Graphene battery different lignin low temperature performance test system

Abstract

The application discloses a kind of different lignin low-temperature performance test system of graphene battery, it is related to the field of graphene battery performance test system, the low-temperature performance test module is used for graphene battery to carry out low-temperature performance experiment, obtains the experimental result data of graphene battery in low-temperature performance experiment, and experimental result data is sent to information processing module;Information processing module calculates the performance coefficient score XN of each group of graphene battery respectively, compares performance coefficient score XN with preset performance threshold respectively, if greater than preset performance threshold YZ, then the cost score of this group experimental result data is carried out;The final score is obtained by formula ZJ=XN×e1+ZJ×e2, compare each group final score, the final score maximum experimental group is the best experimental group;The performance of graphene battery and cost should be reasonably distributed, more reasonable graphene battery group component proportion distribution is obtained, more conducive to the development progress of graphene battery.

Description

Technical Field

[0001] This invention relates to the field of graphene battery performance testing systems, and more particularly to a low-temperature performance testing system for different lignins in graphene batteries. Background Technology

[0002] Lignin is an extremely abundant natural resource with untapped potential. Currently, lignin in pulping wastewater is primarily used as fuel. Lignin sulfonates from sulfite pulping wastewater can be used as adhesives, chelating agents, industrial detergents, water softeners, emulsifiers and emulsion stabilizers, tannin supplements, cement additives, oil drilling dispersants, and soil stabilizers. By conducting low-temperature performance tests on graphene batteries with different lignins, the capacity information of the graphene batteries was determined, and their performance parameters were obtained. Performance testing was performed using automated testing tools to simulate various normal, peak, and abnormal load conditions to test various performance indicators of the system.

[0003] Existing low-temperature performance tests for graphene batteries using different lignins are limited in the types of performance tested, resulting in less objective and accurate evaluation data. The composition of graphene batteries varies, and the content and price of each element also differ. Although graphene batteries composed of certain element combinations may exhibit superior performance, their high cost hinders the widespread adoption of graphene battery products and impedes their development. Summary of the Invention

[0004] The purpose of this invention is to solve the problem that existing low-temperature performance tests for different lignins in graphene batteries only detect a limited range of performance types, resulting in insufficiently objective and accurate test evaluation data. Therefore, this invention proposes a low-temperature performance test system for different lignins in graphene batteries.

[0005] To achieve the above objectives, the present invention adopts the following technical solutions:

[0006] A low-temperature performance testing system for different lignins in graphene batteries includes a low-temperature performance testing module and an information processing module communicatively connected thereto, wherein the information processing module is communicatively connected to a display module.

[0007] The low-temperature performance testing module is used to conduct low-temperature performance experiments on graphene batteries, obtain experimental result data of graphene batteries in low-temperature performance experiments, and send the experimental result data to the information processing module.

[0008] After receiving the experimental results data sent by the low-temperature performance testing module, the information processing module calculates the performance coefficient score XN for each group of graphene batteries, compares the performance coefficient score XN with the preset performance threshold, and if it is greater than the preset performance threshold YZ, then the experimental results data of that group are scored.

[0009] The final score is obtained by the formula ZJ=XN×e1+ZJ×e2, where e1 and e2 are both proportional coefficients. By comparing the final scores of each group, the experimental group with the highest final score is the best experimental group.

[0010] The display module is used to display the data of each experimental group, as well as the performance coefficient score and cost score, on the monitor.

[0011] As a further technical solution of the present invention, the specific experimental steps of the low-temperature performance test are as follows:

[0012] The experiment was divided into 5 groups. Groups 1 to 4 used batteries with K lignin as the slurry, with an addition amount of 0.87 kg / pot (0.0725%), and other lignins were added to make the total lignin content 0.24%. Group 3 had a total lignin content of 0.35%. Group 5 used a 200g negative plate, 184g side negative plate, and 329g positive plate. The negative plate used T lignin slurry + DCA lignin, with a total lignin content of 0.4%.

[0013] Five groups of experimental batteries were subjected to cyclic discharge at -18℃ and -10℃ respectively. The time it took for the graphene battery to discharge completely and the battery capacity after 50 cycles, 100 cycles, 150 cycles, and 200 cycles were obtained.

[0014] As a further technical solution of the present invention, the specific scoring steps for the performance coefficient scoring are as follows:

[0015] In the experimental results at -18℃ and -10℃, the time taken for the graphene battery to completely discharge its charge is labeled as t1 and t2, respectively; the battery capacity after 50 discharge cycles is labeled as r1 and r2, respectively; the battery capacity after 100 discharge cycles is labeled as d1 and d2, respectively; the battery capacity after 150 discharge cycles is labeled as c1 and c2, respectively; and the battery capacity after 200 discharge cycles is labeled as y1 and y2, respectively. The performance coefficient score XN is obtained using the formula XN = 1.8 × t1 × (a1 × r1 + a2 × d1 + a3 × c1 + a4 × y1) + t2 × (a1 × r2 + a2 × d2 + a3 × c2 + a4 × y2), where a1, a2, a3, and a4 are coefficient factors, and a1 <a2<a3<a4。

[0016] As a further technical solution of the present invention, the specific scoring steps of the cost evaluation are as follows:

[0017] The prices of various types of lignin are obtained through the Internet of Things. The unit price of each kilogram of K lignin is marked as JK, the unit price of each kilogram of DCA lignin is marked as JC, and the unit price of each kilogram of T lignin is marked as JT. The cost score ZJ is obtained by the formula ZJ=JK×b1+JC×b2+JT×b3, where b1, b2, and b3 are all coefficient factors.

[0018] As a further technical solution of the present invention, the steps for obtaining the preset performance threshold are as follows:

[0019] The Internet of Things (IoT) is used to acquire battery capacity and discharge time data of graphene batteries with different lignin contents after 50, 100, 150, and 200 cycles of discharge at various temperatures; the intelligent calculation model is used to obtain the corresponding data at -18℃ and -10℃, and to calculate the preset performance threshold.

[0020] As a further technical solution of the present invention, the establishment of the intelligent computing model is specifically as follows:

[0021] The battery capacity data and discharge time data of graphene batteries with different lignin contents obtained through the Internet of Things after 50, 100, 150 and 200 cycles of discharge at various temperatures are used as training data to input into the artificial intelligence model for training. The trained artificial intelligence model is then labeled as an intelligent computing model.

[0022] The beneficial effects of this invention are:

[0023] This invention utilizes a low-temperature performance testing module to conduct low-temperature performance experiments on graphene batteries, acquiring experimental results data and sending this data to an information processing module. Upon receiving the data, the information processing module calculates a performance coefficient score XN for each group of graphene batteries. This score XN is then compared to a preset performance threshold YZ. If XN exceeds the threshold, a cost score is assigned to that group. The final score is obtained using the formula ZJ = XN × e1 + ZJ × e2, where e1 and e2 are proportionality coefficients. Comparing the final scores of each group, the group with the highest final score is identified as the optimal group. This approach aims to rationally allocate the performance and cost of graphene batteries, resulting in a more reasonable component ratio that is more conducive to the development and advancement of graphene batteries. Attached Figure Description

[0024] Figure 1 This is a system block diagram of the present invention. Detailed Implementation

[0025] To further illustrate the technical means and effects of the present invention in order to achieve the intended purpose, the following detailed description of the specific implementation methods, structures, features and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0026] Reference Figure 1 A low-temperature performance testing system for graphene batteries with different lignin content is disclosed, comprising a low-temperature performance testing module and an information processing module communicatively connected thereto, the information processing module being communicatively connected to a display module. The low-temperature performance testing module is used to conduct low-temperature performance experiments on graphene batteries, acquire experimental result data of the graphene batteries in the low-temperature performance experiment, and send the experimental result data to the information processing module. After receiving the experimental result data sent by the low-temperature performance testing module, the information processing module calculates the performance coefficient score XN for each group of graphene batteries, and compares the performance coefficient score XN with a preset performance threshold. The steps for obtaining the preset performance threshold are as follows: acquiring battery capacity data and discharge time data of graphene batteries with different lignin contents after 50, 100, 150, and 200 cycles of discharge at various temperatures through the Internet of Things; obtaining the corresponding data at -18℃ and -10℃ environments through an intelligent calculation model, and calculating the preset performance threshold.

[0027] The establishment of the intelligent computing model is specifically as follows: the battery capacity data and discharge time data of graphene batteries with different lignin contents obtained through the Internet of Things after 50, 100, 150 and 200 cycles of discharge at various temperatures are input into the artificial intelligence model for training, and the trained artificial intelligence model is marked as the intelligent computing model.

[0028] The artificial intelligence model in this embodiment includes classification models such as support vector machine models or deep convolutional neural network models.

[0029] If the cost score is greater than the preset performance threshold YZ, the experimental results data of this group are scored. The final score is obtained by the formula ZJ = XN × e1 + ZJ × e2, where e1 and e2 are proportional coefficients. By comparing the final scores of each group, the experimental group with the highest final score is the best experimental group. The display module is used to display the data of each experimental group, as well as the performance coefficient score and cost score, on the display.

[0030] This can be understood as follows: the best-performing experimental group of graphene batteries has different contents of each component element, and the content and price of each component element are also different. Although the graphene battery composed of individual element combinations has superior performance, the high cost will affect the popularization of graphene battery products and is not conducive to the development of graphene batteries. Therefore, the performance and cost of graphene batteries should be reasonably allocated to be more conducive to the development and progress of graphene batteries.

[0031] Furthermore, the specific experimental steps for the low-temperature performance test are as follows:

[0032] The experiment was divided into 5 groups. Groups 1 to 4 used batteries with K lignin as the slurry, with an addition amount of 0.87 kg / pot (0.0725%), and other lignins were added to make the total lignin content 0.24%. Group 3 had a total lignin content of 0.35%. Group 5 used a 200g negative plate, 184g side negative plate, and 329g positive plate. The negative plate used T lignin slurry + DCA lignin, with a total lignin content of 0.4%.

[0033] Five groups of experimental batteries were subjected to cyclic discharge at -18℃ and -10℃ respectively. The time it took for the graphene battery to discharge completely and the battery capacity after 50 cycles, 100 cycles, 150 cycles, and 200 cycles were obtained.

[0034] Furthermore, the specific scoring steps for the performance coefficient score are as follows:

[0035] In the experimental results at -18℃ and -10℃, the time taken for the graphene battery to completely discharge its charge is labeled as t1 and t2, respectively; the battery capacity after 50 discharge cycles is labeled as r1 and r2, respectively; the battery capacity after 100 discharge cycles is labeled as d1 and d2, respectively; the battery capacity after 150 discharge cycles is labeled as c1 and c2, respectively; and the battery capacity after 200 discharge cycles is labeled as y1 and y2, respectively. The performance coefficient score XN is obtained using the formula XN = 1.8 × t1 × (a1 × r1 + a2 × d1 + a3 × c1 + a4 × y1) + t2 × (a1 × r2 + a2 × d2 + a3 × c2 + a4 × y2), where a1, a2, a3, and a4 are coefficient factors, and a1 <a2<a3<a4。

[0036] Furthermore, the specific scoring steps for the cost assessment are as follows:

[0037] The prices of various types of lignin are obtained through the Internet of Things. The unit price of each kilogram of K lignin is marked as JK, the unit price of each kilogram of DCA lignin is marked as JC, and the unit price of each kilogram of T lignin is marked as JT. The cost score ZJ is obtained by the formula ZJ=JK×b1+JC×b2+JT×b3, where b1, b2, and b3 are all coefficient factors.

[0038] Working principle of the invention:

[0039] A low-temperature performance testing system for graphene batteries using different lignins is disclosed. A low-temperature performance testing module is used to conduct low-temperature performance experiments on graphene batteries, acquiring experimental results data and sending this data to an information processing module. Upon receiving the data, the information processing module calculates a performance coefficient score XN for each group of graphene batteries. The performance coefficient score XN is compared with a preset performance threshold. If it exceeds the preset performance threshold YZ, a cost score is assigned to that group of experimental results. The final score is obtained using the formula ZJ = XN × e1 + ZJ × e2, where e1 and e2 are proportionality coefficients. The experimental group with the highest final score is identified as the optimal experimental group. This system aims to rationally allocate the performance and cost of graphene batteries, resulting in a more reasonable component ratio that is more conducive to the development and advancement of graphene batteries.

[0040] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A low-temperature performance testing system for different lignins in graphene batteries, characterized in that, The system includes a low-temperature performance testing module and an information processing module that are communicatively connected to it, and the information processing module is communicatively connected to a display module. The low-temperature performance testing module is used to conduct low-temperature performance experiments on graphene batteries, obtain experimental result data of graphene batteries in low-temperature performance experiments, and send the experimental result data to the information processing module. After receiving the experimental results data sent by the low-temperature performance testing module, the information processing module calculates the performance coefficient score XN for each group of graphene batteries, compares the performance coefficient score XN with the preset performance threshold, and if it is greater than the preset performance threshold YZ, then the experimental results data of that group are scored. The final score is obtained by the formula Score=XN×e1+ZJ×e2, where e1 and e2 are proportional coefficients, ZJ is the cost score, and Score is the final score. By comparing the final scores of each group, the experimental group with the highest final score is the best experimental group. The display module is used to display the data of each experimental group, as well as the performance coefficient score and cost score, on the monitor. The specific experimental steps for the low-temperature performance test are as follows: The experiment was divided into 5 groups. Groups 1 to 4 used batteries with K lignin as the slurry, with an addition amount of 0.87 kg / pot (0.0725%), followed by the addition of other lignins to achieve a total lignin content of 0.24%. Group 3 had a total lignin content of 0.35%. Group 5 used 200g of negative electrode plate, 184g of edge negative electrode plate, and 329g of positive electrode plate. The negative electrode plate used T lignin slurry + DCA lignin, with a total lignin content of 0.4%. Five groups of experimental batteries were subjected to cyclic discharge at -18℃ and -10℃ respectively. The time it took for the graphene battery to discharge completely and the battery capacity after 50 cycles, 100 cycles, 150 cycles, and 200 cycles were obtained. The specific scoring steps for the performance coefficient score are as follows: In the experimental results at -18℃ and -10℃, the time taken for the graphene battery to discharge completely is labeled as t1 and t2, respectively; the battery capacity after 50 discharge cycles is labeled as r1 and r2, respectively; the battery capacity after 100 discharge cycles is labeled as d1 and d2, respectively; the battery capacity after 150 discharge cycles is labeled as c1 and c2, respectively; and the battery capacity after 200 discharge cycles is labeled as y1 and y2, respectively. The performance coefficient score XN is obtained using the formula XN=1.8×t1×(a1×r1+a2×d1+a3×c1+a4×y1)+t2×(a1×r2+a2×d2+a3×c2+a4×y2); where a1, a2, a3, and a4 are all coefficient factors, and a1 <a2<a3<a4。 2. The low-temperature performance testing system for different lignins in graphene batteries according to claim 1, characterized in that, The specific scoring steps for the cost assessment are as follows: The prices of various types of lignin are obtained through the Internet of Things. The unit price of each kilogram of K lignin is marked as JK, the unit price of each kilogram of DCA lignin is marked as JC, and the unit price of each kilogram of T lignin is marked as JT. The cost score ZJ is obtained by the formula ZJ=JK×b1+JC×b2+JT×b3, where b1, b2, and b3 are all coefficient factors.

3. The low-temperature performance testing system for different lignins in graphene batteries according to claim 1, characterized in that, The steps for obtaining the preset performance threshold are as follows: The Internet of Things (IoT) is used to acquire battery capacity and discharge time data of graphene batteries with different lignin contents after 50, 100, 150, and 200 cycles of discharge at various temperatures; the intelligent calculation model is used to obtain the corresponding data at -18℃ and -10℃, and to calculate the preset performance threshold.

4. The low-temperature performance testing system for different lignins in graphene batteries according to claim 3, characterized in that, The establishment of the intelligent computing model is specifically as follows: The battery capacity data and discharge time data of graphene batteries with different lignin contents obtained through the Internet of Things after 50, 100, 150 and 200 cycles of discharge at various temperatures are used as training data to input into the artificial intelligence model for training. The trained artificial intelligence model is then labeled as an intelligent computing model.

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