CN115101836A - Cloud-end converged battery system management method and device - Google Patents

Cloud-end converged battery system management method and device Download PDF

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CN115101836A
CN115101836A CN202210784034.9A CN202210784034A CN115101836A CN 115101836 A CN115101836 A CN 115101836A CN 202210784034 A CN202210784034 A CN 202210784034A CN 115101836 A CN115101836 A CN 115101836A
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battery
soc
key
battery system
cloud
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张涛
朱文凯
周星
宋元明
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National University of Defense Technology
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    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/425—Structural combination with electronic components, e.g. electronic circuits integrated to the outside of the casing
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/48—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/425—Structural combination with electronic components, e.g. electronic circuits integrated to the outside of the casing
    • H01M2010/4271—Battery management systems including electronic circuits, e.g. control of current or voltage to keep battery in healthy state, cell balancing
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/425—Structural combination with electronic components, e.g. electronic circuits integrated to the outside of the casing
    • H01M2010/4278—Systems for data transfer from batteries, e.g. transfer of battery parameters to a controller, data transferred between battery controller and main controller

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Abstract

The invention discloses a cloud-terminal integrated battery system management method and device. The method and the device are applied to the technical field of battery management, and the terminal BMS and the cloud BMS are reasonably and effectively distributed with functions, so that the computing cost of the terminal BMS is guaranteed, the data transmission cost between the terminal BMS and the cloud BMS is also guaranteed, and the state monitoring and the balance control of a battery system can be more effectively implemented.

Description

Cloud-end converged battery system management method and device
Technical Field
The invention relates to the technical field of battery management, in particular to a cloud-end integrated battery system management method and device.
Background
The lithium ion battery is widely applied to electric vehicles and static energy storage scenes due to the advantages of high energy density, long cycle life, no memory effect and the like. In order to ensure the safe operation of the lithium ion battery pack, a corresponding battery management system is required to be equipped for monitoring and managing the state of the lithium ion battery pack.
A conventional Battery Management System (BMS) is integrated in a Battery System, and signal acquisition, data processing, and charge/discharge control are all implemented on a terminal ic. With the application of a large-scale energy storage system, a battery management system needs to monitor and manage a large number of batteries, and the traditional terminal BMS is not strong in calculation and storage capacity and difficult to meet the requirement of efficient management of the large-scale battery energy storage system. Therefore, the field relevant scholars propose the cloud battery management system, and the defects of the computing power of the terminal battery management system are made up by the strong storage and computing power of the cloud computing platform.
Currently, research on a cloud battery management system only stays in a stage of using the cloud battery management system as a computing and storage platform and using a terminal battery management system as a data acquisition and control device. When the battery system is monitored, the terminal BMS acquires battery data, then transmits all the battery data to the cloud BMS for state monitoring and parameter estimation, and finally transmits the estimation result of the cloud BMS back to the terminal BMS for charging and discharging control of the battery pack. Although the problem that terminal BMS computing power is not strong can be effectively solved to the use mode of cloud BMS as computing platform, in large-scale energy storage, the battery scale is huge, and its produced battery data volume is very big, will all battery data transmit the transmission cost that can greatly increased data, can produce the time delay of data transmission moreover and produce the influence to battery management's timeliness.
Disclosure of Invention
Aiming at the problems that cost cannot be controlled and timeliness is poor due to the fact that data transmission between a terminal BMS and a cloud platform is not effectively selected in the prior art, the cloud-end fusion battery system management method and device are provided.
In order to achieve the above object, the present invention provides a cloud-end converged battery system management method, which comprises the following steps:
step 1, acquiring battery data of all battery monomers in a battery system by a terminal BMS, wherein the battery data comprises voltage, current and temperature data of the battery monomers;
step 2, performing key battery preliminary screening on the terminal BMS based on the acquired battery data to obtain a key battery candidate group consisting of a plurality of battery monomers;
step 3, uploading the battery data of all the battery monomers in the key battery candidate group to a cloud BMS;
step 4, accurately estimating the SOC and the capacity of all the battery monomers in the key battery candidate group in the cloud BMS to obtain estimated values of the SOC and the capacity of all the battery monomers in the key battery candidate group and obtain the key battery monomers in the key battery candidate group;
step 5, obtaining a consistency index of the battery system based on the estimated values of the SOC and the capacity of the key battery monomer;
and 6, performing strategy updating judgment:
if the current consistency index of the battery system is smaller than the index threshold value, performing iterative optimization by taking the number of the balanced battery monomers within the number threshold value and the minimum power consumption as an optimization target to obtain an optimal battery balancing strategy, transmitting the optimal battery balancing strategy back to the terminal BMS for balancing control, and performing the steps 1-6 again after a first time interval;
otherwise steps 1-6 are repeated after a second time interval has elapsed.
In order to achieve the above object, the present invention further provides a cloud-terminal integrated battery system management apparatus, including a cloud BMS and a terminal BMS;
the terminal BMS is electrically connected with the battery system, and includes:
the data acquisition module is used for acquiring battery data of all battery monomers in the battery system, wherein the battery data comprises voltage, current and temperature data;
the key battery screening module is used for carrying out primary screening on a key battery according to the acquired battery data to obtain a key battery candidate group consisting of a plurality of battery monomers;
the charging and discharging control module is used for performing charging and discharging control and balance control on the battery system according to the optimal battery balance strategy;
the cloud BMS with terminal BMS communication links to each other, includes:
the SOC accurate estimation module is used for accurately estimating the SOC of each battery cell according to the battery data uploaded by the terminal BMS to obtain an SOC estimation value of each battery cell;
the accurate capacity estimation module is used for obtaining a capacity estimation value according to the SOC estimation value of each battery monomer;
the battery system consistency evaluation module is used for obtaining the current consistency index of the battery system according to the SOC estimation value and the capacity estimation value of the battery monomer;
and the balancing strategy optimization module is used for optimizing and exploring the optimal balancing strategy according to the current consistency index of the battery system.
Compared with the traditional battery management system utilizing a cloud platform, the cloud-end fusion battery system management method and device provided by the invention have the advantages that the terminal BMS and the cloud BMS are reasonably and effectively distributed through designing the system structure of the cloud-end fusion battery management system, the computing cost of the terminal BMS is ensured, the data transmission cost between the terminal BMS and the cloud BMS is also ensured, and the state monitoring and the balance control of the battery system can be more effectively implemented.
Drawings
In order to more clearly illustrate the embodiments or technical solutions of the present invention, the drawings used in the embodiments or technical solutions of the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the structures shown in the drawings without creative efforts.
FIG. 1 is a flow chart of a battery system management method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a key cell in an embodiment of the invention;
fig. 3 is a block diagram of a battery system management apparatus according to an embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below 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 the directional indicators (such as up, down, left, right, front, and rear … …) in the embodiment of the present invention are only used to explain the relative position relationship between the components, the movement situation, etc. in a specific posture (as shown in the drawing), and if the specific posture is changed, the directional indicator is changed accordingly.
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 the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
In the present invention, unless otherwise explicitly stated or limited, the terms "connected", "fixed", and the like are to be understood broadly, for example, "fixed" may be fixedly connected, may be detachably connected, or may be integrated; the connection can be mechanical connection, electrical connection, physical connection or wireless communication connection; they may be directly connected or indirectly connected through intervening media, or they may be connected internally or in any other suitable relationship, unless expressly stated otherwise. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In addition, the technical solutions in the embodiments of the present invention may be combined with each other, but it must be based on the realization of those skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination of technical solutions should not be considered to exist, and is not within the protection scope of the present invention.
Example 1
The embodiment discloses a cloud-end integrated battery system management method, wherein a battery system is formed by serially connecting a large number of lithium batteries. Referring to fig. 1, the battery system management method includes the steps of:
step 1, acquiring battery data of all battery monomers in a battery system by a terminal BMS, wherein the battery data comprises voltage, current and temperature data of the battery monomers;
step 2, performing key battery preliminary screening on the terminal BMS based on the acquired battery data to obtain a key battery candidate group consisting of a plurality of battery monomers;
and 3, uploading the battery data of all the battery monomers in the key battery candidate group to the cloud BMS through wireless data transmission or wired data transmission.
And 4, accurately estimating the SOC and the capacity of all the battery monomers in the key battery candidate group in the cloud BMS to obtain estimated values of the SOC and the capacity of all the battery monomers in the key battery candidate group and obtain the key battery monomers in the key battery candidate group.
And 5, obtaining the consistency index of the battery system based on the estimated values of the SOC and the capacity of the key battery monomer.
And 6, performing strategy updating judgment:
if the current consistency index of the battery system is smaller than the index threshold value U 0 And performing iterative optimization by taking the number of the single equalizing batteries within the quantity threshold value and the minimum power consumption as an optimization target to obtain an optimal battery equalization strategy, transmitting the optimal battery equalization strategy back to the terminal BMS for equalization control, and performing equalization controlFirst time interval Δ T 1 Then, the steps 1 to 6 are carried out again, namely, the current time is T 0 When the real-time T is equal to T 0 +ΔT 1 Then, carrying out the steps 1-6 again;
otherwise at the lapse of a second time interval Δ T 2 Then, the steps 1 to 6 are carried out again, namely, the current time is T 0 When the real-time T is equal to T 0 +ΔT 2 Then, steps 1 to 6 are repeated.
In a specific application, the key battery is a battery cell that determines the performance of the battery system, as shown in fig. 2, four battery cells A, B, C, D are connected in series to form a group, and the capacity and the current dischargeable amount are shown in the figure, the capacity is sorted in size D > B > a > C, the current dischargeable amount is sorted in size D > a > C > B, and the current chargeable amount is sorted in size B > a > C > D. As can be seen from fig. 2, the dischargeable amount of the current battery pack is the dischargeable amount of the battery cell B, the chargeable amount of the battery pack is the chargeable amount of the battery cell D, and the capacity of the previous battery pack is the capacity of the battery cell C, that is, the battery cell B, C, D is referred to as a key battery cell of the battery pack.
Therefore, the process of primary screening of the key battery in the step 2 specifically comprises the following steps:
all the battery monomers in the battery system are sorted from small to large or from large to small in voltage, and the front 5-10% of the battery monomers and the rear 5-10% of the battery monomers in the sequence are screened out to form a key battery candidate group.
In step 4, obtaining SOC estimation values of all battery cells in the key battery candidate group by using a neural network, wherein the specific implementation manner is as follows:
in an off-line state, training network model parameters of a gate control circulation unit neural network GRU by taking battery data of different temperatures and aging states as samples;
obtaining an SOC estimated value SOC of the single battery i at the time k by adopting 100 hidden nodes and voltage, current and temperature sequence data of the single battery i in N time steps through the trained network GRU i,k The method comprises the following steps:
SOC i,k ＝f GRU (U i (k-N+1,…,k),I i (k-N+1,…,k),T i (k-N+1,…,k),k≥N
wherein N is not less than 1000 and U i (k-N +1, …, k) is voltage sequence data of cell I at N time steps, I i (k-N +1, …, k) is the current sequence data of cell i in N time steps, T i And (k-N +1, …, k) is temperature sequence data of the battery cell i in N time steps.
On the basis of completing the accurate estimation of the SOC, the capacity estimation value is obtained by the back-stepping of the SOC estimation value, namely:
Figure BDA0003731134700000051
in the formula, C i,k Is an estimate of the capacity of cell i at time k, Δ Q i For the cell i at k 0 Integral value of current at time k, Δ SOC i For the cell i at k 0 Value of change of SOC at time k, i.e.
Figure BDA0003731134700000052
As can be seen from the explanation of the critical cells in step 2, the battery system has three critical cells, which are the cell with the minimum dischargeable amount, the cell with the minimum chargeable amount, and the cell with the minimum capacity. The dischargeable amount of the battery cell is the product of the capacity and the SOC, and the chargeable amount is the product of the capacity and (1-SOC). Therefore, the specific implementation of obtaining the key battery cells in the key battery candidate group in step 4 is as follows:
screening out a battery monomer with the minimum product of the capacity estimation value and the SOC estimation value in the key battery candidate group, namely screening out a battery monomer with the minimum dischargeable capacity, and recording as a key battery monomer a;
screening out a battery monomer with the minimum product of the capacity estimation value and the (1-SOC estimation value) in the key battery candidate group, namely the battery monomer with the minimum chargeable quantity, and recording as a key battery monomer b;
and screening out the battery monomer with the minimum capacity estimation value in the key battery candidate group, and recording as the key battery monomer c.
In this embodiment, the consistency index is defined as the current available capacity C of the battery system pack The ratio of the current available capacity of the battery system to the ideal available capacity is the sum of the dischargeable amount of the key battery cell a and the chargeable amount of the key battery b, namely:
C pack ＝C a *SOC a +C b *(1-SOC b )
in the formula, SOC a 、SOC b SOC estimated values, C, of key battery cells a and b, respectively a 、C b Respectively estimating the capacity of the key battery monomer a and the key battery monomer b;
the ideal available capacity of the battery system is the capacity of the key battery monomer c, so that the consistency index U is obtained as follows:
Figure BDA0003731134700000061
in the formula, C c Capacity estimation of key cell c.
In step 6, the iterative optimization process specifically includes:
acquiring the current minimum chargeable quantity Q of the battery system charge And a minimum dischargeable quantity Q discharge Wherein Q is charge The chargeable quantity, Q, corresponding to the battery cell with the minimum chargeable quantity in the battery system discharge The dischargeable amount corresponding to the battery cell with the minimum dischargeable amount in the battery system is as follows:
Q charge ＝min(C·(1-SOC))
Q discharge ＝min(C·SOC)
consistency index U required to be achieved after equalization is determined 1 Then, the capacity of the battery system after equalization can be obtained as C c *U 1 I.e. the available capacity of the battery system is increased by C c *U 1 -C pack ；
Q 'is increased by chargeable amount based on available capacity of battery system' charge And dischargeable amount increased Q' discharge The two parts are formed, namely:
Q′ charge +Q′ discharge ＝C k *U 1 -C pack
as can be seen from the above formula, if it is preset to Q' charge And Q' discharge The value of any one of the above and the value of the other one of the above are determined;
thus can be based on Q' charge And Q' discharge Calculating the number of the battery cells needing to be balanced and the consumed energy, wherein the process is as follows:
less than Q for all chargeable quantities in the battery system charge +Q' charge Until the chargeable amount reaches Q charge +Q' charge Obtaining a battery monomer and discharge electric quantity which need to be subjected to discharge equalization;
for all dischargeable quantities in the battery system less than Q discharge +Q' discharge Until the dischargeable amount of the battery cell reaches Q discharge +Q' discharge Obtaining a battery monomer and charging electric quantity which need to be charged and balanced;
adding the battery monomers with balanced discharge and the battery monomers with balanced charge to obtain the number of the battery monomers with balanced charge, and adding the discharge electric quantity and the charge current to obtain the power consumption;
in the optimization iterative process, Q' charge And Q' discharge One of the cell balance strategies is used as a control parameter, iterative optimization is carried out by taking the number of the balance battery cells within a number threshold as a constraint and taking the minimum power consumption as an optimization target, and an optimal battery balance strategy is obtained, namely the battery cells needing discharge balance or charge balance and the charge amount or discharge amount of each battery cell in the balance process are obtained. The number threshold is 1% of the total number of the battery cells in the battery system, and the number threshold can be rounded up or down.
In an iterative process, the control parameter may be selected to be linearly incremented from 0 to C k *U 1 -C pack Or from C k *U 1 -C pack The starting line is decremented to 0. For example, will be known as C c *U 1 -C pack Equally dividing the control parameters into 100 parts, and adding 1 part of the control parameters from 0 to realize linear incremental updating. Or an updating algorithm can be selected to realize the updating of the control parameters.
In an embodiment, the first time interval Δ T 1 15-30 days, the second time interval is delta T 2 The time is 5 to 10 days. For example, if the first time interval Δ T 1 At 30 days, the second time interval DeltaT 2 For 10 days, there were:
if the currently calculated consistency index U is smaller than the index threshold value U 0 When the battery system needs to be subjected to the equalization control, the equalization control of the battery system is finished in real time, and whether the battery system needs to be subjected to the equalization control is judged again after 30 days, namely the shortest interval between the two equalization controls is 30 days;
if the currently calculated consistency index U is greater than or equal to the index threshold U 0 And when the battery system needs to be subjected to the balance control, the battery system does not need to be subjected to the balance control at present, and whether the battery system needs the balance control is judged again after 10 days.
Example 2
Referring to fig. 3, the battery system is formed by serially connecting a large number of lithium batteries and is an object to be monitored and controlled by the device. The battery system management device comprises a cloud BMS and a terminal BMS.
The terminal BMS is directly connected to the battery system, resulting in poor calculation and storage capabilities due to the limited mass and volume requirements of the terminal BMS. Therefore, the terminal BMS is selected to mainly perform the collection and control of the battery data and the SOC recursion and battery screening with low computational complexity. Therefore, the terminal BMS in this embodiment is composed of a data acquisition module, a key battery pre-screening module, a charge-discharge control module, and an SOC recursion calculation module, specifically:
the data acquisition module is used for acquiring battery data of all battery monomers in the battery system, wherein the battery data comprises voltage, current and temperature data of each battery monomer;
the key battery screening module is used for performing key battery primary screening according to the acquired battery data to obtain a key battery candidate group consisting of a plurality of battery monomers, wherein the primary screening mode is the same as that in embodiment 1, and therefore the description is omitted in this embodiment;
the charging and discharging control module is used for performing charging and discharging control and balance control on the battery system according to the optimal battery balance strategy;
and the SOC recursive calculation module is used for carrying out recursive calculation on the SOC of each battery monomer by adopting an ampere-hour integration method according to the collected battery data and is used for daily monitoring of a battery system, and an initial SOC value and a capacity value of the ampere-hour integration method can be provided by a cloud BMS.
The cloud BMS is a cloud computing platform, performs wireless data transmission or wired data transmission with the terminal BMS, and consists of an SOC (system on chip) accurate estimation module, a capacity accurate estimation module, a battery system consistency evaluation module and a balance strategy optimization module. Specifically, the method comprises the following steps:
the SOC accurate estimation module is used for accurately estimating the SOC of each battery monomer by adopting a neural network according to the battery data uploaded by the terminal BMS to obtain the SOC estimation value of each battery monomer;
the accurate capacity estimation module is used for obtaining a capacity estimation value according to the SOC estimation value of each battery monomer;
the battery system consistency evaluation module is used for obtaining the current consistency index of the battery system according to the SOC estimation value and the capacity estimation value of the battery monomer;
the equalization strategy optimization module is used for optimizing and exploring the optimal equalization strategy according to the current consistency index of the battery system;
the function implementation processes of the SOC precision estimation module, the capacity precision estimation module, the battery system consistency evaluation module, and the equalization policy optimization module are the same as those in embodiment 1, and therefore, this embodiment is not described again.
The overall performance of the battery system depends on the key cells in the battery system, which need to be selected based on accurate SOC and capacity estimates. Considering the computing power of the terminal BMS, it is impossible to perform accurate screening of the key battery cells, and the cost of data transmission is greatly increased if all the battery cell data are transmitted to the cloud BMS for key battery selection. Therefore, the invention adopts a compromise mode, the battery monomer is primarily screened in the terminal BMS, only the battery monomer data obtained by primary screening is transmitted to the cloud BMS, and the terminal BMS and the cloud BMS are reasonably and effectively distributed with functions, so that the computing cost of the terminal BMS is ensured, the data transmission cost between the terminal BMS and the cloud BMS is also ensured, and the state monitoring and the balance control of the battery system can be more effectively implemented.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention, and all modifications and equivalents of the present invention, which are made by the contents of the present specification and the accompanying drawings, or directly/indirectly applied to other related technical fields, are included in the scope of the present invention.

Claims (10)

1. A cloud-end converged battery system management method is characterized by comprising the following steps:
step 1, acquiring battery data of all battery monomers in a battery system by a terminal BMS, wherein the battery data comprises voltage, current and temperature data of the battery monomers;
step 2, performing key battery preliminary screening on the terminal BMS based on the acquired battery data to obtain a key battery candidate group consisting of a plurality of battery monomers;
step 3, uploading the battery data of all the battery monomers in the key battery candidate group to a cloud BMS;
step 4, accurately estimating the SOC and the capacity of all the battery monomers in the key battery candidate group in the cloud BMS to obtain estimated values of the SOC and the capacity of all the battery monomers in the key battery candidate group and obtain the key battery monomers in the key battery candidate group;
step 5, obtaining a consistency index of the battery system based on the estimated values of the SOC and the capacity of the key battery monomer;
and 6, performing strategy updating judgment:
if the current consistency index of the battery system is smaller than the index threshold value, performing iterative optimization by taking the number of the balanced battery monomers within the number threshold value and the minimum power consumption as an optimization target to obtain an optimal battery balancing strategy, transmitting the optimal battery balancing strategy back to the terminal BMS for balancing control, and performing the steps 1-6 again after a first time interval;
otherwise steps 1-6 are repeated after a second time interval has elapsed.
2. The cloud-end converged battery system management method according to claim 1, wherein in step 2, the process of primary screening of the key battery specifically comprises:
all the battery monomers in the battery system are sequenced from small to large or from large to small in voltage, 5% -10% of the battery monomers at the front and 5% -10% of the battery monomers at the back in the sequence are screened out, and a key battery candidate group is formed.
3. The cloud-end integrated battery system management method according to claim 1, wherein in step 4, the neural network is used to obtain SOC estimation values of all battery cells in the key battery candidate group, specifically:
in an off-line state, training network model parameters of a gate control circulation unit neural network GRU by taking battery data of different temperatures and aging states as samples;
through the trained network GRU, the SOC estimated value SOC of the battery monomer i at the k moment is obtained by adopting 100 hidden nodes and voltage, current and temperature sequence data of the corresponding battery monomer i at N time steps i,k The method comprises the following steps:
SOC i,k ＝f GRU (U i (k-N+1，...，k)，I i (k-N+1，...，k)，T i (k-N+1，...，k)，k≥N
wherein N is not less than 1000 and U i (k-N + 1.., k) is the voltage sequence data of the battery cell I in N time steps, I i K is current sequence data of the battery cell i in N time steps, T i And (k-N + 1.., k) is temperature sequence data of the battery cell i in N time steps.
4. The cloud-end converged battery system management method according to claim 3, wherein in step 4, the capacity estimation value is obtained by reverse extrapolation from the SOC estimation value and is as follows:
Figure FDA0003731134690000021
in the formula, C i，k Is an estimate of the capacity of cell i at time k, Δ Q i For the battery cell i at k 0 Integral value of current at time k, Δ SOC i For the cell i at k 0 Value of change of SOC at time k, i.e.
Figure FDA0003731134690000023
5. The cloud-end converged battery system management method according to any one of claims 1 to 4, wherein in step 4, the key battery cells are specifically:
the key battery monomers are three battery monomers with the minimum chargeable quantity, the minimum dischargeable quantity and the minimum capacity in the key battery candidate group;
screening out a battery monomer with the minimum product of the capacity estimation value and the SOC estimation value in the key battery candidate group, namely screening out a battery monomer with the minimum dischargeable capacity, and recording as a key battery monomer a;
screening out a battery monomer with the minimum product of the capacity estimated value and the (1-SOC estimated value) in the key battery candidate group, namely screening out a battery monomer with the minimum chargeable quantity, and recording as a key battery monomer b;
screening out the battery monomer with the minimum capacity estimation value in the key battery candidate group, and recording as a key battery monomer c;
in step 5, the calculation process of the consistency index is as follows:
calculating the current available capacity C of the battery system pack Is a
C pack ＝C a *SOC a +C b *(1-SOC b )
In the formula, SOC a 、SOC b Of critical cells a, b, respectivelyEstimated value of SOC, C a 、C b Respectively estimating the capacity of the key battery monomer a and the key battery monomer b;
obtaining a consistency index U based on the current available capacity of the battery system, wherein the consistency index U comprises the following steps:
Figure FDA0003731134690000022
in the formula, C c Capacity estimation of key cell c.
6. The cloud-end converged battery system management method according to claim 5, wherein in the step 6, the iterative optimization process specifically comprises:
acquiring the current minimum chargeable quantity Q of the battery system charge And a minimum dischargeable quantity Q discharge ；
Determining the consistency index U required to be achieved after equalization 1 And the balanced battery system capacity is C c *U 1 I.e. the available capacity of the battery system is increased by C c *U 1 -C pack ；
Q 'is increased by chargeable quantity based on available capacity of battery system' charge And dischargeable amount increased Q' discharge Two parts constitute, namely:
Q′ charge +Q′ discharge ＝C k *U 1 -C pack
wherein Q 'is preset' charge And Q' discharge The value of either one, and the other is also determined;
according to Q' charge And Q' discharge Calculating the number of the battery cells needing to be equalized and the consumed energy:
less than Q for all chargeable quantities in the battery system charge +Q′ charge Until the chargeable amount reaches Q charge +Q′ charge Obtaining a battery monomer and discharge electric quantity which need to be subjected to discharge equalization;
for all dischargeable quantities in the battery system less than Q discharge +Q′ discharge Until the dischargeable amount of the battery cell reaches Q discharge +Q′ discharge Obtaining a single battery and charging electric quantity which need to be charged and equalized;
adding the discharge-balanced battery monomers and the charge-balanced battery monomers to obtain the number of balanced battery monomers, and adding the discharge electric quantity and the charge current to obtain the power consumption;
is Q' charge And Q' discharge One of the cell balancing parameters is used as a control parameter, the number of the balancing cells is restricted within a number threshold, and iterative optimization is performed by taking the minimum power consumption as an optimization target to obtain an optimal cell balancing strategy.
7. The cloud-end converged battery system management method according to claim 6, wherein the number threshold is 1% of the total number of battery cells in the battery system;
in an iterative process, the control parameter is linearly increased from 0 to C k *U 1 -C pack Or from C k *U 1 -C pack The starting line is decremented to 0.
8. The cloud-end converged battery system management method according to any one of claims 1 to 4, wherein in the step 6, the first time interval is 15 to 30 days, and the second time interval is 5 to 10 days.
9. A cloud-end converged battery system management device, which is characterized in that the battery system management is carried out by adopting the method of any one of claims 1 to 8, and the battery system management device comprises a cloud BMS and a terminal BMS;
the terminal BMS is electrically connected with the battery system, and includes:
the data acquisition module is used for acquiring battery data of all battery monomers in the battery system, wherein the battery data comprises voltage, current and temperature data;
the key battery screening module is used for carrying out primary screening on a key battery according to the acquired battery data to obtain a key battery candidate group consisting of a plurality of battery monomers;
the charging and discharging control module is used for performing charging and discharging control and balance control on the battery system according to the optimal battery balance strategy;
the cloud BMS with terminal BMS communication links to each other, includes:
the SOC accurate estimation module is used for accurately estimating the SOC of each battery cell according to the battery data uploaded by the terminal BMS to obtain an SOC estimation value of each battery cell;
the accurate capacity estimation module is used for obtaining a capacity estimation value according to the SOC estimation value of each battery monomer;
the battery system consistency evaluation module is used for obtaining the current consistency index of the battery system according to the SOC estimation value and the capacity estimation value of the battery monomer;
and the balancing strategy optimization module is used for optimizing and exploring the optimal balancing strategy according to the current consistency index of the battery system.
10. The cloud-end converged battery system management device according to claim 9, wherein the terminal BMS further comprises:
and the SOC recursive calculation module is used for carrying out recursive calculation on the SOC of each battery monomer by adopting an ampere-hour integration method according to the acquired battery data and is used for daily monitoring of the battery system.
CN202210784034.9A 2022-07-05 2022-07-05 Cloud-end converged battery system management method and device Pending CN115101836A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116893357A (en) * 2023-07-07 2023-10-17 中国人民解放军国防科技大学 Key battery screening method, system and storage medium

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116893357A (en) * 2023-07-07 2023-10-17 中国人民解放军国防科技大学 Key battery screening method, system and storage medium
CN116893357B (en) * 2023-07-07 2024-03-19 中国人民解放军国防科技大学 Key battery screening method, system and storage medium

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