CN115575843A - Lithium ion battery life prediction method - Google Patents
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- CN115575843A CN115575843A CN202211327187.7A CN202211327187A CN115575843A CN 115575843 A CN115575843 A CN 115575843A CN 202211327187 A CN202211327187 A CN 202211327187A CN 115575843 A CN115575843 A CN 115575843A
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- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 title claims abstract description 89
- 229910001416 lithium ion Inorganic materials 0.000 title claims abstract description 89
- 238000000034 method Methods 0.000 title claims abstract description 43
- 238000007599 discharging Methods 0.000 claims abstract description 24
- 230000008569 process Effects 0.000 claims abstract description 18
- 239000002245 particle Substances 0.000 claims description 11
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 abstract description 9
- 229910052744 lithium Inorganic materials 0.000 abstract description 9
- 238000001514 detection method Methods 0.000 abstract description 3
- 230000004048 modification Effects 0.000 description 4
- 238000012986 modification Methods 0.000 description 4
- 125000004122 cyclic group Chemical group 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 230000015556 catabolic process Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000006731 degradation reaction Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000004146 energy storage Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 239000007770 graphite material Substances 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000001556 precipitation Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
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- 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
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Abstract
The invention provides a lithium ion battery service life prediction method, which relates to the field of lithium battery detection and comprises the following steps: setting a charging and discharging system, performing multiple charging and discharging cycles on the lithium ion battery in the set charging and discharging system, and performing the charging and discharging cycles at a frequency f 1 Recording the relation between the current and the voltage of the lithium ion battery in the charging and discharging process; setting the capacity threshold of the lithium ion battery, and acquiring the voltage V when the lithium ion battery reaches the capacity threshold in the discharge process of each charge-discharge cycle 1 (ii) a According to the current charge-discharge cycle times and charge-discharge cycle times n of the lithium ion battery 1 The invention discloses a method for predicting the service life of a lithium ion battery, which carries out service life prediction based on the early cycle data of a single battery, can avoid errors caused by the difference of battery consistency and can be used in the process of predicting the service life of the lithium ion batteryThe method is simple to operate, and the service life of the lithium battery can be rapidly predicted.
Description
Technical Field
The invention relates to the field of lithium battery detection, in particular to a lithium ion battery service life prediction method.
Background
Lithium ion batteries are widely used in 3C portable electronic consumer products, power cars and terminal energy storage devices due to their advantages of high energy density, long life, recoverability, etc. The cycle life of the lithium ion battery is a key concern of manufacturers and users, generally, the cycle life of the battery is as high as thousands or even tens of thousands of weeks, similar to that of LFP and ternary batteries, and if the life of the battery is to be tested, the cost of time is consumed, so that the prediction of the cycle life of the lithium ion battery by using the early part cycle data characteristic has high practical value.
At the present stage, the method for predicting the cycle life of the lithium battery mainly comprises the following steps; an electrochemical model method, which simulates the cycle life of a battery by inputting materials and electric core parameters through simulation software similar to comsole and the like, and also simulates the phenomena of charge and discharge multiplying power, lithium precipitation, thermal effect and the like of the battery, is one of the main means for understanding the mechanism of the battery at the present stage; data driving, wherein the data driving comprises big data driving modeling for prediction, and life prediction is carried out according to a battery capacity attenuation trend combined with an empirical model, both methods are based on pure data driving, such as patent CN202110474679.8, a new energy vehicle power battery life prediction method and system, the patent is battery life prediction carried out by means of data driving, but the prediction accuracy of the patent is too dependent on the accuracy of the model and the data; the data driving and electrochemical model is a main means at the present stage for realizing the service life prediction of the lithium battery by combining the electrochemical model with the data driving, so that the service life prediction of the lithium battery has the interpretability of battery degradation. The big data driving mainly depends on the size of a data set, the interpretability of characteristic values and the selection of an algorithm, namely, a model is trained, but the model has no generalization when the conditions of a material system, a process system and the like of a battery are changed; the method of empirical modeling is more practical, but also depends on the size of the data set. And the method for combining the data driving and the electrochemical model has higher complexity and difficulty.
Disclosure of Invention
The invention aims to provide a lithium ion battery service life prediction method, which realizes the rapid and accurate prediction of the service life of a lithium ion battery.
In order to achieve the above purpose, the invention provides the following technical scheme: a lithium ion battery life prediction method comprises the following steps: s1: setting a charging and discharging system, performing multiple charging and discharging cycles on the lithium ion battery in the set charging and discharging system, and performing the charging and discharging cycles at a frequency f 1 Recording the relation between the current and the voltage of the lithium ion battery in the charging and discharging process; s2: setting the capacity threshold of the lithium ion battery, and acquiring the voltage V when the lithium ion battery reaches the capacity threshold in the discharge process of each charge-discharge cycle 1 (ii) a S3: establishing a particle filter model and a double-exponential empirical model; s4: establishing the charge-discharge cycle number and the voltage V of the lithium ion battery through the particle filter model and the double-exponential empirical model 1 Obtaining said voltage V 1 The number n of charge and discharge cycles of the lithium ion battery at the cut-off voltage of the lithium ion battery 1 ；
S5: according to the current charge-discharge cycle times and charge-discharge cycle times n of the lithium ion battery 1 And judging the residual service life of the lithium ion battery by the difference value.
Furthermore, the interval time between each charge and discharge of the lithium ion battery in the charge-discharge cycle of the lithium ion battery is 8min-12min.
Further, the lithium ion battery is charged to the rated highest voltage of the lithium ion battery at a constant current and a constant voltage of 1C, and the charge cut-off rate of the lithium ion battery is 0.05C;
and discharging the lithium ion battery to the cut-off voltage of the lithium ion battery at a constant current of 1C.
Further, the voltage range of the lithium ion battery is 2.5V-3.65V.
Further, the frequency f 1 For 30s each time.
Further, the capacity threshold of the lithium ion battery is 75% -85%.
The method can be used for predicting the service life of the lithium ion battery based on the early cycle data of the single battery, can avoid errors caused by the difference of battery consistency, is simple to operate and can be used for rapidly predicting the service life of the lithium ion battery.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate embodiment(s) of the invention and together with the description serve to explain the invention and not to limit the invention. Wherein:
FIG. 1 is a voltage-capacity graph of a lithium ion battery according to an embodiment of the present invention.
Fig. 2 is a two-dimensional vector diagram of voltage-capacity-cycle count of a lithium ion battery according to an embodiment of the present invention.
Fig. 3 is a graph of voltage versus number of charge and discharge cycles for a lithium ion battery in accordance with an embodiment of the present invention.
Fig. 4 is a graph of extended voltage versus number of charge and discharge cycles for a lithium ion battery in accordance with an embodiment of the present invention.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings in conjunction with embodiments. The various examples are provided by way of explanation of the invention, and not limitation of the invention. In fact, it will be apparent to those skilled in the art that modifications and variations can be made in the present invention without departing from the scope or spirit thereof. For instance, features illustrated or described as part of one embodiment, can be used with another embodiment to yield a still further embodiment. It is therefore intended that the present invention encompass such modifications and variations as fall within the scope of the appended claims and equivalents thereof.
In the description of the present invention, the terms "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", and the like indicate orientations or positional relationships based on orientations or positional relationships shown in the drawings, which are merely for convenience of description of the present invention and do not require that the present invention must be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention. The terms "connected," "connected," and "disposed" as used herein are intended to be broadly construed, and may include, for example, fixed and removable connections; can be directly connected or indirectly connected through intermediate components; the connection may be a wired electrical connection, a wireless electrical connection, or a wireless communication signal connection, and a person skilled in the art can understand the specific meaning of the above terms according to specific situations.
One or more examples of the invention are illustrated in the accompanying drawings. The detailed description uses numerical and letter designations to refer to features in the drawings. Like or similar designations in the drawings and description have been used to refer to like or similar parts of the invention. As used herein, the terms "first," "second," "third," and "fourth," etc. may be used interchangeably to distinguish one component from another and are not intended to indicate the position or importance of an individual component.
According to an embodiment of the present invention, there is provided a lithium ion battery life prediction method, including: s1: setting a charging and discharging system, performing multiple charging and discharging cycles on the lithium ion battery in the set charging and discharging system, and performing the charging and discharging cycles at a frequency f 1 Recording the relation between the current and the voltage of the lithium ion battery in the charging and discharging process, and acquiring the charging and discharging process of the lithium ion battery at a fixed frequency so as to obtain the current and voltage data of the lithium ion battery at a fixed time interval, so that the current and voltage data can be used as basic data for predicting the subsequent state of the lithium ion battery;
s2: setting the capacity threshold of the lithium ion battery, and acquiring the voltage V when the lithium ion battery reaches the capacity threshold in the discharge process of each charge-discharge cycle 1 In the process of cyclic charge and discharge of the battery core, the charge and discharge system of the battery is always the main factor (test environment temperature, charge and discharge multiplying power, SOC, DOD, etc.) influencing the service life of the battery. However, under the condition of a fixed system, the discharge system generally adopts constant current discharge, and under the discharge system, the voltage interval of the battery cell shows nonlinear reduction, so that the capacity and the voltage are in one-to-one correspondence in a cyclic discharge process;
s3: establishing a particle filter model and a double-index empirical model, wherein the particle filter model and the double-index empirical model can realize the prediction of the voltage of the lithium ion battery, and the particle filter model and the double-index empirical model can predict the voltage of the subsequent charge-discharge cycle of the lithium ion battery on the basis of the voltage and current data of the existing lithium ion battery;
s4: establishing the charge-discharge cycle number and the voltage V of the lithium ion battery through the particle filter model and the double-exponential empirical model 1 Obtaining said voltage V 1 The number n of charge and discharge cycles of the lithium ion battery at the cut-off voltage of the lithium ion battery 1 The dual-exponential model is y = a x exp b + c x exp d, wherein the parameters of the particle filter are adjustable, the patent prominently provides a new characteristic for predicting the RUL (remaining service life) of the lithium battery, the particle filter and the dual-exponential model are auxiliary means, wherein a, b, c and d are empirical model parameters;
s5: according to the current charge-discharge cycle times and charge-discharge cycle times n of the lithium ion battery 1 The residual life of the lithium ion battery is judged by the difference, generally speaking, the discharge to the cut-off voltage in the constant current discharge process is considered to be the end of the discharge, so that the battery cell can be considered to reach the end point of the life by setting a capacity threshold value in the circulation process (the battery cell can be considered to reach the end point of the life by judging that the capacity threshold value and the discharge cut-off voltage are on the same time dimension).
Preferably, the interval time between each charge and discharge of the lithium ion battery in the charge-discharge cycle of the lithium ion battery is 8min-12min.
Preferably, the lithium ion battery is charged with a constant current and a constant voltage of 1C to a rated maximum voltage of the lithium ion battery, and the charge cut-off rate of the lithium ion battery is 0.05C;
and discharging the lithium ion battery to the cut-off voltage of the lithium ion battery at a constant current of 1C.
Preferably, the voltage range of the lithium ion battery is 2.5V-3.65V.
Preferably, the frequency f 1 It was 30s each time.
Example 1
Taking a lithium ion battery with an LPF/graphite material system, a voltage range of 2.5V-3.65V and a rated capacity of 5.5Ah as an example, a constant charge-discharge mode (quick charge mode) is formulated before detection: charging to 3.65V with 1C constant current and constant voltage, stopping at a multiplying power of 0.05C, standing for 10min, and discharging with 1C constant current to 2.5V, standing for 10min. Recording data once every 30s, and performing cyclic charge and discharge by adopting the charge and discharge system;
collecting charge and discharge data in a circulation process, setting a capacity threshold limit (threshold) when the battery core circulation life is stopped according to initial capacity, then extracting voltage and capacity data in the discharge data in each circulation process, substituting the voltage data and the capacity data into a two-dimensional coordinate system as shown in fig. 1, wherein an x axis is capacity data, a y axis is voltage data, a vertical line is the capacity threshold limit, an x axis coordinate is a capacity threshold of the lithium ion battery, the relation between the voltage and the capacity of the lithium ion battery can be reflected through a voltage-capacity curve of fig. 1, and each curve in fig. 1 represents the relation between the voltage and the capacity of the lithium ion battery in a certain circulation period;
converting three variables such as voltage, capacity and cycle number in fig. 1 into a two-dimensional vector diagram (fig. 2), and then extracting a voltage corresponding to a capacity threshold value reached in a voltage-capacity curve in the discharge process of each cycle period (when the voltage at the capacity threshold value is not acquired, a sampling interpolation method can be adopted to obtain the voltage corresponding to the capacity threshold value);
v is extracted in FIG. 2 threshold-cycle Cycle (voltage-number of charge-discharge cycles) curve (fig. 3), followed by a method using particle filtering and a bi-exponential empirical model for V threshold-cycle The cycle (voltage-number of charge-discharge cycles) curve is extended up to the cut-off voltage (fig. 4), according to V threshold-cycle Extension of the cycle (voltage-number of charge-discharge cycles) curve yields the number of charge-discharge cycles n of the battery 1 2100 times, the current number of charge and discharge cycles is 1411 times, the residual cycle life is 689 times.
From the above description, it can be seen that the above-described embodiments of the present invention achieve the following technical effects: the service life prediction method is based on the early-stage cycle data of the single battery, can avoid errors caused by the difference of battery consistency, is simple to operate when in use, and can be used for rapidly predicting the service life of the lithium battery.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made to the present invention by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. A lithium ion battery life prediction method is characterized by comprising the following steps:
s1: setting a charging and discharging system, performing multiple charging and discharging cycles on the lithium ion battery in the set charging and discharging system, and controlling the frequency f 1 Recording the relation between the current and the voltage of the lithium ion battery in the charging and discharging process;
s2: setting the capacity threshold of the lithium ion battery, and acquiring the voltage V when the lithium ion battery reaches the capacity threshold in the discharge process of each charge-discharge cycle 1 ；
S3: establishing a particle filter model and a double-exponential empirical model;
s4: establishing the charge-discharge cycle number and the voltage V of the lithium ion battery through the particle filter model and the double-exponential empirical model 1 Obtaining said voltage V 1 The number n of charge and discharge cycles of the lithium ion battery at the cut-off voltage of the lithium ion battery 1 ；
S5: according to the current charge-discharge cycle times and charge-discharge cycle times n of the lithium ion battery 1 And judging the residual service life of the lithium ion battery by the difference value.
2. The method according to claim 1, wherein the interval time between each charge and discharge of the lithium ion battery in a charge-discharge cycle of the lithium ion battery is 8min to 12min.
3. The method for predicting the service life of the lithium ion battery according to claim 1, wherein the lithium ion battery is charged at a constant current and a constant voltage of 1C to a rated maximum voltage of the lithium ion battery, and a charge cutoff rate of the lithium ion battery is 0.05C;
and discharging the lithium ion battery to the cut-off voltage of the lithium ion battery at a constant current of 1C.
4. The method according to claim 1, wherein the voltage of the lithium ion battery ranges from 2.5V to 3.65V.
5. The method of claim 1, wherein the frequency f is determined by a frequency of the battery pack 1 It was 30s each time.
6. The method of claim 1, wherein the capacity threshold of the lithium ion battery is 75% -85%.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115856646A (en) * | 2023-03-03 | 2023-03-28 | 中山大学 | Lithium ion battery early life prediction method based on three-dimensional voltage characteristics |
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Application publication date: 20230106 |