery pack of the previous equalization sub-circuit, the input end of the switching tube in the next equalization sub-circuit is connected with the output end of the switching tube of the previous equalization sub-circuit, the input end of the switching tube in the next equalization sub-circuit is further connected with the positive electrode of the battery pack in the next equalization sub-circuit, the output end of the switching tube in the next equalization sub-circuit is connected with the negative electrode of the battery pack in the next equalization sub-circuit 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 treating the equilibrium of selecting, communicate its corresponding switch tube, of course, here is not directly switching on, but need set up the duty cycle, charge to the inductance that corresponds the target group battery that the electric energy is many, then charge to the target group battery that the electric energy is few through the inductance, specifically can accomplish according to setting up the duty cycle, do not do the restriction as to the setting of duty cycle this application, specifically need set for 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 inductance is many with the electric energy to the target group battery that the electric energy is few. Referring to the drawings, wherein B1, B2, B3, and B4 are different battery packs, each battery pack includes at least one single cell, L1, L2, L3, and L4 are inductors, S1, S2, S3, and S4 are switching tubes, which may be transistors, MOS tubes, and the like, and MOS tubes are preferably used in the present application.
In one embodiment, the voltage detection module comprises a voltage sensor connected in parallel between a positive bus and a negative bus 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 with a positive bus and a negative bus of the power battery pack.
After the voltage sensors collect the relevant temperature, voltage, current and other parameter sets, the parameter sets are converted into digital signals to the bottom layer control unit, the bottom layer control unit is used for receiving the information uploaded by the modules, and meanwhile, the CAN communication module CAN complete the communication work with the upper layer and the lower layer and the same layer, so that the data exchange between the upper layer and the lower layer and the information sharing between the upper layer and the lower layer are realized.
In one embodiment, the equalization circuit further comprises an equalization duration recording unit;
the equalization time length recording unit is used for recording the time length 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 among the battery packs;
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 balancing time is too long, it indicates that the balancing of the battery has failed to meet the conversion efficiency between the electric energies, and at this time, it indicates that the voltage difference between the battery packs has reached a certain value, and in this case, the conversion efficiency of the battery is very slow, and therefore, further repair is required, and the preset time is a preset time.
Referring to fig. 2, the present invention further provides a battery balancing method for a distributed control system of a new energy storage battery, where the battery balancing method is applied to the top layer control unit, the distributed control system of a new energy storage battery includes a plurality of batteries, a top layer control unit, a plurality of middle layer control units, and a plurality of bottom layer control units, where the plurality of 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 unit is controlled by the top layer control unit, and the middle layer control unit controls at least 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, the bottom layer control units are connected with the batteries in a one-to-one correspondence mode and used for acquiring parameter sets of the batteries, a first equalization circuit is further arranged between the battery packs and 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 the battery at a plurality of time points through a bottom layer control unit, and uploading the parameter sets to a middle layer control unit;
s2: calculating SOC capacity sets of a plurality of time points of each middle-layer control unit by a neural network method based on the parameter sets
(ii) a Wherein the content of the first and second substances,
represents time of
The ith middle layer controls the SOC capacity of the cell, an
；
S3: respectively inputting each element in each SOC capacity set into a formula
In the method, conversion parameters corresponding to each SOC capacity set are obtained
(ii) a Wherein the content of the first and second substances,
conversion parameter representing SOC capacity set corresponding to ith middle layer control unit
；
S4: acquiring a real-time first SOC corresponding to each middle-layer control unit, inputting the real-time first SOC and the conversion parameter of each middle-layer control unit into a preset neural network model to obtain a target middle-layer control unit to be balanced and a balance parameter, and uploading the target middle-layer control unit and the balance parameter to the top-layer control unit; wherein the target middle layer control unit is the middle layer control unit; the preset neural network model is formed by taking different real-time SOCs and corresponding conversion parameters as inputs and taking a target middle-layer control unit to be balanced as an output training;
s5: and controlling a target battery pack 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 parameter.
Calculating SOC capacity sets of a plurality of time points of each middle-level control unit by a neural network method based on the parameter sets as described in the above steps
. The SOC capacity set herein refers to the SOC of the entire series-connected battery pack, and a corresponding capacity set is obtained, where the loss values of the batteries are different after the batteries are discharged for a plurality of times, and therefore, it is necessary to calculate the capacity set by obtaining capacity sets at a plurality of time points. Specifically, each element in each SOC capacity set is input into a formula
In the method, conversion parameters corresponding to each SOC capacity set are obtained
(ii) a Wherein the content of the first and second substances,
conversion parameter representing SOC capacity set corresponding to ith middle level control unit
The more the conversion parameters are, the faster the speed of releasing electric quantity of the battery is, at this time, more energy supplement should be considered for the battery, that is, the present application does not only perform calculation based on the real-time SOC of each battery, but needs to balance the battery in combination with the discharge efficiency of each battery pack, and needs to supplement more energy for the battery discharging faster, that is, each conversion parameter is calculated to serve as one parameter of the balance, so that the number of times of the balance can be effectively reduced, and the balance efficiency can be effectively improved. The top-level control unit controls the target battery packs corresponding to the middle-level 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 parameter, wherein the specific form of the first equalization circuit is not limited, the first equalization circuit is generally an equalization circuit formed by inductors, the equalization circuit formed by the inductors can transfer part of electric energy in batteries with large electric energy to other batteries with small electric energy, and compared with the prior art, the top-level control unit can effectively reduce loss, improve the discharge efficiency and reduce the temperature of the batteries during working.
In addition, the SOC is calculated by the existing neural network method, and the principle that the neural network method is applied to the lithium battery state of charge detection is as follows: a large amount of corresponding external data such as voltage, current and the like and the charge state data of the battery are used as training samples, training and modification are repeatedly carried out through forward propagation of input information and backward propagation of error transmission in the learning process of the neural network, and when the predicted charge state reaches the error range of the design requirement, a new data is input to obtain the charge state predicted value of the battery. Since the balance of some batteries is specific to individual batteries, but such a balance method causes the battery energy balance to be slow, which is very unfavorable for a device with many single batteries, so that the batteries are grouped, and of course, if the voltage difference between the batteries in a certain battery pack is too large, the battery electric energy can be balanced in the battery pack.
The invention has the beneficial effects that: the SOC capacity sets of a plurality of time points of each middle-layer control unit are obtained, the conversion parameters are calculated, the corresponding target middle-layer control units and the balance parameters are selected through the neural network model, the battery packs are balanced, the balance circuit formed by the inductors can transfer electric energy in a part of batteries with large electric energy to other batteries with small electric energy, compared with the prior art, the loss can be effectively reduced, the discharging efficiency is improved, and the temperature of the batteries during working is reduced.
Referring to fig. 3, a computer device, which may be a server and whose internal structure may be as shown in fig. 3, is also provided in the embodiment of the present application. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the computer designed processor is used to provide computational and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The memory provides an environment for the operating system and the running of computer programs in the non-volatile storage medium. The database of the computer device is used for storing 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 can implement the distributed control method of the new energy storage battery according to any one of the above embodiments when executed by the processor.
It will be understood by those skilled in the art that the structure shown in fig. 3 is only a block diagram of a part of the structure related to the present application, and does not constitute a limitation to the computer device to which the present application is applied.
The embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, may implement the new energy storage battery distributed control system according to any of the above embodiments.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium provided herein and used in the examples may include non-volatile and/or volatile memory. Non-volatile 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 (Synchlink) DRAM (SLDRAM), rambus (Rambus) direct RAM (RDRAM), direct bused dynamic RAM (DRDRAM), and bused 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 phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of another identical element in a process, apparatus, article, or method comprising the element.
The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, artificial Intelligence (AI) is a theory, method, technique and application method that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
The artificial intelligence infrastructure generally includes 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 the like.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement 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. A new forms of energy storage battery distributed control system, characterized in that includes: the battery pack 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 unit is controlled by the top layer control unit, and the middle layer control unit controls at least 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, the bottom layer control unit is connected with the batteries in a one-to-one correspondence mode and used for acquiring parameter sets of the batteries, a first equalization circuit is further arranged between the battery packs and controlled by the top layer control unit, and the first equalization circuit is used for equalizing energy among 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 the battery at a plurality of time points through a bottom layer control unit, and uploading the parameter sets to a middle layer control unit;
calculating SOC capacity sets of a plurality of time points of each middle-layer control unit by a neural network method based on the parameter sets
(ii) a Wherein the content of the first and second substances,
represents time of
The ith middle layer controls the SOC capacity of the cell, an
；
Respectively inputting each element in each SOC capacity set into a formula
In the method, conversion parameters corresponding to each SOC capacity set are obtained
(ii) a Wherein, the first and the second end of the pipe are connected with each other,
conversion parameter representing SOC capacity set corresponding to ith middle layer control unit
；
Acquiring a real-time first SOC corresponding to each middle-layer control unit, inputting the real-time first SOC and the conversion parameter of each middle-layer control unit into a preset neural network model to obtain a target middle-layer control unit to be balanced and a balance parameter, and uploading the target middle-layer control unit and the balance parameter to the top-layer control unit; wherein the target middle layer control unit is the middle layer control unit; the preset neural network model is formed by taking different real-time SOCs and corresponding conversion parameters as inputs and taking a target middle-layer control unit to be balanced as an output training;
and controlling the target battery packs corresponding to the middle-layer control unit to be communicated with the first equalization circuit through the top-layer control unit until the real-time first SOC difference between the target battery packs meets the equalization parameters.
2. The distributed control system for the new energy storage battery according to claim 1, wherein 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 comprising:
acquiring real-time second SOC of each battery in the battery pack through the intermediate control unit;
and selecting the battery with the largest real-time second SOC and the battery with the smallest real-time second SOC to be communicated with the second equalizing circuit according to the real-time second SOC values of the batteries until the real-time second SOC difference between the batteries is smaller than a second preset difference.
3. The distributed control system for the new energy storage battery according to claim 1, wherein the first equalization circuit comprises n switching tubes, n inductors and n groups of battery packs, and n equalization sub-circuits are formed, 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 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 battery pack;
the positive electrode of the battery pack of the equalization sub-circuit in the next equalization sub-circuit is connected with the negative electrode of the battery pack of the previous equalization sub-circuit, the input end of the switching tube in the next equalization sub-circuit is connected with the output end of the switching tube of the previous equalization sub-circuit, the input end of the switching tube in the next equalization sub-circuit is further connected with the positive electrode of the battery pack in the next equalization sub-circuit, the output end of the switching tube in the next equalization sub-circuit is connected with the negative electrode of the battery pack in the next equalization sub-circuit 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 as claimed in claim 1, wherein the voltage detection module comprises a voltage sensor connected in parallel between a positive bus and a negative bus of the battery, and the current detection module comprises a current sensor connected in series with 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 with a positive bus and a negative bus of the power battery pack.
5. The distributed control system for the new energy storage battery according to claim 1, wherein the balancing circuit further comprises a balancing duration recording unit;
the equalization time length recording unit is used for recording the time length 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 among the battery packs;
and when the maximum difference value is larger than the preset value, determining that the battery pack needs to be repaired.
6. A battery balancing method of a new energy storage battery distributed control system is characterized in that the battery balancing method is applied to the new energy storage battery distributed control system of any one of claims 1 to 5, 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 unit is controlled by the top layer control unit, and the middle layer control unit controls at least 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, the bottom layer control unit is connected with the batteries in a one-to-one correspondence mode and used for acquiring parameter sets of the batteries, a first equalization circuit is further arranged between the battery packs and controlled by the top layer control unit, and the first equalization circuit is used for equalizing energy among 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 the battery at a plurality of time points through a bottom layer control unit, and uploading the parameter sets to a middle layer control unit;
calculating SOC capacity sets of a plurality of time points of each middle layer control unit by a neural network method based on the parameter sets
(ii) a Wherein the content of the first and second substances,
represents time of
The ith middle layer controls the SOC capacity of the unit, and
；
respectively inputting each element in each SOC capacity set into a formula
In the method, conversion parameters corresponding to each SOC capacity set are obtained
(ii) a Wherein the content of the first and second substances,
conversion parameter representing SOC capacity set corresponding to ith middle level control unit
；
Acquiring a real-time first SOC corresponding to each middle-layer control unit, inputting the real-time first SOC and the conversion parameter of each middle-layer control unit into a preset neural network model to obtain a target middle-layer control unit to be balanced and a balance parameter, and uploading the target middle-layer control unit and the balance parameter to the top-layer control unit; wherein the target middle layer control unit is the middle layer control unit; the preset neural network model is formed by taking different real-time SOCs and corresponding conversion parameters as input and taking a target middle-layer control unit to be balanced as output training;
and controlling a target battery pack 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, wherein 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, which, when being executed by a processor, carries out the steps of the method as claimed in claim 6.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102231546A (en) * | 2011-06-30 | 2011-11-02 | 武汉市菱电汽车电子有限责任公司 | Battery management system with balanced charge and discharge functions and control method thereof |
| CN105553026A (en) * | 2016-01-29 | 2016-05-04 | 华南理工大学 | Battery pack electricity equalization circuit and equalization method |
| CN109216803A (en) * | 2018-09-20 | 2019-01-15 | 东北大学 | A kind of UMDs battery management system |
| CN109617185A (en) * | 2018-12-31 | 2019-04-12 | 浙江高泰昊能科技有限公司 | A kind of passive equalization methods of battery 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 |
| US20220324350A1 (en) * | 2021-04-13 | 2022-10-13 | Hyundai Motor Company | Method for equalizing states of charge (socs) of battery packs in electric vehicle |
-
2023
- 2023-01-10 CN CN202310036267.5A patent/CN115765121B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102231546A (en) * | 2011-06-30 | 2011-11-02 | 武汉市菱电汽车电子有限责任公司 | Battery management system with balanced charge and discharge functions and control method thereof |
| CN105553026A (en) * | 2016-01-29 | 2016-05-04 | 华南理工大学 | Battery pack electricity equalization circuit and equalization method |
| CN109216803A (en) * | 2018-09-20 | 2019-01-15 | 东北大学 | A kind of UMDs battery management system |
| CN109617185A (en) * | 2018-12-31 | 2019-04-12 | 浙江高泰昊能科技有限公司 | A kind of passive equalization methods of battery 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 |
| US20220324350A1 (en) * | 2021-04-13 | 2022-10-13 | Hyundai Motor Company | Method for equalizing states of charge (socs) of battery packs in electric vehicle |
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