CN114859234A - Lithium battery SOC real-time evaluation method considering precision and cost - Google Patents
Lithium battery SOC real-time evaluation method considering precision and cost Download PDFInfo
- Publication number
- CN114859234A CN114859234A CN202210540467.XA CN202210540467A CN114859234A CN 114859234 A CN114859234 A CN 114859234A CN 202210540467 A CN202210540467 A CN 202210540467A CN 114859234 A CN114859234 A CN 114859234A
- Authority
- CN
- China
- Prior art keywords
- soc
- time
- battery
- real
- ekf
- 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.)
- Pending
Links
Images
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]
- G01R31/367—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]
- G01R31/378—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] specially adapted for the type of battery or accumulator
-
- 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
- G01R31/3842—Arrangements for monitoring battery or accumulator variables, e.g. SoC combining voltage and current measurements
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/10—Energy storage using batteries
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Tests Of Electric Status Of Batteries (AREA)
Abstract
The invention relates to a lithium battery SOC real-time evaluation method giving consideration to both precision and cost.A current and voltage data of a working battery monomer is collected in real time according to an ampere-hour integration (Ah) method to evaluate the SOC of the battery monomer; when the accumulated working time reaches the 'ampere-hour integral working time', correcting the SOC obtained by the ampere-hour integral method by adopting a double-extended Kalman filtering algorithm; when the calibration accumulated time reaches the Kalman working time, stopping calibration; until the battery cell operation is finished. In the invention, the ampere-hour integral working time (T) is determined by an error threshold value Ah ) Therefore, the real-time SOC evaluation error does not exceed the error threshold value specified by the project; because the double-EKF algorithm has stronger correction capability, the Kalman working time T ekf Generally smaller, and therefore the computation time increases compared to the Ah method aloneIs not large; the method has good practical value in the application of the underwater vehicle electric power system SOC.
Description
Technical Field
The invention belongs to a lithium battery SOC real-time evaluation method, and relates to a lithium battery SOC real-time evaluation method considering both precision and cost.
Background
The power type lithium battery has the characteristics of high safety, long cycle life, excellent power characteristics, low use cost, simple later maintenance and the like, and is the most ideal power source of the current industrial scientific underwater vehicle. According to the state of charge (SOC) of the lithium battery, the battery management system reasonably distributes the energy of the battery and ensures that the aircraft can fully finish the set task. The SOC represents a ratio of an available charge of the battery in a current state to an available charge of the battery in a full state, which is a state quantity that cannot be directly measured. And evaluating by a corresponding algorithm.
Currently, among all SOC estimation methods, the ampere-hour integration (Ah) method is very good for equalizing the accuracy and computational complexity of the method, and is therefore favored in engineering applications. However, the method belongs to an open-loop algorithm, is easily interfered by external environment and measurement noise, and estimation errors are gradually accumulated along with the working time, so that the SOC estimation precision is greatly reduced at the end of discharging. The Kalman filtering algorithm is a model-based SOC (system on chip) evaluation method, belongs to a closed-loop control algorithm, and can be adjusted in real time according to an evaluation error, so that the method has higher prediction precision. Correspondingly, the algorithm needs real-time evaluation of circuit model parameters, EKF updating and calibration calculation steps, and the calculation load is heavy, so that the application of the method in the engineering field is limited.
Therefore, how to establish a lithium battery SOC real-time evaluation method which gives consideration to both precision and cost according to the existing method to be applied to practical engineering is very urgent.
Disclosure of Invention
Technical problem to be solved
In order to avoid the defects of the prior art, the invention provides a lithium battery SOC real-time evaluation method considering both precision and cost.
Technical scheme
A lithium battery SOC real-time evaluation method giving consideration to both precision and cost is characterized by comprising the following steps:
step 1: and (3) evaluating the SOC of the battery monomer in real time by adopting an ampere-hour integral (Ah) method:
wherein: k and k-1 represent experimental data sampling time points; i.e. i L The sampling current of the working battery monomer at the k-1 moment is represented; η represents the coulombic efficiency; data sampling interval denoted at; c n Represents the maximum available capacity of the battery;
step 2: when the accumulated working time reaches 'ampere hour' integral working time, namely T Ah Correcting the SOC obtained by the ampere-hour integration method by adopting a double extended Kalman filtering algorithm double-EKF; when the calibration accumulated time reaches' Kalman working time T EKF When yes, stopping calibration and returning to the step 1;
and step 3: and (3) repeating the step 1 and the step 2 all the time during the working time of the single battery, and evaluating the SOC of the lithium battery in real time.
The ampere-hour integral working time is T Ah "and" Kalman operating time T EKF "is determined as the following steps:
1. adding random noise to current data obtained by actual operation of the battery as the determined T Ah And T EKF Current data of (a);
2. evaluating the SOC of the battery in real time by using the current data of the noise through an ampere-hour integration method, comparing the SOC with an experimental result, and calculating the deviation delta SOC of the SOC and the SOC;
3. when Δ SOC>3% and recording the cumulative time as T Ah_1 (ii) a Repeating the steps 2 and 3 until the battery is discharged to the lower cut-off voltage, and recording the time T Ah_i
4. Replacing an Ah method with a double-EKF algorithm, evaluating the SOC of the battery in real time according to current data of noise and voltage data obtained by actual operation of the battery, comparing the SOC with an experimental result, and calculating the deviation delta SOC of the SOC and the SOC;
5. when Δ SOC<1%, recording the cumulative time as T EKF_1 (ii) a Until the battery is discharged to the lower cut-off voltageRepeating the step 4 and the step 5, and recording the time as T EKF_i ；
6. To ensure SOC prediction accuracy, take T Ah_i The smallest value in the above is taken as T Ah Taking T EKF_i The maximum value of T EKF 。
The random noise adopts random normal distribution noise with the mean value of 0 and the variance of 0.5A.
The coulombic efficiency eta takes a value of 1.
The sampling time frequency value is 1.
The double extended Kalman filter algorithm double-EKF is as follows: identifying battery model parameters and the current SOC of the battery in real time respectively by adopting two extended Kalman filtering algorithms; wherein, the battery model parameter adopts a first-order RC equivalent circuit model.
The ampere-hour integral working time T Ah The deviation of the SOC estimation from the true value is greater than 3%.
The Kalman operating time T EKF The deviation of the SOC estimation from the true value is less than 1%.
Advantageous effects
According to the lithium battery SOC real-time evaluation method considering both precision and cost, the SOC of a single battery is evaluated according to an ampere-hour integral (Ah) method and current and voltage data of the working battery monomer collected in real time; when the accumulated working time reaches the 'ampere-hour integral working time', correcting the SOC obtained by the ampere-hour integral method by adopting a double-extended Kalman filtering algorithm; when the calibration accumulated time reaches the Kalman working time, stopping calibration; until the battery cell operation is finished.
The beneficial effects are as follows:
1. in the invention, the ampere-hour integral working time (T) is determined by an error threshold value Ah ) Therefore, the real-time SOC evaluation error does not exceed the error threshold value specified by the project;
2. in the invention, because the double-EKF algorithm has stronger correction capability, the Kalman working time T ekf Generally, the method is small, so that compared with the Ah method alone, the calculation time is not greatly increased;
3. the method has good practical value in the application of the underwater vehicle electric power system SOC.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention
FIG. 2 shows T of the present invention Ah And T EKF Flow chart of determination mode
Detailed Description
The invention will now be further described with reference to the following examples and drawings:
the lithium battery SOC real-time evaluation method considering both precision and cost comprises the following steps:
step 1: collecting current and voltage data of a working battery monomer in real time and recording the current and voltage data;
step 2: estimating the SOC of the battery monomer in real time according to an ampere-hour integration (Ah) method;
and step 3: integrating the working time (T) when the accumulated working time reaches' ampere hour Ah ) Correcting the SOC obtained by the ampere-hour integration method by adopting a double extended Kalman filter algorithm (double-EKF); when the calibration accumulated time reaches' Kalman working time (T) EKF ) When yes, stopping calibration;
and 4, step 4: and repeating the second step and the third step until the work of the battery cell is finished.
In the step 1, the collection frequency of the voltage and the current of the battery monomer is fixed, and the frequency value is 1.
The method for evaluating the SOC of the battery in real time according to the ampere-hour integral method in the step 2 comprises the following steps:
in the formula (I), the compound is shown in the specification,
k and k-1 represent experimental data sampling time points;
i L represents the current at time k-1;
η represents the coulombic efficiency, and here takes the value of 1;
data sampling interval denoted at;
C n representing the maximum available capacity of the battery.
And 3, representing that the double-EKF identifies the battery model parameters and the current SOC of the battery respectively in real time by adopting two extended Kalman filtering algorithms. Wherein, the battery model parameter adopts a first-order RC equivalent circuit model.
Step 3, ampere-hour integral working time (T) Ah ) "and" Kalman time of operation (T) EKF ) The determining method is determined by a project precision allowable threshold, and in the invention, when the deviation of the SOC evaluation value and the true value is more than 3 percent (maximum error), a double-EKF algorithm is required for calibration; when the deviation of the SOC estimated value and the true value is lower than 1 percent (error negligible value), the error is considered to be negligible, and the ampere-hour integration method can be continuously adopted to carry out real-time SOC estimation on the single battery. Determination of T Ah And T EKF The specific procedure of (1) is as follows.
1) In order to increase the reliability of the calibration time, a certain degree of random noise is added to the current data obtained by the actual operation of the battery and is used as the determined T Ah And T EKF Current data of (a). The invention adopts the random normal distribution noise with the mean value of 0 and the variance of 0.5A.
2) According to the current data with noise added in the i, estimating the SOC of the battery in real time by an ampere-hour integration method, comparing the SOC with an experimental result, and calculating the deviation delta SOC of the SOC and the SOC;
3) when Δ SOC>3% and recording the cumulative time as T Ah_1 ；
4) And (3) replacing an Ah method with a double-EKF algorithm, evaluating the SOC of the battery in real time according to the current data of the noise added in the i and the voltage data obtained by the actual operation of the battery, comparing the SOC with an experimental result, and calculating the deviation delta SOC of the SOC and the SOC.
5) When Δ SOC<1%, recording the cumulative time as T EKF_1 ；
6) Repeating steps ii, iii with a recording time T Ah_2 ；
7) Repeating steps iv, v with a recording time T EKF_2 ；
8) Repeating and recording T Ah_1 ，T EKF_1 ，T Ah_2 ，T EKF_2 … … until the battery discharges to a lower cutoff voltage;
to ensure SOC prediction accuracy, take T Ah_i The smallest value in the above is taken as T Ah Taking T EKF_i The maximum value of T EKF 。
Claims (8)
1. A lithium battery SOC real-time evaluation method giving consideration to both precision and cost is characterized by comprising the following steps:
step 1: and (3) evaluating the SOC of the battery monomer in real time by adopting an ampere-hour integral (Ah) method:
wherein: k and k-1 represent experimental data sampling time points; i.e. i L The sampling current of the working battery monomer at the k-1 moment is represented; η represents the coulombic efficiency; data sampling interval denoted at; c n Represents the maximum available capacity of the battery;
step 2: when the accumulated working time reaches 'ampere hour' integral working time, namely T Ah Correcting the SOC obtained by the ampere-hour integration method by adopting a double extended Kalman filtering algorithm double-EKF; when the calibration accumulated time reaches' Kalman working time T EKF When yes, stopping calibration and returning to the step 1;
and step 3: and (3) repeating the step 1 and the step 2 all the time during the working time of the single battery, and evaluating the SOC of the lithium battery in real time.
2. The lithium battery SOC real-time evaluation method considering both precision and cost according to claim 1, wherein: the ampere-hour integral working time is T Ah "and" Kalman operating time T EKF "is determined as the following steps:
1) adding random noise to current data obtained by actual operation of the battery as the determined T Ah And T EKF Current data of (a);
2) evaluating the SOC of the battery in real time by using current data of noise through an ampere-hour integration method, comparing the SOC with an experimental result, and calculating the deviation delta SOC of the battery and the SOC;
3) when inΔSOC>3% and recording the cumulative time as T Ah_1 (ii) a Repeating the steps 2 and 3 until the battery is discharged to the lower cut-off voltage, and recording the time T Ah_i
4) Replacing an Ah method with a double-EKF algorithm, evaluating the SOC of the battery in real time according to current data of noise and voltage data obtained by actual operation of the battery, comparing the SOC with an experimental result, and calculating the deviation delta SOC of the SOC and the SOC;
5) when delta SOC<1%, recording the cumulative time as T EKF_1 (ii) a Repeating the steps 4 and 5 until the battery is discharged to the lower cut-off voltage, and recording the time T EKF_i ；
6) Taking T to ensure the SOC prediction precision Ah_i The smallest value in the above is taken as T Ah Taking T EKF_i The maximum value of T EKF 。
3. The lithium battery SOC real-time evaluation method considering both precision and cost according to claim 1, wherein: the random noise adopts random normal distribution noise with the mean value of 0 and the variance of 0.5A.
4. The lithium battery SOC real-time evaluation method considering both precision and cost according to claim 1, wherein: the coulombic efficiency eta takes a value of 1.
5. The lithium battery SOC real-time evaluation method considering both precision and cost according to claim 1, wherein: the sampling time frequency value is 1.
6. The lithium battery SOC real-time evaluation method considering both precision and cost according to claim 1, wherein: the double extended Kalman Filter algorithm double-EKF is as follows: identifying battery model parameters and the current SOC of the battery in real time respectively by adopting two extended Kalman filtering algorithms; wherein, the battery model parameter adopts a first-order RC equivalent circuit model.
7. The lithium battery SOC real-time evaluation method considering both precision and cost according to claim 1The method is characterized in that: the ampere-hour integral working time T Ah The deviation of the SOC estimation from the true value is greater than 3%.
8. The lithium battery SOC real-time evaluation method considering both precision and cost according to claim 1, wherein: the Kalman operating time T EKF The deviation of the SOC estimation from the true value is less than 1%.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210540467.XA CN114859234A (en) | 2022-05-17 | 2022-05-17 | Lithium battery SOC real-time evaluation method considering precision and cost |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210540467.XA CN114859234A (en) | 2022-05-17 | 2022-05-17 | Lithium battery SOC real-time evaluation method considering precision and cost |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114859234A true CN114859234A (en) | 2022-08-05 |
Family
ID=82638357
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210540467.XA Pending CN114859234A (en) | 2022-05-17 | 2022-05-17 | Lithium battery SOC real-time evaluation method considering precision and cost |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114859234A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117572269A (en) * | 2023-11-09 | 2024-02-20 | 东莞市科路得新能源科技有限公司 | SOC measuring and calculating method and method for displaying value thereof |
-
2022
- 2022-05-17 CN CN202210540467.XA patent/CN114859234A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117572269A (en) * | 2023-11-09 | 2024-02-20 | 东莞市科路得新能源科技有限公司 | SOC measuring and calculating method and method for displaying value thereof |
| CN117572269B (en) * | 2023-11-09 | 2024-05-31 | 东莞市科路得新能源科技有限公司 | SOC measuring and calculating method and method for displaying value thereof |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP6897748B2 (en) | Power storage element state estimation device and power storage element state estimation method | |
| CN107368619B (en) | Extended Kalman filtering SOC estimation method | |
| CN107576918B (en) | Method and system for estimating residual electric quantity of lithium battery | |
| CN109507611B (en) | SOH correction method and system for electric vehicle | |
| JP6863560B2 (en) | Battery resistance estimation device and method | |
| CN103163480B (en) | The appraisal procedure of lithium battery health status | |
| CN104617623B (en) | A kind of electric automobile power battery group balance control method | |
| CN111679212B (en) | Method, system and device for calculating SOC of battery under different temperatures and charge-discharge multiplying power | |
| CN107219466A (en) | A kind of lithium battery SOC estimation method for mixing EKF | |
| CN109633473B (en) | Distributed battery pack state of charge estimation algorithm | |
| CN104360285A (en) | Battery capacity correction method based on improved ampere-hour integral method | |
| Kim et al. | Adaptive battery state-of-charge estimation method for electric vehicle battery management system | |
| CN108287316B (en) | Storage battery residual capacity estimation method based on threshold value extended Kalman algorithm | |
| JP2013531780A (en) | Lithium ion battery charge state calculation method | |
| CN110895310A (en) | SOC (state of charge) estimation system of lithium iron phosphate battery | |
| CN104502847A (en) | Pre-estimate method for SOH (state of health) of power battery of electric car | |
| CN105548906A (en) | Method for dynamically estimating battery state of charge | |
| CN113625174B (en) | Lithium ion battery SOC and capacity joint estimation method | |
| Khalil et al. | SOC prediction of lithium-ion battery using extended Kalman filter | |
| CN112858920B (en) | SOC estimation method of all-vanadium redox flow battery fusion model based on adaptive unscented Kalman filtering | |
| CN113589179B (en) | Power battery formation process state estimation method based on convex spatial filtering | |
| CN114859234A (en) | Lithium battery SOC real-time evaluation method considering precision and cost | |
| CN112649747A (en) | Fractional order extended Kalman lithium battery SOC estimation method | |
| CN112528472A (en) | Multi-innovation hybrid Kalman filtering and H-infinity filtering algorithm | |
| CN112946481A (en) | Based on federation H∞Filtering sliding-mode observer lithium ion battery SOC estimation method and battery management system |
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 |