CN115980592A - Battery energy storage system reliability assessment method and system considering battery thermal fault - Google Patents
Battery energy storage system reliability assessment method and system considering battery thermal fault Download PDFInfo
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Abstract
The invention discloses a method and a system for evaluating the reliability of a battery energy storage system by considering battery thermal faults, and belongs to the field of battery energy storage systems. Firstly, considering uncertainty and fuzzy characteristics in battery state distribution, establishing a fuzzy multi-state model of a battery monomer under the condition of no fault based on fuzzy normal distribution, further considering thermal runaway fault, establishing a fuzzy multi-state model of the battery monomer under the condition of considering the thermal runaway fault based on a propagation model of the thermal runaway fault, obtaining the fuzzy multi-state model of the energy storage battery system under the condition of considering the thermal runaway fault by the fuzzy multi-state model of the battery monomer, and further performing reliability evaluation on the battery energy storage system by utilizing the fuzzy multi-state model of the energy storage battery system under the condition of considering the thermal runaway fault, so that the reliability is calculated more comprehensively and accurately.
Description
Technical Field
The invention relates to the field of battery energy storage systems, in particular to a method and a system for evaluating the reliability of a battery energy storage system by considering battery thermal faults.
Background
At present, new energy such as wind and light is in large-scale grid connection, and large-scale application is obtained for stabilizing wind and light fluctuation and storing energy. The safety and reliability degree of the energy storage system directly influences the operation level of the wind and light storage station and finally influences the reliable operation of the power system. The battery energy storage system is most widely applied to large-scale wind and light storage stations due to high maturity, so that the reliability of the battery energy storage system needs to be comprehensively and accurately evaluated.
In the continuous charge-discharge circulation process of the energy storage battery, the performance state of the energy storage battery is gradually attenuated, and the maximum available capacity of the battery is directly reduced. However, the capacity attenuation process of the battery is not all the time, and in the process of continuously reducing the performance of the battery, a plurality of health states exist, and the battery can have different reliability performances in different capacity states, so that how to comprehensively and accurately describe the multi-state of the battery, and the reliability evaluation of the energy storage battery system has important significance by considering the uncertainty and the fuzzy characteristic in the state division process. Meanwhile, the battery energy storage system has a fault condition except normal capacity attenuation, and the thermal runaway fault is serious as the damage of the battery, so that the fault occurs frequently, and the reliability of the battery is greatly influenced. Therefore, how to reflect the propagation of the thermal runaway fault of the battery and the influence of the fault result on the reliability is a problem that the reliability evaluation of the battery energy storage system needs to pay important attention under the fault state. In the current stage, the energy storage battery system is modularly combined by different energy storage battery monomers, the series-connection relation of the battery monomers in the energy storage system needs to be considered, and an integral state model is formed by a multi-state model of the battery monomers so as to realize the evaluation of the reliability of the integral energy storage system.
Disclosure of Invention
The invention aims to provide a method and a system for evaluating the reliability of a battery energy storage system by considering the thermal fault of a battery, which can more comprehensively and accurately evaluate the reliability of the battery energy storage system.
In order to achieve the purpose, the invention provides the following scheme:
a battery energy storage system reliability evaluation method considering battery thermal faults comprises the following steps:
considering uncertainty and fuzzy characteristics in battery state distribution, and establishing a fuzzy multi-state model of the battery monomer under the fault-free condition based on fuzzy normal distribution; the fuzzy multi-state model is used for calculating fuzzy probability values of all capacity states of the single battery;
determining a fuzzy multi-state model of the battery monomer under the thermal runaway fault consideration according to the fuzzy multi-state model of the battery monomer under the fault-free condition and based on a propagation model of the thermal runaway fault;
acquiring a fuzzy multi-state model of the energy storage battery system under the thermal runaway fault by adopting a general generating function method and through a fuzzy multi-state model of a battery monomer under the thermal runaway fault;
according to the fuzzy multi-state model of the energy storage battery system under the thermal runaway fault, sampling simulation is carried out on the battery by adopting a Monte Carlo simulation method, and the reliability of the energy storage battery system is obtained.
Optionally, the establishing a fuzzy multi-state model of the battery cell under the fault-free condition based on the fuzzy normal distribution in consideration of the uncertainty and the fuzzy characteristic in the battery state distribution specifically includes:
the capacity state of the battery is divided, and different probability values corresponding to different capacity states are specified;
making the performance level of the battery obey normal distribution with the mean value mu and the standard deviation sigma, considering the uncertainty in the battery state distribution and adopting fuzzy number
To represent the mean and variance of a normal distribution;
considering fuzzy characteristics in battery state distribution, and adopting fuzzy numbers to represent upper and lower limits of the divided capacity states;
based on the fuzzy normal distribution, a fuzzy multi-state model of the battery monomer under the condition of no fault is established as
In combination with>
Indicates the battery state L i The fuzzy probability of (2); a represents the degree of membership of the fuzzy number, and alpha is in the range of 0,1]；Z 1 (alpha) represents the upper limit of the integration,
Z 2 (alpha) represents a lower limit of integration, in conjunction with a number of pixels>
[SoH i_lower ,SoH i_upper ]Indicates the battery state L i Corresponding volume status interval, ->
Is the lower bound SoH i_lower Based on the number of fuzzy bits in the image signal>
To an upper bound SoH i_upper The fuzzy number of (1); z is an integration variable, is greater than or equal to>
Optionally, the determining, according to the fuzzy multi-state model of the single battery under the no-fault condition, the fuzzy multi-state model of the single battery under the thermal runaway fault considering the thermal runaway fault based on the propagation model of the thermal runaway fault specifically includes:
considering the thermal runaway fault state of the battery, and determining the temperature set of each battery monomer at each moment after the battery triggers the thermal runaway;
according to the temperature set of each battery monomer at each moment, using a formula
And
calculating the mean value mu' of the battery at each moment under the thermal runaway fault; in the formula, Q ini Indicates the initial capacity, Q, of the battery loss Represents the decay of the battery capacity, T represents the absolute temperature, T is equal to T t ，T t The temperature set of the battery at each moment is shown as B, which represents a parameter factor before the index, and R is a gas constant; ah is the total amount of current flowing at time t, ah =Q ini ×DOD×C rate X t, DOD represents the depth of charge and discharge, C rate Represents the charge-discharge rate;
determining the fuzzy mean value and the fuzzy variance set of the battery at each moment under the thermal runaway fault according to the mean value of the battery at each moment under the thermal runaway fault
In the formula (I), the compound is shown in the specification,
represents a fuzzy mean value of the battery at time t under the thermal runaway fault>
Representing the fuzzy variance of the battery at the t moment under the thermal runaway fault;
determining the fuzzy multi-state model of the battery monomer considering the thermal runaway fault as
In the formula (II)>
Optionally, the determining, in consideration of a thermal runaway fault state of the battery, a temperature set of each battery cell at each time after the battery triggers the thermal runaway specifically includes:
considering the thermal runaway fault state of the battery, the heat absorbed by the battery after the thermal runaway is
In the formula, Q ji (t) represents the energy absorbed by cell i from cell j by time t; t is t 0 Indicating the moment when thermal runaway of the battery occurs; />
Representing the exothermic power of cell j at time tau,
P REL (τ) represents the heat release power at time τ, SOC, corresponding to the typical thermal runaway heat release rate curve for a lithium ion battery j (t 0 ) Battery state of charge, l, indicating the moment of triggering thermal runaway j (t 0 ) Indicates the number of normal branch currents flowing through the defective cell at the moment of triggering the thermal runaway, and/or>
Is a constant; eta ji Indicates the efficiency of the heat release from cell j to cell i @>
γ thc 、γ thr Respectively represents the reference efficiency of heat conduction and heat radiation under a given environment, m is a distance coefficient, and->
k ji Denotes the distance, k, of battery j from battery i 0,j Represents the effective propagation range of thermal runaway;
the temperature of the battery changes under thermal runaway failure after the battery absorbs heat as
In the formula, T i (t) represents the temperature of battery i at time t; t is a unit of i (t 0 ) Is shown at t 0 The temperature of the battery i at the moment; />
Represents a set of cells that triggered thermal runaway and transferred heat to battery i; c. C i Is the specific heat capacity of cell i; m is i Is the mass of cell i;
when the battery is out of control by touch, the temperature of each battery cell at each moment is integrated into T t ＝[T 1 (t),T 2 (t),...,T n (t)](ii) a In the formula, T n (t) represents the temperature of the battery cell n at time t.
Optionally, the obtaining, by using a general generation function method and using the fuzzy multi-state model of the battery cell under consideration of the thermal runaway fault, the fuzzy multi-state model of the energy storage battery system under consideration of the thermal runaway fault specifically includes:
according to a fuzzy multi-state model of the single battery under the thermal runaway fault, a general generating function of the single battery is established as
In the formula u j (z) a generic generation function for battery j, based on a predetermined criterion>
Is in the battery state>
The representation after z-transformation;
according to the general generating function of the battery monomers, obtaining the general generating function of a battery rack formed by connecting the battery monomers in series and in parallel into
In the formula u string (z) represents a general generation function for the battery rack, Φ () represents an operator of the general generation function, and +>
Indicates the battery state L s,i In based on the fuzzy probability in the combined area>
Is in a battery state L s,i The representation after z-transformation;
according to the general generating function of the battery rack, generating a general generating function of the energy storage battery system into
In the formula，u bat (z) represents a generic generation function for the energy storage battery system, based on>
Indicates the battery state L b,i Is based on the fuzzy probability of->
Is in a battery state L b,i The representation after z-transform;
and determining a universal generating function of the energy storage battery system as a fuzzy multi-state model of the energy storage battery system under the condition of considering the thermal runaway fault.
Alternatively, when the battery rack is formed by serially connecting battery cells, the reliability of the battery rack is determined by the battery cell with the worst condition;
when the battery rack is formed by parallel connection of battery monomers, the rule of weighted combination operation is adopted, the highest weight is distributed to the battery monomer with the best battery performance state, the low weight is distributed to the battery monomer with the poor performance state, and finally the performance state of the parallel structure is obtained by weighted combination of all parts.
Optionally, the sampling simulation is performed on the battery by using a monte carlo simulation method according to the fuzzy multi-state model of the energy storage battery system under consideration of the thermal runaway fault, so as to obtain the reliability of the energy storage battery system, and the method specifically includes:
setting various simulation operation scenes;
sampling and detecting the batteries in the system, acquiring relevant data of the sampled batteries by using a measuring device of the batteries,
substituting the related data of the sampled battery into a formula
Determining the SoH mean value of the sampled battery; wherein SoH represents the SoH mean value of the sampled cell, μ 0 Representing the initial battery capacity state, Δ Q loss Representing a battery capacity fade variation;
dividing the states of the sampled batteries, constructing a fuzzy multi-state model of the sampled batteries based on the SoH mean value of the sampled batteries, and further forming a fuzzy multi-state model for simulating the battery energy storage system;
simulating the sampled battery in each simulated operation scene by adopting a Monte Carlo simulation method, and calculating the fuzzy probability of the energy storage battery system in each simulated operation scene by utilizing a fuzzy multi-state model for simulating the energy storage battery system;
according to the fuzzy probability of the energy storage battery system under each simulated operation scene, a formula is utilized
And
calculating the reliability and the health state expectation under each simulated operation scene; in the formula, R b Represents the degree of reliability, g b Represents a performance demand level of the energy storage battery system, gamma represents a minimum performance demand level of the energy storage battery system, and->
Representing a fuzzy probability of the energy storage battery system; e b Indicating a health state expectation;
and according to the reliability under each simulated operation scene, combining the occurrence probability of each simulated operation scene to obtain the reliability and the health state expectation of the battery energy storage system.
A battery energy storage system reliability evaluation system that accounts for battery thermal faults, comprising:
the fault-free model establishing module is used for considering uncertainty and fuzzy characteristics in battery state distribution and establishing a fuzzy multi-state model of the battery monomer under the fault-free condition based on fuzzy normal distribution; the fuzzy multi-state model is used for calculating fuzzy probability values of all capacity states of the single battery;
the battery pack failure fault analysis module is used for analyzing the battery pack failure and the battery pack failure to obtain a battery pack state model;
the system out-of-control fault model establishing module is used for obtaining a fuzzy multi-state model of the energy storage battery system under the thermal out-of-control fault by adopting a general generating function method and a fuzzy multi-state model of the battery monomer under the thermal out-of-control fault;
and the sampling simulation module is used for sampling and simulating the battery by adopting a Monte Carlo simulation method according to the fuzzy multi-state model of the energy storage battery system under the thermal runaway fault, so as to obtain the reliability of the energy storage battery system.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention discloses a method and a system for evaluating the reliability of a battery energy storage system by considering battery thermal failure.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a flowchart of a method for evaluating reliability of a battery energy storage system in consideration of thermal failure of a battery according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a method for evaluating reliability of a battery energy storage system in consideration of thermal failure of a battery according to an embodiment of the present invention;
fig. 3 is a flowchart for evaluating reliability of an energy storage electromagnetic system by using a monte carlo simulation method according to an embodiment of the present invention.
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.
The invention aims to provide a method and a system for evaluating the reliability of a battery energy storage system by considering the thermal fault of a battery, which can more comprehensively and accurately evaluate the reliability of the battery energy storage system.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
An embodiment of the present invention provides a method for evaluating reliability of a battery energy storage system in consideration of thermal failure of a battery, as shown in fig. 1 and 2, including:
step S1, considering uncertainty and fuzzy characteristics in battery state distribution, and establishing a fuzzy multi-state model of a battery monomer under the fault-free condition based on fuzzy normal distribution; the fuzzy multi-state model is used for calculating fuzzy probability values of each capacity state of the battery monomer.
And calculating the SoH attenuation change of the battery based on a capacity attenuation model of the battery, wherein the SoH value of the battery calculated at each moment is used as the average value of the normal distribution of the battery at each moment.
Taking a lithium iron phosphate battery as an example, the factors influencing the capacity attenuation can be summarized as temperature, charge and discharge depth and charge and discharge rate, namely: q loss ＝f(t,T,DOD,C rate ). The change in cell SoH (i.e., the mean of the cell distribution) over time can be expressed in terms of the relationship between the capacity fade and the definition of cell SoHComprises the following steps:
wherein, mu 0 The initial battery SoH value is the distribution average value of the battery at the initial moment; q ini Is the initial capacity C of the battery rate Is the charge/discharge rate; r is a gas constant equal to 8.31J/mol.K; t is the absolute temperature; ah is the total amount of current flowing at time t, proportional to the time per charge-discharge rate, and can be expressed as: ah = Q ini ×DOD×C rate X t; z is a power coefficient equal to 0.55 for a lithium ion battery. B is a pre-exponential parameter whose value decreases as the charge and discharge rate increases.
The health state of the battery is divided, different states correspond to different probability values, the mean value of distribution and the boundary of state division are represented by fuzzy numbers, and the fuzzy probability value of each state of the battery monomer is calculated based on fuzzy normal distribution.
Dividing the SoH level of the cell into n different states, using L 1 、L 2 、L 3 、...L i 、...L n Indicating different battery states. Corresponding to different intervals of battery SoH, such as 90% -100%, 80% -90%, 8230, etc.
The SoH levels of a large number of cells are considered to fit a normal distribution. The battery corresponds to different probabilities P in each state 1 、P 2 、P 3 、...P i 、...P n . At any time t, the battery can only be in one performance state in the state set and corresponds to a corresponding value in the probability set. Assuming that the cells are in perfect operation right after being put into use, the performance levels of a large number of batteries are subject to normal distribution with the mean value mu and the standard deviation sigma at the rest of the time.
Taking into account uncertainty in battery state distribution, fuzzy numbers are used
To represent the mean of the distribution andthe variance. The state of the energy storage battery falls within a certain interval [ SoH i_lower ,SoH i_upper ](i.e., falling in state L) i Up) may be expressed as £ based>
At alpha epsilon [0,1 ∈ ]]，/>
Then, the fuzzy probability can be obtained by the following formula:
wherein the content of the first and second substances,
upper limit of integration, in>
Is an integration lower limit, Z is an integration variable, and has a relationship with the variable x of->
a is the membership of the fuzzy number.
Meanwhile, the fuzzy characteristics of the battery health status can be further considered, and the battery health status can be represented by fuzzy numbers. For the fuzzy number of the upper and lower limits, a triangular fuzzy number can be used to represent:
where TFN represents the number of triangular ambiguities, l 1 ,l 2 ,l 3 To define triangular blur number
Three values of the membership function, u 1 ,u 2 ,u 3 Define triangle blur number>
Three values of the membership function.
Therefore, the probabilities of the battery cell j in the various performance states are reconstructed into fuzzy sets
Wherein the content of the first and second substances,
is in the battery state>
The alpha intercept of (1). />
The formula should be (2), (3) and (4) and the state of the corresponding battery, i.e. L 1 、L 2 、L 3 、...L i 、...L n And jointly forming a fuzzy multi-state model of the battery cells under the condition of no fault.
And S2, determining the fuzzy multi-state model of the single battery under the thermal runaway fault according to the fuzzy multi-state model of the single battery under the fault-free condition and based on the propagation model of the thermal runaway fault.
In addition to life decay in the normal state, batteries also experience failure conditions. And calculating the working temperature of the energy storage battery monomer at each moment under the thermal runaway fault in consideration of the thermal runaway fault state of the battery based on the propagation model of the thermal runaway fault. And (3) calculating the state mean value of the energy storage battery under the thermal runaway fault based on the relation between the battery mean value and the temperature in the step (1), and calculating the state probability of the energy storage battery under the thermal runaway fault based on the fuzzy normal distribution.
After thermal runaway occurred, the amount of heat absorbed by the surrounding cells was:
in the formula, Q ji (t) is the energy absorbed by battery i from battery j by time t; t is t 0 The moment when thermal runaway of the battery occurs;
represents the heat release power, eta, of cell j at time τ ji Indicating the efficiency of cell j in releasing heat to cell i.
And η ji The following formula is adopted for the calculation:
wherein, P REL (t) is the heat release power at t moment, SOC, corresponding to the typical thermal runaway heat release rate curve of the lithium ion battery j (t 0 ) The battery state of charge at the moment of triggering thermal runaway; l. the j (t 0 ) The number of the normal branch current flowing through the failure battery monomer at the moment of triggering thermal runaway is set;
is a constant; gamma ray thc 、γ thr Are constants that are reference efficiencies for thermal conduction and radiation, respectively, in a given environment. m is a distance coefficient,the values are as follows:
wherein k is ji Represents the distance, k, between cell j and cell i 0,j Indicating the effective propagation range of thermal runaway.
After the battery absorbs heat, the change of the battery temperature is as follows:
in the formula, T i (t) is the temperature of cell i at time t; t is i (t 0 ) Is at t 0 The temperature of battery i at time;
represents a set of cells that triggered thermal runaway and transferred heat to battery i; c. C i Is the specific heat capacity of the battery monomer i; m is a unit of i Is the mass of cell i.
When the temperature of the battery reaches a critical value, thermal runaway is triggered, and the criterion for triggering the thermal runaway is as follows:
wherein, T C To trigger the critical temperature for thermal runaway when the temperature is greater than T C Thermal runaway may be triggered; tr i And (t) is a coefficient for reflecting whether the battery monomer i triggers thermal runaway at the time t, when the value is 1, the thermal runaway is triggered, and when the value is 0, the thermal runaway is not triggered.
When the battery is in an abnormal state of overheating after the battery is out of control due to heat shock, energy is transmitted to the surrounding battery to enable the surrounding battery to be in the abnormal state of overheating, the battery is accelerated to age at the moment, therefore, the performance distribution of the battery at the moment is different from the performance distribution of the battery at normal temperature, and the performance distribution and the corresponding probability of each battery need to be recalculated according to the temperature condition of each battery at the moment.
If the temperature set of each battery cell at time t is:
T t ＝[T 1 (t),T 2 (t),...,T n (t)] 13)
then the fuzzy mean and the fuzzy variance of the battery cell under the thermal fault at each moment are reformed and considered, and the set is expressed as:
and then, based on normal fuzzy distribution, solving a fuzzy probability set of the battery under each state value considering the thermal runaway fault:
wherein the content of the first and second substances,
equation 15) is a fuzzy multi-state model of the battery cell under consideration of the thermal runaway fault.
And S3, acquiring a fuzzy multi-state model of the energy storage battery system under the thermal runaway fault by adopting a general generating function method and through a fuzzy multi-state model of the battery monomer under the thermal runaway fault.
And forming a general generation function of the energy storage battery monomer by adopting a general generation function method, and constructing a general function operator to form a general generation function of the battery box body.
For an energy storage cell, its UGF function can be expressed as:
u j (z) means: universal generating function for representing state combination of battery cellsThe variable Z is a function of the variable Z (a functional expression formed by combining each state of the battery cell and the probability, and only one combination relation between the battery probability and the state is expressed).
The meaning is as follows: where Z is the variable Z of the Z transform and L represents each state of the battery. />
Then it is battery status->
Z-transformed representation of (a).
The energy storage battery system is formed by combining a large number of energy storage battery monomers in series and parallel, and for the battery monomers connected in series, the overall reliability of the battery monomers is determined by the battery monomer with the worst condition; for the parallel battery monomer, in order to reflect the overall performance level of the battery more comprehensively and accurately, a rule of adopting weighted combination operation is provided, the highest weight is distributed to the battery monomer with the best battery performance state, the lower weight is distributed to the battery monomer with the poorer performance state, and finally the performance state of the parallel structure is obtained by weighted combination of all parts. For the specific weight, a hierarchical analysis method can be adopted to obtain the specific weight.
Wherein the content of the first and second substances,
is a random variable function in a series structure mode:
the energy storage battery module is characterized in that battery racks are formed by connecting battery monomers in series, and then battery strings of all parts form an integral battery module through a certain series-parallel connection relation.
Wherein, g s,i Is the SoH performance level of the battery rack after series-parallel connection,
the SoH horizontal probability in the i state corresponding to the battery rack is obtained, n is the state number of the system, g b,i Is the SoH performance level, or->
The SoH level probability in the i state corresponding to the battery system is obtained, and n is the state number of the system. u. of string (z) is the general generating function for battery rack, Φ () means: an operator that is a defined generic generation function. u. u bat (z) means: a functional expression is generated for a generic use of the battery system.
The formulas (16), (20) and (21) are general generating functions, and are general generating function expressions of the battery cells, general generating function expressions of the battery rack and general generating function expressions of the battery system. The whole energy storage battery system is divided into three stages, namely a battery monomer, a battery rack and a battery system; the battery cells are connected in series and parallel to form a battery rack, the battery rack is connected in series and parallel to form a battery system, therefore, general generating functions of the battery rack and the battery rack are sequentially constructed, firstly, the general generating functions of the battery cells are calculated through defined operators to form the general generating functions of the battery rack, and then, the general generating functions of the battery system are formed through operator calculation.
According to the finally obtained general generating function of the energy storage battery system, fuzzy probability values of the battery energy storage system in various states can be obtained, and a fuzzy multi-state model of the battery energy storage system under the thermal runaway fault is formed.
And S4, sampling and simulating the battery by adopting a Monte Carlo simulation method according to the fuzzy multi-state model of the energy storage battery system under the thermal runaway fault, so as to obtain the reliability of the energy storage battery system.
After the state probability of the battery energy storage system is calculated by applying the proposed model assuming that the minimum performance requirement level of the energy storage battery system is γ, the reliability and the health state expectation of the energy storage battery can be calculated by using the following equations:
because the state probability of the battery adopts the fuzzy probability, a fuzzy number related to the reliability can be obtained finally, namely the reliability of the stored energy is not a unique fixed numerical value, a possible value interval of the reliability and the membership degrees of different values of the reliability can be given. Based on the membership functions of the reliability, confidence intervals of reliability values under different confidence levels can be further obtained. Likewise, it is also a corresponding fuzzy number representation and confidence intervals of different confidence levels can be determined on the basis of its membership function.
Sampling simulation is carried out on the battery by adopting a Monte Carlo simulation method, the reliability and the expected index of the battery are calculated, and an evaluation flow of the reliability is formed.
When the reliability of the energy storage battery system is evaluated, a certain number of battery monomers in the system are sampled, relevant battery performance attenuation data are obtained based on a measurement device of a battery, and relevant parameters of a battery attenuation model are set. And calculating the fuzzy mean value and the fuzzy variance of the performance distribution of the battery at each moment by combining the service condition of the battery and the working environment temperature. And then, based on the thermal runaway fault probability of the battery, randomly simulating the thermal fault of the battery monomer, calculating the working environment temperature of each battery monomer based on a thermal fault propagation model, reforming the fuzzy mean value and the fuzzy variance of each battery monomer, and solving the fuzzy probability of each performance state of the battery monomer based on the fuzzy normal distribution. On the basis, based on a general generating function, a fuzzy multi-state model of the battery energy storage system and under the battery fault is constructed, fuzzy state probabilities of the system at different performance levels are obtained, then minimum performance requirements are compared, and the reliability and state expectation of the energy storage system under the condition of considering the battery thermal fault are calculated.
Because a large number of battery monomers are contained in the battery energy storage system and the influence factors of the heat propagation model design are more, the operating scenes of the battery energy storage system are sampled by adopting a Monte Carlo method, and the reliability of the battery energy storage system is obtained by combining the occurrence probability of each scene. As shown in fig. 3, the specific evaluation flow is as follows:
(1) Acquiring capacity attenuation data of the battery by using the battery detected by actual sampling so as to set relevant parameters of the model and relevant parameters of the battery thermal fault model
(2) Determining the Performance distribution g of the cells i 、
And the probability of thermal failure occurring;
(3) Setting a reliability evaluation environment, and determining parameters such as environment temperature, battery charge and discharge multiplying power and the like and battery monomer connection topology in a battery energy storage system;
(4) Calculating the performance distribution mean and variance of the batteries at each moment, and assuming that all the batteries are in a perfect working state at the initial moment;
(5) Randomly extracting the battery monomer with the fault based on the set fault probability, calculating the battery environment temperature at the moment based on a thermal fault propagation model, newly forming a fuzzy mean value and a variance sequence of each battery monomer, and calculating the fuzzy probability of each state;
(6) And forming a fuzzy multi-state model of the battery energy storage system based on a general generating function method. And performing operation scene simulation on the battery energy storage system for specified times by using a Monte Carlo simulation method. In each simulation, the system performance distribution g at each moment is recorded bi 、
And propagation of thermal runaway. And calculating the reliability R of the battery energy storage system b Health State expectation E b And membership functions under corresponding membership degrees;
(7) And obtaining a reliability calculation result of the battery energy storage system by combining the reliability data and the occurrence probability under each operation scene.
The method comprises the steps of firstly dividing the health state of the energy storage battery, solving the multi-state fuzzy probability of the battery based on fuzzy normal distribution, and forming a fuzzy multi-state model of a battery monomer. The thermal runaway fault of the energy storage battery system is considered, the fuzzy mean value of the energy storage battery under the thermal runaway fault is calculated based on the thermal runaway fault propagation model of the battery energy storage system, and the multi-state fuzzy probability of the battery under the thermal runaway fault is formed based on the fuzzy normal distribution. And then, forming a fuzzy multi-state model of the energy storage system by using the fuzzy multi-state model of the single energy storage battery by adopting a general generating function method. And finally, sampling and simulating the energy storage battery system based on a Monte Carlo simulation method, and calculating the reliability and the health state expectation of the energy storage battery system.
The beneficial effects of the invention are as follows:
1. the fuzzy multi-state distribution of the energy storage battery monomer is constructed by considering the fuzzy characteristics of the energy storage battery under each state membership and adopting fuzzy normal distribution to calculate the fuzzy probability of the energy storage battery under different states. Compared with the traditional normal distribution description, more information in the process is reserved, and the probability result has more flexibility. And according to the actual structural hierarchy of the large-scale energy storage battery system, universal generating functions of all hierarchies are respectively constructed from the energy storage battery monomer, the energy storage battery rack and the energy storage battery system, and finally, the fuzzy multi-state model of the energy storage battery monomer is obtained to consider more uncertainty of the calculation reliability, so that the calculation of the final reliability is more accurate and more comprehensive.
2. The invention considers the typical thermal runaway fault of the energy storage battery and the specific fault propagation mechanism and influence, calculates the lower temperature sequence of the battery in the fault state based on the relation between the fault propagation model and the battery mean value and the temperature, and forms the battery and energy storage system fuzzy multi-state model considering the thermal fault state by combining the fuzzy state of the battery. The accuracy of the reliability evaluation model of the battery energy storage system is improved, the reliability evaluation is more comprehensive and is more practical.
3. When the general generating function is used for describing the overall level of the parallel battery structure, the general generating function operator of the weighted combination based on the weight determined by the hierarchical analysis method is constructed, so that the overall level of the battery is more comprehensively and accurately described.
The invention also provides a battery energy storage system reliability evaluation system considering battery thermal faults, which comprises the following steps:
the fault-free model establishing module is used for considering uncertainty and fuzzy characteristics in battery state distribution and establishing a fuzzy multi-state model of the battery monomer under the fault-free condition based on fuzzy normal distribution; the fuzzy multi-state model is used for calculating fuzzy probability values of all capacity states of the battery monomer;
the single body runaway fault model establishing module is used for determining a fuzzy multi-state model of the single battery under the thermal runaway fault according to the fuzzy multi-state model of the single battery under the fault-free condition and based on a propagation model of the thermal runaway fault;
the system out-of-control fault model establishing module is used for obtaining a fuzzy multi-state model of the energy storage battery system under the thermal out-of-control fault by adopting a general generating function method and a fuzzy multi-state model of the battery monomer under the thermal out-of-control fault;
and the sampling simulation module is used for sampling and simulating the battery by adopting a Monte Carlo simulation method according to the fuzzy multi-state model of the energy storage battery system under the thermal runaway fault, so as to obtain the reliability of the energy storage battery system.
The reliability evaluation system of the battery energy storage system considering the thermal fault of the battery provided by the embodiment of the invention is similar to the reliability evaluation method of the battery energy storage system considering the thermal fault of the battery described in the embodiment, and therefore, the working principle and the beneficial effect are not described in detail here, and specific contents can be referred to the introduction of the embodiment of the method.
In the present specification, the embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (8)
1. A method for evaluating reliability of a battery energy storage system by considering thermal faults of a battery is characterized by comprising the following steps:
considering uncertainty and fuzzy characteristics in battery state distribution, and establishing a fuzzy multi-state model of the battery monomer under the fault-free condition based on fuzzy normal distribution; the fuzzy multi-state model is used for calculating fuzzy probability values of all capacity states of the battery monomer;
determining a fuzzy multi-state model of the battery monomer under the thermal runaway fault condition according to the fuzzy multi-state model of the battery monomer under the fault-free condition and based on a propagation model of the thermal runaway fault;
acquiring a fuzzy multi-state model of the energy storage battery system under the thermal runaway fault by adopting a general generating function method and through a fuzzy multi-state model of a battery monomer under the thermal runaway fault;
according to the fuzzy multi-state model of the energy storage battery system under the thermal runaway fault, sampling simulation is carried out on the battery by adopting a Monte Carlo simulation method, and the reliability of the energy storage battery system is obtained.
2. The method according to claim 1, wherein the method for evaluating the reliability of the battery energy storage system in consideration of the thermal fault of the battery is characterized in that uncertainty and fuzzy characteristics in the state distribution of the battery are considered, and a fuzzy multi-state model of the battery under the fault-free condition is established based on fuzzy normal distribution, and specifically comprises the following steps:
dividing the capacity states of the batteries, and specifying different probability values corresponding to different capacity states;
making the performance level of the battery obey normal distribution with the mean value mu and the standard deviation sigma, considering the uncertainty in the battery state distribution and adopting fuzzy number
To represent the mean and variance of a normal distribution;
considering fuzzy characteristics in battery state distribution, and adopting fuzzy numbers to represent upper and lower limits of the divided capacity states;
based on the fuzzy normal distribution, a fuzzy multi-state model of the battery monomer under the condition of no fault is established as
In combination with>
Indicates the battery state L i The fuzzy probability of (2); a represents the membership of a fuzzy number, and alpha belongs to [0,1 ]]；Z 1 (alpha) represents the upper limit of the integration,
Z 2 (alpha) represents a lower limit of integration, in conjunction with a number of pixels>
[SoH i_lower ,SoH i_upper ]Indicates the battery state L i Corresponding volume status interval, ->
Is the lower bound of SoH i_lower Based on the number of fuzzy bits in the image signal>
Is the upper bound SoH i_upper The fuzzy number of (1); z is an integration variable, is greater than or equal to>
3. The method for evaluating the reliability of the battery energy storage system considering the thermal failure of the battery according to claim 2, wherein the determining the fuzzy multi-state model of the battery cell considering the thermal runaway fault based on the propagation model of the thermal runaway fault according to the fuzzy multi-state model of the battery cell under the fault-free condition specifically comprises:
considering the thermal runaway fault state of the battery, and determining the temperature set of each battery monomer at each moment after the battery triggers the thermal runaway;
according to the temperature set of each battery monomer at each moment, using a formula
And
calculating the mean value mu' of the battery at each moment under the thermal runaway fault; in the formula, Q ini Indicates the initial capacity, Q, of the battery loss Represents the battery capacity fade, T represents the absolute temperature, T ∈ T t ，T t The temperature set of the battery at each moment is shown as B, which represents a parameter factor before the index, and R is a gas constant; ah is the total amount of current flowing at time t, ah = Q ini ×DOD×C rate X t, DOD denotes depth of charge and discharge, C rate Represents a charge-discharge rate;
determining the fuzzy mean value and the fuzzy variance set of the battery at each moment under the thermal runaway fault according to the mean value of the battery at each moment under the thermal runaway fault
In the formula (I), the compound is shown in the specification,
represents a fuzzy mean value of the battery at time t under the thermal runaway fault>
Representing the fuzzy variance of the battery at the t moment under the thermal runaway fault;
determining the fuzzy multi-state model of the battery monomer considering the thermal runaway fault as
In the formula (I), the compound is shown in the specification,
4. the method according to claim 3, wherein the method for evaluating the reliability of the battery energy storage system in consideration of the thermal failure of the battery determines the temperature set of each battery cell at each time after the thermal runaway is triggered by the battery, and specifically comprises:
considering the thermal runaway fault state of the battery, the heat absorbed by the battery after the thermal runaway is
In the formula, Q ji (t) represents the energy absorbed by cell i from cell j by time t; t is t 0 Indicating the moment when thermal runaway of the battery occurs; />
Representing the exothermic power of cell j at time tau,
P REL (τ) represents the heat release power at time τ, SOC, corresponding to the typical thermal runaway heat release rate curve for a lithium ion battery j (t 0 ) State of charge of the battery indicating the moment of triggering thermal runaway,/ j (t 0 ) Indicates the number of normal branch currents flowing through the failed cell at the moment of triggering the thermal runaway, and/or>
Is a constant; eta ji Indicates the efficiency of the heat release from cell j to cell i @>
γ thc 、γ thr Respectively represents the reference efficiency of heat conduction and heat radiation under a given environment, m is a distance coefficient, and->
k ji Represents the distance, k, between battery j and battery i 0,j Represents the effective propagation range of thermal runaway;
the temperature of the battery changes in thermal runaway failure after the battery absorbs heat into
In the formula, T i (t) represents the temperature of battery i at time t; t is i (t 0 ) Is shown at t 0 The temperature of battery i at time; />
Represents a set of battery cells that triggered thermal runaway and transferred heat to battery i; c. C i Is the specific heat capacity of battery i; m is i Is the mass of cell i;
when the battery is out of control by touch, the temperature of each battery cell at each moment is integrated into T t ＝[T 1 (t),T 2 (t),...,T n (t)](ii) a In the formula, T n (t) represents the temperature of the battery cell n at time t.
5. The method for evaluating the reliability of the battery energy storage system considering the thermal fault of the battery according to claim 4, wherein the obtaining the fuzzy multi-state model of the energy storage battery system considering the thermal runaway fault from the fuzzy multi-state model of the battery cells considering the thermal runaway fault by using a general generating function method specifically comprises:
according to a fuzzy multi-state model of the single battery under the thermal runaway fault, a general generating function of the single battery is established as
In the formula u j (z) a generic generation function for battery j, based on a predetermined criterion>
Is in the battery state>
The representation after z-transformation;
according to the general generating function of the battery monomers, obtaining the general generation of the battery rack formed by connecting the battery monomers in series and in parallelIs a function of
In the formula u string (z) represents a general generation function of the battery rack, Φ () represents an operator of the general generation function, and->
Indicates the battery state L s,i Is based on the fuzzy probability of->
Is in a battery state L s,i The representation after z-transform;
according to the general generating function of the battery rack, generating a general generating function of the energy storage battery system into
In the formula u bat (z) represents a generic generation function for the energy storage battery system, based on>
Indicates the battery state L b,i Is based on the fuzzy probability of->
Is in a battery state L b,i The representation after z-transformation;
and determining a universal generating function of the energy storage battery system as a fuzzy multi-state model of the energy storage battery system under the condition of considering the thermal runaway fault.
6. The battery energy storage system reliability evaluation method considering thermal failure of a battery according to claim 5,
when the battery rack is formed by serially connecting the battery cells, the reliability of the battery rack is determined by the battery cell with the worst condition;
when the battery rack is formed by parallel connection of battery monomers, the rule of weighted combination operation is adopted, the highest weight is distributed to the battery monomer with the best battery performance state, the low weight is distributed to the battery monomer with the poor performance state, and finally the performance state of the parallel structure is obtained by weighted combination of all parts.
7. The method according to claim 6, wherein the obtaining the reliability of the energy storage battery system by sampling and simulating the battery by using a monte carlo simulation method according to the fuzzy multi-state model of the energy storage battery system under consideration of the thermal runaway fault comprises:
setting various simulation operation scenes;
sampling and detecting the batteries in the system, acquiring relevant data of the sampled batteries by using a measuring device of the batteries,
substituting the related data of the sampled battery into a formula
Determining the SoH mean value of the sampled battery; wherein SoH represents the SoH mean, μ of the sampled cell 0 Representing the initial battery capacity state, Δ Q loss Representing the capacity fade variation of the battery;
dividing the states of the sampled batteries, constructing a fuzzy multi-state model of the sampled batteries based on the SoH mean value of the sampled batteries, and further forming a fuzzy multi-state model for simulating the battery energy storage system;
simulating the sampled battery in each simulated operation scene by adopting a Monte Carlo simulation method, and calculating the fuzzy probability of the energy storage battery system in each simulated operation scene by utilizing a fuzzy multi-state model for simulating the energy storage battery system;
according to the fuzzy probability of the energy storage battery system in each simulation operation scene, a formula is utilized
And
calculating the reliability and the health state expectation in each simulation operation scene; in the formula, R b Represents the degree of reliability, g b Represents the performance requirement level of the energy storage battery system, gamma represents the minimum performance requirement level of the energy storage battery system, and/or>
Representing a fuzzy probability of the energy storage battery system; e b Indicating a health state expectation;
and according to the reliability under each simulated operation scene, combining the occurrence probability of each simulated operation scene to obtain the reliability and the health state expectation of the battery energy storage system.
8. A battery energy storage system reliability evaluation system that considers thermal failure of a battery, comprising:
the fault-free model establishing module is used for considering uncertainty and fuzzy characteristics in battery state distribution and establishing a fuzzy multi-state model of the battery monomer under the fault-free condition based on fuzzy normal distribution; the fuzzy multi-state model is used for calculating fuzzy probability values of all capacity states of the battery monomer;
the single body runaway fault model establishing module is used for determining a fuzzy multi-state model of the single battery under the thermal runaway fault according to the fuzzy multi-state model of the single battery under the fault-free condition and based on a propagation model of the thermal runaway fault;
the system out-of-control fault model establishing module is used for acquiring a fuzzy multi-state model of the energy storage battery system under the thermal out-of-control fault by adopting a general generating function method and through a fuzzy multi-state model of the battery monomer under the thermal out-of-control fault;
and the sampling simulation module is used for sampling and simulating the battery by adopting a Monte Carlo simulation method according to the fuzzy multi-state model of the energy storage battery system under the thermal runaway fault, so as to obtain the reliability of the energy storage battery system.
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