CN113900033B - Lithium battery online service life prediction method based on charging data spatial distribution characteristics - Google Patents
Lithium battery online service life prediction method based on charging data spatial distribution characteristics Download PDFInfo
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- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 title claims abstract description 104
- 229910052744 lithium Inorganic materials 0.000 title claims abstract description 104
- 238000007600 charging Methods 0.000 title claims abstract description 76
- 238000009826 distribution Methods 0.000 title claims abstract description 32
- 238000000034 method Methods 0.000 title claims abstract description 17
- 238000007599 discharging Methods 0.000 claims abstract description 35
- 238000012549 training Methods 0.000 claims abstract description 11
- 238000010277 constant-current charging Methods 0.000 claims description 20
- 238000010801 machine learning Methods 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 abstract description 4
- 238000005259 measurement Methods 0.000 description 5
- 230000032683 aging Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000013461 design Methods 0.000 description 2
- 238000000638 solvent extraction Methods 0.000 description 2
- 230000002238 attenuated effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
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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]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
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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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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
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Abstract
The invention discloses an online lithium battery service life prediction method based on charging data spatial distribution characteristics. The invention comprises the following steps: collecting charging voltage and current data and cycle life of a brand new lithium battery in a preset charging and discharging cycle interval; calculating corresponding spatial distribution characteristics; repeating the steps to collect and calculate each lithium battery, obtaining the cycle life and the corresponding spatial distribution characteristics of each lithium battery, and forming a training set; training the lithium battery life prediction regression model to obtain a trained lithium battery life prediction regression model; during online prediction, charging voltage and current data of the lithium battery to be predicted in a preset charging and discharging cycle interval are collected, corresponding spatial distribution characteristics are obtained through calculation and input into a regression model for prediction, and the cycle life of the lithium battery to be predicted at present is output. The invention realizes the accurate prediction of the service life of the lithium battery and improves the reliability and safety of the lithium battery.
Description
Technical Field
The invention belongs to an online lithium battery life prediction method in the field of lithium battery application, and particularly relates to an online lithium battery life prediction method based on charging data spatial distribution characteristics.
Background
The lithium battery has the advantages of low cost, high energy density, long cycle life and the like, and is widely applied to the fields of fixing, portability, traffic and the like. The service life prediction technology plays an important role in the aspects of safe operation, prediction maintenance, secondary use and the like of the lithium battery. However, since lithium batteries have a complex aging mechanism, and the aging path is influenced by many factors in the design, production and application processes, it becomes a great challenge to achieve simple, fast and accurate prediction of the life of lithium batteries under complex aging paths, wide device variability and variable dynamic operating conditions. Furthermore, for a large lithium battery pack consisting of thousands of cells, since there are various intrinsic and extrinsic differences inevitable between the cells, individual life prediction for each cell is required, which brings a huge data storage burden, calculation burden, and cost burden. Meanwhile, in practical application, the discharge modes of the lithium battery are random, so that the characteristics cannot be extracted based on a specific discharge test to predict the online service life of the lithium battery. In general, the charging mode of a lithium battery is more fixed than the discharging mode, so that it is a more ideal choice to extract features from charging data to predict the life of the lithium battery. In addition, a large number of lithium battery life prediction methods at present rely on accurate capacity measurement, and the accurate capacity measurement requires complete charging process data of the lithium battery from zero charge state, which is not in line with the practical use mode of the lithium battery. Therefore, it is of great significance to design and develop better lithium battery life prediction methods based on charging data and independent of capacity measurement.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides an online lithium battery service life prediction method based on charging data spatial distribution characteristics.
The scheme adopted by the invention is as follows:
the invention comprises the following steps:
1) collecting charging voltage and current data of a brand new lithium battery at the same constant current charging mode stage in a preset charging and discharging cycle interval, and simultaneously collecting the cycle life of the lithium battery in the preset charging and discharging cycle interval;
2) calculating and obtaining the spatial distribution characteristics of the current lithium battery in a preset charge-discharge cycle interval according to all charge voltage and current data collected by the current lithium battery in the preset charge-discharge cycle interval;
3) repeating the steps 1) -2) to collect and calculate each lithium battery, obtaining the cycle life and the corresponding spatial distribution characteristics of each lithium battery in a preset charge-discharge cycle interval, and forming a training set;
4) training the lithium battery life prediction regression model based on a training set to obtain a trained lithium battery life prediction regression model;
5) during online prediction, charging voltage and current data of a brand-new lithium battery to be predicted in the same constant-current charging mode stage in a preset charging and discharging cycle interval are collected, spatial distribution characteristics of the lithium battery to be predicted in the preset charging and discharging cycle interval are obtained through calculation, the obtained spatial distribution characteristics are input into a trained lithium battery life prediction regression model for prediction, and the current cycle life of the lithium battery to be predicted is output.
The step 2) is specifically as follows:
according to charging voltage and current data of the current lithium battery in a preset charging and discharging cycle interval, drawing a two-dimensional coordinate axis space by taking the charging voltage as a horizontal axis and the charging current as a vertical axis, drawing all the charging voltage and current data of the current lithium battery in the preset charging and discharging cycle interval in the two-dimensional coordinate axis space, and dividing the horizontal axis and the vertical axis into m equidistant voltage sub-intervals and n equidistant current sub-intervals according to the ranges of the charging voltage and the current to obtain m multiplied by n two-dimensional sub-spaces; counting the number of points of all charging voltage and current data accumulated in a preset charging and discharging cycle interval of the current lithium battery in each two-dimensional subspace, and taking the points as the spatial distribution characteristics of the current lithium battery.
The preset charging and discharging cycle interval in the step 5) is the same as the preset charging and discharging cycle interval in the step 1), and the same constant current charging mode stage in the step 5) is the same as the same constant current charging mode stage in the step 1).
And selecting a machine learning regression model from the lithium battery life prediction regression model.
The invention has the beneficial effects that:
the invention solves the problem that the lithium battery online life prediction depends on a specific discharge mode and accurate capacity measurement in practical application. The method has the advantages that the spatial distribution characteristics of the charging voltage and current data are applied to the online life prediction of the lithium battery, the number distribution characteristics of the lithium battery in each two-dimensional subspace are obtained only by collecting the charging voltage and current data of the lithium battery in the same constant current charging mode stage in the preset charging and discharging cycle interval and counting, the cycle life of the lithium battery is accurately predicted, the method does not need to depend on a specific discharging mode and accurate capacity measurement which do not exist in the actual lithium battery application, the method can be used for the online life prediction of the lithium battery in different actual application scenes, and the lithium battery in the actual application can be better safely operated, predicted to be maintained and secondarily used.
Drawings
Fig. 1 is a flow chart of an online lithium battery life prediction method based on spatial distribution characteristics of charging data according to the present invention.
Fig. 2 is a schematic diagram of the same constant current charging mode stage of the lithium battery selected in the embodiment of the present invention.
Fig. 3 is a schematic diagram of the spatial distribution of all charging voltage and current data of the lithium battery in the same constant current charging mode stage in the 10 th and 200 th charging/discharging cycle intervals according to the embodiment of the present invention.
Fig. 4 is a diagram illustrating the difference between the predicted lifetime of the lithium battery and the actual lifetime of the lithium battery based on the spatial distribution characteristics of the charging voltage and current data in the embodiment of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments.
The invention comprises the following steps:
1) collecting charging voltage and current data of a brand-new lithium battery at the same constant current charging mode stage in each charging and discharging cycle of a preset charging and discharging cycle interval, and simultaneously collecting the cycle life of the lithium battery in each charging and discharging cycle of the preset charging and discharging cycle interval; the preset charge-discharge cycle interval specifically comprises the following steps: the minimum value of the preset charge-discharge cycle interval is one of the charge-discharge cycles 1-20, and the maximum value is one of the charge-discharge cycles 50 and above. In a specific embodiment, the minimum value is the 10 th charge-discharge cycle, and the maximum value is the 200 th charge-discharge cycle. In specific implementation, the same constant current charging mode stage is as shown in fig. 2, where (a) in fig. 2 is a voltage variation curve of the same constant current charging mode stage, and (b) in fig. 2 is a current variation curve of the same constant current charging mode stage. The cycle life of the lithium battery is obtained through experiments, the lithium battery is continuously subjected to charge and discharge cycles, and when the actual total capacity of the lithium battery is attenuated to 80% of the initial total capacity of the lithium battery, the number of the currently performed charge and discharge cycles is the cycle life of the lithium battery.
2) Calculating and obtaining the spatial distribution characteristics of the current lithium battery in a preset charge-discharge cycle interval according to all charge voltage and current data acquired by the current lithium battery in the preset charge-discharge cycle interval;
the step 2) is specifically as follows:
according to all charging voltage and current data collected by the current lithium battery in a preset charging and discharging cycle interval, drawing a two-dimensional coordinate axis space by taking the charging voltage as a horizontal axis and the charging current as a vertical axis, drawing the charging voltage and current data of the current lithium battery in the preset charging and discharging cycle interval in the two-dimensional coordinate axis space, and respectively dividing the horizontal axis and the vertical axis into m equidistant voltage sub-intervals and n equidistant current sub-intervals according to the ranges of the charging voltage and the current to obtain m multiplied by n two-dimensional sub-spaces; counting the number of points of all charging voltage and current data accumulated in a preset charging and discharging cycle interval of the current lithium battery in each two-dimensional subspace, and taking the points as the spatial distribution characteristics of the current lithium battery. Wherein the point falling on the lower current and voltage boundary belongs to the current two-dimensional subspace and the point falling on the upper current and voltage boundary does not belong to the current two-dimensional subspace.
3) Repeating the steps 1) -2) to collect and calculate each lithium battery, obtaining the cycle life and the corresponding spatial distribution characteristics of each lithium battery in a preset charge-discharge cycle interval, and forming a training set;
in specific implementation, all lithium batteries have the same model. As shown in table 1 and table 2, the horizontal axis is divided into 19 equidistant voltage subintervals, the vertical axis is divided into 12 equidistant current subintervals, and 228 two-dimensional subspaces are obtained, as shown in fig. 3.
TABLE 1 Voltage subinterval partitioning (V) of the constant-current charging phase
| 3.40-3.41 | 3.41-3.42 | 3.42-3.43 | 3.43-3.44 | 3.44-3.45 | 3.45-3.46 | 3.46-3.47 | 3.47-3.48 | 3.48-3.49 | 3.49-3.50 |
| 3.50-3.51 | 3.51-3.52 | 3.52-3.53 | 3.53-3.54 | 3.54-3.55 | 3.55-3.56 | 3.56-3.57 | 3.57-3.58 | 3.58-3.59 |
TABLE 2 Current subinterval partitioning (A) of the constant-current charging phase
| 0.9970-0.9975 | 0.9975-0.9980 | 0.9980-0.9985 | 0.9985-0.9990 | 0.9990-0.9995 | 0.9995-1.0000 |
| 1.0000-1.0005 | 1.0005-1.0010 | 1.0010-1.0015 | 1.0015-1.0020 | 1.0020-1.0025 | 1.0025-1.0030 |
4) Training the lithium battery life prediction regression model based on a training set to obtain a trained lithium battery life prediction regression model; and selecting a machine learning regression model from the lithium battery life prediction regression model. In specific implementation, an XGboost model is adopted.
5) During online prediction, charging voltage and current data of a brand-new lithium battery to be predicted in the same constant-current charging mode stage in a preset charging and discharging cycle interval are collected, spatial distribution characteristics of the lithium battery to be predicted in the preset charging and discharging cycle interval are obtained through calculation, the obtained spatial distribution characteristics are input into a trained lithium battery life prediction regression model for prediction, and the current cycle life of the lithium battery to be predicted is output. The deviation of the predicted life of the lithium battery from the actual life of the lithium battery based on the spatial distribution characteristics of the charging voltage and current data is shown in fig. 4.
The times of the preset charging and discharging cycle intervals in the step 5) are the same as the times of the different preset charging and discharging cycle intervals in the step 1), and the same constant current charging mode stage in the step 5) is the same as the same constant current charging mode stage in the step 1).
Claims (3)
1. A lithium battery online service life prediction method based on charging data spatial distribution characteristics is characterized by comprising the following steps:
1) collecting charging voltage and current data of a brand new lithium battery at the same constant current charging mode stage in a preset charging and discharging cycle interval, and simultaneously collecting the cycle life of the lithium battery in the preset charging and discharging cycle interval;
2) calculating and obtaining the spatial distribution characteristics of the current lithium battery in a preset charge-discharge cycle interval according to all charge voltage and current data collected by the current lithium battery in the preset charge-discharge cycle interval;
3) repeating the steps 1) -2) to collect and calculate each lithium battery, obtaining the cycle life and the corresponding spatial distribution characteristics of each lithium battery in a preset charge-discharge cycle interval, and forming a training set;
4) training the lithium battery life prediction regression model based on a training set to obtain a trained lithium battery life prediction regression model;
5) during online prediction, collecting charging voltage and current data of a lithium battery to be predicted at the same constant-current charging mode stage in a preset charging and discharging cycle interval, calculating to obtain spatial distribution characteristics of the lithium battery to be predicted in the preset charging and discharging cycle interval, inputting the obtained spatial distribution characteristics into a trained lithium battery life prediction regression model for prediction, and outputting the current cycle life of the lithium battery to be predicted;
the step 2) is specifically as follows:
according to charging voltage and current data of the current lithium battery in a preset charging and discharging cycle interval, drawing a two-dimensional coordinate axis space by taking the charging voltage as a horizontal axis and the charging current as a vertical axis, drawing all the charging voltage and current data of the current lithium battery in the preset charging and discharging cycle interval in the two-dimensional coordinate axis space, and dividing the horizontal axis and the vertical axis into m equidistant voltage sub-intervals and n equidistant current sub-intervals according to the ranges of the charging voltage and the current to obtain m multiplied by n two-dimensional sub-spaces; counting the number of points of all charging voltage and current data accumulated in a preset charging and discharging cycle interval of the current lithium battery in each two-dimensional subspace, and taking the points as the spatial distribution characteristics of the current lithium battery.
2. The method for predicting the online service life of the lithium battery based on the spatial distribution characteristics of the charging data as claimed in claim 1, wherein the preset charging and discharging cycle interval in the step 5) is the same as the preset charging and discharging cycle interval in the step 1), and the same constant current charging mode stage in the step 5) is the same as the same constant current charging mode stage in the step 1).
3. The lithium battery online life prediction method based on the charge data spatial distribution characteristics as claimed in claim 1, wherein the lithium battery life prediction regression model is a machine learning regression model.
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Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103326428A (en) * | 2013-06-08 | 2013-09-25 | 东北电力大学 | Energy storage system operation optimization control method for prolonging service life of lithium battery |
| CN103389471A (en) * | 2013-07-25 | 2013-11-13 | 哈尔滨工业大学 | Cycle life indirect prediction method for lithium ion battery provided with uncertain intervals on basis of GPR (general purpose register) |
| CN105093124A (en) * | 2015-07-30 | 2015-11-25 | 武汉美格科技有限公司 | High-magnification type lithium battery grouping method |
| CN106885994A (en) * | 2017-04-01 | 2017-06-23 | 湖南银杏数据科技有限公司 | Lithium battery residual life fast detection method based on constant-voltage charge fragment |
| CN107015157A (en) * | 2017-04-01 | 2017-08-04 | 湖南银杏数据科技有限公司 | The lithium battery cycles left life-span online fast test method of fragment is risen based on constant current equipressure |
| CN107024664A (en) * | 2017-04-01 | 2017-08-08 | 湖南银杏数据科技有限公司 | Lithium battery residual life fast prediction method based on constant-current charge fragment |
| CN108510128A (en) * | 2018-04-11 | 2018-09-07 | 华南理工大学广州学院 | A kind of region electric vehicle charging load spatial and temporal distributions prediction technique |
| CN109031153A (en) * | 2018-10-16 | 2018-12-18 | 北京交通大学 | A kind of health status On-line Estimation method of lithium ion battery |
| CN110728396A (en) * | 2019-09-25 | 2020-01-24 | 国网山西省电力公司经济技术研究院 | Electric vehicle charging load comprehensive modeling method considering space-time distribution |
| JP2020028195A (en) * | 2018-08-13 | 2020-02-20 | 一般財団法人電力中央研究所 | Photovoltaic power generation system distribution estimation device, photovoltaic power generation system distribution estimation method, photovoltaic power generation system distribution estimation program, and photovoltaic power generation output prediction device |
| CN111007399A (en) * | 2019-11-15 | 2020-04-14 | 浙江大学 | Lithium battery state of charge prediction method based on improved generation countermeasure network |
| CN111832221A (en) * | 2020-06-23 | 2020-10-27 | 华中科技大学无锡研究院 | Lithium battery life prediction method based on feature screening |
| CN112666480A (en) * | 2020-12-02 | 2021-04-16 | 西安交通大学 | Battery life prediction method based on charging process characteristic attention |
| CN112881916A (en) * | 2021-01-21 | 2021-06-01 | 湘潭大学 | Method and system for predicting health state and remaining usable life of lithium battery |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR102468895B1 (en) * | 2015-07-21 | 2022-11-21 | 삼성전자주식회사 | Method and apparatus for estimating state of battery |
| US10324135B2 (en) * | 2016-06-06 | 2019-06-18 | Mitsubishi Electric Research Laboratories, Inc. | Methods and systems for data-driven battery state of charge (SoC) estimation |
| US10564222B2 (en) * | 2017-07-20 | 2020-02-18 | Mitsubishi Electric Research Laboratories, Inc. | Methods and systems for battery state of power prediction |
| KR102283109B1 (en) * | 2019-06-04 | 2021-07-29 | 한국과학기술원 | Multi-electrode electron excitation based simulation method for non-equilibrium electronic structures of nanodevices and apparatus therefore |
| WO2021163056A1 (en) * | 2020-02-10 | 2021-08-19 | Titan Advanced Energy Solutions Inc. | Battery testing systems and methods |
-
2021
- 2021-09-23 CN CN202111113576.5A patent/CN113900033B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103326428A (en) * | 2013-06-08 | 2013-09-25 | 东北电力大学 | Energy storage system operation optimization control method for prolonging service life of lithium battery |
| CN103389471A (en) * | 2013-07-25 | 2013-11-13 | 哈尔滨工业大学 | Cycle life indirect prediction method for lithium ion battery provided with uncertain intervals on basis of GPR (general purpose register) |
| CN105093124A (en) * | 2015-07-30 | 2015-11-25 | 武汉美格科技有限公司 | High-magnification type lithium battery grouping method |
| CN106885994A (en) * | 2017-04-01 | 2017-06-23 | 湖南银杏数据科技有限公司 | Lithium battery residual life fast detection method based on constant-voltage charge fragment |
| CN107015157A (en) * | 2017-04-01 | 2017-08-04 | 湖南银杏数据科技有限公司 | The lithium battery cycles left life-span online fast test method of fragment is risen based on constant current equipressure |
| CN107024664A (en) * | 2017-04-01 | 2017-08-08 | 湖南银杏数据科技有限公司 | Lithium battery residual life fast prediction method based on constant-current charge fragment |
| CN108510128A (en) * | 2018-04-11 | 2018-09-07 | 华南理工大学广州学院 | A kind of region electric vehicle charging load spatial and temporal distributions prediction technique |
| JP2020028195A (en) * | 2018-08-13 | 2020-02-20 | 一般財団法人電力中央研究所 | Photovoltaic power generation system distribution estimation device, photovoltaic power generation system distribution estimation method, photovoltaic power generation system distribution estimation program, and photovoltaic power generation output prediction device |
| CN109031153A (en) * | 2018-10-16 | 2018-12-18 | 北京交通大学 | A kind of health status On-line Estimation method of lithium ion battery |
| CN110728396A (en) * | 2019-09-25 | 2020-01-24 | 国网山西省电力公司经济技术研究院 | Electric vehicle charging load comprehensive modeling method considering space-time distribution |
| CN111007399A (en) * | 2019-11-15 | 2020-04-14 | 浙江大学 | Lithium battery state of charge prediction method based on improved generation countermeasure network |
| CN111832221A (en) * | 2020-06-23 | 2020-10-27 | 华中科技大学无锡研究院 | Lithium battery life prediction method based on feature screening |
| CN112666480A (en) * | 2020-12-02 | 2021-04-16 | 西安交通大学 | Battery life prediction method based on charging process characteristic attention |
| CN112881916A (en) * | 2021-01-21 | 2021-06-01 | 湘潭大学 | Method and system for predicting health state and remaining usable life of lithium battery |
Non-Patent Citations (3)
| Title |
|---|
| An improved exponential model for predicting the remaining useful life of lithium-ion batteries;Peijun Ma等;《2015 IEEE Conference on Prognostics and Health Management (PHM)》;20150910;全文 * |
| Regional Spatial Distribution of Electric Vehicles – A Data-Driven Approach for Distribution Grid Impact Case Studies;Johannes Hiry等;《ETG Congress 2021》;20210701;全文 * |
| 基于LSTM长短期记忆网络的超短期风速预测;魏昱洲等;《电子测量与仪器仪表》;20190215;第33卷(第2期);全文 * |
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