Abstract
In this paper, the GSP655060Fe soft pack lithium-ion battery with a capacity of 1600 mAh is utilized, employing lithium iron phosphate as the positive electrode and graphite as the negative electrode. In order to comprehensively evaluate the performance of lithium batteries under the conditions of multi-application scenarios, the operating conditions of the battery were simulated under various external confinement pressures of 300 N, 400 N, 500 N, and 600 N, respectively, and the ambient temperatures of 10 ℃, 25 ℃, and 40 ℃, respectively, were controlled to thoroughly test the battery. One charge/discharge test was conducted on six batteries of the same model at multiplicities of 0.5 C, 1 C, 1.5 C, and 2 C, respectively. To ensure the accuracy and reliability of the experimental data, a Battery comprehensive tester Neware BTS-5V12A was utilized, which possesses high-precision voltage and current measurement capabilities with an error rate of only 0.05 %. This data plays an important role in battery research and development, new energy vehicles, electronic products, and other fields.
Keywords: Lithium-ion battery, Constrained pressure, Constant-current constant-voltage charging, Constant-current discharging, Lithium battery dataset
Specifications Table
| Subject | Electrical and Electronic Engineering |
| Specific subject area | Properties of Lithium polymer batteries |
| Data format | Raw |
| Type of data | Table |
| Data collection | Under the simulated external constraint pressure and specific temperature conditions, the current and voltage data of Li-ion batteries are collected at different charge/discharge multipliers. Before data acquisition, it is necessary to ensure that all the batteries to be tested have been completely discharged. Afterwards, the batteries are placed in a pressurized device and the simulated external confinement pressure is precisely set. The batteries were then placed in a thermostat at a preset temperature. A single charge–discharge test was performed on six batteries of the same type, all at multiplicities of 0.5 C, 1 C, 1.5 C and 2 C. The battery was then subjected to a series of tests. In order to study the battery performance in depth, the above charge/discharge test procedure was repeated after adjusting the ambient temperature and external confinement pressure. |
| Data source location | Organization: Henan Institute of Science and Technology City: Xinxiang, Henan Country: China |
| Data accessibility | Repository name: Mendeley Data Data identification number: 10.17632/dszd9mmd5j.1 Direct URL to data: https://data.mendeley.com/datasets/dszd9mmd5j/1 |
| Related research article | None |
1. Value of the Data
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This dataset provides the new energy battery field with data on the performance of the GSP655060Fe model 1600 mAh lithium-ion soft-coated battery under a variety of externally constrained pressure and temperature conditions. These data are invaluable to battery manufacturers and R&D teams, as they can help understand battery performance under different operating environments, thereby optimizing battery design and improving battery efficiency and safety.
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Through detailed testing of battery performance at different charge/discharge multipliers, this dataset provides an important reference for Battery Management System (BMS) optimization, which is the key to ensuring battery safety, prolonging battery life, and improving battery efficiency. This data can help the BMS predict battery behavior more accurately and thus manage the battery charging and discharging process more effectively.
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Lithium iron phosphate batteries are favored by the new energy vehicle industry for their safety, stability and long life. This dataset provides data on the performance of such batteries under various conditions, which helps manufacturers of new energy vehicles choose the right battery configuration to improve the overall performance and range of the vehicle.
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With the development of AI technology, its application in battery management has become increasingly widespread. The dataset can provide training data for algorithms based on artificial intelligence for battery state estimation, fault prediction and health management. By training these algorithms, real-time monitoring and prediction of battery status can be realized, further improving the safety and efficiency of battery use.
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The dataset can also be used as a scientific research and educational resource to help researchers and students gain a deeper understanding of the working principle and performance characteristics of lithium-ion batteries. By analyzing and mining this data, research progress in battery science and related fields can be promoted.
2. Background
In order to deeply understand the characteristic changes of lithium batteries under pressure, researchers have carried out a lot of exploratory work. They simulate the force on the battery in the actual use scenario by setting a suitable preload force, and then observe and analyze the various property changes of the battery under pressure. For example, Lv et al. [1] through experimental research, found that the expansion force of lithium batteries changes when they are subjected to external pressure, and this change is closely related to the failure state of the battery. Based on this finding, they proposed a fast and high-confidence battery fault detection method, which can realize early warning of multiple faults. In addition, Li et al. [2] experimentally investigated the external expansion force, voltage and temperature behavior of batteries at different SOC (state of charge) under thermal abuse scenarios. They found that the expansion force, voltage and temperature parameters of Li-ion batteries change significantly under thermal abuse conditions, and these changes can be used as an important basis for determining the degree of danger of battery failure. Based on these findings, they proposed a novel safety warning strategy for the classification of battery failure danger, which provides an important guarantee for the safe use of lithium batteries. From the existing literature survey, the datasets obtained from testing batteries under simulated external constraint stress conditions are relatively few and not sufficiently diverse. Therefore, we believe that there is a need to develop and publish such a dataset to facilitate research and innovation in lithium batteries. The dataset contains current and voltage data for batteries under various externally constrained pressure and temperature conditions. This enables researchers to gain a more comprehensive understanding of the behavior of batteries under various conditions and to mine key features related to battery state and lifetime. By publicly releasing such a representative dataset, we hope to provide researchers in the field of lithium batteries with a valuable resource to advance the application of machine learning to battery performance prediction and management. This will help accelerate innovation in lithium battery technology and improve battery performance, safety, and lifetime, thereby promoting the development of new energy vehicles, wearable devices, and other fields.
In summary, the study of the characteristic changes of lithium batteries under pressure is crucial for understanding their performance limits, optimizing their design, and improving their safety. Through continuous and in-depth research and exploration, we would like to provide more reliable data and technical support for the application of lithium batteries and promote the rapid development of new energy vehicles and other industries.
3. Data Description
In this work, a 1600 mAh soft pack lithium-ion battery model GSP655060Fe, which is a high-performance energy storage device, was selected. Its positive electrode material is lithium iron phosphate (LFP), characterized by high safety and stability, effectively reducing the risk of thermal runaway during battery charging and discharging, thereby ensuring safety during use [3]. The negative electrode material is graphite, a common and well-performing material providing stable charge transfer and discharge performance. The basic parameters of the battery are shown in Table 1, and the sample of the test battery is shown in Fig. 1. By selecting this battery, a reliable and stable energy supply was obtained during the test, ensuring the accuracy and reproducibility of the test.
Table 1.
Basic parameters of GSP655060Fe battery.
| Items | Specifications |
|---|---|
| Nominal capacity | 1600 mAh |
| Charge upper limited voltage | 3.65 V |
| Discharge lower limited voltage | 2.0 V |
| Nominal voltage | 3.2 V |
| Maximum charge current | 0.5C |
| Maximum discharge current | 1C |
Fig. 1.
Sample of test battery.
The overall structure of the dataset is illustrated in Fig. 2. Within the parent folder 'Lithium_battery_dataset', there is a subfolder named 'instructions', which contains a file named 'manufacturer_specifications.pdf'. This file includes the manufacturer data for the studied batteries. Additionally, there are three subfolders named after temperatures (10 °C, 25 °C, and 40 °C). Each temperature-named subfolder contains four subfolders named after simulated external constraint pressure values (300 N, 400 N, 500 N, and 600 N). Each pressure-named subfolder further contains four subfolders named after different rate capacities (0.5 C, 1 C, 1.5, and 2 C). Within each of these subfolders, six tables in .xls format are stored (Battery No.1.xls, Battery No.2.xls, Battery No.3.xls, Battery No.4.xls, Battery No.5.xls, and Battery No.6.xls). This constitutes the structure of the experimental data saved for the entire test.
Fig. 2.
Data file structure.
4. Experimental Design, Materials and Methods
The experimental test was divided into three phases, as shown in Table 2. In order to easily distinguish the data of each battery, the six batteries were numbered as battery No.1, battery No.2, battery No.3, battery No.4, battery No.5, and battery No.6. The six batteries were first tested in Stage I, and then used for Stage II and Stage III tests. The following briefly describes the Stage I test procedure. Initially, the temperature of the thermostat was set to 10 °C, and the simulated external constraint pressure was set to 300 N. The six batteries were charged and discharged once at a multiplication rate of 0.5 C. If the simulated external constraint pressure changed during this process, it was adjusted to 300 N to maintain consistency with the initial setting, ensuring the authority of the data. Subsequently, the batteries were charged and discharged once at a multiplication rate of 1 C.
Table 2.
Three different testing phases.
| Stage No. | Stage 1 | Stage 2 | Stage 3 |
|---|---|---|---|
| Test object | Battery No.1-6 | Battery No.1-6 | Battery No.1-6 |
| Test environment | 10 °C | 25 °C | 40 °C |
| Test simulation constraint pressure | 300 N 400 N 500 N 600 N |
300 N 400 N 500 N 600 N |
300 N 400 N 500 N 600 N |
| Test content | 0.5 C 1 C 1.5 C 2 C |
0.5 C 1 C 1.5 C 2 C |
0.5 C 1 C 1.5 C 2 C |
After completing these four charging and discharging multiplication tests, the simulated external constraint pressure was set to 400 N, and the same four charging and discharging multiplication tests were repeated, followed by 500 N and 600 N. At this point, the first stage of the test was completed. The steps of the second and third stages were the same, so they will not be described in detail. The detailed test steps are shown in Table 3.
Table 3.
Test steps.
| Step | Concrete content |
|---|---|
| 1 | The incubator temperature was set to 10 ℃ |
| 2 | (1) Set the simulated external restraint pressure to 300 N (2) Charge at constant current with a test multiplier of 0.5 C to a voltage of 3.65 V, then switch to constant voltage mode until the charge rate drops to 0.08 A (3) Set aside for 2 h (4) Constant current discharge at a test multiplication of 0.5 C to a cutoff voltage of 2.0 V (5) Set aside for 2 h |
| 3 | Change (2) and (4) test magnification in step 2 to 1 C and repeat step 2 |
| 4 | Change (2) and (4) test magnification in step 2 to 1.5 C and repeat step 2 |
| 5 | Change (2) and (4) test magnification in step 2 to 2 C and repeat step 2 |
| 6 | Set the (1) simulated external restraint pressure in step 2 to 400 N, and repeat steps 2 to 5. |
| 7 | Set the (1) simulated external restraint pressure in step 2 to 500 N, and repeat steps 2 to 5 |
| 8 | Set the (1) simulated external restraint pressure in step 2 to 600 N, and repeat steps 2 to 5 |
| 9 | Set the thermostat temperature in step 1 to 25°C and repeat steps 2 to 8 |
| 10 | Set the thermostat temperature in step 1 to 40°C and repeat steps 2 to 8 |
The equipment used to collect data in the Intelligent Battery System Laboratory at Henan Institute of Science and Technology is shown in Fig. 3, including (1) Neware BTS-5V12A Battery Comprehensive Tester; (2) Sanwood, SMG-150-CC Thermostat; (3) Mainframe; (4) DY-940 (9-channel Digital Transmitter); and (5) Analog External Constraint Pressure Device. A detailed description of how the data communication between the different devices took place is described next.1. Connection between the host computer and the Neware BTS-5V12A battery synthesizer. Neware BTS-5V12A battery integrated tester control unit RS485 and battery test unit back of the RS485 with a straight-through network cable in series, after the control unit of the TCP / IP network port and the computer's network port with a straight-through network cable connection, through the computer side of the IP for the success of the configuration and the installation of the NEWARE BTS7.6.0 software to complete the host and the data communication between the battery integrated tester. The data communication between the host and the integrated battery tester can be completed by installing NEWARE BTS7.6.0 software, realizing real-time monitoring and data recording of the battery charging and discharging process.2. Connection between the host and DY-940 and the pressure sensor. By connecting the pressure sensor in the simulated external constraint pressure device to the DY-940, the DY-940 is connected to the computer interface through the USB to RS-485/422 converter, and the multi-channel test and recording system software is installed on the computer, and the simulated constraint pressure value of each battery can be displayed after the debugging is completed, and we can manually adjust and set the desired value of constraint pressure, in this test, we set the constraint pressure value to 300 mm. In this test, we set 300 N, 400 N, 500 N and 600 N. 3. Sanwood, SMG-150-CC thermostat directly connected to the power supply to set the desired temperature.
Fig. 3.
Experimental test setup.
The data obtained through the NEWARE BTS 7.6.0 software in the computer terminal can be collected in the form of exported reports in .xls format.
Limitations
None
Ethics Statement
The proposed data does not involve any human subjects, animal experiments, or data collected from social media platforms.
CRediT Author Statement
Chong Yan: Conceptualization, Methodology, Software, Writing-original draft, Investigation. Xiaoying Wu: Validation, Writing –review & editing. Ye Yuan: Validation,Writing –review & editing. Yaning Xie: Validation,Writing –review & editing. Jianping Wang: Validation, Writing –review & editing. Guohong Gao: Validation, Writing –review & editing. Yuqian Fan*: Supervision, Validation, Writing –review & editing.
Acknowledgments
Funding: This work was supported by the Key Scientific and Technological Project of Henan Province, China (242102241001, 222102210165, 232102111124).
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Data Availability
References
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Associated Data
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