CN113514770A - Lithium battery residual capacity SOC prediction algorithm based on open-circuit voltage and battery temperature drive - Google Patents
Lithium battery residual capacity SOC prediction algorithm based on open-circuit voltage and battery temperature drive Download PDFInfo
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- CN113514770A CN113514770A CN202110528373.6A CN202110528373A CN113514770A CN 113514770 A CN113514770 A CN 113514770A CN 202110528373 A CN202110528373 A CN 202110528373A CN 113514770 A CN113514770 A CN 113514770A
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- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 title claims abstract description 28
- 229910052744 lithium Inorganic materials 0.000 title claims abstract description 28
- 238000000034 method Methods 0.000 claims abstract description 11
- 230000008569 process Effects 0.000 claims abstract description 5
- 238000001914 filtration Methods 0.000 claims description 3
- 238000013178 mathematical model Methods 0.000 claims description 3
- 230000009467 reduction Effects 0.000 claims description 3
- 238000012360 testing method Methods 0.000 claims description 3
- 230000006870 function Effects 0.000 description 5
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 4
- 229910001416 lithium ion Inorganic materials 0.000 description 4
- 238000004146 energy storage Methods 0.000 description 3
- 238000011161 development Methods 0.000 description 2
- 230000036541 health Effects 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 239000002253 acid Substances 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 238000007599 discharging Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000004880 explosion Methods 0.000 description 1
- 229910001385 heavy metal Inorganic materials 0.000 description 1
- 229910052739 hydrogen Inorganic materials 0.000 description 1
- 239000001257 hydrogen Substances 0.000 description 1
- 230000002427 irreversible effect Effects 0.000 description 1
- 230000008447 perception Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
Classifications
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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]
- G01R31/385—Arrangements for measuring battery or accumulator variables
- G01R31/387—Determining ampere-hour charge capacity or SoC
-
- 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
Abstract
The invention discloses a lithium battery residual capacity SOC prediction algorithm based on open-circuit voltage and battery temperature drive, which is characterized in that discharge parameters in the discharge process of a lithium battery are collected according to frequency, the discharge parameters comprise discharge current, open-circuit voltage, battery temperature and discharge time, a battery residual capacity model is established based on an ampere-hour method, the estimated battery residual capacity and the open-circuit voltage are subjected to linear fitting to obtain a mathematical relation between the open-circuit voltage and the battery residual capacity SOC, and further the predicted value of the real-time residual capacity of the battery is obtained. The method has higher state estimation precision, plays a role in early warning of thermal runaway of the lithium battery, and improves the use safety of the lithium battery.
Description
Technical Field
The invention belongs to the technical field of management and control of lithium battery BMS (battery management system), and particularly relates to a prediction algorithm of a battery residual capacity SOC (state of charge).
Background
The development of energy storage systems and new energy automobile industry has pushed the development of energy storage batteries and power batteries. Compared with lead-acid and nickel-hydrogen batteries, the lithium ion battery has the advantages of high energy ratio, long service life and single body
The lithium battery has the advantages of high working voltage, low self-discharge rate, strong high-low temperature adaptability, no harmful heavy metal and the like, so that the lithium battery becomes the first choice of energy storage batteries and power batteries and is widely applied to practical systems. For lithium ion batteries, in the actual use process, the requirements of large capacity and high voltage are usually required to be met, and a single battery often cannot meet the requirements, so the lithium ion batteries are usually used by forming a battery pack through series-parallel connection. However, safety and consistency limit the widespread use of lithium ion batteries. Lithium batteries must operate in a reliable region, i.e., limited to a specific temperature, voltage and current range. Exceeding this area can result in irreversible damage or even explosion of the battery. Therefore, the lithium Battery pack requires a Battery Management System (BMS) to manage it. By monitoring the basic parameters of the single batteries in the battery pack in real time, the BMS realizes parameter acquisition, fault diagnosis and fault protection on the batteries so that each single battery can operate within a safety range, and the inconsistency among the batteries is balanced through an equalizing circuit. State estimation is a main function of BMS, including real-time estimation of soh (state of health), soc (state of charge), and thermal state, etc. The SOH reflects the ratio of the current state of the battery to the ideal state for the state of health of the battery. The SOC is an important parameter of the battery, reflects the relative size of the remaining capacity of the battery, and is an important task of the battery management system to estimate the SOC in real time. The thermal state is the temperature change condition of the lithium battery under different loads. High-precision modeling is a core technology of a lithium battery module, so that state estimation is performed on the SOC, the SOH and the thermal state of a lithium battery, and the state estimation is used as a basis for charging and discharging, balance control and state monitoring of a lithium Battery Management System (BMS).
At present, a great deal of research is carried out at home and abroad aiming at battery management, but the existing method only uses single open-circuit voltage or ampere-hour integral as a state estimation model, ignores other factors such as a thermal model and the like, and causes the problems of low state estimation precision and lack of perception capability on hidden dangers such as thermal runaway and the like.
Disclosure of Invention
In order to solve the technical problems, the invention provides the SOC prediction algorithm for driving the residual capacity of the lithium battery based on the open-circuit voltage and the battery temperature, which has higher state estimation precision, plays a role in early warning of thermal runaway of the lithium battery and improves the use safety of the lithium battery.
The technical scheme adopted by the invention is as follows: a lithium battery residual capacity SOC prediction algorithm based on open-circuit voltage and battery temperature driving realizes the safety control of a battery by accurately estimating and feeding back the battery residual capacity, and is characterized by comprising the following steps:
s1, carrying out discharge test on the lithium battery, collecting discharge parameters in the discharge process of the lithium battery according to frequency, wherein the discharge parameters comprise discharge current, open-circuit voltage, battery temperature and discharge time, and carrying out filtering and noise reduction treatment;
s2, according to the data collected in the step S1, a mathematical model of the battery residual capacity SOC is established based on an ampere-hour method to obtain the battery residual capacity SOC in the current state;
in the formula, SOCk+1The estimated value of the residual capacity of the battery is obtained when the (k + 1) th data is acquired; SOCkThe k-th data acquisition time is the residual capacity value; η is the battery discharge efficiency; c0For rated capacity of battery, ikIs the load current of the battery; delta t is the time interval from the kth to the k +1 th data acquisition; k and n are constants of a Pockets empirical formula; t is the battery temperature;
s3, performing linear fitting on the correspondence relationship between the open-circuit voltage and the current battery remaining capacity SOC to obtain a mathematical relationship between the open-circuit voltage and the battery remaining capacity SOC, where:
f(z)＝a+bz+cz2+dz3 0≤z≤100％ (2)
where z represents the battery remaining capacity SOC, f (z) is a function of the open circuit voltage with respect to the battery remaining capacity, and the parameters a, b, c, d are linear fitting parameters.
S4, the battery real-time remaining capacity prediction value y is obtained by the following formula:
wherein y is the real-time remaining capacity of the battery, T is the battery temperature, z is the remaining capacity of the battery, and f is the turn-off of the battery open-circuit voltage and SOCA system function, X is a battery system state quantity and represents the dynamic characteristic of the battery, ikAnd c and d are linear fitting parameters in the formula (2).
Further, in step S2, SOC values of the battery remaining capacity at different battery temperatures are obtained, and a quadratic polynomial is used to perform smooth fitting regression to obtain a relationship between K and n and the battery temperature T.
Has the advantages that: compared with the prior art, the invention has the advantages of simple structure, low cost and high efficiency.
Detailed Description
The present invention will be described in detail with reference to specific embodiments in order to make those skilled in the art better understand the technical solutions of the present invention.
The invention discloses a lithium battery residual capacity SOC prediction algorithm, which realizes the safety control of a battery by accurately estimating and feeding back the residual capacity of the battery, and is characterized by comprising the following steps:
s1, carrying out discharge test on the lithium battery, collecting discharge parameters in the discharge process of the lithium battery according to frequency, wherein the discharge parameters comprise discharge current, open-circuit voltage, battery temperature and discharge time, and carrying out filtering and noise reduction treatment;
s2, according to the data collected in the step S1, a mathematical model of the battery residual capacity SOC is established based on an ampere-hour method to obtain the battery residual capacity SOC in the current state;
in the formula, SOCk+1The estimated value of the residual capacity of the battery is obtained when the (k + 1) th data is acquired; SOCkThe k-th data acquisition time is the residual capacity value; η is the battery discharge efficiency; c0For rated capacity of battery, ikIs the load current of the battery; delta t is the time interval from the kth to the k +1 th data acquisition; k and n are constants of a Pockets empirical formula; t is the battery temperature;
s3, performing linear fitting on the correspondence relationship between the open-circuit voltage and the current battery remaining capacity SOC to obtain a mathematical relationship between the open-circuit voltage and the battery remaining capacity SOC, where:
f(z)＝a+bz+cz2+dz3 0≤z≤100％ (2)
where z represents the battery remaining capacity SOC, f (z) is a function of the open circuit voltage with respect to the battery remaining capacity, and the parameters a, b, c, d are linear fitting parameters.
S4, the battery real-time remaining capacity prediction value y is obtained by the following formula:
wherein y is the real-time remaining capacity of the battery, T is the temperature of the battery, z is the remaining capacity of the battery, f is a function of the open-circuit voltage of the battery and the SOC, X is the state quantity of the battery system and represents the dynamic characteristic of the battery, and ikAnd c and d are linear fitting parameters in the formula (2).
Further, in step S2, SOC values of the battery remaining capacity at different battery temperatures are obtained, and a quadratic polynomial is used to perform smooth fitting regression to obtain a relationship between K and n and the battery temperature T.
Finally, it should be noted that the above-mentioned description is only a preferred embodiment of the present invention, and those skilled in the art can make various similar representations without departing from the spirit and scope of the present invention.
Claims (2)
1. A lithium battery residual capacity SOC prediction algorithm based on open-circuit voltage and battery temperature driving is characterized by comprising the following steps:
s1, carrying out discharge test on the lithium battery, collecting discharge parameters in the discharge process of the lithium battery according to frequency, wherein the discharge parameters comprise discharge current, open-circuit voltage, battery temperature and discharge time, and carrying out filtering and noise reduction treatment;
s2, according to the data collected in the step S1, a mathematical model of the battery residual capacity SOC is established based on an ampere-hour method to obtain the battery residual capacity SOC in the current state;
in the formula, SOCk+1The estimated value of the residual capacity of the battery is obtained when the (k + 1) th data is acquired; SOCkThe k-th data acquisition time is the residual capacity value; η is the battery discharge efficiency; c0For rated capacity of battery, ikIs the load current of the battery; delta t is the time interval from the kth time to the k +1 th time of data acquisition; k and n are constants of a Pockets empirical formula; t is the battery temperature;
s3, performing linear fitting on the correspondence relationship between the open-circuit voltage and the current battery remaining capacity SOC to obtain a mathematical relationship between the open-circuit voltage and the battery remaining capacity SOC, where:
f(z)＝a+bz+cz2+dz3 0≤z≤100％ (2)
where z represents the battery remaining capacity SOC, f (z) is a function of the open circuit voltage with respect to the battery remaining capacity, and the parameters a, b, c, d are linear fitting parameters.
S4, the battery real-time remaining capacity prediction value y is obtained by the following formula:
wherein y is the real-time remaining capacity of the battery, T is the temperature of the battery, z is the remaining capacity of the battery, f is a function of the open-circuit voltage of the battery and the SOC, X is the state quantity of the battery system and represents the dynamic characteristic of the battery, and ikAnd c and d are linear fitting parameters in the formula (2).
2. The SOC prediction algorithm for lithium battery driven based on open circuit voltage and battery temperature according to claim 1, wherein: in step S2, battery remaining capacity SOC values at different battery temperatures are obtained, and a quadratic polynomial is used to perform smooth fitting regression to obtain the relationship between K and n and the battery temperature T.
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Cited By (5)
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| CN114325427A (en) * | 2021-11-16 | 2022-04-12 | 深圳供电局有限公司 | Method and device for estimating residual capacity of storage battery and storage medium |
| CN115639482A (en) * | 2022-12-22 | 2023-01-24 | 江苏欧力特能源科技有限公司 | Method and device for estimating remaining battery capacity |
| WO2023116524A1 (en) * | 2021-12-24 | 2023-06-29 | 长城汽车股份有限公司 | Battery soc estimation method and related apparatus |
| CN116699412A (en) * | 2023-05-17 | 2023-09-05 | 盐城工学院 | Residual capacity estimation method of energy storage battery module |
| CN117686918A (en) * | 2024-01-31 | 2024-03-12 | 深圳市卓芯微科技有限公司 | Battery SOC prediction method, device, battery management equipment and storage medium |
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| CN116699412A (en) * | 2023-05-17 | 2023-09-05 | 盐城工学院 | Residual capacity estimation method of energy storage battery module |
| CN117686918A (en) * | 2024-01-31 | 2024-03-12 | 深圳市卓芯微科技有限公司 | Battery SOC prediction method, device, battery management equipment and storage medium |
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