CN112858928B - Lithium battery SOC estimation method based on online parameter identification - Google Patents
Lithium battery SOC estimation method based on online parameter identification Download PDFInfo
- Publication number
- CN112858928B CN112858928B CN202110264962.8A CN202110264962A CN112858928B CN 112858928 B CN112858928 B CN 112858928B CN 202110264962 A CN202110264962 A CN 202110264962A CN 112858928 B CN112858928 B CN 112858928B
- Authority
- CN
- China
- Prior art keywords
- lithium battery
- soc
- parameter
- state
- equivalent circuit
- 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
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 title claims abstract description 97
- 229910052744 lithium Inorganic materials 0.000 title claims abstract description 97
- 238000000034 method Methods 0.000 title claims abstract description 35
- 238000001914 filtration Methods 0.000 claims abstract description 25
- 230000008859 change Effects 0.000 claims abstract description 12
- 230000008569 process Effects 0.000 claims abstract description 9
- 238000004364 calculation method Methods 0.000 claims abstract description 8
- 238000007599 discharging Methods 0.000 claims description 9
- 230000010287 polarization Effects 0.000 claims description 6
- 238000005070 sampling Methods 0.000 claims description 6
- 238000002474 experimental method Methods 0.000 claims description 5
- 239000003990 capacitor Substances 0.000 claims description 3
- 239000011159 matrix material Substances 0.000 claims description 3
- 238000005259 measurement Methods 0.000 claims description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 238000007796 conventional method Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000005284 excitation Effects 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 230000003446 memory effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- 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]
- G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
- G01R31/3835—Arrangements for monitoring battery or accumulator variables, e.g. SoC involving only voltage measurements
-
- 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]
- G01R31/3644—Constructional arrangements
- G01R31/3648—Constructional arrangements comprising digital calculation means, e.g. for performing an algorithm
-
- 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
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Tests Of Electric Status Of Batteries (AREA)
Abstract
The invention discloses a lithium battery SOC estimation method based on online parameter identification, which comprises the steps of establishing a second-order equivalent circuit model of a lithium battery; determining the functional relation between each parameter of the equivalent circuit and the SOC, establishing a state space equation based on the on-line parameters of the lithium battery, and initializing the SOC state variables and the parameter state variables; estimating the SOC of the lithium battery by using an extended Kalman filtering algorithm under a microscopic time scale; when the lithium battery SOC estimation reaches the preset time, switching the time scale, identifying the equivalent circuit parameters by using an unscented Kalman filtering algorithm under the macroscopic time scale, and finally updating the equivalent circuit parameters and a state space equation of the lithium battery to perform the next round of calculation; according to the invention, the unscented Kalman filtering algorithm is used for carrying out online parameter identification on the lithium battery model, and the extended Kalman filtering algorithm is combined to estimate the SOC of the lithium battery, so that the problem of parameter change in the working process of the lithium battery is solved, the accuracy of the SOC estimation of the lithium battery is improved, and the method has practical significance.
Description
Technical Field
The invention relates to the field of lithium battery SOC estimation, in particular to a lithium battery SOC estimation method based on online parameter identification.
Background
With the increasing consumption of non-renewable energy sources and the increasing environmental pollution, the adoption of green energy sources to replace traditional non-renewable energy sources becomes a current research hotspot, so that new energy electric vehicles are rapidly developed. The lithium battery is widely applied to electric automobiles due to the advantages of high energy density, high cycle times, wide application temperature range, no memory effect and the like.
The lithium battery SOC estimation is used as the core of the battery management system of the electric automobile, and the estimation accuracy directly influences the charge and discharge limit, the service life and the driving safety of the lithium battery. The main methods of current lithium battery SOC estimation include an ampere-hour integration method, an open-circuit voltage method, a Kalman filtering algorithm, a neural network algorithm, a fuzzy logic algorithm and the like, wherein the most studied is to establish a mathematical model according to the charge and discharge characteristics of the lithium battery and combine the Kalman filtering technology to carry out lithium battery SOC estimation.
At present, most of methods for estimating the SOC by combining a lithium battery equivalent circuit model with a Kalman filtering technology are based on an offline battery model, namely the offline equivalent circuit model of the lithium battery is obtained through offline parameter identification, and then the Kalman filtering algorithm is adopted to estimate the SOC of the lithium battery. The parameters of the equivalent circuit model of the lithium battery established by the method are fixed, but when the lithium battery actually works, the internal parameters of the lithium battery tend to change slowly, and the parameters of the lithium battery also change along with the change of the cycle times. Therefore, the fixed off-line circuit model cannot be well matched with the actual working condition of the lithium battery, and the estimation accuracy is insufficient.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a lithium battery SOC estimation method based on online parameter identification.
The technical scheme adopted by the invention is as follows:
an on-line parameter identification-based lithium battery SOC estimation method mainly comprises the following steps:
step 1: establishing a second-order equivalent circuit model of the lithium battery
Step 2: determining the functional relation between each parameter of the equivalent circuit and the SOC, and establishing a state space equation based on the on-line parameters of the lithium battery
Step 3: initializing SOC state variables and parameter state variables
Step 4: estimating the SOC of the lithium battery by using an extended Kalman filtering algorithm under a microscopic time scale
Step 5: when the lithium battery SOC estimation reaches the preset time, switching the time scale
Step 6: under the macroscopic time scale, the unscented Kalman filtering algorithm is used for identifying the equivalent circuit parameters
Step 7: updating equivalent circuit parameters and state space equations of the lithium battery, and returning to the step 4 to perform the next round of calculation
Preferably, the lithium battery equivalent circuit model adopts an integer-order second-order equivalent circuit model, and the circuit parameters comprise: ohmic internal resistance R 0 Polarization resistance R 1 、R 2 Polarization capacitor C 1 、C 2 。
Further, step 2, determining the functional relation between each parameter of the equivalent circuit and the SOC, and establishing a state space equation based on the on-line parameters of the lithium battery:
step 2.1: discharging the fully charged battery with a constant current of 30A, wherein each discharging time is 3min, namely 0.5 SOC value, standing the battery for 2h after discharging, recording the open circuit voltage of the battery, and repeating the operation for 20 times.
Step 2.2: according to experiments, 20 groups of U ocv Data points related to SOC, the SOC is used as a variable, and the formula (4) is used for the U ocv Performing eighth-order fitting with the data points of the SOC to obtain U ocv Function with SOC:
y(x)＝p 1 x 8 +p 2 x 7 +p 3 x 6 +p 4 x 5 +p 5 x 4 +p 6 x 3 +p 7 x 2 +p 8 x+p 9
step 2.3: according to the relation between the equivalent circuit model of the lithium battery and the circuit voltage, the relation between each parameter and the SOC is shown:
observation equation:
U 0 ＝U ocv (SOC)-R 0 I-U 1 -U 2
U 0 is the terminal voltage, T s For sampling time, Q n Is the battery capacity.
Step 2.4: discretizing the equation difference in the step 2.3:
step 2.5: further simplified to obtain:
medium output matrixCombining the lithium battery parameter change to obtain a state space equation based on the lithium battery parameter:
state variables of medium parametersw k 、v k ρ is the process noise and the observation noise of the system k Process noise is a model parameter.
Further, the SOC state variables and parameter state variables are initialized: initializing SOC state variables: x is x 0,0 ＝E(x 0,0 )，Parameter state variable initialization: θ 0 ＝E(θ 0 )，
Further, in step 4, under the microscopic time scale, estimating the SOC of the lithium battery by using an extended kalman filter algorithm:
step 4.1: dividing a time scale, wherein a macro scale L=60 s, a micro scale L epsilon (1-L), calculating a state filter under each micro scale, namely calculating a state variable predicted value under the time scale when a sequence of the bust time scale l= 1:LAnd state variable prediction error covariance +>
Step 4.2: in the measurement updating stage, the extended Kalman gain is calculated respectivelyUpdating to obtain state variable estimated value +.>And state variable error covariance estimate +.>
The result returns to step 4.1.
Further, when the time sequence of the micro time scale reaches the macro time scale, i.e. l=l=60 s, L is set to zero, the micro time scale is switched to the macro time scale, and the unscented Kalman filtering algorithm is adopted for carrying out primary parameter identification.
Further, step 6:
step 6.1: firstly, calculating a k moment sampling point under a macroscopic time scale:
wherein M is the length of the state vector, herein the state vector length is 5;
step 6.2: weight value calculation:
wherein, take α=0.01, k i ＝0，β＝2；
Step 6.3: calculating parameter state variable predictive valuesAnd a system variance prediction value P xx 。
Step 6.4: predicting the parameter state variableReplacement of θ in step 2.5 k Updating U according to the SOC value obtained in the step 4 ocv Finally, updating to obtain a lithium battery state space equation based on the prediction parameters:
step 6.5: updating the observations based on the state space equations of step 6.4And observed variance pre-predictionMeasurement value P yy :
Step 6.6: obtaining covariance of parameter state variables and unscented Kalman gainParameter estimation value->And parameter state error covariance estimate P k 。
Further, the new parameters obtained in step 6.6 are usedThe state space equations in step 2.5 are updated and the next cycle is performed.
Compared with the air pressure detection system of the existing air collection pipe, the invention has the advantages that:
1. according to the method, the unscented Kalman filtering algorithm is used for carrying out on-line parameter identification on the lithium battery equivalent circuit model, and the extended Kalman filtering algorithm is combined to estimate the lithium battery SOC, so that the problem of fixing the lithium battery off-line equivalent circuit model is solved, the unscented Kalman filtering algorithm is used for estimating the parameters of the lithium battery model on line, the battery model is enabled to be more matched with the lithium battery characteristics under the complex working conditions, and the precision of the lithium battery model and the precision of SOC estimation are improved.
2. According to the SOC estimation method based on the on-line parameter identification, the unscented Kalman filtering algorithm under the macroscopic time scale is adopted to identify the on-line parameters of the lithium battery model, and the parameters obtained through identification are updated to the equivalent circuit model of the lithium battery, so that the accuracy of estimating the SOC by the extended Kalman filtering algorithm under the microscopic time scale is improved; the multi-time scale joint estimation method greatly saves the computing resource of a computer, is quite compatible with the characteristic of slow change of the parameters of the lithium battery and rapid change of the SOC, and has quite wide application prospect.
Drawings
Fig. 1 is a flowchart of lithium battery SOC estimation according to a preferred embodiment of the present invention.
Fig. 2 is a schematic diagram of an equivalent circuit of a lithium battery according to a preferred embodiment of the present invention.
Fig. 3 is a graph of current excitation and voltage response waveforms in accordance with a preferred embodiment of the present invention.
FIG. 4 is a diagram showing the results of online parameter identification according to a preferred embodiment of the present invention.
Fig. 5 is a graph comparing the SOC estimation result of the lithium battery according to the preferred embodiment of the present invention with that of the conventional method.
Fig. 6 is a diagram showing the comparison of the error of the SOC estimation result of the lithium battery according to the preferred embodiment of the present invention and the conventional method.
Detailed Description
The invention is described in detail below with reference to the attached drawings:
as shown in fig. 1, the lithium battery SOC estimation method based on online parameter identification mainly includes the following steps:
step 1: establishing a second-order equivalent circuit model of the lithium battery;
step 2: determining the functional relation between each parameter of the equivalent circuit and the SOC, and establishing a state space equation based on the on-line parameters of the lithium battery;
step 3: initializing SOC state variables and parameter state variables;
step 4: estimating the SOC of the lithium battery by using an extended Kalman filtering algorithm under a microscopic time scale;
step 5: switching a time scale when the lithium battery SOC estimation reaches a preset time;
step 6: under the macroscopic time scale, identifying the equivalent circuit parameters by using an unscented Kalman filtering algorithm;
step 7: updating the equivalent circuit parameters and the state space equation of the lithium battery, and returning to the step 4 to perform the next round of calculation.
And step 1, establishing a second-order equivalent circuit model of the lithium battery, as shown in fig. 2. The parameters of the second-order equivalent circuit model of the lithium battery comprise ohmic internal resistance R 0 Polarization resistance R 1 、R 2 Polarization capacitor C 1 、C 2 。
And (3) entering a step (2) according to the circuit model and the parameters, determining the functional relation between each parameter of the equivalent circuit and the SOC, and establishing a state space equation based on the on-line parameters of the lithium battery. In the embodiment of the invention, the open-circuit voltage U of the lithium battery is firstly determined through a constant-current discharge experiment ocv Fitting U by using eighth order polynomial according to relation with SOC ocv The function with SOC comprises the following specific steps:
step 2.1: discharging the fully charged battery with a constant current of 30A, wherein each discharging time is 3min, namely 0.05 SOC value, standing the battery for 2h after discharging, recording the open circuit voltage of the battery, and repeating the operation for 20 times.
Step 2.2: according to experiments, 20 groups of U ocv Data points related to SOC, the SOC is used as a variable, and the formula (4) is used for the U ocv Performing eighth-order fitting with the data points of the SOC to obtain U ocv Function with SOC:
y(x)＝p 1 x 8 +p 2 x 7 +p 3 x 6 +p 4 x 5 +p 5 x 4 +p 6 x 3 +p 7 x 2 +p 8 x+p 9
step 2.3: according to the relation between the equivalent circuit model of the lithium battery and the circuit voltage, the relation between each parameter and the SOC is shown:
observation equation:
U 0 ＝U ocv (SOC)-R 0 I-U 1 -U 2
U 0 is the terminal voltage, T s For sampling time, Q n For battery capacity
Step 2.4: discretizing the equation difference in the step 2.3:
step 2.5: further simplified to obtain:
medium output matrixCombining the lithium battery parameter change to obtain a state space equation based on the lithium battery parameter:
state variables of medium parametersw k 、v k For the process noise and the observed noise of the system, Q is the process noise covariance, R is the observed noise covariance, ρ k Process noise is a model parameter.
After the state space equation of the second-order equivalent circuit model of the lithium battery is established, the preferred embodiment adopts the U.S. city circulation condition (UDDS) as an actual condition to perform on-line parameter identification and SOC estimation in the actual working process of the lithium battery, and the excitation current and the response voltage of the condition are shown in fig. 3.
Step 3, initializing an SOC state variable and a parameter state variable, namely, performing algorithm initial assignment on the basis of an on-line parameter identification lithium battery SOC estimation method, and taking parameters obtained through off-line data identification as initial values of the parameter state variables:
initializing SOC state variables: x is x 0,0 ＝E(x 0,0 )，Parameter state variable initialization: θ 0 ＝E(θ 0 )，/>
After initialization is completed, the step 4 is carried out, and under the microscopic time scale, the extended Kalman filtering algorithm is used for estimating the SOC of the lithium battery; under the microscopic time scale, estimating the SOC of the lithium battery by using an extended Kalman filtering algorithm, wherein the method comprises the following specific steps of:
step 4.1: dividing a time scale, wherein a macro scale L=60 s, a micro scale L epsilon (1-L), calculating a state filter under each micro scale, namely calculating a state variable predicted value under the time scale when a sequence of the bust time scale l= 1:LAnd state variable prediction error covariance +>
Step 4.2: in the measurement updating stage, the extended Kalman gain is calculated respectivelyUpdating to obtain state variable estimated value +.>And state variable error covariance estimate +.>
The result returns to step 4.1.
When the lithium battery SOC estimation is continuously carried out until reaching the preset time, entering a step 5, and switching the time scale; and 4, under the microscopic time scale, continuously estimating the SOC of the lithium battery by using an extended Kalman filtering algorithm, when the time sequence of the microscopic time scale reaches the macroscopic time scale, namely l=L=60 s, setting L to be zero, switching the microscopic time scale to the macroscopic time scale, and carrying out primary parameter identification by using an unscented Kalman filtering algorithm.
And 6, under the macroscopic time scale, identifying the equivalent circuit parameters by using an unscented Kalman filtering algorithm, wherein the method comprises the following specific steps of:
step 6.1: firstly, calculating a k moment sampling point under a macroscopic time scale:
wherein M is the length of the state vector, and the length of the state vector is 5 in the invention;
step 6.2: weight value calculation:
wherein, take α=0.01, k i ＝0，β＝2；
Step 6.3: calculating parameter state variable predictive valuesAnd tie upUnified variance prediction value P xx 。
Step 6.4: predicting the parameter state variableReplacement of θ in step 2.5 k Updating U according to the SOC value obtained in the step 4 ocv Finally, updating to obtain a lithium battery state space equation based on the prediction parameters:
step 6.5: updating the observations based on the state space equations of step 6.4And observed variance prediction value P yy :
Step 6.6: obtaining covariance of parameter state variables and unscented Kalman gainParameter estimation value->And parameter state error covariance estimate P k 。
The algorithm flow under a macroscopic time scale is ended, the on-line identification of the new circuit model parameters is obtained, the new parameters are substituted into the circuit model, and the state space equation is updated, namely, the step 7 is entered.
Step 7: updating the equivalent circuit parameters and the state space equation of the lithium battery, and returning to the step 4 to perform the next round of calculation.
The result of parameter identification in the preferred embodiment of the present invention is shown in fig. 4; the SOC estimation result is shown in fig. 5, and the SOC estimation result is compared with the Extended Kalman Filter (EKF) estimation SOC result under the conventional offline parameter identification, and the error is shown in fig. 6. From the result, the invention provides an on-line estimation algorithm of the lithium battery SOC, which has high precision, fast following and strong stability; the lithium battery circuit model can be updated online in real time by collecting the voltage and current data of the lithium battery in real time, and the SOC of the lithium battery is estimated, so that the problem that the circuit model in the traditional offline circuit model cannot be changed along with the change of complex working conditions is solved; the multi-time scale combined estimation method is suitable for lithium battery SOC estimation under complex working conditions, greatly saves the computing resources of a computer, is quite compatible with the characteristics of slow change of parameters and rapid change of the SOC of the lithium battery, has quite wide application prospect, and is a new practice for applying novel algorithms.
The present invention is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention.
Claims (3)
1. An on-line parameter identification-based lithium battery SOC estimation method mainly comprises the following steps:
step 1: establishing a second-order equivalent circuit model of the lithium battery, wherein the model is an integer-order equivalent circuit model, and the circuit parameters comprise ohmic internal resistance R 0 Polarization resistance R 1 、R 2 Polarization capacitor C 1 、C 2 ；
Step 2: determining functional relation between each parameter of an equivalent circuit and SOC, establishing a state space equation based on-line parameters of a lithium battery, and determining open-circuit voltage U of the lithium battery through a constant-current discharge experiment ocv The relation with the SOC comprises the following specific steps:
step 2.1: discharging the fully charged battery with a constant current of 30A, wherein each discharging time is 3min, namely 0.5 SOC value, standing the battery for 2h after discharging, recording the open circuit voltage of the battery, and repeating the operation for 20 times;
step 2.2: according to experiments, 20 groups of U ocv Data points related to SOC, the SOC is used as a variable, and the formula (4) is used for the U ocv Performing eighth-order fitting with the data points of the SOC to obtain U ocv Function with SOC:
y(x)＝p 1 x 8 +p 2 x 7 +p 3 x 6 +p 4 x 5 +p 5 x 4 +p 6 x 3 +p 7 x 2 +p 8 x+p 9
step 2.3: according to the relation between the equivalent circuit model of the lithium battery and the circuit voltage, the relation between each parameter and the SOC is shown:
observation equation:
U 0 ＝U ocv (SOC)-R 0 I-U 1 -U 2
U 0 is the terminal voltage, T s For sampling time, Q n Is the battery capacity;
step 2.4: discretizing the equation difference in the step 2.3:
step 2.5: further simplified to obtain:
medium output matrixCombining the lithium battery parameter change to obtain a state space equation based on the lithium battery parameter:
state variables of medium parametersw k 、v k ρ is the process noise and the observation noise of the system k Process noise is model parameter;
step 3: initializing SOC state variables and parameter state variables;
step 4: under the microscopic time scale, estimating the SOC of the lithium battery by using an extended Kalman filtering algorithm, wherein the method comprises the following specific steps of:
step 4.1: dividing a time scale, wherein a macro scale L=60 s, a micro scale L epsilon (1-L), calculating a state filter under each micro scale, namely calculating a state variable predicted value under the time scale when a micro time scale sequence l= 1:LAnd state variable prediction error covariance +>
Step 4.2: in the measurement updating stage, the extended Kalman gain is calculated respectivelyUpdating to obtain state variable estimated valueAnd state variable error covariance estimate +.>
Returning the obtained result to the step 4.1;
step 5: switching a time scale when the lithium battery SOC estimation reaches a preset time;
step 6: under the macroscopic time scale, the equivalent circuit parameters are identified by using an unscented Kalman filtering algorithm, and the specific steps are as follows:
step 6.1: firstly, calculating a k moment sampling point under a macroscopic time scale:
wherein M is the length of the state vector, herein the state vector length is 5;
step 6.2: weight value calculation:
wherein, take α=0.01, k i ＝0，β＝2；
Step 6.3: calculating parameter state variable predictive valuesAnd a system variance prediction value P xx ；
Step 6.4: predicting the parameter state variableReplacement of θ in step 2.5 k Updating U according to the SOC value obtained in the step 4 ocv Finally, updating to obtain a lithium battery state space equation based on the prediction parameters:
step 6.5: updating the observations based on the state space equations of step 6.4And observed variance prediction value P yy ：
Step 6.6: obtaining covariance of parameter state variables and unscented Kalman gainParameter estimation value->And parameter state error covariance estimate P k ；
Step 7: updating the second-order equivalent circuit parameters and the state space equation of the lithium battery, and returning to the step 4 to perform the next round of calculation.
2. The lithium battery SOC estimation method based on-line parameter identification according to claim 1, wherein the method comprises the following steps: base groupIn a state space equation of lithium battery parameters, initializing state of charge (SOC) state variables: x is x 0,0 ＝E(x 0,0 )，Parameter state variable initialization: θ 0 ＝E(θ 0 )，/>
3. The lithium battery SOC estimation method based on-line parameter identification according to claim 1, wherein the method comprises the following steps: and 6, substituting the new parameters into the circuit model, updating the state space equation in the step 2.5, and performing the next cycle.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110264962.8A CN112858928B (en) | 2021-03-08 | 2021-03-08 | Lithium battery SOC estimation method based on online parameter identification |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110264962.8A CN112858928B (en) | 2021-03-08 | 2021-03-08 | Lithium battery SOC estimation method based on online parameter identification |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112858928A CN112858928A (en) | 2021-05-28 |
| CN112858928B true CN112858928B (en) | 2024-02-06 |
Family
ID=75994053
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110264962.8A Active CN112858928B (en) | 2021-03-08 | 2021-03-08 | Lithium battery SOC estimation method based on online parameter identification |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112858928B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113420444A (en) * | 2021-06-23 | 2021-09-21 | 南通大学 | Lithium ion battery SOC estimation method based on parameter online identification |
| CN113608121A (en) * | 2021-08-18 | 2021-11-05 | 合肥工业大学 | Lithium battery SOC estimation method based on fuzzy fractional order unscented Kalman filtering |
| CN114239463B (en) * | 2021-12-16 | 2022-11-01 | 杭州科工电子科技有限公司 | Battery cluster state of charge correction method based on big data |
| CN114839550B (en) * | 2022-04-14 | 2024-05-10 | 安徽理工大学 | UKF-AUKF-based lithium battery SOC joint estimation method |
| CN116632386A (en) * | 2023-07-20 | 2023-08-22 | 山东科技大学 | Battery auxiliary device and battery working method |
Citations (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106291381A (en) * | 2016-08-16 | 2017-01-04 | 北京理工大学 | A kind of Combined estimator electrokinetic cell system state-of-charge and the method for health status |
| CN106443471A (en) * | 2016-09-20 | 2017-02-22 | 首都师范大学 | System on chip (SOC) estimation method for lithium ion battery and hardware implementation of estimation method |
| CN107741569A (en) * | 2017-11-16 | 2018-02-27 | 温州大学 | A kind of evaluation method of the lithium battery charge state based on segment extension Kalman filtering |
| CN108872873A (en) * | 2018-06-29 | 2018-11-23 | 长沙理工大学 | A kind of lithium iron phosphate dynamic battery state-of-charge joint estimate method based on GA-AUKF |
| CN109633479A (en) * | 2019-01-11 | 2019-04-16 | 武汉理工大学 | Lithium battery SOC estimation on line method based on built-in capacitor G-card Kalman Filtering |
| CN109856556A (en) * | 2019-03-21 | 2019-06-07 | 南京工程学院 | A kind of power battery SOC estimation method |
| CN110068772A (en) * | 2019-05-06 | 2019-07-30 | 重庆大学 | Based on the charge states of lithium ion battery estimation method for improving fractional model |
| CN110082684A (en) * | 2019-05-13 | 2019-08-02 | 南京邮电大学 | A kind of lithium battery SOC estimation method based on the more new breath Extended Kalman filter of weighting |
| CN110361652A (en) * | 2019-06-26 | 2019-10-22 | 河南理工大学 | A kind of Kalman filtering lithium battery SOC estimation method based on Model Parameter Optimization |
| CN110398691A (en) * | 2019-06-26 | 2019-11-01 | 重庆大学 | Based on the lithium-ion-power cell SoC estimation method for improving adaptive double Unscented kalman filtering devices |
| CN110502778A (en) * | 2019-07-02 | 2019-11-26 | 江苏大学 | A kind of adaptive optimization method based on Kalman filtering frame estimation battery SOC |
| CN111007400A (en) * | 2019-11-22 | 2020-04-14 | 西安工程大学 | Lithium battery SOC estimation method based on self-adaptive double-extended Kalman filtering method |
| CN111060834A (en) * | 2019-12-19 | 2020-04-24 | 中国汽车技术研究中心有限公司 | Power battery state of health estimation method |
| CN111428433A (en) * | 2020-04-10 | 2020-07-17 | 湖南经研电力设计有限公司 | Lithium ion battery state calculation method based on hybrid filtering |
| CN111458646A (en) * | 2020-05-25 | 2020-07-28 | 安徽理工大学 | Lithium battery SOC estimation method based on PSO-RBF neural network |
| CN111948539A (en) * | 2019-05-17 | 2020-11-17 | 天津科技大学 | Kalman filtering lithium ion battery SOC estimation method based on deep reinforcement learning |
| CN112327166A (en) * | 2020-10-21 | 2021-02-05 | 合肥工业大学 | Lithium battery SOC estimation method based on fractional order square root unscented Kalman filter |
| CN112733411A (en) * | 2020-12-18 | 2021-04-30 | 武汉大学 | Lithium battery SOC estimation method and system based on second-order differential particle filter |
| CN114839550A (en) * | 2022-04-14 | 2022-08-02 | 安徽理工大学 | UKF-AUKF-based lithium battery SOC joint estimation method |
-
2021
- 2021-03-08 CN CN202110264962.8A patent/CN112858928B/en active Active
Patent Citations (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106291381A (en) * | 2016-08-16 | 2017-01-04 | 北京理工大学 | A kind of Combined estimator electrokinetic cell system state-of-charge and the method for health status |
| CN106443471A (en) * | 2016-09-20 | 2017-02-22 | 首都师范大学 | System on chip (SOC) estimation method for lithium ion battery and hardware implementation of estimation method |
| CN107741569A (en) * | 2017-11-16 | 2018-02-27 | 温州大学 | A kind of evaluation method of the lithium battery charge state based on segment extension Kalman filtering |
| CN108872873A (en) * | 2018-06-29 | 2018-11-23 | 长沙理工大学 | A kind of lithium iron phosphate dynamic battery state-of-charge joint estimate method based on GA-AUKF |
| CN109633479A (en) * | 2019-01-11 | 2019-04-16 | 武汉理工大学 | Lithium battery SOC estimation on line method based on built-in capacitor G-card Kalman Filtering |
| CN109856556A (en) * | 2019-03-21 | 2019-06-07 | 南京工程学院 | A kind of power battery SOC estimation method |
| CN110068772A (en) * | 2019-05-06 | 2019-07-30 | 重庆大学 | Based on the charge states of lithium ion battery estimation method for improving fractional model |
| CN110082684A (en) * | 2019-05-13 | 2019-08-02 | 南京邮电大学 | A kind of lithium battery SOC estimation method based on the more new breath Extended Kalman filter of weighting |
| CN111948539A (en) * | 2019-05-17 | 2020-11-17 | 天津科技大学 | Kalman filtering lithium ion battery SOC estimation method based on deep reinforcement learning |
| CN110361652A (en) * | 2019-06-26 | 2019-10-22 | 河南理工大学 | A kind of Kalman filtering lithium battery SOC estimation method based on Model Parameter Optimization |
| CN110398691A (en) * | 2019-06-26 | 2019-11-01 | 重庆大学 | Based on the lithium-ion-power cell SoC estimation method for improving adaptive double Unscented kalman filtering devices |
| CN110502778A (en) * | 2019-07-02 | 2019-11-26 | 江苏大学 | A kind of adaptive optimization method based on Kalman filtering frame estimation battery SOC |
| CN111007400A (en) * | 2019-11-22 | 2020-04-14 | 西安工程大学 | Lithium battery SOC estimation method based on self-adaptive double-extended Kalman filtering method |
| CN111060834A (en) * | 2019-12-19 | 2020-04-24 | 中国汽车技术研究中心有限公司 | Power battery state of health estimation method |
| CN111428433A (en) * | 2020-04-10 | 2020-07-17 | 湖南经研电力设计有限公司 | Lithium ion battery state calculation method based on hybrid filtering |
| CN111458646A (en) * | 2020-05-25 | 2020-07-28 | 安徽理工大学 | Lithium battery SOC estimation method based on PSO-RBF neural network |
| CN112327166A (en) * | 2020-10-21 | 2021-02-05 | 合肥工业大学 | Lithium battery SOC estimation method based on fractional order square root unscented Kalman filter |
| CN112733411A (en) * | 2020-12-18 | 2021-04-30 | 武汉大学 | Lithium battery SOC estimation method and system based on second-order differential particle filter |
| CN114839550A (en) * | 2022-04-14 | 2022-08-02 | 安徽理工大学 | UKF-AUKF-based lithium battery SOC joint estimation method |
Non-Patent Citations (3)
| Title |
|---|
| State of charge estimation for lithium-ion battery based on artificial neural network;Ying Wei 等;2021 IAEAC》;第2021-03-12卷;2454-2458 * |
| 基于安时积分和无迹卡尔曼滤波的锂离子电池SOC估算方法研究;丁镇涛;邓涛;李志飞;尹燕莉;;中国机械工程(15);63-70 * |
| 电动车辆锂离子动力电池建模方法综述;胡晓松;唐小林;;机械工程学报(16);34-45 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112858928A (en) | 2021-05-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112858928B (en) | Lithium battery SOC estimation method based on online parameter identification | |
| CN113030752B (en) | Online parameter identification and SOC joint estimation method based on variable forgetting factor | |
| CN111505506A (en) | Battery SOC estimation method integrating multi-scale Kalman filtering and unscented Kalman filtering | |
| CN113049962B (en) | LSTM-based energy storage device operation situation deduction method | |
| Ma et al. | Co-estimation of state of charge and state of health for lithium-ion batteries based on fractional-order model with multi-innovations unscented Kalman filter method | |
| CN112067998A (en) | Lithium ion battery state of charge estimation method based on deep neural network | |
| CN113740736B (en) | Electric vehicle lithium battery SOH estimation method based on deep network self-adaption | |
| CN107741568B (en) | Lithium battery SOC estimation method based on state transition optimization RBF neural network | |
| CN110208703A (en) | The method that compound equivalent-circuit model based on temperature adjustmemt estimates state-of-charge | |
| CN110658459B (en) | Lithium ion battery state of charge estimation method based on bidirectional cyclic neural network | |
| CN107831448B (en) | A kind of state-of-charge estimation method of parallel connection type battery system | |
| Li et al. | A novel state estimation approach based on adaptive unscented Kalman filter for electric vehicles | |
| CN111458646A (en) | Lithium battery SOC estimation method based on PSO-RBF neural network | |
| CN112083331A (en) | Fusion method for improving lithium ion battery state of charge estimation precision | |
| CN111428433B (en) | Lithium ion battery state calculation method based on hybrid filtering | |
| CN109901072B (en) | Retired battery parameter detection method based on historical data and laboratory test data | |
| CN109633470B (en) | Estimation method for battery real-time full charge time based on EKF-GPR and daily segment data | |
| CN112269133B (en) | SOC estimation method based on pre-charging circuit model parameter identification | |
| CN113777510A (en) | Lithium battery state of charge estimation method and device | |
| CN114114038A (en) | Lithium battery SOC and available capacity joint estimation method under full-life and full-temperature conditions | |
| CN112946481A (en) | Based on federation H∞Filtering sliding-mode observer lithium ion battery SOC estimation method and battery management system | |
| CN113608126B (en) | Lithium battery SOC online prediction method under different temperatures | |
| CN114839550A (en) | UKF-AUKF-based lithium battery SOC joint estimation method | |
| CN115754741A (en) | SOC estimation method and device of energy storage system, equipment and storage medium | |
| CN110232432B (en) | Lithium battery pack SOC prediction method based on artificial life 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 |