CN113687240A - Power battery SOH estimation method based on big data drive - Google Patents
Power battery SOH estimation method based on big data drive Download PDFInfo
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- 239000000178 monomer Substances 0.000 claims description 5
- 238000004364 calculation method Methods 0.000 abstract description 9
- 238000005259 measurement Methods 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 2
- GELKBWJHTRAYNV-UHFFFAOYSA-K lithium iron phosphate Chemical compound [Li+].[Fe+2].[O-]P([O-])([O-])=O GELKBWJHTRAYNV-UHFFFAOYSA-K 0.000 description 2
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- 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
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- 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
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Abstract
The invention discloses a power battery SOH estimation method based on big data driving, which comprises the following working steps: (1) carrying out laboratory aging test on the power battery, and immediately carrying out standard power battery capacity test to obtain real SOH (state of health) after each aging test in one stage0And recording the test process data; (2) the laboratory aging test process data and the real SOH are compared0Substituting a fitting formula to calculate parameters; (3) determining parameters through multilayer iteration until the error is less than 1%; (4) and acquiring the running data of the power battery of the single vehicle through the big data. The algorithm integrates laboratory standard power battery capacity test data, effectively eliminates the calculation accumulated error of the SOH of the single vehicle power battery, the SOH calculated by the integrated single vehicle power battery can effectively reflect the special working condition of the operation of the single vehicle power battery, and the SOH calculation precision can be effectively improved after the SOH calculated by weighted average is used for replacing the SOH of the single vehicle.
Description
Technical Field
The invention relates to the technical field of new energy power batteries, in particular to a power battery SOH estimation method based on big data driving.
Background
With the rapid development of new energy automobiles, the echelon utilization of the power battery becomes an important way for solving the problem of power battery recovery, the accurate estimation of the SOH (index for evaluating the aging degree of the power battery) of the power battery is significant, the estimation accuracy of the mainstream SOH of the current lithium iron phosphate power battery is about 5%, and the echelon utilization detection accuracy of the lithium iron phosphate power battery can not be basically met.
The comparison document CN110988725A discloses an estimation system and method for a power battery SoH based on a big data platform, which includes a data acquisition end, a microprocessor, a storage unit, a communication module, a cloud computing center, a display end and a vehicle networking database, wherein an output end of the data acquisition end is connected with an input end of the microprocessor through a data line, and the estimation method for the estimation system for the power battery SoH based on the big data platform specifically includes the following steps: s1: when leaving the factory, measuring and uploading battery data; s2: analyzing and storing the charge and discharge of the battery; s3: analyzing and storing the energy consumption of the battery; s4: obtaining a fitting curve relative to SoH, analyzing massive data, and avoiding misjudgment caused by data limitation; and a rich data pool related to the battery is convenient for the system to perform comprehensive operation. And (3) establishing charging and discharging habit analysis and driving behavior analysis, and obviously improving the SOH estimation accuracy. ", the above-mentioned device has low accuracy in data estimation and is inconvenient to perform effective data estimation, resulting in inconvenient use.
In the prior art, a big data drive-based power battery SoH estimation method has the following defects:
1. in the prior art, the SOH estimation method of the power battery based on big data driving has the problems that the SOH precision of the power battery is not high, the actual residual performance of the power battery cannot be accurately detected, the difficulty is brought to the echelon utilization of the power battery in a group matching mode, the evaluation of a power battery recycling manufacturer is difficult, and the like;
2. in the prior art, when the power battery SoH estimation method based on big data driving is used, workers need to effectively convert the estimation method, but most of conversion effects are complex, so that the use is inconvenient.
3. In the prior art, when the power battery SoH estimation method based on big data driving is used, workers need to accurately budget data of the estimation method, and under the condition that the numerical value is unstable, return stroke calculation needs to be carried out, so that most calculation modes are complex and inconvenient to use.
Disclosure of Invention
The invention aims to provide a power battery SOH estimation method based on big data driving, so as to solve the problems in the background technology.
In order to achieve the above object, the present invention provides the following technical solution, a method for estimating SOH of a power battery based on big data driving, comprising the following working steps:
(1) carrying out laboratory aging test on the power battery, and immediately carrying out standard power battery capacity test to obtain real SOH (state of health) after each aging test in one stage0And recording the test process data;
(2) the laboratory aging test process data and the real SOH are compared0Substituting a fitting formula to calculate parameters;
(3) determining parameters through multilayer iteration until the error is less than 1%;
(4) obtaining the running data of the power battery of a single vehicle (the running data is consistent with the cell model, the grouping mode and the like of the power battery of the laboratory) through big data, and substituting the running data into the fitting formula to calculate the SOH of the power battery of the single vehiclex；
(5) Power battery SOH calculated by laboratory0SOH calculated by single vehicle power batteryxCarrying out weighted average to obtain new SOHnFor replacing the SOH of the power battery of the bicycle.
Preferably, in the step (1), the power battery is subjected to a laboratory aging test, and a standard power battery capacity test is immediately performed every time the aging test of one stage is performed to obtain a true SOH0And recording the measurementTest procedure data; the recorded data comprises the lowest voltage, the total voltage and the discharge electric quantity of the power battery monomer when the discharge is cut off, and data recording is carried out; and then carrying out data recording on the highest voltage, the total voltage and the charged electric quantity of the single body when the charging is stopped, and detecting the average temperature, the total using time length, the average power during discharging and the peak power ratio during discharging of the power battery.
Preferably, in the step (2), the laboratory aging test process data and the real SOH are compared0Substituting a fitting formula to calculate parameters; the SOH fitting equation is:
in the above formula, V is the voltage of the power battery cell, U is the total voltage of the power battery, E is the charge amount, T is the average temperature of the power battery, T0 is the conventional temperature of the battery (generally 20-30 according to practical situations), P is the average discharge power of the power battery, P0 is the nominal discharge power of the power battery, C is the proportion of the peak discharge power, and F is the power.
Preferably, in the step (3), the parameters are determined through multiple iterations until the error is less than 1%: theta1θ2...θn，K。
Preferably, in the step (4), the operation data of the single vehicle power battery (with the same height as the cell model of the laboratory power battery, the grouping mode and the like) is obtained through the big data, and the operation data is substituted into the fitting formula to calculate the SOH of the single vehicle power batteryx；
Preferably, in the step (5), the SOH of the power battery calculated in the laboratory is used0SOH calculated by single vehicle power batteryxCarrying out weighted average to obtain new SOHnFor replacing the SOH of the power battery of the bicycle.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention integrates the laboratory standard capacity test data of the power battery to effectively eliminate the accumulated error of the single vehicle SOH calculation.
2. The SOH calculated by fusing the running data of the power battery of the single vehicle effectively reflects the special working condition of the running of the single vehicle.
3. According to the invention, the calculation of the SOH fitting formula is convenient for effectively estimating the value of the device, the calculation mode is extremely simple and convenient, and the estimated data is more accurate, so that the calculation mode is convenient to popularize and calculate.
4. According to the method, the SOH of the power battery is estimated through actual measurement data and a formula, when the data in comparison has a large error, the modification is convenient, the modification steps are simple, and errors can be found conveniently in the calculation process, so that the result of the worker in modifying and estimating the test data is achieved conveniently.
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FIG. 1 is a flow chart of the operation of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that the terms "upper", "lower", "inner", "outer", "front", "rear", "both ends", "one end", "the other end", and the like indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it is to be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "disposed," "connected," and the like are to be construed broadly, such as "connected," which may be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
The first embodiment is as follows:
when the actual measurement error is less than one percent;
(1) carrying out laboratory aging test on the power battery, and immediately carrying out standard power battery capacity test to obtain real SOH (state of health) after each aging test in one stage0And recording the test process data; the recorded data comprises the lowest voltage, the total voltage and the discharge electric quantity of the power battery monomer when the discharge is cut off, and data recording is carried out; then carrying out data recording on the highest voltage, the total voltage and the charging electric quantity of the single body when the charging is stopped, and detecting the average temperature, the total using time length, the average power during discharging and the peak power ratio during discharging of the power battery;
(2) the laboratory aging test process data and the real SOH are compared0Substituting a fitting formula to calculate parameters; the SOH fitting equation is:
in the above formula, V is the voltage of the power battery cell, U is the total voltage of the power battery, E is the charge capacity, T is the average temperature of the power battery, T0 is the conventional temperature of the battery (generally 20-30 according to the actual situation), P is the average discharge power of the power battery, P0 is the nominal discharge power of the power battery, C is the proportion of the peak discharge power, and F is the power;
(4) through multiple iterations until the error is less than 1%, the parameters can be determined: theta1θ2...θn，K；
(5) Obtaining singles by big dataThe running data of the vehicle power battery (the model of the battery cell of the power battery in the laboratory, the grouping mode and the like are consistent in height) is substituted into the fitting formula to calculate the SOH of the single vehicle power batteryx；
(6) Power battery SOH calculated by laboratory0SOH calculated by single vehicle power batteryxCarrying out weighted average to obtain new SOHnFor replacing the SOH of the power battery of the bicycle.
Example two:
when the actual measurement error equals one percent;
(1) carrying out laboratory aging test on the power battery, and immediately carrying out standard power battery capacity test to obtain real SOH (state of health) after each aging test in one stage0And recording the test process data; the recorded data comprises the lowest voltage, the total voltage and the discharge electric quantity of the power battery monomer when the discharge is cut off, and data recording is carried out; then carrying out data recording on the highest voltage, the total voltage and the charging electric quantity of the single body when the charging is stopped, and detecting the average temperature, the total using time length, the average power during discharging and the peak power ratio during discharging of the power battery;
(2) the laboratory aging test process data and the real SOH are compared0Substituting a fitting formula to calculate parameters; the SOH fitting equation is:
in the above formula, V is the voltage of the power battery cell, U is the total voltage of the power battery, E is the charge capacity, T is the average temperature of the power battery, T0 is the conventional temperature of the battery (generally 20-30 according to the actual situation), P is the average discharge power of the power battery, P0 is the nominal discharge power of the power battery, C is the proportion of the peak discharge power, and F is the power;
(4) performing multi-layer iteration until the error is equal to 1%, and returning to the step one for testing;
(5) obtaining the height of a single vehicle power battery (the battery core type of the power battery in a laboratory, the grouping mode and the like) through big dataConsistent in degree) operating data, and substituting the operating data into the fitting formula to calculate the SOH of the single-vehicle power batteryx；
(6) Power battery SOH calculated by laboratory0SOH calculated by single vehicle power batteryxCarrying out weighted average to obtain new SOHnFor replacing the SOH of the power battery of the bicycle.
Example three:
when the actual measurement error is greater than one percent;
(1) carrying out laboratory aging test on the power battery, and immediately carrying out standard power battery capacity test to obtain real SOH (state of health) after each aging test in one stage0And recording the test process data; the recorded data comprises the lowest voltage, the total voltage and the discharge electric quantity of the power battery monomer when the discharge is cut off, and data recording is carried out; then carrying out data recording on the highest voltage, the total voltage and the charging electric quantity of the single body when the charging is stopped, and detecting the average temperature, the total using time length, the average power during discharging and the peak power ratio during discharging of the power battery;
(2) the laboratory aging test process data and the real SOH are compared0Substituting a fitting formula to calculate parameters; the SOH fitting equation is:
in the above formula, V is the voltage of the power battery cell, U is the total voltage of the power battery, E is the charge capacity, T is the average temperature of the power battery, T0 is the conventional temperature of the battery (generally 20-30 according to the actual situation), P is the average discharge power of the power battery, P0 is the nominal discharge power of the power battery, C is the proportion of the peak discharge power, and F is the power;
(4) performing multi-layer iteration until the error is more than 1%, and returning to the step one for testing;
(5) obtaining the running data of the power battery of a single vehicle (the running data is consistent with the cell model of the power battery in a laboratory, the grouping mode and the like) through big data, and substituting the running data into the fitting formula to calculate the power battery of the single vehicleSOH of (1)x；
(6) Power battery SOH calculated by laboratory0SOH calculated by single vehicle power batteryxCarrying out weighted average to obtain new SOHnFor replacing the SOH of the power battery of the bicycle.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (6)
1. A power battery SOH estimation method based on big data drive is characterized in that: the method comprises the following working steps:
(1) carrying out laboratory aging test on the power battery, and immediately carrying out standard power battery capacity test to obtain real SOH (state of health) after each aging test in one stage0And recording the test process data;
(2) the laboratory aging test process data and the real SOH are compared0Substituting a fitting formula to calculate parameters;
(3) determining parameters through multilayer iteration until the error is less than 1%;
(4) obtaining the running data of the power battery of a single vehicle (the running data is consistent with the cell model, the grouping mode and the like of the power battery of the laboratory) through big data, and substituting the running data into the fitting formula to calculate the SOH of the power battery of the single vehiclex；
(5) Power battery SOH calculated by laboratory0SOH calculated by single vehicle power batteryxCarrying out weighted average to obtain new SOHnFor replacing the SOH of the power battery of the bicycle.
2. The SOH estimation method for the power battery based on the big data drive as claimed in claim 1, wherein: in the step (1), the power battery is subjected to laboratory aging test, and standard power battery capacity test is immediately carried out after each aging test in one stage to obtain real SOH0And recording the test process data; the recorded data comprises the lowest voltage, the total voltage and the discharge electric quantity of the power battery monomer when the discharge is cut off, and data recording is carried out; and then carrying out data recording on the highest voltage, the total voltage and the charged electric quantity of the single body when the charging is stopped, and detecting the average temperature, the total using time length, the average power during discharging and the peak power ratio during discharging of the power battery.
3. The SOH estimation method for the power battery based on the big data drive as claimed in claim 1, wherein: in the step (2), the laboratory aging test process data and the real SOH are compared0Substituting a fitting formula to calculate parameters; the SOH fitting equation is:
in the above formula, V is the voltage of the power battery cell, U is the total voltage of the power battery, E is the charge amount, T is the average temperature of the power battery, T0 is the conventional temperature of the battery (generally 20-30 according to practical situations), P is the average discharge power of the power battery, P0 is the nominal discharge power of the power battery, C is the proportion of the peak discharge power, and F is the power.
4. The SOH estimation method for the power battery based on the big data drive as claimed in claim 1, wherein: in the step (3), the parameters can be determined through multi-layer iteration until the error is less than 1 percent: theta1 θ2...θn，K。
5. Power battery based on big data drive according to claim 1The SOH estimation method is characterized in that: in the step (4), the operation data of the power battery of a single vehicle (the operation data is consistent with the cell model of the power battery in a laboratory, the grouping mode and the like) is obtained through big data, and the SOH of the power battery of the single vehicle is calculated by substituting the operation data into the fitting formulax。
6. The SOH estimation method for the power battery based on the big data drive as claimed in claim 1, wherein: in the step (5), the SOH of the power battery calculated in the laboratory is used0SOH calculated by single vehicle power batteryxCarrying out weighted average to obtain new SOHnFor replacing the SOH of the power battery of the bicycle.
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| WO2020251854A1 (en) * | 2019-06-14 | 2020-12-17 | Cummins Inc. | Methods and devices for determining battery state of health using incremental capacity analysis and support vector regression |
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- 2021-09-24 CN CN202111119039.1A patent/CN113687240A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2597479A2 (en) * | 2011-11-25 | 2013-05-29 | Honeywell International Inc. | Method and apparatus for online determination of battery state of charge and state of health |
| CN108549035A (en) * | 2018-04-20 | 2018-09-18 | 成都雅骏新能源汽车科技股份有限公司 | A method of SOH is estimated based on big data |
| CN108535661A (en) * | 2018-05-18 | 2018-09-14 | 东北大学 | A kind of power battery health status On-line Estimation method based on model error spectrum |
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