Battery parameter determination method and device and nonvolatile storage medium

Abstract

The invention discloses a battery parameter determination method and device and a nonvolatile storage medium. The method comprises the steps that initial battery parameters are obtained, constraint conditions and multiple objective functions for optimizing the initial battery parameters are obtained, and the initial battery parameters are used for processing a battery in the whole life cycle of the battery; according to a preset carbon emission determination mode, a preset processing cost determination mode, a preset constraint condition and a plurality of target functions, a multi-target optimization model is constructed, and the target functions are respectively related to the carbon emission and the processing cost; and inputting the initial battery parameters into a multi-objective optimization model, solving the multi-objective optimization model by adopting a non-dominated sorting genetic algorithm to obtain multiple groups of optimized battery parameters, and determining target battery parameters. According to the invention, the technical problems that the battery design lacks an effective method for optimizing the carbon emission and the cost of the whole life cycle at the same time, and an optimal balance point between environmental protection and economy is difficult to find are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of batteries, and in particular, to a method, device, and non-volatile storage medium for determining battery parameters. Background Art

[0002] At present, the global new energy power battery market maintains a rapid growth trend, but the export of new energy power batteries overseas faces policy regulations and access barriers. Countries and regions such as the European Union and the United States have introduced strict battery access policies, mainly involving information such as the material composition, carbon footprint, and supply chain of batteries, which increases the difficulty for power battery enterprises to enter overseas markets.

[0003] In addition, power battery technologies are constantly updated and iterated, and enterprises need to innovate and research and develop to meet the performance requirements of high safety, high energy density, long cycle life, and fast charging ability in domestic and foreign markets. Power batteries face the problem of too high costs, and it is necessary to comprehensively consider aspects such as raw material procurement, production process optimization, and logistics transportation routes to further reduce costs to improve competitiveness.

[0004] In order to meet the requirements of policy regulations, technological iteration, and cost control, etc., it is necessary to calculate the greenhouse gas emissions generated during the entire life cycle of battery products (including raw material acquisition, battery production, sales, use, and waste treatment), that is, carbon footprint accounting, and based on this, explore appropriate power battery emission reduction paths.

[0005] In response to the above problems, no effective solutions have been proposed yet. Summary of the Invention

[0006] Embodiments of the present invention provide a method, device, and non-volatile storage medium for determining battery parameters, so as to at least solve the technical problem that the battery design lacks an effective method to simultaneously optimize the carbon emissions and costs of the entire life cycle and it is difficult to find the best balance between environmental protection and economy.

[0007] According to one aspect of the embodiments of the present invention, a method for determining battery parameters is provided, including: obtaining initial battery parameters, as well as constraint conditions and multiple objective functions for optimizing the initial battery parameters, where the initial battery parameters are used to process the battery during the entire life cycle of the battery; constructing a multi-objective optimization model according to a preset carbon emission determination method, a processing cost determination method, constraint conditions, and multiple objective functions, where the multiple objective functions are respectively related to carbon emissions and processing costs; inputting the initial battery parameters into the multi-objective optimization model, and using a non-dominated sorting genetic algorithm to solve the multi-objective optimization model to obtain multiple sets of optimized battery parameters; and determining target battery parameters among the multiple sets of optimized battery parameters.

[0008] Optionally, input the initial battery parameters into the multi-objective optimization model, and use the non-dominated sorting genetic algorithm to solve the multi-objective optimization model to obtain multiple sets of optimized battery parameters, including: generating multiple sets of battery parameters that meet the constraint conditions in the multi-objective optimization model based on the initial battery parameters as the initial population; determining the multiple objective function values corresponding to each of the multiple sets of battery parameters according to the multiple objective functions in the multi-objective optimization model; grading the multiple sets of battery parameters according to the multiple objective function values corresponding to each of the multiple sets of battery parameters to obtain the grading result of the initial population; updating the initial population according to the grading result of the initial population until the number of updates meets the predetermined conditions, obtaining the updated population, and determining the Pareto optimal solution of the updated population to obtain multiple sets of optimized battery parameters. In this optional embodiment, through the iterative optimization of the genetic algorithm, the battery parameters that balance carbon emissions and processing costs can be found faster, which is applicable to the early stage of battery design to quickly locate the best battery parameter design scheme.

[0009] Optionally, grading the multiple sets of battery parameters according to the multiple objective function values corresponding to each of the multiple sets of battery parameters to obtain the grading result of the initial population, including: taking the multiple sets of battery parameters as multiple individuals respectively, and determining the multiple objective function values of each of the multiple individuals; determining the dominance relationship between the multiple individuals according to the magnitude relationship between the multiple objective function values of each of the multiple individuals, where the dominance relationship includes that the target individual dominates another individual, that is, all the values of the objective functions corresponding to the target individual are not worse than those of another individual, and at least one value of the objective function is better than that of another individual; grading the multiple sets of battery parameters according to the dominance relationship between the multiple individuals to obtain the grading result of the initial population. In this optional embodiment, the grading mechanism determined according to the dominance relationship helps to quickly identify which parameter combinations are closer to the optimal solution in multi-objective optimization, is applicable to dealing with large-scale battery parameter optimization problems, and improves the optimization efficiency.

[0010] Optionally, updating the initial population according to the grading result of the initial population until the number of updates meets the predetermined conditions, obtaining the updated population, and determining the Pareto optimal solution of the updated population to obtain multiple sets of optimized battery parameters, including: selecting some individuals from the initial population according to the grading result of the initial population; performing crossover processing and mutation processing on the selected individuals to obtain the offspring population; updating the offspring population until the number of updates meets the predetermined conditions, obtaining the updated population, and determining the Pareto optimal solution of the updated population to obtain multiple sets of optimized battery parameters. In this optional embodiment, through crossover and mutation operations, new parameter combinations can be introduced to avoid local optimal solutions and ensure the diversity and globality of the optimization results, which is applicable to battery R & D projects that require continuous improvement and innovation.

[0011] Optionally, the carbon emission determination method is determined as follows, including: obtaining the total amount of substances involved in multiple stages of the battery's entire life cycle; determining the type of substances to which the total amount of substances involved in multiple stages belongs; determining the carbon emissions generated during the battery's entire life cycle based on the carbon emission factors corresponding to the substance types and the total amount of substances involved in multiple stages; determining the first correlation relationship between the parameters included in the initial battery parameters and the carbon emissions generated during the battery's entire life cycle; and determining the carbon emission determination method based on the first correlation relationship. In this optional embodiment, the impact of the battery on the environment can be quantified, which is applicable to markets with strict environmental protection regulations and obvious green consumption trends, helping enterprises better meet environmental standards and enhance their brand images.

[0012] Optionally, the processing cost determination method is determined as follows, including: obtaining the total amount of substances involved in multiple stages of the battery's entire life cycle; determining the cost required during the battery's entire life cycle based on the total amount of substances involved in multiple stages; determining the second correlation relationship between the parameters included in the initial battery parameters and the cost required during the battery's entire life cycle; and determining the processing cost determination method based on the second correlation relationship. In this optional embodiment, the costs in stages such as battery production, use, and recycling can be comprehensively considered, which is applicable to industries with strict cost control and fierce market competition, helping enterprises optimize their cost structures and improve their market competitiveness.

[0013] According to another aspect of the embodiments of the present invention, there is also provided a battery parameter determination device, including: an acquisition module, configured to acquire initial battery parameters, as well as the constraint conditions and multiple objective functions for optimizing the initial battery parameters, where the initial battery parameters are used to process the battery during its entire life cycle; a modeling module, configured to construct a multi-objective optimization model according to the preset carbon emission determination method, processing cost determination method, constraint conditions, and multiple objective functions, where the multiple objective functions are respectively related to carbon emissions and processing costs; a solution module, configured to input the initial battery parameters into the multi-objective optimization model and solve the multi-objective optimization model using the non-dominated sorting genetic algorithm to obtain multiple sets of optimized battery parameters; and a determination module, configured to determine the target battery parameters from the multiple sets of optimized battery parameters.

[0014] According to yet another aspect of the embodiments of the present invention, there is also provided a non-volatile storage medium, where the non-volatile storage medium includes a stored program, and when the program runs, it controls the device where the non-volatile storage medium is located to execute any one of the above battery parameter determination methods.

[0015] According to still another aspect of the embodiments of the present invention, there is also provided a computer device, where the computer device includes a processor, and the processor is used to run a program, and when the program runs, it executes any one of the above battery parameter determination methods.

[0016] According to another aspect of the embodiments of the present invention, there is also provided a computer program product, including a computer program, which when executed by a processor implements any one of the above battery parameter determination methods.

[0017] In the embodiments of the present invention, by obtaining initial battery parameters, as well as optimizing the constraint conditions and multiple objective functions of the initial battery parameters, wherein the initial battery parameters are used to process the battery throughout its entire life cycle; according to the preset carbon emission determination method, processing cost determination method, constraint conditions and multiple objective functions, a multi-objective optimization model is constructed, wherein the multiple objective functions are respectively related to carbon emissions and processing costs; the initial battery parameters are input into the multi-objective optimization model, and the non-dominated sorting genetic algorithm is used to solve the multi-objective optimization model to obtain multiple sets of optimized battery parameters; among the multiple sets of optimized battery parameters, the target battery parameters are determined, which can systematically optimize the environmental impact and economic cost in the entire life cycle of the battery, find the best balance point between carbon emissions and costs, thereby effectively improving the environmental protection performance and market competitiveness of battery products, and further solving the technical problem that the battery design lacks an effective method to simultaneously optimize the carbon emissions and costs in the entire life cycle and it is difficult to find the best balance point between environmental protection and economy. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention. In the drawings:

[0019] Figure 1 A hardware structure block diagram of a computer terminal for implementing the battery parameter determination method is shown;

[0020] Figure 2 It is a flowchart of the battery parameter determination method provided by the embodiments of the present invention;

[0021] Figure 3 It is a structure block diagram of the battery parameter determination device provided by the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0023] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present invention described here can be implemented in an order different from those illustrated or described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0024] According to an embodiment of the present invention, an embodiment of a method for determining battery parameters is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. And although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0025] The method embodiment provided by the first embodiment of the present application can be executed on a mobile terminal, a computer terminal, or a similar computing device. Figure 1 A hardware structure block diagram of a computer terminal for implementing the battery parameter determination method is shown. As Figure 1 shown, the computer terminal 10 may include one or more (shown as 102a, 102b,..., 102n in the figure) processors (the processor may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA), and a memory 104 for storing data. In addition, it may further include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which can be included as one of the ports of the BUS bus), a network interface, a power supply, and / or a camera. Those of ordinary skill in the art can understand that Figure 1 the structure shown is only schematic and does not limit the structure of the above-mentioned electronic device. For example, the computer terminal 10 may further include more or fewer components than Figure 1 shown, or have a different configuration from Figure 1 shown.

[0026] It should be noted that one or more of the above-mentioned processors and / or other data processing circuits can generally be referred to as "data processing circuits" herein. The data processing circuit can be embodied in software, hardware, firmware, or any combination thereof, in whole or in part. In addition, the data processing circuit can be a single independent processing module, or be incorporated in whole or in part into any one of other elements in the computer terminal 10. As involved in the embodiments of the present application, the data processing circuit is a kind of processor control (for example, the selection of a variable resistance terminal path connected to an interface).

[0027] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage devices corresponding to the battery parameter determination method in the embodiments of the present invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, implements the battery parameter determination method of the above-mentioned application program. The memory 104 can include high-speed random access memory, and can also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memories. In some instances, the memory 104 can further include memories remotely provided with respect to the processor, and these remote memories can be connected to the computer terminal 10 through a network. Examples of the above-mentioned network include but are not limited to the Internet, enterprise intranets, local area networks, mobile communication networks, and combinations thereof.

[0028] The display can be, for example, a touch-screen liquid crystal display (LCD), which enables a user to interact with the user interface of the computer terminal 10.

[0029] Figure 2 is a schematic flowchart of the battery parameter determination method provided according to the embodiments of the present invention, as Figure 2 shown, the method includes the following steps:

[0030] Step S202, obtain initial battery parameters, as well as constraint conditions and multiple objective functions for optimizing the initial battery parameters, wherein the initial battery parameters are used to process the battery throughout the entire life cycle of the battery.

[0031] In this step, the initial battery parameters are the parameters used to describe the processing of the battery throughout its entire life cycle, which may specifically include the production parameters, sales parameters, usage parameters, and recycling parameters of the battery. Among them, the production parameters mainly include the bill of materials such as the cathode material, anode material, current collector, separator material, electrolyte material, and other auxiliary materials, as well as the battery design parameters such as the types of cathode and anode materials, separator type, electrolyte type, cell structure type, cell volume and capacity, and cell heat dissipation parameters. It also includes parameters such as material preparation, electrode sheet production, battery assembly, sealing and formation, testing and grading, and packaging; the sales parameters mainly include battery transportation, battery storage, operation consumption, etc.; the usage parameters include battery charging, battery usage, and battery maintenance and repair parameters, and the recycling parameters include battery collection, battery pretreatment, material separation and extraction, material reuse, waste treatment, etc.

[0032] It should be noted that the initial battery parameters are a set of parameters including specific data, and the optimization process is a process of updating the specific data of the parameters. The goal is to select more appropriate data as the final battery processing parameters to achieve the balance between environmental protection and economy. Some of the parameters in the initial battery parameters can be used as decision variables during the optimization process and gradually select the most suitable data as the optimization progresses.

[0033] In this step, the constraint conditions can be boundary conditions based on policies and regulations, the enterprise's own conditions, or industry standards, which are used to limit the optimization search space and ensure the feasibility of the solution. Specifically, they can include the upper limit of material costs, production carbon emission limits, battery performance index limits, etc. Since the purpose of this solution is to achieve the balance between economy and environmental protection in the entire life cycle of the battery, multiple objective functions can include minimizing carbon emissions and minimizing processing costs, which respectively represent two optimization directions of environmental impact and economic benefits. Moreover, when designing the objective functions, all stages of the entire life cycle of the battery are considered.

[0034] Specifically, the constraint conditions can include raw material cost constraints, carbon emissions constraints of key components, carbon emissions constraints in the production organization process, distance constraints between the product production point and the sales point, etc. The decision variables can include all variables or all variables of the design scheme, such as the source of material procurement, selection of production bases, transportation methods, storage methods, and recycling methods.

[0035] Step S204, construct a multi-objective optimization model according to the pre-set carbon emission determination method, processing cost determination method, constraint conditions, and multiple objective functions, where the multiple objective functions are respectively related to carbon emissions and processing costs.

[0036] In this step, a carbon emission determination method can be set to calculate the carbon emissions of the battery during its entire life cycle (including raw material acquisition, production, sales, use, and recycling stages). Specifically, it involves a detailed analysis of the material and energy flows in each stage of the cycle, combined with emission coefficients such as greenhouse gas emission factors, to calculate the overall carbon footprint.

[0037] A treatment cost determination method can also be set to determine the total treatment cost of the battery's entire life cycle, which specifically involves raw material costs, production costs (such as labor and energy consumption), logistics costs, usage costs (such as charging fees and maintenance costs), and recycling costs (such as material extraction and waste treatment).

[0038] Then, some parameters in the initial battery parameters can be selected as decision variables. Combining the carbon emission determination method and the treatment cost determination method, as well as multiple objective functions and constraint conditions, a multi-objective optimization model is formed, aiming to optimize environmental and economic indicators simultaneously.

[0039] Step S206: Input the initial battery parameters into the multi-objective optimization model, and use the Non-dominated Sorting Genetic Algorithm (NSGA-II) to solve the multi-objective optimization model to obtain multiple sets of optimized battery parameters.

[0040] In this step, the initial battery parameters in step S202 can be used as input and imported into the multi-objective optimization model. Then, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is used to solve the model. Through iterative search, multiple sets of possible battery parameter combinations are generated. These combinations form the Pareto front solutions, representing the optimal trade-off points between carbon emissions and costs.

[0041] Step S208: Determine the target battery parameters among multiple sets of optimized battery parameters.

[0042] In this step, one or more sets can be selected from the multiple sets of optimized battery parameters obtained by the NSGA-II algorithm as the target battery parameters. This selection is based on the decision-maker's preferences for environmental impact and economic cost. Usually, the solutions in the Pareto optimal solution set are selected. Then, the selected target battery parameters can be applied to battery design, production processes, and supply chain management to achieve the optimal combination of carbon footprint and cost.

[0043] Through the above steps, it is possible to systematically optimize the environmental impact and economic cost in the entire life cycle of the battery, find the best balance point between carbon emissions and costs, thereby effectively improving the environmental performance and market competitiveness of battery products, and further solving the technical problem that the battery design lacks an effective method to simultaneously optimize the carbon emissions and costs in the entire life cycle and is difficult to find the best balance point between environmental protection and economy.

[0044] As an alternative embodiment, the initial battery parameters are input into the multi-objective optimization model, and the non-dominated sorting genetic algorithm is used to solve the multi-objective optimization model, obtaining multiple sets of optimized battery parameters, including: generating multiple sets of battery parameters that meet the constraint conditions in the multi-objective optimization model based on the initial battery parameters as the initial population; determining the multiple objective function values corresponding to each of the multiple sets of battery parameters according to the multiple objective functions in the multi-objective optimization model; grading the multiple sets of battery parameters according to the multiple objective function values corresponding to each of the multiple sets of battery parameters to obtain the grading result of the initial population; updating the initial population according to the grading result of the initial population until the number of updates meets the predetermined conditions, obtaining the updated population, and determining the Pareto optimal solution of the updated population to obtain multiple sets of optimized battery parameters.

[0045] As an alternative embodiment, grading the multiple sets of battery parameters according to the multiple objective function values corresponding to each of the multiple sets of battery parameters to obtain the grading result of the initial population includes: taking the multiple sets of battery parameters as multiple individuals respectively, and determining the multiple objective function values of each of the multiple individuals; determining the dominance relationship between the multiple individuals according to the magnitude relationship between the multiple objective function values of each of the multiple individuals, where the dominance relationship includes that the target individual dominates another individual, that is, all the values of the objective functions corresponding to the target individual are not worse than those of another individual, and at least one value of the objective function is better than that of another individual; grading the multiple sets of battery parameters according to the dominance relationship between the multiple individuals to obtain the grading result of the initial population.

[0046] As an alternative embodiment, updating the initial population according to the grading result of the initial population until the number of updates meets the predetermined conditions, obtaining the updated population, and determining the Pareto optimal solution of the updated population to obtain multiple sets of optimized battery parameters includes: selecting some individuals from the initial population according to the grading result of the initial population; performing crossover processing and mutation processing on the selected individuals to obtain the offspring population; updating the offspring population until the number of updates meets the predetermined conditions, obtaining the updated population, and determining the Pareto optimal solution of the updated population to obtain multiple sets of optimized battery parameters.

[0047] Optionally, in the embodiment of using the non-dominated sorting genetic algorithm (NSGA-II) to solve the multi-objective optimization model, the process can be detailedly decomposed into the following steps:

[0048] (1) Initialize the population: According to the initial battery parameters of the battery and the constraint conditions in the model, randomly generate multiple sets of battery parameters, and this set of parameters should meet the basic feasibility requirements of the model to form the initial population, where each individual (i.e., a set of battery parameters) represents a possible battery processing solution.

[0049] (2) Objective function value calculation: Substitute each set of battery parameters in the initial population into the multi-objective optimization model, and calculate multiple objective function values related to minimizing carbon emissions and minimizing treatment costs respectively, to evaluate the performance of each individual in the objective space, that is, the performance in the two dimensions of carbon emissions and cost.

[0050] (3) Non-dominated sorting: Perform non-dominated sorting according to the multiple objective function values of each individual. Non-dominated sorting is a method to evaluate the superiority and inferiority relationship between individuals in a multi-objective optimization problem. If individual A is not inferior to individual B in all objective functions and is superior to individual B in at least one objective, then individual A is said to dominate individual B. Through non-dominated sorting, the individuals in the population can be divided into different non-dominated levels, that is, the sorting result. Within each non-dominated level, calculate the crowding degree between individuals to evaluate the solution distribution in the objective function space. The crowding degree calculation is used for further selection among individuals at the same level to avoid individuals being too concentrated in a certain area of the objective space, thus maintaining the diversity of the population.

[0051] (4) Population update: Update the current population through genetic operations such as selection (based on crowding degree and non-dominated level), crossover, and mutation to generate a new offspring population. The updated population should contain more non-dominated individuals, that is, individuals with better performance in the objective space.

[0052] (5) Pareto optimal solution determination: Repeat steps 2 to 5 until the predetermined stop conditions (such as the number of iterations, convergence criteria, etc.) are met. Finally, determine one or more sets of Pareto optimal solutions from the updated population, that is, the battery parameter combinations that cannot be further improved in both the two objectives of carbon emissions and treatment costs, and output the Pareto optimal solution set as multiple sets of optimized battery parameters.

[0053] As an optional embodiment, the carbon emission determination method is determined by the following method, including: obtaining the total amount of substances involved in multiple stages of the battery's entire life cycle; determining the substance types to which the total amount of substances involved in multiple stages belongs; determining the carbon emissions generated during the battery's entire life cycle according to the carbon emission factors corresponding to the substance types and the total amount of substances involved in multiple stages; determining the first correlation relationship between the parameters included in the initial battery parameters and the carbon emissions generated during the battery's entire life cycle; and determining the carbon emission determination method according to the first correlation relationship.

[0054] Optionally, when determining the method for calculating carbon emissions, it is necessary to collect the total amount of substances involved in each stage (such as raw material acquisition, production, sales, use, and recycling stages) throughout the entire life cycle of the battery from raw material acquisition to final disposal, including the usage amount of raw materials, energy consumption during production, logistics transportation volume, energy consumption during use, and resource consumption during recycling, etc. Then, the total amount of substances obtained can be classified to identify the type of substances, such as metal materials, organic materials, energy, etc. Determining the type of substances helps to select the correct carbon emission factors when calculating carbon emissions subsequently. The carbon emission factor represents the amount of greenhouse gas emissions generated per unit mass of the substance during the production, transportation, or treatment process of the substance. Then, multiply the total amount of substances involved in each stage by the corresponding carbon emission factor and accumulate to obtain the total carbon emissions of the battery's entire life cycle.

[0055] After determining the carbon emissions of the battery's entire life cycle, it is possible to analyze the influence degree of each initial battery parameter (such as the types of positive and negative electrode materials, battery capacity, production process, logistics method, etc.) on the carbon emissions, and construct a mathematical model or algorithm for determining carbon emissions. This model can accept battery parameters as input and output the corresponding carbon emissions. Among them, the construction of the model and the selection of parameters should be able to reflect the comprehensive influence of the substance type, total amount of substances, and carbon emission factors on the carbon emissions.

[0056] Specifically, taking the calculation of the carbon emissions generated by the electricity used for baking a single battery cell as an example, the carbon emissions of baking a single battery cell = the electricity consumption for baking a single battery cell × the electricity emission factor, and the electricity consumption for baking a single battery cell = the electricity consumption per single baking of the baking equipment ÷ the total number of battery cells baked in a single baking. Among them, the electricity emission factor can be selected as the average carbon dioxide emission factor announced in the relevant regulations in the most recent year.

[0057] As an optional embodiment, the method for determining the processing cost is determined in the following way, including: obtaining the total amount of substances involved in multiple stages within the entire life cycle of the battery; determining the cost required within the entire life cycle of the battery according to the total amount of substances involved in multiple stages; determining the second correlation relationship between the parameters included in the initial battery parameters and the cost required within the entire life cycle of the battery; and determining the method for determining the processing cost according to the second correlation relationship. In this optional embodiment, it is possible to comprehensively consider the costs in stages such as the production, use, and recycling of the battery, which is applicable to industries with strict cost control and intense market competition, and helps enterprises optimize their cost structures and improve their market competitiveness.

[0058] Optionally, when determining how to calculate the processing cost, the total amount of substances involved in each stage of the battery from raw material acquisition to final disposal can be collected, including the usage amount of raw materials, the energy and materials consumed during production, the logistics transportation volume, the energy consumption during the usage stage, and the resource consumption during the recycling stage, etc. Then, based on the collected information on the total amount of substances, the costs of each stage can be calculated, which may include raw material costs, energy and equipment usage costs during production, logistics and transportation costs, operation and maintenance costs during the usage stage, and processing and reuse costs during the recycling stage. By accumulating these costs, the total processing cost of the battery's entire life cycle can be obtained.

[0059] After determining the total processing cost of the battery's entire life cycle, it is possible to analyze how each initial battery parameter (such as the selected cathode and anode materials, battery capacity, production process, logistics mode, etc.) affects the cost of the battery's entire life cycle. A mathematical model can be constructed between the parameter changes and the cost changes to identify which parameters are the key factors for cost control. This model takes the battery parameters as inputs and outputs the corresponding processing costs. This model should be able to reflect the comprehensive impact of the total amount of substances, activities at each stage, and battery parameter changes on the total cost.

[0060] Specifically, the input of the battery cost accounting model is the preset parameters of the battery design scheme, and the output is the cost of the battery's entire province cycle. Specifically, taking the cost calculation of the electricity consumption for baking a single battery cell as an example, the cost of baking a single battery cell = the electricity consumption for baking a single battery cell × the electricity price, and the electricity consumption for baking a single battery cell = the electricity consumption per single baking of the baking equipment ÷ the total number of battery cells baked in a single baking. Among them, the electricity price policy for industrial electricity varies due to different regions and times, and it needs to be calculated according to specific circumstances.

[0061] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0062] Through the description of the above embodiments, those skilled in the art can clearly understand that the battery parameter determination method according to the above embodiments can be implemented by means of software plus a necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions for causing a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in various embodiments of the present invention.

[0063] According to an embodiment of the present invention, there is also provided a battery parameter determination device for implementing the above battery parameter determination method. Figure 3 It is a structural block diagram of the battery parameter determination device provided according to an embodiment of the present invention. As Figure 3 shown, the battery parameter determination device includes: an acquisition module 32, a modeling module 34, a solution module 36, and a determination module 38. The battery parameter determination device will be described below.

[0064] The acquisition module 32 is used to acquire initial battery parameters, as well as constraint conditions and multiple objective functions for optimizing the initial battery parameters. Among them, the initial battery parameters are used to process the battery during the entire life cycle of the battery.

[0065] The modeling module 34 is connected to the acquisition module 32 and is used to construct a multi-objective optimization model according to the pre-set carbon emission determination method, processing cost determination method, constraint conditions, and multiple objective functions. Among them, the multiple objective functions are respectively related to carbon emissions and processing costs.

[0066] The solution module 36 is connected to the modeling module 34 and is used to input the initial battery parameters into the multi-objective optimization model and solve the multi-objective optimization model using the non-dominated sorting genetic algorithm to obtain multiple sets of optimized battery parameters.

[0067] The determination module 38 is connected to the solution module 36 and is used to determine the target battery parameters from multiple sets of optimized battery parameters.

[0068] It should be noted here that the above acquisition module 32, modeling module 34, solution module 36, and determination module 38 correspond to steps S202 to S206 in the embodiment. The instances and application scenarios implemented by the multiple modules and the corresponding steps are the same, but are not limited to the content disclosed in the above embodiments. It should be noted that the above modules, as part of the device, can run in the computer terminal 10 provided in the embodiment.

[0069] Embodiments of the present invention can provide a computer device. Optionally, in this embodiment, the above computer device can be located in at least one of multiple network devices in a computer network. The computer device includes a memory and a processor.

[0070] Among them, the memory can be used to store software programs and modules, such as program instructions / modules corresponding to the battery parameter determination method and device in the embodiments of the present invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, to implement the above battery parameter determination method. The memory can include a high-speed random access memory, and can also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memories. In some instances, the memory can further include a memory remotely set relative to the processor, and these remote memories can be connected to the computer terminal through a network. Examples of the above network include but are not limited to the Internet, enterprise intranet, local area network, mobile communication network, and their combinations.

[0071] The processor can call the information and application programs stored in the memory through a transmission device to execute the following steps: obtaining initial battery parameters, as well as constraints and multiple objective functions for optimizing the initial battery parameters, where the initial battery parameters are used to process the battery throughout its life cycle; constructing a multi-objective optimization model according to the pre-set carbon emission determination method, processing cost determination method, constraints, and multiple objective functions, where the multiple objective functions are respectively related to carbon emissions and processing costs; inputting the initial battery parameters into the multi-objective optimization model, and using the non-dominated sorting genetic algorithm to solve the multi-objective optimization model to obtain multiple sets of optimized battery parameters; determining the target battery parameters among the multiple sets of optimized battery parameters.

[0072] An embodiment of the present invention provides a solution for determining battery parameters. By obtaining initial battery parameters, as well as constraint conditions and multiple objective functions for optimizing the initial battery parameters, where the initial battery parameters are used to process the battery throughout its entire life cycle; according to a pre-set carbon emission determination method, a processing cost determination method, constraint conditions and multiple objective functions, a multi-objective optimization model is constructed, where the multiple objective functions are respectively related to carbon emissions and processing costs; the initial battery parameters are input into the multi-objective optimization model, and the non-dominated sorting genetic algorithm is used to solve the multi-objective optimization model to obtain multiple sets of optimized battery parameters; among the multiple sets of optimized battery parameters, the target battery parameters are determined, which can systematically optimize the environmental impact and economic cost in the entire life cycle of the battery, find the best balance point between carbon emissions and costs, thereby effectively improving the environmental protection performance and market competitiveness of battery products, and further solving the technical problem that the battery design lacks an effective method to simultaneously optimize the carbon emissions and costs in the entire life cycle and it is difficult to find the best balance point between environmental protection and economy.

[0073] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the relevant hardware of the terminal device through a program, and this program can be stored in a non-volatile storage medium. The storage medium can include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disc, etc.

[0074] The embodiment of the present invention also provides a non-volatile storage medium. Optionally, in this embodiment, the above non-volatile storage medium can be used to store the program code executed by the battery parameter determination method provided in the above embodiment.

[0075] Optionally, in this embodiment, the above non-volatile storage medium can be located in any one of the computer terminals in the computer terminal group in the computer network, or in any one of the mobile terminals in the mobile terminal group.

[0076] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: obtaining initial battery parameters, as well as constraints and multiple objective functions for optimizing the initial battery parameters, where the initial battery parameters are used to process the battery throughout its entire life cycle; constructing a multi-objective optimization model according to a preset carbon emission determination method, a processing cost determination method, constraints, and multiple objective functions, where the multiple objective functions are respectively related to carbon emissions and processing costs; inputting the initial battery parameters into the multi-objective optimization model, and using the non-dominated sorting genetic algorithm to solve the multi-objective optimization model to obtain multiple sets of optimized battery parameters; and determining target battery parameters from the multiple sets of optimized battery parameters.

[0077] An embodiment of the present invention further provides a computer program product, including a computer program, which when executed by a processor, implements the steps of the battery parameter determination method in each embodiment of the present application.

[0078] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0079] In the above embodiments of the present invention, the descriptions of the respective embodiments have their own emphases. For parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0080] In several embodiments provided by the present application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only illustrative. For example, the division of the units can be a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the units or modules can be in electrical or other forms.

[0081] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0082] In addition, the functional units in each embodiment of the present invention can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0083] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a non-volatile storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: various media that can store program codes, such as USB flash drives, read-only memories (ROMs), random access memories (RAMs), mobile hard disks, magnetic disks, or optical discs.

[0084] The foregoing are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A method for determining battery parameters, characterized in that: include: Obtaining initial battery parameters, and constraints and multiple objective functions for optimizing the initial battery parameters, wherein the initial battery parameters are used to process the battery over its entire life cycle; Constructing a multi-objective optimization model according to a preset carbon emission determination method, a processing cost determination method, the constraint conditions and the multiple objective functions, wherein the multiple objective functions are respectively related to the carbon emission and the processing cost; Inputting the initial battery parameters into the multi-objective optimization model, solving the multi-objective optimization model using a non-dominant sorting genetic algorithm, and obtaining multiple sets of optimized battery parameters; Among the multiple groups of optimized battery parameters, target battery parameters are determined.

2. The method according to claim 1, characterized in that: The inputting of the initial battery parameters into the multi-objective optimization model, and solving the multi-objective optimization model using a non-dominant sorting genetic algorithm to obtain multiple groups of optimized battery parameters include: Based on the initial battery parameters, generating multiple groups of battery parameters that meet the constraint conditions in the multi-objective optimization model as an initial population; According to the multiple objective functions in the multi-objective optimization model, respectively determine multiple objective function values ​​corresponding to the multiple groups of battery parameters; Classifying the multiple groups of battery parameters according to multiple objective function values ​​corresponding to each of the multiple groups of battery parameters to obtain a classification result of the initial population; According to the classification result of the initial population, the initial population is updated until the number of updates meets the predetermined conditions, and an updated population is obtained, and the Pareto optimal solution of the updated population is determined to obtain the multiple groups of optimized battery parameters.

3. The method according to claim 2, characterized in that The step of grading the plurality of groups of battery parameters according to the plurality of objective function values ​​corresponding to the plurality of groups of battery parameters to obtain the grading result of the initial population includes: Taking the multiple groups of battery parameters as multiple individuals, respectively, and determining multiple objective function values ​​of the multiple individuals; Determine a dominance relationship among the multiple individuals according to the size relationship among the multiple objective function values ​​of the multiple individuals, wherein the dominance relationship includes that the target individual dominates another individual, that is, all the objective function values ​​corresponding to the target individual are not worse than those of the other individual, and at least one objective function value is better than that of the other individual; The plurality of groups of battery parameters are classified according to the dominance relationship between the plurality of individuals to obtain a classification result of the initial population.

4. The method according to claim 2, characterized in that: The method of updating the initial population according to the classification result of the initial population until the number of updates meets a predetermined condition to obtain an updated population, and determining the Pareto optimal solution of the updated population to obtain the multiple groups of optimized battery parameters includes: Selecting some individuals from the initial population according to the classification result of the initial population; Perform crossover and mutation processing on the selected individuals to obtain the offspring population; The offspring population is updated until the number of updates meets the predetermined condition, thereby obtaining an updated population, and the Pareto optimal solution of the updated population is determined to obtain the multiple groups of optimized battery parameters.

5. The method according to any one of claims 1 to 4, characterized in that: The carbon emissions are determined by the following methods: Obtaining the total amount of substances involved in multiple stages of the battery's entire life cycle; Determining the substance types to which the total amount of substances involved in the multiple stages belongs; Determine the carbon emissions generated during the entire life cycle of the battery based on the carbon emission factors corresponding to the substance types and the total amount of substances involved in the multiple stages; Determine a first correlation relationship between the parameters included in the initial battery parameters and the carbon emissions generated during the entire life cycle of the battery; The method for determining the carbon emissions is determined according to the first association relationship.

6. The method according to any one of claims 1 to 4, characterized in that The processing cost is determined by the following method, including: Obtaining the total amount of substances involved in multiple stages of the battery's entire life cycle; Determine the cost required for the entire life cycle of the battery based on the total amount of materials involved in the multiple stages; Determine a second correlation between the parameters included in the initial battery parameters and the cost required for the battery over its entire life cycle; The processing cost determination method is determined according to the second association relationship.

7. A battery parameter determination device, characterized in that: include: An acquisition module, used to acquire initial battery parameters, and optimize constraints and multiple objective functions of the initial battery parameters, wherein the initial battery parameters are used to process the battery during its entire life cycle; A modeling module, used for constructing a multi-objective optimization model according to a preset carbon emission determination method, a processing cost determination method, the constraint conditions and the multiple objective functions, wherein the multiple objective functions are respectively related to the carbon emission and the processing cost; A solution module, used for inputting the initial battery parameters into the multi-objective optimization model, solving the multi-objective optimization model using a non-dominant sorting genetic algorithm, and obtaining multiple groups of optimized battery parameters; The determination module is used to determine the target battery parameters among the multiple groups of optimized battery parameters.

8. A non-volatile storage medium, characterized in that: The non-volatile storage medium includes a stored program, wherein when the program is executed, the device where the non-volatile storage medium is located is controlled to execute the battery parameter determination method according to any one of claims 1 to 6.

9. A computer device, characterized in that: include: Memory and processor, The memory stores a computer program; The processor is used to execute the computer program stored in the memory, and when the computer program is running, the processor executes the battery parameter determination method according to any one of claims 1 to 6.

10. A computer program product comprising computer instructions, characterized in that The computer instructions are executed by a processor to implement the battery parameter determination method according to any one of claims 1 to 6.

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