CN114781176B - Equivalent circuit parameter identification method for lumped parameters of lithium ion battery energy storage system - Google Patents
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- 229910001416 lithium ion Inorganic materials 0.000 title claims abstract description 14
- GELKBWJHTRAYNV-UHFFFAOYSA-K lithium iron phosphate Chemical compound [Li+].[Fe+2].[O-]P([O-])([O-])=O GELKBWJHTRAYNV-UHFFFAOYSA-K 0.000 claims abstract description 19
- 238000011156 evaluation Methods 0.000 claims abstract description 12
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- QHGJSLXSVXVKHZ-UHFFFAOYSA-N dilithium;dioxido(dioxo)manganese Chemical compound [Li+].[Li+].[O-][Mn]([O-])(=O)=O QHGJSLXSVXVKHZ-UHFFFAOYSA-N 0.000 description 1
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Abstract
The invention relates to an equivalent circuit parameter identification method for lumped parameters of a lithium ion battery energy storage system. The method comprises the following steps: firstly, performing HPPC test experiments on selected battery monomers, and recording relevant experimental data such as voltage, current, temperature and the like; then, calculating the SOC variation of the battery by adopting an ampere-hour integration method, and calculating an equivalent internal resistance value R according to a Thevenin equivalent circuit model 0 Simultaneously respectively establishing fuzzy evaluation factor sets of SOC/temperature T, and finally realizing f (SOC, T) =R by applying fuzzy logic 0 Is used for identifying functional relation parameters. According to the invention, the influence of temperature and SOC variation on the performance of the lithium ion battery is fully considered, the parameter identification precision of the battery model is improved by adopting a fuzzy logic algorithm, and a foundation is laid for establishing a relatively accurate electric-thermal coupling model of the lithium iron phosphate battery. The invention has wide applicability and good universality.
Description
Technical field:
the invention belongs to the field of lithium ion battery energy storage, and particularly relates to an equivalent circuit parameter identification method for lumped parameters of a lithium ion battery energy storage system.
The background technology is as follows:
currently, the installed capacity of renewable energy sources is rapidly increasing, and for the digestion of renewable energy sources and solving the intermittent and fluctuating problems, the best solution is undoubtedly to develop large-scale energy storage technologies.
The batteries used in energy storage engineering are of various kinds, including lithium manganate batteries, lithium titanate batteries, ternary lithium batteries, flow batteries and lithium iron phosphate batteries which have been developed rapidly in recent years. From the aspects of one-time investment cost, cycle life and safety, the lithium iron phosphate battery has the advantages of high stability, long cycle life and the like: the energy density of the lithium iron phosphate battery for energy storage is 120-150 Wh/kg, the energy conversion efficiency of the system is 85-88%, the small-multiplying power charge-discharge cycle life is 3500-5000 times, the investment cost of the energy storage system is 1600-2000 yuan/kWh, and the electricity-measuring cost is 0.7-1.0 yuan/kWh. In recent years, the technology is widely applied to various links of power system transmission and distribution under the influence of cost reduction and comprehensive performance improvement of lithium iron phosphate batteries.
In order to realize accurate estimation of the charge state of the lithium iron phosphate battery and safe and stable operation of the battery, students at home and abroad deeply explore the relevant models of the energy storage system to a certain extent. Until now, the development of the battery cell model to the present stage forms the following three common model building ideas: electrochemical models, black box models, and equivalent circuit models. The electrochemical model (first principle model) is studied from the aspect of mechanism and is mainly applied to charge and discharge state estimation and aging prediction, but is difficult to obtain a complete parameter set of a battery manufacturer due to complex calculation, is generally used for research and development and battery assembly manufacturing, and is difficult to realize in large-scale energy storage engineering; secondly, the black box model (experience model) is based on external characteristics, a large amount of data training is needed, the precision and the calculation burden of the model are influenced by the selection and the number of input variables, but the precision is poor under the condition of insufficient data quantity or unsuitable training method; the equivalent circuit model is based on the system identification method of the circuit theory to obtain relevant parameters, records the input and output data of the battery, and further simulates the nonlinear characteristics of the lithium ion battery, belongs to a semi-empirical simulation model, has a simple structure, fewer parameters and higher simulation precision compared with the former two models, reduces the calculation complexity of the battery, can write out an analytic mathematical equation to model the battery, and is more suitable for modeling analysis of a battery module or a larger scale.
Among a plurality of equivalent circuit models, the Thevenin equivalent circuit model has simple structure, definite physical meaning and easy parameter identification, can describe electrode potential change caused by polarization effect, predicts the influence of SOC change on load, and combines a table look-up method or a self-adaptive algorithm to realize engineering application.
Currently, common estimation methods for battery SOC include: (1) The ampere-hour integration method has higher precision under the condition of knowing the initial state of the battery, the current acquisition precision and the sampling duration time; (2) The open circuit voltage method is used only in the open circuit state of the circuit, but the battery needs to stand for a long time after being opened due to the capacitance characteristic of the battery so as to recover the true open circuit voltage potential; (3) The Kalman filtering method has strong dependence on the battery model, and the estimation accuracy has strong relevance with the battery model parameters.
In addition, the least square method is often applied to the expression fitting about battery modeling, but in the practical application process, the least square method can obtain a fitting result of a higher degree to a larger extent, but is not perfect, and a small gap is still left from the practical situation.
The invention comprises the following steps:
the invention provides a fuzzy algorithm-based lithium battery modeling lumped parameter identification method, which estimates the R of the influence of the change of SOC and temperature T 0 And the value is changed, and the fuzzy logic is utilized to realize more accurate modeling parameter identification. The specific technical scheme is as follows:
an equivalent circuit parameter identification method for lumped parameters of a lithium ion battery energy storage system comprises the following steps:
step S1: performing HPPC test on a plurality of lithium iron phosphate battery cells to obtain battery open-circuit voltage and current, and measuring temperature data in real time;
step S2: estimating the SOC variation of the lithium iron phosphate battery by utilizing an ampere-hour integration method according to relevant test data such as the battery voltage, the current and the like, and marking the SOC variation as small, medium and large in a fuzzy way;
step S3: according to the recorded temperature T changes, the temperature T changes into 3 fuzzy states, and fuzzy marks are low, medium and high;
step S4: respectively establishing a fuzzy evaluation factor set of the SOC and a fuzzy evaluation factor set of the T, and then establishing a comprehensive factor evaluation set;
step S5: establishing a two-dimensional fuzzy control rule about the SOC and the temperature T, and simultaneously setting or adopting expert experience to determine a weight coefficient;
step S6: establishing a judgment matrix about the SOC and the temperature T, performing fuzzy reasoning, and performing defuzzification treatment; the specific process comprises the following steps:
step S6.1: consider the equivalent resistance R 0 Influence factors, determining factors of fuzzy comprehensive evaluationThe set is the change of state of charge SOC and temperature T, and the set of available factors is:
v= { state of charge SOC (V SOC ) Temperature change T (V T )}；
Step S6.2: comprehensively considering the influence of the temperature T change and the state of charge (SOC) change on the equivalent internal resistance of the battery and the like, and establishing a relevant membership function of the SOC to the fuzzy mark:
V SOC = { low v 1 V of (v) 2 High v 3 ，…}；
Establishing a relevant membership function of the temperature T to the fuzzy label:
V T = { small v 4 V of (v) 5 Large v 6 ，…}；
Step S6.3: and carrying out fuzzy judgment on the consideration to obtain a consideration judgment set:
R 1 ＝(a b c…)；
R 2 ＝(d e f…)；
obtaining a judgment matrix R:
wherein a+b+c+ … is less than or equal to 1; d+e+f+ … is less than or equal to 1;
step S6.4: according to the experience of the relevant expert, determining the fuzzy control rule of the influencing factors, namely determining the equivalent resistance R of the input SOC and the temperature T change to the output 0 The fuzzy control rule of (2) is:
step S6.5: the input variable SOC/T is fuzzified to obtain the corresponding membership degree, and the equivalent internal resistance R of the SOC or T to the output is determined 0 Is used for the influence weight of (1): a= (g, h),
wherein g+h=1;
step S6.5: calculating the intensity of the fuzzy control rule, and obtaining a judgment model and performing f (SOC-T) =R 0 Weighted flatAverage (minimum-maximum) fuzzy reasoning is carried out to obtain a fuzzy state value of the relation coefficient of the output model:
wherein, the first element i of B is calculated from ((g ∈a) and (g ∈d)), wherein ∈d is a smaller value and V is a larger value; the rest and so on;
step S6.6: and performing defuzzification processing on the fuzzy reasoning result:
a weighted average method is selected as shown in the following formula:
wherein k is 1 、k 2 …k n (g, h) is the weight size, a 1 、a 2 …a n Is a fuzzy reasoning result;
step S7: combining the Thevenin equivalent circuit model, and fitting the internal resistance R of the lithium ion battery while identifying model parameters 0 Corresponding to variation of SOC/T, i.e. find f (SOC, T) =R 0 A relational expression;
step S8: determining SOC-T-R according to the output calculation result 0 And obtaining a relatively accurate lithium iron phosphate battery model by using the function parameter values, and constructing an electrothermal coupling model of the lithium iron phosphate battery.
By adopting the technical scheme, the voltage, the current, the temperature and the internal resistance of the lithium iron phosphate battery are used as fuzzy control inputs, and compared with a least square method, the fuzzy logic prediction result is more accurate. The battery attribute changes along with the battery aging and other problems, so that the parameter identification of the battery model is affected. Therefore, the factor of the internal resistance of the battery is considered through the fuzzy algorithm, and a better expression effect is obtained.
Description of the drawings:
FIG. 1 is a Thevenin equivalent circuit model; having a plurality of equivalent circuit elements including an open circuit voltage U oc Equivalent resistance R 0 The series structure of the first-order RC resistance-capacitance network is formed; r is R th Represents a polarization resistance; c (C) th Representing the polarization capacitance.
Fig. 2 is a basic calculation execution flow of the fuzzy logic algorithm.
FIG. 3 (a) is a graph showing the relative membership function of SOC to fuzzy labels.
FIG. 3 (b) is a graph of T versus membership functions for fuzzy labels.
FIG. 4 is a diagram of SOC-T-R 0 A correspondence map; in the figure, the Temp-Table coordinate axis is the temperature T, the SOC-Table coordinate axis represents the SOC value, and the R0-Table coordinate axis represents the output resistance R 0 。
The specific embodiment is as follows:
the present invention will be described in further detail with reference to the accompanying drawings.
An equivalent circuit parameter identification method for lumped parameters of a lithium ion battery energy storage system comprises the following steps:
step S1: performing HPPC test on a plurality of lithium iron phosphate battery cells to obtain battery open-circuit voltage and current, and measuring temperature data in real time;
step S2: estimating the SOC variation of the lithium iron phosphate battery by utilizing an ampere-hour integration method according to relevant test data such as the battery voltage, the current and the like, and marking the SOC variation as small, medium and large in a fuzzy way; the ampere-hour integration method is realized by the following formula:
wherein Q is max The maximum charge-discharge capacity allowed for the battery can be understood as the rated capacity SOH; i eff Charging to be negative for charge-discharge current or self-discharge current; η is the coulombic efficiency of charge and discharge;
step S3: according to the recorded temperature T changes, the temperature T changes into 3 fuzzy states, and fuzzy marks are low, medium and high;
step S4: respectively establishing a fuzzy evaluation factor set of the SOC and a fuzzy evaluation factor set of the T, and then establishing a comprehensive factor evaluation set;
step S5: establishing a two-dimensional fuzzy control rule about the SOC and the temperature T, and simultaneously setting or adopting expert experience to determine a weight coefficient;
step S6: establishing a judgment matrix about the SOC and the temperature T, performing fuzzy reasoning, and performing defuzzification treatment; the specific process comprises the following steps:
step S6.1: consider the equivalent resistance R 0 Influence factors, determining a factor set of fuzzy comprehensive evaluation as the change of the state of charge (SOC) and the temperature (T), and obtaining the factor set as follows:
v= { state of charge SOC (V SOC ) Temperature change T (V T )}；
Step S6.2: comprehensively considering the influence of the temperature T change and the SOC change on the equivalent internal resistance of the battery and the like, and establishing a relevant membership function of the SOC to the fuzzy mark, as shown in a figure 3 (a),
V SOC = { low v 1 V of (v) 2 High v 3 ，…}；
The temperature T is established as a function of the relative membership of the fuzzy label, as shown in figure 3 (b),
V T = { small v 4 V of (v) 5 Large v 6 ，…}；
The membership function adopts a triangle membership function (trimf), and the expression is y=trimf (x [ a, b, c. ]), wherein the parameter x represents the domain range of the variable, the parameters a and c correspond to the left vertex and the right vertex of the lower part of the triangle, and the parameter b corresponds to the vertex of the upper part of the triangle;
step S6.3: and carrying out fuzzy judgment on the consideration to obtain a consideration judgment set:
R 1 ＝(a b c…)；
R 2 ＝(d e f…)；
obtaining a judgment matrix R:
wherein a+b+c+ … is less than or equal to 1; d+e+f+ … is less than or equal to 1;
step S6.4: according to the experience of the relevant expert, determining the fuzzy control rule of the influencing factors, namely determining the equivalent resistance R of the input SOC and the temperature T change to the output 0 The fuzzy control rule of (2) is:
step S6.5: the input variable SOC/T is fuzzified to obtain the corresponding membership degree, and the equivalent internal resistance R of the SOC or T to the output is determined 0 Is used for the influence weight of (1): a= (g, h),
wherein g+h=1;
step S6.5: fuzzy reasoning is carried out according to the statement of 'if A and B, C and D, E', and SOC-T-R shown in figure 4 is combined 0 Relation, calculating the intensity of the fuzzy control rule, and obtaining a judgment model and performing f (SOC-T) =R 0 Weighted average (min-max) fuzzy reasoning, obtaining fuzzy state value of output model relation coefficient:
wherein, the first element i of B is calculated from ((g ∈a) and (g ∈d)), wherein ∈d is a smaller value and V is a larger value; the rest and so on;
step S6.6: and performing defuzzification processing on the fuzzy reasoning result:
a weighted average method is selected as shown in the following formula:
wherein k is 1 、k 2 …k n (g, h) is the weight size, a 1 、a 2 …a n Is a fuzzy reasoning result;
step S7: combining the Thevenin equivalent circuit model, and fitting the internal resistance R of the lithium ion battery while identifying model parameters 0 With SOThe variation correspondence of C/T, i.e. find f (SOC, T) =r 0 A relational expression;
step S8: determining SOC-T-R according to the output calculation result 0 And obtaining a relatively accurate lithium iron phosphate battery model by using the function parameter values, and constructing an electrothermal coupling model of the lithium iron phosphate battery.
Claims (2)
1. The method for identifying the equivalent circuit parameters of the lumped parameters of the lithium ion battery energy storage system is characterized by comprising the following steps:
step S1: performing HPPC test on a plurality of lithium iron phosphate battery cells to obtain battery open-circuit voltage and current, and measuring temperature data in real time;
step S2: estimating the SOC variation of the lithium iron phosphate battery by utilizing an ampere-hour integration method according to relevant test data such as the battery voltage, the current and the like, and marking the SOC variation as small, medium and large in a fuzzy way;
step S3: according to the recorded temperature T changes, the temperature T changes into 3 fuzzy states, and fuzzy marks are low, medium and high;
step S4: respectively establishing a fuzzy evaluation factor set of the SOC and a fuzzy evaluation factor set of the T, and then establishing a comprehensive factor evaluation set;
step S5: establishing a two-dimensional fuzzy control rule about the SOC and the temperature T, and simultaneously setting or adopting expert experience to determine a weight coefficient;
step S6: establishing a judgment matrix about the SOC and the temperature T, performing fuzzy reasoning, and performing defuzzification treatment; the specific process comprises the following steps:
step S6.1: consider the equivalent resistance R 0 Influence factors, determining a factor set of fuzzy comprehensive evaluation as the change of the state of charge (SOC) and the temperature (T), and obtaining the factor set as follows:
v= { state of charge SOC (V SOC ) Temperature change T (V T )}；
Step S6.2: comprehensively considering the influence of the temperature T change and the SOC change on the equivalent internal resistance of the battery and the like, establishing a fuzzy mark for comprehensive judgment, and establishing a relevant membership function of the SOC to the fuzzy mark:
V SOC = { low v 1 V of (v) 2 High v 3 ，…}；
Establishing a relevant membership function of the temperature T to the fuzzy label:
V T = { small v 4 V of (v) 5 Large v 6 ，…}；
Step S6.3: and carrying out fuzzy judgment on the consideration to obtain a consideration judgment set:
R 1 ＝(a b c…)；
R 2 ＝(d e f…)；
obtaining a judgment matrix R:
wherein a+b+c+ … is less than or equal to 1; d+e+f+ … is less than or equal to 1;
step S6.4: according to the experience of the relevant expert, determining the fuzzy control rule of the influencing factors, namely determining the equivalent resistance R of the input SOC and the temperature T change to the output 0 The fuzzy control rule of (2) is:
step S6.5: the input variable SOC/T is fuzzified to obtain the corresponding membership degree, and the equivalent internal resistance R of the SOC or T to the output is determined 0 Is used for the influence weight of (1): a= (g, h),
wherein g+h=1;
step S6.5: calculating the intensity of the fuzzy control rule, and obtaining a judgment model and performing f (SOC-T) =R 0 Weighted average (min-max) fuzzy reasoning, obtaining fuzzy state value of output model relation coefficient:
wherein, the first element i of B is calculated from ((g ∈a) and (g ∈d)), wherein ∈d is a smaller value and V is a larger value; the rest and so on;
step S6.6: and performing defuzzification processing on the fuzzy reasoning result:
a weighted average method is selected as shown in the following formula:
wherein k is 1 、k 2 …k n (g, h) is the weight size, a 1 、a 2 …a n Is a fuzzy reasoning result;
step S7: combining the Thevenin equivalent circuit model, and fitting the internal resistance R of the lithium ion battery while identifying model parameters 0 Corresponding to variation of SOC/T, i.e. find f (SOC, T) =R 0 A relational expression;
step S8: determining SOC-T-R according to the output calculation result 0 And obtaining a relatively accurate lithium iron phosphate battery model by using the function parameter values, and constructing an electrothermal coupling model of the lithium iron phosphate battery.
2. The method for identifying the equivalent circuit parameters of the lumped parameters of the lithium ion battery energy storage system according to claim 1, wherein the ampere-hour integration method in the step S2 is realized by the following formula:
wherein Q is max The maximum charge-discharge capacity allowed for the battery can be understood as the rated capacity SOH; i eff Charging to be negative for charge-discharge current or self-discharge current; η is the coulombic efficiency of charge and discharge.
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