CN114781176B - Equivalent circuit parameter identification method for lumped parameters of lithium ion battery energy storage system - Google Patents
Equivalent circuit parameter identification method for lumped parameters of lithium ion battery energy storage system Download PDFInfo
- Publication number
- CN114781176B CN114781176B CN202210503147.7A CN202210503147A CN114781176B CN 114781176 B CN114781176 B CN 114781176B CN 202210503147 A CN202210503147 A CN 202210503147A CN 114781176 B CN114781176 B CN 114781176B
- Authority
- CN
- China
- Prior art keywords
- soc
- fuzzy
- battery
- temperature
- model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 33
- 238000004146 energy storage Methods 0.000 title claims abstract description 15
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 title claims abstract description 14
- 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
- 230000010354 integration Effects 0.000 claims abstract description 7
- 238000012360 testing method Methods 0.000 claims abstract description 7
- 230000008878 coupling Effects 0.000 claims abstract description 4
- 238000010168 coupling process Methods 0.000 claims abstract description 4
- 238000005859 coupling reaction Methods 0.000 claims abstract description 4
- 230000008859 change Effects 0.000 claims description 18
- 238000004364 calculation method Methods 0.000 claims description 7
- 239000011159 matrix material Substances 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 4
- 238000012545 processing Methods 0.000 claims description 3
- 238000004422 calculation algorithm Methods 0.000 abstract description 5
- 238000002474 experimental method Methods 0.000 abstract 1
- 239000000178 monomer Substances 0.000 abstract 1
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 229910052744 lithium Inorganic materials 0.000 description 3
- 230000010287 polarization Effects 0.000 description 3
- 230000032683 aging Effects 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- RTAQQCXQSZGOHL-UHFFFAOYSA-N Titanium Chemical compound [Ti] RTAQQCXQSZGOHL-UHFFFAOYSA-N 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000029087 digestion Effects 0.000 description 1
- QHGJSLXSVXVKHZ-UHFFFAOYSA-N dilithium;dioxido(dioxo)manganese Chemical compound [Li+].[Li+].[O-][Mn]([O-])(=O)=O QHGJSLXSVXVKHZ-UHFFFAOYSA-N 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000012827 research and development Methods 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/385—Arrangements for measuring battery or accumulator variables
- G01R31/387—Determining ampere-hour charge capacity or SoC
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/04—Inference or reasoning models
- G06N5/048—Fuzzy inferencing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/04—Ageing analysis or optimisation against ageing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/08—Thermal analysis or thermal optimisation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Mathematical Physics (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Computational Mathematics (AREA)
- Computing Systems (AREA)
- Algebra (AREA)
- Databases & Information Systems (AREA)
- Evolutionary Computation (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Evolutionary Biology (AREA)
- Operations Research (AREA)
- Probability & Statistics with Applications (AREA)
- Automation & Control Theory (AREA)
- Fuzzy Systems (AREA)
- Artificial Intelligence (AREA)
- Computational Linguistics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Computer Hardware Design (AREA)
- Geometry (AREA)
- Secondary Cells (AREA)
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.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202210503147.7A CN114781176B (en) | 2022-05-10 | 2022-05-10 | Equivalent circuit parameter identification method for lumped parameters of lithium ion battery energy storage system |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202210503147.7A CN114781176B (en) | 2022-05-10 | 2022-05-10 | Equivalent circuit parameter identification method for lumped parameters of lithium ion battery energy storage system |
Publications (2)
Publication Number | Publication Date |
---|---|
CN114781176A CN114781176A (en) | 2022-07-22 |
CN114781176B true CN114781176B (en) | 2024-04-05 |
Family
ID=82437289
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202210503147.7A Active CN114781176B (en) | 2022-05-10 | 2022-05-10 | Equivalent circuit parameter identification method for lumped parameters of lithium ion battery energy storage system |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN114781176B (en) |
Families Citing this family (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN116106750B (en) * | 2023-01-28 | 2023-11-10 | 上海玫克生储能科技有限公司 | Battery temperature measuring point identification and abnormality detection method and device and electronic equipment |
CN116680938B (en) * | 2023-08-04 | 2023-10-20 | 通达电磁能股份有限公司 | Modeling method and system for lithium battery electric heating coupling model |
Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2016134496A1 (en) * | 2015-02-28 | 2016-09-01 | 北京交通大学 | Method and apparatus for estimating state of charge of lithium ion battery |
CN112858929A (en) * | 2021-03-16 | 2021-05-28 | 上海理工大学 | Battery SOC estimation method based on fuzzy logic and extended Kalman filtering |
-
2022
- 2022-05-10 CN CN202210503147.7A patent/CN114781176B/en active Active
Patent Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2016134496A1 (en) * | 2015-02-28 | 2016-09-01 | 北京交通大学 | Method and apparatus for estimating state of charge of lithium ion battery |
CN112858929A (en) * | 2021-03-16 | 2021-05-28 | 上海理工大学 | Battery SOC estimation method based on fuzzy logic and extended Kalman filtering |
Non-Patent Citations (2)
Title |
---|
在线参数辨识和扩展卡尔曼算法的锂离子电池SOC估算研究;李博文;王顺利;于春梅;李建超;谢伟;;自动化仪表;20200320(03);全文 * |
基于模型与双卡尔曼滤波的锂电池参数辨识;刘政;黄和悦;赵振华;;桂林航天工业学院学报;20200915(03);全文 * |
Also Published As
Publication number | Publication date |
---|---|
CN114781176A (en) | 2022-07-22 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Ouyang et al. | Improved parameters identification and state of charge estimation for lithium-ion battery with real-time optimal forgetting factor | |
CN114781176B (en) | Equivalent circuit parameter identification method for lumped parameters of lithium ion battery energy storage system | |
CN113049962B (en) | LSTM-based energy storage device operation situation deduction method | |
CN106777786A (en) | A kind of lithium ion battery SOC estimation method | |
Li et al. | A novel state estimation approach based on adaptive unscented Kalman filter for electric vehicles | |
CN109839599B (en) | Lithium ion battery SOC estimation method based on second-order EKF algorithm | |
CN111366864B (en) | Battery SOH on-line estimation method based on fixed voltage rise interval | |
CN110824363A (en) | Lithium battery SOC and SOE joint estimation method based on improved CKF | |
CN105974320B (en) | A kind of liquid or semi-liquid metal battery charge state method of estimation | |
Rajanna et al. | Comparison of one and two time constant models for lithium ion battery. | |
CN109917299A (en) | A kind of three layers of filtering evaluation method of lithium battery charge state | |
CN113484771A (en) | Method for estimating wide-temperature full-life SOC and capacity of lithium ion battery | |
CN112946481A (en) | Based on federation H∞Filtering sliding-mode observer lithium ion battery SOC estimation method and battery management system | |
Zhang et al. | Fuzzy-kalman-filter-based short-circuit fault diagnosis design for lithium-ion battery | |
Guo et al. | An unscented kalman filtering method for estimation of state-of-charge of lithium-ion battery | |
Tran et al. | Comparative Study of Equivalent Circuit Models Performance in Four Common Lithium-Ion Batteries: LFP, NMC, LMO, NCA. Batteries 2021; 7: 51 | |
CN116774045A (en) | Lithium battery health state prediction method based on HHO-SVR | |
CN112946480B (en) | Lithium battery circuit model simplification method for improving SOC estimation real-time performance | |
Liu et al. | Fast screening of capacity and internal resistance for cascade utilization of the retired power lithium-ion batteries | |
CN114779105A (en) | Lithium battery pack inconsistency estimation method | |
Zhixiang et al. | Joint estimation of battery state-of-charge based on the genetic algorithm-adaptive unscented Kalman filter | |
CN113740735A (en) | Method for estimating SOC of lithium ion battery | |
Liu et al. | Fault diagnosis of series batteries based on GWO-SVM | |
Yan et al. | SOC prediction method of wireless sensor nodes batteries based on Attention-LSTM | |
Wang et al. | A hybrid approach on estimating state of charge of lithium-ion batteries based on data driven Model |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant |