Battery selection analysis method, system, and electronic device
By automating the acquisition of target design parameters for batteries, the problem of errors in manual data collection during the selection process of batteries for new energy vehicles has been solved, achieving high efficiency in battery design and a balance between cost and performance.
Patent Information
- Application Number
- CN202610405571.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-03-30
- Publication Date
- 2026-07-21
AI Technical Summary
In existing technologies, the selection process for new energy vehicle batteries relies on manual data collection, which is prone to errors, resulting in long design cycles and difficulty in ensuring a balance between battery cost and performance.
By acquiring the cell performance parameters, battery pack prices, and vehicle power requirements of different material systems, and using relationship curves and multi-objective optimization algorithms, the target design parameters of the battery are automatically matched, thus achieving fully automated analysis of the battery selection process.
It improves the efficiency of battery design, ensures a balance between battery cost and performance, and reduces errors from human intervention and design cycles.
Smart Images
Figure CN122433283A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of battery technology, and in particular to a battery selection analysis method, system, and electronic device. Background Technology
[0002] Currently, the new energy vehicle industry is developing rapidly, and research on batteries is also becoming increasingly in-depth. The power batteries or hybrid batteries used in new energy vehicles require OEMs to manually collect and compile data on various materials, cell capacity, power output, and cost into tables. Engineers then select battery materials and configurations based on experience and these tables. Manually collecting battery data is inefficient and prone to errors, and the final selection of battery materials and configurations relies heavily on the engineer's personal experience, resulting in long battery design cycles and making it difficult to ensure a balance between battery cost and performance. Summary of the Invention
[0003] This application provides a battery selection analysis method, system, and electronic device, aiming to automate battery selection analysis to improve battery design efficiency and ensure a balance between battery cost and performance.
[0004] The first aspect of this application provides a battery selection analysis method, the selection analysis method comprising: To obtain the performance parameters of battery cells with different material systems, the prices of battery packs with different capacities, and the overall vehicle power requirements for various vehicles; Obtain the target design requirements for the battery; Based on the performance parameters, the prices of battery packs with different capacities, and the overall vehicle power requirements, a set of balance points that meet the target design requirements is obtained. The target design parameters of the battery are obtained by querying the battery configuration database based on the set of equilibrium points. The target design parameters include at least the target material system and the target cell configuration.
[0005] In this embodiment, by acquiring the performance parameters of cells with different material systems, the prices of battery packs with different capacities, the overall vehicle power requirements of various vehicles, and the target design requirements of the battery, a set of balance points that meet the target design requirements is obtained based on the performance parameters, the prices of battery packs with different capacities, and the overall vehicle power requirements. Then, the target design parameters of the battery are obtained by querying the battery configuration database based on the set of balance points. This enables the automated collection and integration of battery-related parameters, and the automatic matching of target design parameters based on the target design requirements of the battery. This achieves fully automated analysis of the battery selection process, improves the efficiency of battery design, and ensures a balance between battery cost and performance.
[0006] In one embodiment, obtaining a set of balance points that meet the target design requirements based on the performance parameters, the prices of battery packs with different capacities, and the overall vehicle power requirements includes: The first relationship curve between the cost per watt-hour of a battery and its capacity is obtained based on the price of battery packs with different capacities; A second relationship curve between the cost per watt-hour and the vehicle rate performance is obtained based on the performance parameters, the vehicle power requirements, and the prices of battery packs with different capacities. The set of equilibrium points that satisfy the target design requirements is obtained based on the first relationship curve and the second relationship curve.
[0007] In one embodiment, obtaining the first relationship curve between the cost per watt-hour of a battery and its capacity based on the price of battery packs with different capacities includes: The cost per watt-hour is calculated based on the price of battery packs with different capacities. The first relationship curve is generated by fitting the mapping point between the cost per watt-hour and the amount of electricity.
[0008] In one embodiment, the performance parameters include the plateau voltage and cutoff voltage of cells with different material systems. Obtaining a second relationship curve between the cost per watt-hour and the vehicle rate performance based on the performance parameters, the vehicle power requirements, and the prices of battery packs with different capacities includes: The vehicle rate performance is calculated based on the platform voltage, the cutoff voltage, the vehicle power requirement, and the price of the corresponding battery pack. The second relationship curve is generated by fitting the mapping point between the vehicle's rate performance and the cost per watt-hour.
[0009] In one embodiment, the target design requirements include a target power range, a target cost range, and a target rate performance. Obtaining the set of balance points that satisfy the target design requirements based on the first relationship curve and the second relationship curve includes: Find the feasible region on the first relationship curve and the second relationship curve that satisfies the target power range, the target cost range and the target rate performance; A preset algorithm is used to search the feasible region to obtain the set of equilibrium points.
[0010] In one embodiment, the selection analysis method further includes: A selection analysis report is generated based on the target design parameters, and the selection analysis report includes each parameter of the target design parameters; Output and display the selection analysis report.
[0011] A second aspect of this application provides a battery selection analysis system, comprising: The data integration module is used to collect the performance parameters of cells with different material systems, the prices of battery packs with different capacities, and the overall vehicle power requirements of various vehicles, in order to build a cell performance database, a battery cost database, and a battery configuration database. The input module is used to receive the target design requirements for obtaining the battery; The selection module is connected to the data integration module and the input module. The selection module is used to obtain a set of balance points that meet the target design requirements based on the performance parameters, the price of battery packs with different capacities and the power requirements of the vehicle. The selection module also queries the battery configuration database based on the set of balance points to obtain the target design parameters of the battery. The target design parameters include at least the target material system and the target cell configuration.
[0012] In one embodiment, the selection analysis system further includes a curve fitting module, which connects the data integration module and the selection module. The curve fitting module is used to obtain a first relationship curve between the cost per watt-hour of the battery and the capacity based on the price of battery packs with different capacities, and to obtain a second relationship curve between the cost per watt-hour of the battery and the vehicle rate performance based on the performance parameters, the vehicle power requirements, and the price of battery packs with different capacities. The selection module is also used to obtain a set of balance points that meet the target design requirements based on the first relationship curve and the second relationship curve.
[0013] In one embodiment, the selection analysis system further includes an interactive output module connected to the selection module; the selection module is further configured to generate a selection analysis report based on the target design parameters and output the selected selection analysis report to the interactive output module; the interactive output module is configured to visualize the selection analysis report.
[0014] A third aspect of this application provides an electronic device, including a processor and a memory, wherein... Memory, used to store computer programs; The processor is used to execute the program stored in memory to implement the method described above.
[0015] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described above. Attached Figure Description
[0016] Figure 1 This is a schematic diagram of the modules of a battery selection analysis system provided in an embodiment of this application; Figure 2This is a schematic diagram of a battery selection analysis system provided in another embodiment of this application; Figure 3 This is a schematic diagram of a first relationship curve provided in an embodiment of this application; Figure 4 This is a schematic diagram of the second relationship curve provided in an embodiment of this application; Figure 5 This is a schematic flowchart of a battery selection analysis method provided in an embodiment of this application; Figure 6 yes Figure 5 Detailed flowchart of step S300; Figure 7 yes Figure 6 Detailed flowchart of step S310; Figure 8 yes Figure 6 Detailed flowchart of step S320; Figure 9 yes Figure 6 Detailed flowchart of step S330; Figure 10 This is a partial flowchart of a battery selection analysis method provided in another embodiment of this application; Figure 11 This is a structural diagram of the electronic device provided in the embodiments of this application. Detailed Implementation
[0017] To make the technical problems, technical solutions, and beneficial effects solved by this application clearer, the following detailed description is provided in conjunction with embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.
[0018] The relevant terms used in this application are explained as follows: Power battery: Provides power to high-voltage components in a vehicle.
[0019] HEV batteries: provide power for hybrid electric vehicles during start-up and low-speed driving.
[0020] Ternary materials (NCM) are ternary cathode materials composed of three metal elements: nickel, cobalt, and manganese (or aluminum). They have advantages such as high voltage tolerance, high specific capacity, and low cobalt content.
[0021] Lithium iron phosphate (LFP): also known as lithium iron phosphate battery cathode material, is an advanced material widely used in the field of lithium-ion batteries. It is mainly produced by high-temperature solid-state reaction or other synthesis methods using iron phosphate and lithium source (such as lithium carbonate, lithium hydroxide, etc.).
[0022] Pareto front search: a core concept in multi-objective optimization problems, also known as the Pareto optimal boundary. Essentially, it describes the set of solutions where, under multi-objective conditions, it is impossible to further optimize one objective without compromising the others.
[0023] Please refer to Figure 1 , Figure 1 This is a schematic diagram of the modules of a battery selection analysis system 100 provided in an embodiment of this application. Figure 1 As shown, the selection analysis system 100 includes a data integration module 110, an input module 120, and a selection module 130.
[0024] In this embodiment, the data integration module 110, input module 120, and selection module 130 can be implemented through functional modules in a software program. The selection analysis system 100 of this embodiment can be used for selection analysis of power batteries for new energy vehicles, and also for selection analysis of HEV batteries in hybrid electric vehicles.
[0025] The data integration module 110 is used to collect performance parameters of battery cells with different material systems, cost parameters of battery packs with different capacities, and the overall vehicle power requirements of various vehicles to construct a battery cell performance database, a battery cost database, and a battery configuration database. The performance parameters of battery cells with different material systems can at least include the rate performance, plateau voltage, and cutoff voltage of the cells for each material system. The cost parameters can at least include battery packs with different capacities and their corresponding prices (which can be real-time or historical prices), and the structural component costs of different cell configurations (such as cylindrical configuration, square aluminum shell configuration, pouch, etc.). Here, "material system" refers to the material composition of the battery cells; NCM is a type of battery cell material system, LFP is another type, and different material systems used in the battery pack will exhibit different characteristics.
[0026] In this embodiment, the data integration module 110 can automatically collect the aforementioned data through preset data interfaces (such as API interfaces, direct database connection with CDC (Change Data Capture), and automatic file parsing). The data integration module 110 can automatically retrieve and clean data from cells, battery packs, and the entire vehicle scattered across multiple systems, files, and devices according to unified rules, and then summarize and integrate the cleaned data into the cell performance database, battery cost database, and battery configuration database. The cell performance database can include the rate performance, platform voltage, cutoff voltage, energy density, and cycle life of cells with different material systems. The battery cost database contains battery packs with different capacities and their corresponding prices. It can be understood that the battery cost database includes the prices of battery packs with different capacities, while the battery configuration database contains the mapping relationship between different cell configurations and material systems, process parameters (such as the range of parallel connections), and structural component costs. Thus, the required data can be directly retrieved from the cell performance database, battery cost database, and battery configuration database for selection analysis.
[0027] In this embodiment of the application, the data integration module 110 can automatically collect the total power requirements of various types of vehicles.
[0028] The input module 120 is used to receive the target design requirements of the battery. Specifically, the input module 120 can receive target design requirements input by the user, which may include, for example, the target capacity range, target cost range, and target rate performance of the battery pack to be designed.
[0029] In some embodiments, the target design requirements may also include a target energy density range, a target cycle life range, and so on.
[0030] The selection module 130 is connected to the data integration module 110 and the input module 120. The selection module 130 obtains a set of balance points that meet the target design requirements based on performance parameters, the price of battery packs with different capacities, and the overall vehicle power requirements. It then queries the battery configuration database based on this set of balance points to obtain the target design parameters for the battery. These target design parameters include at least the target material system and the target cell configuration. The selection module 130 can obtain the set of balance points based on data from the cell performance database and the battery cost database, and then query the battery configuration database based on this set of balance points to obtain the target design parameters. Therefore, the user can confirm the selectable material system and cell configuration based on the target design parameters.
[0031] In the selection analysis system 100 of this application embodiment, the data integration module 110 automatically collects performance parameters of battery cells with different material systems, cost parameters of battery packs with different capacities, and overall vehicle power requirements for various vehicles to construct a battery cell performance database, a battery cost database, and a battery configuration database. The input module 120 receives the target design requirements of the battery. Then, the selection module 130 obtains a set of balance points that meet the target design requirements based on the performance parameters in the battery cell performance database, the cost parameters in the battery cost database, and the obtained overall vehicle power requirements. Finally, it queries the battery configuration database based on the balance point set to obtain the target design parameters of the battery. Therefore, using the selection analysis system 100 of this application embodiment, the user only needs to input the target design requirements through the input module 120. The selection analysis system 100 can automatically obtain relevant battery parameters, construct a database based on each parameter, obtain a set of balance points that meet the target design requirements, and query the battery configuration database based on the balance point set to obtain the target design parameters. This achieves automated analysis of battery selection, improves the efficiency of battery design, and ensures a balance between battery cost and performance.
[0032] The selection analysis system 100 of this application embodiment can automatically calculate and analyze to obtain target design parameters, realizing a fully automated process from data acquisition, database construction and target design parameter output. Compared with the traditional manual data processing and engineers' experience in determining battery design parameters, it can realize intelligent processing of battery design, improve efficiency and ensure a balance between battery cost and performance.
[0033] In some embodiments, such as Figure 2 As shown, the selection analysis system 100 also includes a curve fitting module 140, which is connected to the data integration module 110 and the selection module 130.
[0034] Curve fitting module 140 can be used to obtain a first relationship curve between the cost per watt-hour of a battery and its capacity based on the price of battery packs with different capacities (e.g., ...). Figure 3 (As shown). Specifically, the curve fitting module 140 can calculate the cost per watt-hour of the battery according to the following first formula: C single = C pack / P pack Among them, C single For cost per watt-hour, P pack For the battery pack's capacity, C packThe price is the cost per watt-hour of the battery pack. The curve fitting module 140 calculates the cost per watt-hour of the battery pack for each capacity according to the first formula, and then generates a first relationship curve based on the relationship between cost per watt-hour and capacity. In this embodiment, the curve fitting module 140 can construct a first coordinate system with capacity on the horizontal axis and cost per watt-hour on the vertical axis (see...). Figure 3 Then, based on the correspondence between cost per watt-hour and electricity, mapping points are marked in the first coordinate system, and curve fitting is performed on the mapping points of each cost per watt-hour and electricity to obtain the first relationship curve.
[0035] In some embodiments, the curve fitting module 140 can calculate the cost per watt-hour corresponding to each material system based on the battery price of the battery pack at different capacities, and then fit and generate a first relationship curve corresponding to the battery pack of different material systems based on the correspondence between cost per watt-hour and capacity. That is, each material system battery pack corresponds to a first relationship curve. For example, the curve fitting module 140 can calculate the corresponding cost per watt-hour based on the battery price of the battery pack composed of ternary materials at different capacities, and then fit and generate a first relationship curve corresponding to the battery pack composed of ternary materials based on the correspondence between cost per watt-hour and capacity. The curve fitting module 140 can also calculate the corresponding cost per watt-hour based on the battery price of the battery pack composed of lithium iron phosphate at different capacities, and then fit and generate a first relationship curve corresponding to the battery pack composed of lithium iron phosphate based on the correspondence between cost per watt-hour and capacity.
[0036] In other embodiments, the curve fitting module 140 may also directly calculate the cost per watt-hour based on the prices and capacities of all battery packs collected in the battery cost database of the data integration module 110, and fit a first relationship curve based on the correspondence between cost per watt-hour and capacity.
[0037] The curve fitting module 140 can also obtain a second relationship curve between the cost per watt-hour and the vehicle's rate performance based on performance parameters, vehicle power requirements, and the price of battery packs with different capacities (e.g., ...). Figure 4 (As shown). Specifically, the curve fitting module 140 can calculate the vehicle ratio performance corresponding to the vehicle power demand according to the following second formula: D = (W car * V platform ) / (P pack * V cutoff * S) Where D represents the vehicle's ratio performance, and W... car For the vehicle's power requirements, V platform V is the platform voltage. cutoffHere, S represents the cutoff voltage, and S represents the number of cells in series. This formula can be understood as the vehicle rate performance achievable with a battery pack using a specific material system and a specific capacity under a specific vehicle power requirement. Therefore, the relationship between vehicle rate performance and cost per watt-hour can be obtained by combining the battery pack's capacity with the first formula. Then, a second relationship curve can be generated by fitting the relationship between vehicle rate performance and cost per watt-hour. In this embodiment, the curve fitting module 140 can construct a second coordinate system with vehicle rate performance on the horizontal axis and cost per watt-hour on the vertical axis (see...). Figure 4 Then, based on the correspondence between cost per watt-hour and vehicle performance ratio, mapping points are marked in the second coordinate system, and curve fitting is performed on the mapping points of each cost per watt-hour and vehicle performance ratio to obtain the second relationship curve.
[0038] Based on the first and second relationship curves generated by the curve fitting module 140, the selection module 130 can also be used to obtain a set of balance points that meet the target design requirements according to the first and second relationship curves.
[0039] The selection module 130 can use the user-input target design requirements as constraints, perform intersection calculations on the first and second relationship curves, and automatically determine a feasible region that satisfies the target power range, target cost range, and target rate performance. The selection module 130 can use a preset algorithm to perform multi-objective search within the feasible region to obtain a set of balance points. This set of balance points represents a balance between parameters such as cost, power, and rate performance; for example, achieving the battery design performance with the lowest cost, highest power, and highest rate performance. In this embodiment, the selection module 130 can use a multi-objective optimization algorithm to perform multi-objective search within the feasible region. Specifically, the multi-objective optimization algorithm can be, for example, the Pareto front search algorithm.
[0040] Furthermore, the selection module 130 queries the battery configuration database based on the balance point set, and matches the target design parameters that can achieve the battery design performance in the balance point set. The target design parameters may include at least the target material system, the target cell configuration, and the target cost. Specifically, if a balance point in the balance point set obtained by the selection module 130 has a target battery pack capacity of 7.5 kWh, a target rate performance of 5C, and a target cost per watt-hour of 0.8 kWh, the target design parameters matched by querying the battery configuration database may be: a target material system of high-nickel ternary system, a target battery configuration of prismatic VDA size, and a recommended parallel connection quantity of 3. Of course, in this embodiment, the balance point set may include one or more balance points, and the target design parameters matched by querying the battery configuration database may also include one or more corresponding balance points.
[0041] In this embodiment of the application, the selection analysis system 100 can be a software program built into a computer device, and the data integration module 110, input module 120, selection analysis module and curve fitting module 140 can be part of the software program.
[0042] In some embodiments, the selection analysis system 100 further includes an interactive output module connected to the selection module 130; the selection module 130 is further configured to generate a selection analysis report based on the target design parameters and output the selected analysis report to the interactive output module. The interactive output module is used to visualize the selection analysis report.
[0043] The selection analysis report may include target design parameters. When the set of equilibrium points includes multiple equilibrium points, multiple target design parameters are obtained accordingly. At this time, the selection module 130 can generate a selection analysis report based on the multiple target design parameters. The selection analysis report may include a list of battery configurations formed by the multiple target design parameters, as well as a comparative analysis between the multiple target design parameters, etc.
[0044] The interactive output module can be a display screen of an electronic device with the selection and classification system of this application built in. Thus, the selection module 130 generates a selection analysis report based on the target design parameters and outputs it to the interactive output module. The interactive output module can display the selection analysis report so that the user can directly view the selection analysis report and select the best battery design scheme based on the selection analysis report.
[0045] Please see Figure 5 , Figure 5 A flowchart illustrating the battery selection analysis method provided in an embodiment of this application is shown. In this embodiment, the selection analysis method can be executed by the aforementioned selection analysis system 100.
[0046] like Figure 5 As shown, the selection analysis method provided in this application embodiment may include the following steps: Step S100: Obtain the performance parameters of cells with different material systems, the prices of battery packs with different capacities, and the overall vehicle power requirements for various vehicles.
[0047] The prices of battery packs with different capacities can be real-time or historical prices. Performance parameters include the plateau voltage and cutoff voltage of cells with different material systems. In this embodiment, the data integration module 110 can automatically collect the performance parameters of cells with different material systems, the prices of battery packs with different capacities, and the overall vehicle power requirements of various vehicles. The data integration module 110 then summarizes and integrates the collected data into the cell performance database, battery cost database, and battery configuration database. The selection module 130 can obtain the cell performance parameters, cost parameters, and overall vehicle power requirements of various vehicles from the cell performance database, battery cost database, and battery configuration database, respectively.
[0048] Step S200: Obtain the target design requirements for the battery.
[0049] Users can input their target design requirements through input module 120, and selection module 130 receives the target design requirements input by users through input module 120.
[0050] Step S300: Obtain a set of balance points that meet the target design requirements based on performance parameters, the price of battery packs with different capacities, and the power requirements of the vehicle.
[0051] The selection module 130 performs calculations based on performance parameters, the price of battery packs with different capacities, and the power requirements of the vehicle, thereby obtaining a set of balance points that meet the target design requirements.
[0052] Step S400: Query the battery configuration database based on the balance point set to obtain the target design parameters of the battery.
[0053] The target design parameters include at least the target material system and the target cell configuration. The selection module 130 can retrieve the matching target design parameters from the battery configuration database based on the set of equilibrium points, thereby achieving automated configuration analysis of the battery.
[0054] In this embodiment, by acquiring the performance parameters of cells with different material systems, the prices of battery packs with different capacities, the overall vehicle power requirements of various vehicles, and the target design requirements of the battery, a set of balance points that meet the target design requirements is obtained based on the performance parameters, the prices of battery packs with different capacities, and the overall vehicle power requirements. Then, the target design parameters of the battery are obtained by querying the battery configuration database based on the set of balance points. This enables the automated collection and integration of battery-related parameters, and the automatic matching of target design parameters based on the target design requirements of the battery. This achieves fully automated analysis of the battery selection process, improves the efficiency of battery design, and ensures a balance between battery cost and performance.
[0055] In some embodiments, such as Figure 6 As shown, step S300 may include the following steps: Step S310: Obtain the first relationship curve between the cost per watt-hour of the battery and the capacity based on the price of battery packs with different capacities.
[0056] In this embodiment of the application, the curve fitting module 140 can obtain the prices of battery packs with different capacities from the battery cost database in the data integration module 110, and then obtain the first relationship curve between the unit watt-hour cost and the capacity of the battery based on the price of the battery packs with different capacities.
[0057] Furthermore, such as Figure 7 As shown, in some embodiments, step S310 may include: Step S311: Calculate the corresponding cost per watt-hour based on the price of battery packs with different capacities.
[0058] The curve fitting module 140 can calculate the cost per watt-hour of each battery pack according to the aforementioned first formula.
[0059] Step S312: Generate the first relationship curve by fitting the mapping point of cost per watt-hour and power consumption.
[0060] After calculating the cost per watt-hour, the curve fitting module 140 generates a first relationship curve by fitting the mapping point between the cost per watt-hour and the amount of electricity.
[0061] Step S320: Obtain a second relationship curve between cost per watt-hour and vehicle rate performance based on performance parameters, vehicle power requirements, and the price of battery packs with different capacities.
[0062] Furthermore, such as Figure 8 As shown, in some embodiments, step S320 may include: Step S321: Calculate the vehicle rate performance based on the platform voltage, cutoff voltage, vehicle power requirements, and the price of the corresponding battery pack.
[0063] The curve fitting module 140 can calculate the vehicle ratio performance based on the aforementioned second formula.
[0064] Step S322: Generate a second relationship curve by fitting the mapping point between the vehicle's rate performance and cost per watt-hour.
[0065] After calculating the vehicle's rate performance, the curve fitting module 140 generates a second relationship curve by fitting the mapping point between the vehicle's rate performance and the cost per watt-hour.
[0066] Step S330: Obtain the set of equilibrium points that meet the target design requirements based on the first relationship curve and the second relationship curve.
[0067] Furthermore, such as Figure 9As shown, in some embodiments, step S330 may include: Step S331: Find the feasible region that satisfies the target power range, target cost range, and target rate performance on the first and second relationship curves.
[0068] The selection module 130 can perform intersection calculations on the first and second relationship curves based on the target design requirements to determine a feasible region that meets the target power range, target cost range, and target rate performance.
[0069] Step S332: Use a preset algorithm to search the feasible region to obtain a set of equilibrium points.
[0070] The preset algorithm can be set according to the application needs. For example, the selection module 130 can use a multi-objective optimization algorithm to perform multi-objective search in the feasible region to obtain a set of equilibrium points.
[0071] like Figure 10 As shown, in some embodiments, the selection analysis method may further include: Step S500: Generate a selection analysis report based on the target design parameters.
[0072] The selection analysis report includes all parameters of the target design parameters. The selection module 130 queries the battery configuration database based on the set of equilibrium points to obtain the target design parameters, and generates the selection analysis report based on the target design parameters.
[0073] Step S600: Output and display the selection analysis report.
[0074] The selection module 130 can output the generated selection analysis report to the interactive output module, which will then display the selection analysis report.
[0075] For details on the specific implementation of the selection analysis method in this application embodiment, please refer to the aforementioned selection analysis system 100, which will not be repeated here.
[0076] This application also provides an electronic device 200, please refer to... Figure 11 The electronic device 200 includes a processor 210 and a memory 220. The memory 210 is used to store computer programs, and the processor 220 is used to execute the programs stored in the memory 210 to implement the selection analysis method described in any embodiment of this application.
[0077] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the selection analysis method described in any embodiment of this application.
[0078] In this application, "multiple" refers to two or more.
[0079] In this application, unless otherwise expressly defined, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.
[0080] The terms “first,” “second,” “third,” “fourth,” etc., in this application (if present) are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.
[0081] In this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, in this application, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.
[0082] Unless otherwise specified, all steps in this application may be performed sequentially or randomly. For example, if a method includes steps A and B, it means that the method may include steps A and B performed sequentially, or it may include steps B and A performed sequentially. For example, if a method may also include step C, it means that step C may be added to the method in any order. For example, the method may include steps A, B, and C, or it may include steps A, C, and B, or it may include steps C, A, and B, etc.
[0083] The above are merely preferred embodiments of this application and are not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the protection scope of this application.
Claims
1. A method for battery selection and analysis, characterized in that, The selection analysis method includes: To obtain the performance parameters of battery cells with different material systems, the prices of battery packs with different capacities, and the overall vehicle power requirements for various vehicles; Obtain the target design requirements for the battery; Based on the performance parameters, the prices of battery packs with different capacities, and the overall vehicle power requirements, a set of balance points that meet the target design requirements is obtained. The target design parameters of the battery are obtained by querying the battery configuration database based on the set of equilibrium points. The target design parameters include at least the target material system and the target cell configuration.
2. The selection analysis method according to claim 1, characterized in that, The process of obtaining a set of balance points that meet the target design requirements based on the performance parameters, the prices of battery packs with different capacities, and the overall vehicle power requirements includes: The first relationship curve between the cost per watt-hour of a battery and its capacity is obtained based on the price of battery packs with different capacities; A second relationship curve between the cost per watt-hour and the vehicle rate performance is obtained based on the performance parameters, the vehicle power requirements, and the prices of battery packs with different capacities. The set of equilibrium points that satisfy the target design requirements is obtained based on the first relationship curve and the second relationship curve.
3. The selection analysis method according to claim 1, characterized in that, The method of obtaining the first relationship curve between the cost per watt-hour of a battery and its capacity based on the price of battery packs with different capacities includes: The cost per watt-hour is calculated based on the price of battery packs with different capacities. The first relationship curve is generated by fitting the mapping point between the cost per watt-hour and the amount of electricity.
4. The selection analysis method according to claim 3, characterized in that, The performance parameters include the plateau voltage and cutoff voltage of cells with different material systems. The second relationship curve between the cost per watt-hour and the vehicle rate performance, obtained based on the performance parameters, the vehicle power requirements, and the prices of battery packs with different capacities, includes: The vehicle rate performance is calculated based on the platform voltage, the cutoff voltage, the vehicle power requirement, and the price of the corresponding battery pack. The second relationship curve is generated by fitting the mapping point between the vehicle's rate performance and the cost per watt-hour.
5. The selection analysis method according to any one of claims 2 to 4, characterized in that, The target design requirements include a target power range, a target cost range, and a target rate performance. Obtaining the set of balance points that satisfy the target design requirements based on the first and second relationship curves includes: Find the feasible region on the first relationship curve and the second relationship curve that satisfies the target power range, the target cost range and the target rate performance; A preset algorithm is used to search the feasible region to obtain the set of equilibrium points.
6. The selection analysis method according to claim 1, characterized in that, The selection analysis method also includes: A selection analysis report is generated based on the target design parameters, and the selection analysis report includes each parameter of the target design parameters; Output and display the selection analysis report.
7. A battery selection and analysis system, characterized in that, include: The data integration module is used to collect the performance parameters of cells with different material systems, the prices of battery packs with different capacities, and the overall vehicle power requirements of various vehicles, in order to build a cell performance database, a battery cost database, and a battery configuration database. The input module is used to receive the target design requirements for obtaining the battery; The selection module is connected to the data integration module and the input module. The selection module is used to obtain a set of balance points that meet the target design requirements based on the performance parameters, the price of battery packs with different capacities and the power requirements of the vehicle. The selection module also queries the battery configuration database based on the set of balance points to obtain the target design parameters of the battery. The target design parameters include at least the target material system and the target cell configuration.
8. The selection analysis system as described in claim 7, characterized in that, It also includes a curve fitting module, which is connected to the data integration module and the selection module. The curve fitting module is used to obtain a first relationship curve between the cost per watt-hour of the battery and the capacity based on the price of battery packs with different capacities, and to obtain a second relationship curve between the cost per watt-hour of the battery and the rate performance of the vehicle based on the performance parameters, the vehicle power requirements and the price of battery packs with different capacities. The selection module is also used to obtain a set of balance points that meet the target design requirements based on the first relationship curve and the second relationship curve.
9. The selection analysis system as described in claim 7 or 8, characterized in that, It also includes an interactive output module, which is connected to the selection module; the selection module is further used to generate a selection analysis report based on the target design parameters and output the selection analysis report to the interactive output module; the interactive output module is used to visualize the selection analysis report.
10. An electronic device, characterized in that, Including processor and memory, among which Memory, used to store computer programs; A processor is used to execute a program stored in memory to implement the selection analysis method according to any one of claims 1 to 6.