Battery cell welding method and device, electronic equipment, storage medium and program product

Abstract

The embodiment of the invention provides a battery cell welding method and device, electronic equipment, a readable storage medium and a computer program product, and relates to the technical field of battery cell manufacturing. The method comprises the steps that a to-be-welded battery cell is obtained, at least one time of welding operation is executed on the to-be-welded battery cell till the welding state of the to-be-welded battery cell is successful welding, and the welding operation comprises the steps that first welding state information and a value prediction model of the current welding operation are obtained, and welding energy needed by the current welding operation is determined; and the to-be-welded battery cell is welded, the welding state in the second welding state information is determined, if welding fails, the value prediction model is updated, the second welding state information serves as the next first welding state information, and the updated value prediction model serves as a value prediction model corresponding to the next welding operation. According to the embodiment of the invention, the welding parameters are adjusted according to the welding state feedback, various complex working conditions can be adapted, and the battery cell welding efficiency is improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of battery cell manufacturing, and in particular to a battery cell welding method, device, electronic device, storage medium and program product. Background Art

[0002] With the development of the battery cell manufacturing industry, ultrasonic welding is widely used in battery cell manufacturing. Battery cell welding technology is the core process of battery manufacturing. Through laser welding, ultrasonic welding, resistance welding and other methods, the positive and negative electrodes of the battery cell and other components are precisely connected to ensure the integrity and electrical performance of the battery's internal circuit. The process requires strict control of welding quality, thermal impact and environment to ensure the consistency, safety and reliability of the battery. In the battery cell welding process, heat is a key factor, which achieves the melting and bonding of battery cell materials through the high concentration and precise control of the laser beam.

[0003] Conventional ultrasonic welding usually uses fixed welding parameters, and the welding energy depends on manual experience. However, due to differences in workpiece materials, sizes, environments and other factors, fixed welding energy is difficult to adapt to different working conditions. When working conditions change, the quality of the welds will be poor, resulting in low battery cell welding efficiency. Summary of the invention

[0004] The embodiments of the present disclosure provide a battery core welding method, device, electronic device, computer-readable storage medium and computer program product, which aim to solve the technical problem that fixed parameters are difficult to adapt to different working conditions, resulting in low welding efficiency.

[0005] In a first aspect, a battery core welding method is provided, the method comprising: obtaining a battery core to be welded; Perform at least one welding operation on the battery cell to be welded until the welding state of the battery cell to be welded is successful; Welding operations include: Acquire first welding state information corresponding to the current welding operation of the battery cell to be welded; the first welding state information includes welding state and welding power; Obtaining a value prediction model corresponding to a current welding operation, and determining a welding energy required for the current welding operation from at least one candidate energy based on the first welding state information and the value prediction model corresponding to the current welding operation; Using welding energy to weld the battery core to be welded, and obtaining corresponding second welding state information and reward value; If the welding state in the second welding state information is welding failure, the welding energy required for the next welding operation is predicted from at least one candidate energy based on the second welding state information by using the value prediction model; based on the welding energy required for the current welding operation, the welding energy required for the next welding operation and the reward value, the value prediction model is updated; The second welding state information is used as the first welding state information for the next time, and the updated value prediction model is used as the value prediction model corresponding to the next welding operation.

[0006] Optionally, obtaining a value prediction model corresponding to the current welding operation, and determining the welding energy required for the current welding operation from at least one candidate energy based on the first welding state information and the value prediction model corresponding to the current welding operation, includes: Acquire a value prediction model corresponding to the current welding operation, use the welding power in the first welding state information as an input of the value prediction model, and obtain a value function corresponding to at least one candidate energy; Determine the selection probability corresponding to each candidate energy based on the value function corresponding to each candidate energy and the sum of the value functions of the candidate energies; The candidate energy with the highest probability is selected from the at least one candidate energy as the welding energy required for the current welding operation.

[0007] Optionally, determining the selection probability corresponding to each candidate energy based on the value function corresponding to each candidate energy and the sum of the value functions of the candidate energies includes: Based on the value functions and preset temperature parameters corresponding to the candidate energies, the first probability parameters corresponding to the candidate energies are determined; the preset temperature parameters are used to simulate the actual temperature conditions, and the temperature parameters gradually decrease as the number of welding operations increases; Based on the first probability parameter corresponding to each candidate energy and the sum of the first probability parameters corresponding to the candidate energy, the selection probability corresponding to each candidate energy is determined.

[0008] Optionally, based on the welding energy required for the current welding operation, the welding energy required for the next welding operation and the reward value, the value prediction model is updated, including: Determine a corresponding first value function based on the welding energy required for the current welding operation through a preset value prediction model; Determine a corresponding second value function based on the welding energy required for the next welding operation through a preset value prediction model; Based on the first value function, the second value function and the reward value, the value prediction model is updated.

[0009] Optionally, updating the value prediction model based on the first value function, the second value function and the reward value includes: Determine a first evaluation value corresponding to the current welding operation based on the first value function and the welding parameters corresponding to the current welding operation; Determine a second evaluation value corresponding to the next welding operation based on the second value function and the welding parameters corresponding to the next welding operation; obtain a target evaluation value based on the second evaluation value and the reward value; obtaining an error value based on a difference between the target evaluation value and the first evaluation value; Update the value prediction model based on the error value.

[0010] Optionally, welding the battery cell to be welded using welding energy to obtain corresponding second welding state information and reward value, including: Using welding energy to weld the battery cell to be welded, and obtaining corresponding second welding state information; Through a preset reward function, a corresponding reward value is determined based on the second welding state information.

[0011] Optionally, if the welding state in the second welding state information is welding failure, the welding energy required for the next welding operation is predicted from at least one candidate energy based on the second welding state information by using a value prediction model, including: If the welding state in the second welding state information is welding failure, the welding power in the second welding state information is used as an input of the value prediction model to obtain a value function corresponding to at least one candidate energy; Determine the selection probability corresponding to each candidate energy based on the value function corresponding to each candidate energy and the sum of the value functions of the candidate energies; A candidate energy with the highest probability is selected from the at least one candidate energy as the welding energy required for the next welding operation.

[0012] In a second aspect, a core welding device is provided, the device comprising: A battery cell acquisition module is used to acquire the battery cells to be welded; A cell welding module, used for performing at least one welding operation on the cell to be welded, until the welding state of the cell to be welded is successful; Cell welding module, also used for: Acquire first welding state information corresponding to the current welding operation of the battery cell to be welded; the first welding state information includes welding state and welding power; Obtaining a value prediction model corresponding to a current welding operation, and determining a welding energy required for the current welding operation from at least one candidate energy based on the first welding state information and the value prediction model corresponding to the current welding operation; Using welding energy to weld the battery core to be welded, and obtaining corresponding second welding state information and reward value; If the welding state in the second welding state information is welding failure, the welding energy required for the next welding operation is predicted from at least one candidate energy based on the second welding state information by using the value prediction model; based on the welding energy required for the current welding operation, the welding energy required for the next welding operation and the reward value, the value prediction model is updated; The second welding state information is used as the first welding state information for the next time, and the updated value prediction model is used as the value prediction model corresponding to the next welding operation.

[0013] In a third aspect, an electronic device is provided, the electronic device comprising: A memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of any one of the methods in the first aspect of the present disclosure.

[0014] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, characterized in that when the computer program is executed by a processor, the battery core welding method shown in any one of the first aspects of the present disclosure is implemented.

[0015] In a fifth aspect, a computer program product is provided, comprising a computer program, characterized in that when the computer program is executed by a processor, the steps of any one of the methods in the first aspect of the present disclosure are implemented.

[0016] The technical solution provided by the embodiments of the present disclosure brings the following beneficial effects: The battery cell welding method provided by the present invention performs at least one welding operation on the battery cell to be welded, including: obtaining first welding state information corresponding to the current welding operation of the battery cell to be welded, including welding state and welding power, and obtaining a value prediction model corresponding to the current welding operation, determining the welding energy required for the current welding operation from at least one candidate energy, welding the battery cell to be welded using the welding energy, and obtaining corresponding second welding state information and a reward value; if the welding state in the second welding state information is welding failure, predicting the welding energy required for the next welding operation from at least one candidate energy based on the second welding state information through the value prediction model; updating the value prediction model based on the welding energy required for the current welding operation, the welding energy required for the next welding operation and the reward value, using the second welding state information as the first welding state information for the next time, and using the updated value prediction model as the value prediction model corresponding to the next welding operation; for each welding operation, updating the value prediction model according to the feedback information corresponding to the welding operation, being able to adjust the parameter selection in time according to the feedback information, and effectively improving the welding quality and welding efficiency.

[0017] Furthermore, by using the value prediction model corresponding to the welding operation to predict at least one candidate welding energy and the corresponding value function, and determining the selection probability of each candidate welding energy based on the value function, since the value prediction model will be adjusted according to the feedback information of the welding operation, the selection probability corresponding to each candidate energy will also change according to the adjusted value function. The selection probability obtained in this way can make the selected welding energy more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings required for describing the embodiments of the present disclosure are briefly introduced below.

[0019] Figure 1 A schematic diagram of an application scenario of a battery core welding method provided by an embodiment of the present disclosure; Figure 2 A schematic diagram of a process flow of a battery core welding method provided in an embodiment of the present disclosure; Figure 3 A schematic diagram of a welding process in a battery core welding method provided by an embodiment of the present disclosure; Figure 4 A simplified schematic diagram of a battery core welding method provided in an embodiment of the present disclosure; Figure 5 A schematic flow chart of an example of a battery core welding method provided in an embodiment of the present disclosure; Figure 6 A schematic structural diagram of a battery core welding device provided in an embodiment of the present disclosure; Figure 7 A schematic structural diagram of an electronic device applicable to a battery core welding method provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0020] The embodiments of the present disclosure are described below in conjunction with the drawings in the present disclosure. It should be understood that the implementation methods described below in conjunction with the drawings are exemplary descriptions for explaining the technical solutions of the embodiments of the present disclosure and do not constitute a limitation on the technical solutions of the embodiments of the present disclosure.

[0021] It will be understood by those skilled in the art that, unless specifically stated, the singular forms "one", "an", "said" and "the" used herein may also include plural forms. It should be further understood that the terms "including" and "comprising" used in the embodiments of the present disclosure refer to that the corresponding features can be implemented as the presented features, information, data, steps, operations, elements and / or components, but do not exclude the implementation as other features, information, data, steps, operations, elements, components and / or combinations thereof supported by the technical field. It should be understood that when we say that an element is "connected" or "coupled" to another element, the one element may be directly connected or coupled to the other element, or it may refer to that the one element and the other element establish a connection relationship through an intermediate element. In addition, the "connection" or "coupling" used herein may include wireless connection or wireless coupling. The terms "or", "and / or", "including at least one of the following" used in the present disclosure may be interpreted as inclusive, or mean any one or any combination. For example, “including at least one of the following: A, B, C” means “any of the following: A; B; C; A and B; A and C; B and C; A and B and C” and, for example, “A, B or C” or “A, B and / or C” means “any of the following: A; B; C; A and B; A and C; B and C; A and B and C”.

[0022] In the embodiments of the present disclosure, the term "module" or "unit" refers to a computer program or a part of a computer program that has a predetermined function and works together with other related parts to achieve a predetermined goal, and can be implemented in whole or in part by using software, hardware (such as processing circuits or memories), or a combination thereof. Similarly, a processor (or multiple processors or memories) can be used to implement one or more modules or units. In addition, each module or unit can be part of an overall module or unit that includes the function of the module or unit.

[0023] In order to make the objectives, technical solutions and advantages of the present disclosure more clear, the embodiments of the present disclosure will be further described in detail below with reference to the accompanying drawings.

[0024] First, the technical terms involved in this disclosure are introduced and explained: Welding energy: Welding energy refers to the energy required to melt metal and form a weld during welding. It is an important parameter to measure the heat input during welding and has a significant impact on welding quality, efficiency and cost.

[0025] Welding power: Welding power refers to the energy provided by the welding equipment to the welding arc or heat source such as laser during the welding process. The demand for welding energy directly affects the selection and adjustment of welding power. The welding operation needs to control the welding energy by adjusting the welding parameters according to the specific requirements of the welding task, thereby optimizing the use of welding power to achieve efficient and high-quality welding results.

[0026] In the prior art, ultrasonic welding is a common welding method, which is widely used in battery cell manufacturing. Traditional ultrasonic welding usually adopts fixed welding parameters (such as energy, time, etc.), which rely on manual experience to set. However, due to differences in workpiece materials, sizes, environments and other factors, fixed parameters are difficult to adapt to different working conditions, which can easily lead to problems such as poor weld quality and high energy consumption. Problems existing in the prior art: fixed welding parameters cannot adapt to changes in working conditions, and the quality stability of welds is poor; parameter settings mainly rely on manual experience, lack of scientific theoretical guidance, low efficiency, and lack of versatility; welding parameters cannot be adjusted according to real-time feedback, and energy utilization is low.

[0027] The battery core welding method, device, electronic device, computer-readable storage medium and computer program product provided by the present disclosure are intended to solve at least one of the above technical problems in the prior art.

[0028] In view of at least one of the above-mentioned technical problems or areas that need improvement in the related art, the present disclosure proposes a battery cell welding method, device, electronic device, computer-readable storage medium and computer program product. The battery cell welding method provided by the scheme performs at least one welding operation on the battery cell to be welded, including: obtaining first welding state information corresponding to the current welding operation of the battery cell to be welded, including welding state and welding power, and obtaining a value prediction model corresponding to the current welding operation, determining the welding energy required for the current welding operation from at least one candidate energy, using the welding energy to weld the battery cell to be welded, and obtaining corresponding second welding state information and a reward value; if the second If the welding status in the welding status information is welding failure, the value prediction model is used to predict the welding energy required for the next welding operation from at least one candidate energy based on the second welding status information. The value prediction model is updated based on the welding energy required for the current welding operation, the welding energy required for the next welding operation, and the reward value. The second welding status information is used as the first welding status information for the next time, and the updated value prediction model is used as the value prediction model corresponding to the next welding operation. For each welding operation, the value prediction model is updated according to the feedback information corresponding to the welding operation, and the parameter selection can be adjusted in time according to the feedback information, thereby effectively improving the welding quality and welding efficiency.

[0029] Furthermore, by using the value prediction model corresponding to the welding operation to predict at least one candidate welding energy and the corresponding value function, and determining the selection probability of each candidate welding energy based on the value function, since the value prediction model will be adjusted according to the feedback information of the welding operation, the selection probability corresponding to each candidate energy will also change according to the adjusted value function. The selection probability obtained in this way can make the selected welding energy more accurate.

[0030] The following describes several exemplary embodiments to illustrate the technical solutions of the embodiments of the present disclosure and the technical effects produced by the technical solutions of the present disclosure. It should be noted that the following embodiments can refer to, draw on or combine with each other, and the same terms, similar features and similar implementation steps in different embodiments will not be described repeatedly.

[0031] Figure 1 A schematic diagram of an application scenario of the battery cell welding method provided in an embodiment of the present disclosure, wherein the application environment may include a terminal 101 equipped with a welding control system, and the terminal 101 may be connected to an ultrasonic welder to control the ultrasonic welder to perform welding operations on the battery cells to be welded.

[0032] Specifically, the welding control system in the terminal 101 obtains the battery cell to be welded, and performs at least one welding operation on the battery cell to be welded until the welding state of the battery cell to be welded is successful welding, wherein the welding operation includes: obtaining first welding state information corresponding to the current welding operation of the battery cell to be welded, the first welding state information including welding state and welding power, obtaining a value prediction model corresponding to the current welding operation, determining the welding energy required for the current welding operation from at least one candidate energy based on the first welding state information and the value prediction model corresponding to the current welding operation, welding the battery cell to be welded using the welding energy, and obtaining corresponding second welding state information and a reward value. If the welding state in the second welding state information is welding failure, predicting the welding energy required for the next welding operation from at least one candidate energy based on the second welding state information through the value prediction model, updating the value prediction model based on the welding energy required for the current welding operation, the welding energy required for the next welding operation, and the reward value, using the second welding state information as the first welding state information for the next time, and using the updated value prediction model as the value prediction model corresponding to the next welding operation.

[0033] The above application scenario is only an example and does not limit the application scenario of the battery core welding method disclosed in the present invention.

[0034] Technicians in this technical field can understand that the terminal can be a smart phone (such as Android phone, iOS phone, etc.), a tablet computer, a laptop computer, a digital broadcast receiver, a MID (Mobile Internet Devices), a PDA (Personal Digital Assistant), a desktop computer, a smart home appliance, a vehicle-mounted terminal (such as a vehicle-mounted navigation terminal, a vehicle-mounted computer, etc.), a smart speaker, a smart watch, etc. The terminal and the server can be directly or indirectly connected via wired or wireless communication, but is not limited to this.

[0035] The server may include a server that is installed with a computer capable of processing database operations. The server may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It may also be a cloud server or server cluster that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. The embodiments of the present invention can be applied to various scenarios, including but not limited to cloud technology, artificial intelligence, smart transportation, assisted driving, etc. The specific requirements can also be determined based on the actual application scenario requirements, which is not limited here.

[0036] In some possible implementations, taking the execution subject as a welding control system as an example, the embodiment of the present disclosure provides a core welding method, such as Figure 2 As shown, the following steps may be included: S201, obtaining a battery cell to be welded.

[0037] Specifically, when the production process of the battery cell reaches the welding step, the battery cell is used as the battery cell to be welded in the embodiment, and the basic information of the battery cell to be welded is obtained. The basic information includes: battery cell type, size, tab material and position, welding method and parameters, heat-affected zone, safety specifications, welding quality standards, and battery cell electrical characteristics and production environment, etc. Based on the basic information, the welding method corresponding to the battery cell to be welded is selected, so that the battery cell after welding can be more reliable and stable. In the specific implementation process, the production steps of the battery cell include electrode preparation, winding or lamination, assembly, liquid injection, sealing, aging test and other steps; welding usually occurs in the assembly stage, which is performed after the battery cell is assembled and before liquid injection and sealing. This process ensures that the battery cell can be effectively connected to other battery cells and external devices in the battery pack.

[0038] S202, performing at least one welding operation on the battery cell to be welded until the welding state of the battery cell to be welded is successful.

[0039] After each welding of the battery cell to be welded, the welding status of the battery cell will be detected. If the welding fails, the parameters will continue to be adjusted for welding. If the welding is successful, the welding operation will be terminated.

[0040] Specifically, the welding operation includes determining corresponding welding parameters based on the battery cells to be welded, welding the battery cells to be welded based on the welding parameters, and obtaining the battery cell status after welding to determine whether to continue the welding operation. This allows the welding resources to be stopped in time after success to avoid waste of resources.

[0041] Among them, the above welding operation, such as Figure 3As shown, the following steps are included: S211, obtaining first welding state information corresponding to a current welding operation of a battery cell to be welded. Among them, the first welding status information includes welding status and welding power; the welding status is used to indicate whether the welding of the above-mentioned battery cell to be welded is successful; the welding power includes the initial condition of the battery cell to be welded during the first welding operation, and is used to indicate the battery cell condition after the corresponding welding operation is executed in the second and subsequent welding operations.

[0042] Specifically, if it is the first welding operation, the first welding state information corresponding to the current welding operation is the initial value, wherein the initial values ​​of the welding state and the welding power can be determined based on the historical welding data of the battery cells of the same batch of the battery cells to be welded, or can be received by the operator input in the welding control system for the battery cells to be welded. If it is not the first welding operation, the first welding state information is obtained after the last welding operation is performed.

[0043] S212, obtaining a value prediction model corresponding to the current welding operation, and determining the welding energy required for the current welding operation from at least one candidate energy based on the first welding state information and the value prediction model corresponding to the current welding operation.

[0044] The value prediction model may include a RBF neural network (radial basis function neural network), the input of the value prediction model is the first welding state information, and the output is a value function corresponding to at least one candidate welding energy.

[0045] Specifically, the first welding state information corresponding to the current welding operation is input into the value prediction model corresponding to the current welding operation to obtain a value function corresponding to at least one candidate energy, and the welding energy required for the current welding operation is selected based on each candidate energy and the value function. The method of selecting the welding energy from the candidate energies may include ranking-based selection and probability-based selection, and may also receive the operator's selection operation for energy to determine the welding energy. The method of selecting the welding energy is relatively flexible, so that the welding method disclosed in the present invention has strong adaptability.

[0046] S213, using welding energy to weld the battery cell to be welded, and obtaining corresponding second welding state information and reward value.

[0047] The second welding state information may also include welding state and welding power, and the reward value may be pre-set, and the reward value may change as the number of welding operations increases.

[0048] Specifically, after determining the welding energy, the ultrasonic welder can be controlled to use the welding energy to weld the battery cell to be welded, obtain the second welding state information, determine the welding power corresponding to the welding energy, and detect whether the battery cell to be welded is successful, and obtain the reward value corresponding to the current welding. If the welding is successful, the welding operation is stopped. If the welding fails, the next welding operation is continued based on the reward value and the second welding state information.

[0049] In the specific implementation process, the welding control system is also constantly optimized and updated. The sum of the reward values ​​corresponding to the welding operations in the welding steps is an important basis for system optimization. Generally, the goal is to achieve a higher reward value. The reward value can be expressed based on the following formula: G t = r t+1 +γ r t+2 +γ 2 r t+3 +…+γ n-1 r t+n Among them, G t represents the reward value, t represents the number of welding operations, r t is the immediate reward for the t-th welding operation, γ∈[0,1] is the discount factor, the smaller the value is, the more emphasis is placed on immediate rewards, and the closer it is to 1, the more emphasis is placed on future rewards, and n is a natural number.

[0050] S214, if the welding state in the second welding state information is welding failure, the welding energy required for the next welding operation is predicted from at least one candidate energy based on the second welding state information through the value prediction model; based on the welding energy required for the current welding operation, the welding energy required for the next welding operation and the reward value, the value prediction model is updated.

[0051] Specifically, if the welding state in the second welding state information is welding failure, the value prediction model is used, and the second welding state information is used as the input of the model to predict the welding energy required for the next welding operation from at least one candidate energy. Based on the welding energy required for the current welding operation, the welding energy required for the next welding operation and the corresponding reward value, the value prediction model corresponding to the current welding operation is updated to obtain the value prediction model to be used for the next welding operation. Based on the selection and prediction of the two welding energies and the reward value of the timely feedback of the welding operation, the value prediction model is updated in real time, which can make the selection of welding energy more accurate, reduce the number of trial and error and waste of resources, and reduce welding costs.

[0052] S215, using the second welding state information as the first welding state information for the next time, and using the updated value prediction model as the value prediction model corresponding to the next welding operation.

[0053] Specifically, only one welding is performed in one welding operation, and only the welding corresponding to the current welding energy is performed, and the second welding state information obtained is used as the first welding state information for the next welding operation. The welding energy required for the next welding operation is not executed, but is only used to update the value prediction model; the value prediction model will be updated every time a welding operation is performed and the welding is not successful, so each time a welding operation is performed, the updated value prediction model must be determined to make the determination of the welding energy more accurate.

[0054] In some possible implementations, in the above step S212, a value prediction model corresponding to the current welding operation is obtained, and based on the first welding state information and the value prediction model corresponding to the current welding operation, a welding energy required for the current welding operation is determined from at least one candidate energy, including: (1) obtaining a value prediction model corresponding to the current welding operation, taking the welding power in the first welding state information as an input of the value prediction model, and obtaining a value function corresponding to at least one candidate energy; (2) Determine the selection probability corresponding to each candidate energy based on the value function corresponding to each candidate energy and the sum of the value functions of the candidate energies; (3) Selecting the candidate energy with the highest probability from the at least one candidate energy as the welding energy required for the current welding operation.

[0055] The candidate energies are all energies that can be used on the battery cells to be welded, and can be preset based on the input of the operator.

[0056] Specifically, a value prediction model corresponding to the current welding operation is obtained, and the welding power in the first welding state information is used as the input of the value prediction model to obtain the value functions corresponding to at least one candidate energy, and the sum of the value functions of the candidate energies is obtained. Based on the value function and the sum of each candidate energy, the selection probability corresponding to each candidate energy is determined, and the candidate energy with the highest probability of being selected from at least one candidate energy is used as the welding energy required for the current welding operation.

[0057] In the specific implementation process, the value function is used to evaluate the expected return of taking a specific action under a given state. In this embodiment, the specific action is welding energy, and the value function of welding energy can be expressed as:

[0058] Among them, π is the strategy, that is, how to choose the welding energy, q π(s, a) represents the value function (expected return) of selecting welding energy a when the welding power is s in the first welding state information, t represents the number of welding operations, R t It represents the reward value obtained by using welding energy a in the t-th welding operation. γ∈[0,1] is the discount factor, which is used to measure the importance of future rewards relative to current rewards. The closer it is to 1, the more important the future rewards are. The closer it is to 0, the less important the future rewards are.

[0059] In some possible implementations, the above steps determine the selection probability corresponding to each candidate energy based on the value function corresponding to each candidate energy and the sum of the value functions of the candidate energies, including: (1) determining first probability parameters corresponding to each candidate energy based on the value function and preset temperature parameter corresponding to each candidate energy; (2) Determine the selection probability corresponding to each candidate energy based on the first probability parameter corresponding to each candidate energy and the sum of the first probability parameters corresponding to the candidate energy.

[0060] Among them, the preset temperature parameters are used to simulate the actual temperature conditions, and the temperature parameters gradually decrease as the number of welding operations increases.

[0061] Specifically, based on the value function and preset temperature parameters corresponding to each candidate energy, the first probability parameter corresponding to each candidate energy is determined, and based on the first probability parameter corresponding to each candidate energy and the sum of the first probability parameters corresponding to the candidate energy, the selection probability corresponding to each candidate energy is determined, and the selection probabilities corresponding to each candidate energy are compared to select the welding energy.

[0062] In the specific implementation process, when reinforcement learning is applied to the embodiments of the present disclosure, in order to optimize the selection probability of the action as much as possible and help learn a good strategy faster, a probability weight can be assigned to each action, and the selection probability can be obtained based on the softmax selection strategy (soft maximization strategy). The function of the selection probability can be expressed by the following formula:

[0063] Among them, P (a i ) is the candidate energy a when the welding power is s i The probability of selection, a i is the welding energy that can be selected for the ith time, Q(s, a i ) is the welding energy a taken under state s i The value of >0 is the temperature control parameter. When the value is large, the selection probability of all actions is close to uniform distribution; when When the value is small, the probability distribution is sharper and the probability of high-value actions being selected is higher. Setting the temperature parameter can make the decay over time more in line with the actual situation, avoid tedious parameter trials, and effectively balance the relationship between exploration and utilization.

[0064] The above formula can also be written in logarithmic form:

[0065] Among them, P (a i ) is to select the candidate energy a in state s i The probability of selection, a i is the welding energy that can be selected for the ith time, Q(s, a i ) is the welding energy a taken under state s i The value of >0 is the temperature control parameter, is the initial temperature parameter, and n is the time step, i.e. the number of welding operations.

[0066] In some possible implementations, in the above steps, updating the value prediction model based on the welding energy required for the current welding operation, the welding energy required for the next welding operation, and the reward value includes: (1) determining a corresponding first value function based on the welding energy required for the current welding operation through a preset value prediction model; (2) determining a corresponding second value function based on the welding energy required for the next welding operation through a preset value prediction model; (3) Based on the first value function, the second value function and the reward value, update the value prediction model.

[0067] Specifically, the welding energy required for the current welding is obtained, and the energy corresponding to the next welding is predicted. The value function of the welding energy corresponding to the two welding operations is predicted through a value prediction model. Based on the first value function corresponding to the welding energy selected for the current welding operation, the second value function corresponding to the next welding operation, and the reward value, the welding energy and the welding power in the first welding state information are substituted into the first value function, the predicted next welding energy and the welding power in the second welding state information are substituted into the second value function, the error value is obtained based on the first value function and the second value function, and the value prediction model is updated based on the error value.

[0068] In some possible implementations, in the above steps, updating the value prediction model based on the first value function, the second value function and the reward value includes: (1) determining a first evaluation value corresponding to the current welding operation based on the first value function and welding parameters corresponding to the current welding operation; (2) determining a second evaluation value corresponding to the next welding operation based on the second value function and the welding parameters corresponding to the next welding operation; (3) Obtaining a target evaluation value based on the second evaluation value and the reward value; (4) obtaining an error value based on a difference between the target evaluation value and the first evaluation value; (5) Update the value prediction model based on the error value.

[0069] Specifically, the welding parameters corresponding to the current welding operation are substituted into the first value function to obtain the corresponding first evaluation value, and the welding parameters corresponding to the next welding operation are substituted into the second value function to obtain the second evaluation value. Based on the second evaluation value and the reward value, the target evaluation value is obtained. Based on the difference between the target evaluation value and the first evaluation value, the error value is obtained. With the goal of minimizing the error value, the corresponding update parameters are obtained, and the value prediction model is updated based on the update parameters.

[0070] In the specific implementation process, the above steps can be implemented based on the SARSA algorithm (State-Action-Reward-State-Action), where:

[0071] Where a is the welding energy currently used, is the learning rate, which determines how much new information affects existing estimates, The larger the value, the greater the impact of new information on the Q value; The smaller the value, the slower the update of Q value. R is the reward value for performing welding with welding energy a. is the discount factor that determines the importance of the reward of future welding operations relative to the current reward, Q(s t+1 , a t+1 ) is the next welding status information is s t+1 When welding energy a is taken t+1 The estimated value of. In the above formula, Q(s, a) is the first value function, Q(s t+1 , a t+1 ) is the second value function, R is the reward value, and the welding parameters are substituted into The above-mentioned target evaluation value can be obtained.

[0072] In the specific implementation process, the difference between the target evaluation value and the first evaluation value (prediction value), the loss function can be:

[0073] in, It is the TD error (Temporal-Difference Error). Specifically, the TD error can be expressed by the following formula:

[0074] in, It is the TD error, which represents the difference between the value function predicted at the t-th welding operation and the actual value and reward value at the t+1-th welding operation. The goal is to minimize the error value, which can make the value function closer to the actual value.

[0075] In the specific implementation process, the value function can be parameterized, and the update of the parameters is equivalent to the update of the value function. At this time, the approximate expression of the state-action value function is:

[0076] Among them, w is the approximator parameter, and each parameter value determines a value function accordingly.

[0077] Specifically, updating the value prediction model can be regarded as updating w in the above formula, and performing gradient descent update on the above TD error to obtain the updated parameters. The update rule is:

[0078] in, is the TD error, is the gradient of the above loss function with respect to w.

[0079] In some possible implementations, in the above step S213, welding the battery core to be welded is performed using welding energy to obtain corresponding second welding state information and reward value, including: (1) welding the battery cell to be welded using welding energy to obtain corresponding second welding state information; (2) Determine a corresponding reward value based on the second welding state information through a preset reward function.

[0080] Specifically, the reward function may be preset, and the reward value corresponding to the welding energy of each welding operation obtained based on the reward function is different. The welding energy is used to weld the battery cell to be welded to obtain the corresponding second welding state information, and the corresponding reward value is determined based on the second welding state information through the preset reward function.

[0081] In the specific implementation process, Figure 4As shown in the figure, the reward R is usually defined by the environment. The ultrasonic welder selects the corresponding welding energy A for welding based on the welding power S. The environment determines the reward R by feeding back the welding quality based on the welding energy A and the new state S' taken, combined with the rules or goals of the welding environment. The reward is set through environmental design in reinforcement learning. It provides positive and negative feedback based on the behavior and task goals of the agent to guide the agent to learn the correct strategy; the size and frequency of the reward need to be adjusted according to the characteristics of the task.

[0082] In some possible implementations, in the above step S214, if the welding state in the second welding state information is welding failure, the welding energy required for the next welding operation is predicted from at least one candidate energy based on the second welding state information by using the value prediction model, including: (1) If the welding state in the second welding state information is welding failure, the welding power in the second welding state information is used as an input of the value prediction model to obtain a value function corresponding to at least one candidate energy; (2) Determine the selection probability corresponding to each candidate energy based on the value function corresponding to each candidate energy and the sum of the value functions of the candidate energies; (3) Selecting a candidate energy with the highest probability from at least one candidate energy as the welding energy required for the next welding operation.

[0083] The second welding state information also includes welding state and welding power.

[0084] Specifically, if the welding state in the second welding state information is welding failure, the welding power in the second welding state information is used as the input of the value prediction model to obtain the value function corresponding to at least one candidate energy, and the selection probability corresponding to each candidate energy is determined based on the value function corresponding to each candidate energy and the sum of the value functions of the candidate energies. The candidate energy with the highest probability is selected from at least one candidate energy as the welding energy required for the next welding operation.

[0085] In the above embodiment, at least one welding operation is performed on the battery cell to be welded, including: obtaining first welding state information corresponding to the current welding operation of the battery cell to be welded, including welding state and welding power, and obtaining a value prediction model corresponding to the current welding operation, determining the welding energy required for the current welding operation from at least one candidate energy, using the welding energy to weld the battery cell to be welded, and obtaining corresponding second welding state information and a reward value; if the welding state in the second welding state information is welding failure, then based on the second welding state information, predicting the welding energy required for the next welding operation from at least one candidate energy through the value prediction model, based on the welding energy required for the current welding operation, the welding energy required for the next welding operation and the reward value, updating the value prediction model, using the second welding state information as the first welding state information for the next time, and using the updated value prediction model as the value prediction model corresponding to the next welding operation; for each welding operation, updating the value prediction model according to the feedback information corresponding to the welding operation, being able to adjust the parameter selection in time according to the feedback information, and effectively improving the welding quality and welding efficiency.

[0086] Furthermore, by using the value prediction model corresponding to the welding operation to predict at least one candidate welding energy and the corresponding value function, and determining the selection probability of each candidate welding energy based on the value function, since the value prediction model will be adjusted according to the feedback information of the welding operation, the selection probability corresponding to each candidate energy will also change according to the adjusted value function. The selection probability obtained in this way can make the selected welding energy more accurate.

[0087] In one example, the battery core welding method of the present disclosure is as follows: Figure 5 As shown, it may include: Obtain a battery cell to be welded, and perform at least one welding operation on the battery cell to be welded until the welding state of the battery cell to be welded is successful.

[0088] Among them, welding operations include: Acquire first welding state information corresponding to the current welding operation of the battery cell to be welded; the first welding state information includes welding state and welding power; Acquire a value prediction model corresponding to the current welding operation, use the welding power in the first welding state information as an input of the value prediction model, and obtain a value function corresponding to at least one candidate energy; Based on the value functions and preset temperature parameters corresponding to the candidate energies, the first probability parameters corresponding to the candidate energies are determined; the preset temperature parameters are used to simulate the actual temperature conditions, and the temperature parameters gradually decrease as the number of welding operations increases; Determining the selection probability corresponding to each candidate energy based on the first probability parameter corresponding to each candidate energy and the sum of the first probability parameters corresponding to the candidate energy; Selecting a candidate energy with the highest probability from among the at least one candidate energy as the welding energy required for the current welding operation; The welding energy is used to weld the battery core to be welded, and corresponding second welding state information and reward value are obtained.

[0089] If the welding status in the second welding status information is welding success, the welding operation on the battery cell to be welded is ended.

[0090] If the welding state in the second welding state information is welding failure, the welding power in the second welding state information is used as an input of the value prediction model to obtain a value function corresponding to at least one candidate energy; Based on the value function corresponding to each candidate energy and the sum of the value functions of the candidate energies, the selection probability corresponding to each candidate energy is determined; and the candidate energy with the highest probability is selected from at least one candidate energy as the welding energy required for the next welding operation.

[0091] Determine the corresponding first value function based on the welding energy required for the current welding operation through a preset value prediction model; determine the corresponding second value function based on the welding energy required for the next welding operation through a preset value prediction model; Based on the first value function and the welding parameters corresponding to the current welding operation, determine the first evaluation value corresponding to the current welding operation; based on the second value function and the welding parameters corresponding to the next welding operation, determine the second evaluation value corresponding to the next welding operation; based on the second evaluation value and the reward value, obtain the target evaluation value; based on the difference between the target evaluation value and the first evaluation value, obtain the error value to update the value prediction model; The second welding state information is used as the first welding state information for the next time, and the updated value prediction model is used as the value prediction model corresponding to the next welding operation.

[0092] The above-mentioned battery cell welding method, by performing at least one welding operation on the battery cell to be welded, includes: obtaining first welding state information corresponding to the current welding operation of the battery cell to be welded, including welding state and welding power, and obtaining a value prediction model corresponding to the current welding operation, determining the welding energy required for the current welding operation from at least one candidate energy, using the welding energy to weld the battery cell to be welded, and obtaining corresponding second welding state information and a reward value; if the welding state in the second welding state information is welding failure, then using the value prediction model, based on the second welding state information, predicting the welding energy required for the next welding operation from at least one candidate energy; based on the welding energy required for the current welding operation, the welding energy required for the next welding operation and the reward value, updating the value prediction model, using the second welding state information as the first welding state information for the next time, and using the updated value prediction model as the value prediction model corresponding to the next welding operation; for each welding operation, updating the value prediction model according to the feedback information corresponding to the welding operation, being able to adjust parameter selection in time according to the feedback information, and effectively improving welding quality and welding efficiency.

[0093] Furthermore, by using the value prediction model corresponding to the welding operation to predict at least one candidate welding energy and the corresponding value function, and determining the selection probability of each candidate welding energy based on the value function, since the value prediction model will be adjusted according to the feedback information of the welding operation, the selection probability corresponding to each candidate energy will also change according to the adjusted value function. The selection probability obtained in this way can make the selected welding energy more accurate.

[0094] The present disclosure provides a core welding device, such as Figure 6 As shown, the battery core welding device 60 may include: a battery core acquisition module 601 and a battery core welding module 602, wherein: A battery cell acquisition module 601 is used to acquire battery cells to be welded; A cell welding module 602, used to perform at least one welding operation on the cell to be welded until the welding state of the cell to be welded is successful; The battery core welding module 602 is also used for: Acquire first welding state information corresponding to the current welding operation of the battery cell to be welded; the first welding state information includes welding state and welding power; Obtaining a value prediction model corresponding to a current welding operation, and determining a welding energy required for the current welding operation from at least one candidate energy based on the first welding state information and the value prediction model corresponding to the current welding operation; Using welding energy to weld the battery core to be welded, and obtaining corresponding second welding state information and reward value; If the welding state in the second welding state information is welding failure, the welding energy required for the next welding operation is predicted from at least one candidate energy based on the second welding state information by using the value prediction model; based on the welding energy required for the current welding operation, the welding energy required for the next welding operation and the reward value, the value prediction model is updated; The second welding state information is used as the first welding state information for the next time, and the updated value prediction model is used as the value prediction model corresponding to the next welding operation.

[0095] As an optional embodiment, in the device, the battery core welding module 602 is specifically used for: Obtaining a value prediction model corresponding to the current welding operation, and determining a welding energy required for the current welding operation from at least one candidate energy based on the first welding state information and the value prediction model corresponding to the current welding operation, including: Acquire a value prediction model corresponding to the current welding operation, use the welding power in the first welding state information as an input of the value prediction model, and obtain a value function corresponding to at least one candidate energy; Determine the selection probability corresponding to each candidate energy based on the value function corresponding to each candidate energy and the sum of the value functions of the candidate energies; The candidate energy with the highest probability is selected from the at least one candidate energy as the welding energy required for the current welding operation.

[0096] As an optional embodiment, in the device, the battery core welding module 602 is specifically used for: Based on the value functions corresponding to the candidate energies and the sum of the value functions of the candidate energies, the selection probability corresponding to the candidate energies is determined, including: Based on the value functions and preset temperature parameters corresponding to the candidate energies, the first probability parameters corresponding to the candidate energies are determined; the preset temperature parameters are used to simulate the actual temperature conditions, and the temperature parameters gradually decrease as the number of welding operations increases; Based on the first probability parameter corresponding to each candidate energy and the sum of the first probability parameters corresponding to the candidate energy, the selection probability corresponding to each candidate energy is determined.

[0097] As an optional embodiment, in the device, the battery core welding module 602 is specifically used for: Based on the welding energy required for the current welding operation, the welding energy required for the next welding operation and the reward value, the value prediction model is updated, including: Determine a corresponding first value function based on the welding energy required for the current welding operation through a preset value prediction model; Determine a corresponding second value function based on the welding energy required for the next welding operation through a preset value prediction model; Based on the first value function, the second value function and the reward value, the value prediction model is updated.

[0098] As an optional embodiment, in the device, the battery core welding module 602 is specifically used for: Based on the first value function, the second value function and the reward value, the value prediction model is updated, including: Determine a first evaluation value corresponding to the current welding operation based on the first value function and the welding parameters corresponding to the current welding operation; Determine a second evaluation value corresponding to the next welding operation based on the second value function and the welding parameters corresponding to the next welding operation; obtain a target evaluation value based on the second evaluation value and the reward value; obtaining an error value based on a difference between the target evaluation value and the first evaluation value; Update the value prediction model based on the error value.

[0099] As an optional embodiment, in the device, the battery core welding module 602 is specifically used for: The welding energy is used to weld the core to be welded, and the corresponding second welding state information and reward value are obtained, including: Using welding energy to weld the battery cell to be welded, and obtaining corresponding second welding state information; Through a preset reward function, a corresponding reward value is determined based on the second welding state information.

[0100] As an optional embodiment, in the device, the battery core welding module 602 is specifically used for: If the welding state in the second welding state information is welding failure, the welding energy required for the next welding operation is predicted from at least one candidate energy based on the second welding state information by using the value prediction model, including: If the welding state in the second welding state information is welding failure, the welding power in the second welding state information is used as an input of the value prediction model to obtain a value function corresponding to at least one candidate energy; Determine the selection probability corresponding to each candidate energy based on the value function corresponding to each candidate energy and the sum of the value functions of the candidate energies; A candidate energy with the highest probability is selected from the at least one candidate energy as the welding energy required for the next welding operation.

[0101] The battery cell welding device provided by the present disclosure performs at least one welding operation on the battery cell to be welded, including: obtaining first welding state information corresponding to the current welding operation of the battery cell to be welded, including welding state and welding power, and obtaining a value prediction model corresponding to the current welding operation, determining the welding energy required for the current welding operation from at least one candidate energy, welding the battery cell to be welded using the welding energy, and obtaining corresponding second welding state information and a reward value; if the welding state in the second welding state information is welding failure, then using the value prediction model, based on the second welding state information, predicting the welding energy required for the next welding operation from at least one candidate energy; based on the welding energy required for the current welding operation, the welding energy required for the next welding operation and the reward value, updating the value prediction model, using the second welding state information as the first welding state information for the next time, and using the updated value prediction model as the value prediction model corresponding to the next welding operation; for each welding operation, updating the value prediction model according to the feedback information corresponding to the welding operation, being able to timely adjust parameter selection according to the feedback information, and effectively improving welding quality and welding efficiency.

[0102] Furthermore, by using the value prediction model corresponding to the welding operation to predict at least one candidate welding energy and the corresponding value function, and determining the selection probability of each candidate welding energy based on the value function, since the value prediction model will be adjusted according to the feedback information of the welding operation, the selection probability corresponding to each candidate energy will also change according to the adjusted value function. The selection probability obtained in this way can make the selected welding energy more accurate.

[0103] The device of the embodiment of the present disclosure can execute the method provided by the embodiment of the present disclosure, and its implementation principle is similar and has corresponding technical effects. The actions performed by each module in the device of each embodiment of the present disclosure correspond to the steps in the method of each embodiment of the present disclosure. For the detailed functional description of each module of the device, please refer to the description in the corresponding method shown in the previous text, which will not be repeated here.

[0104] An embodiment of the present disclosure provides an electronic device (computer device / equipment / system), including a memory, a processor, and a computer program stored in the memory, and the processor executes the above computer program to implement the steps of the method provided in any optional embodiment of the present disclosure. Compared with the prior art, the present invention can realize: performing at least one welding operation on the battery cell to be welded, including: obtaining first welding state information corresponding to the current welding operation of the battery cell to be welded, including welding state and welding power, and obtaining a value prediction model corresponding to the current welding operation, determining the welding energy required for the current welding operation from at least one candidate energy, welding the battery cell to be welded using the welding energy, and obtaining corresponding second welding state information and a reward value; if the welding state in the second welding state information is welding failure, then using the value prediction model, based on the second welding state information, predicting the welding energy required for the next welding operation from at least one candidate energy; based on the welding energy required for the current welding operation, the welding energy required for the next welding operation and the reward value, updating the value prediction model, using the second welding state information as the first welding state information for the next time, and using the updated value prediction model as the value prediction model corresponding to the next welding operation; for each welding operation, updating the value prediction model according to the feedback information corresponding to the welding operation, being able to adjust the parameter selection in time according to the feedback information, and effectively improving the welding quality and welding efficiency.

[0105] In an alternative embodiment, an electronic device is provided, such as Figure 7 As shown, Figure 7 The electronic device 7000 shown includes: a processor 7001 and a memory 7003. The processor 7001 and the memory 7003 are connected, such as through a bus 7002. Optionally, the electronic device 7000 may also include a transceiver 7004, which may be used for data interaction between the electronic device and other electronic devices, such as data transmission and / or data reception. It should be noted that in actual applications, the transceiver 7004 is not limited to one, and the structure of the electronic device 7000 does not constitute a limitation on the embodiments of the present disclosure.

[0106] Processor 7001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of the present invention. Processor 7001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0107] The bus 7002 may include a path to transmit information between the above components. The bus 7002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 7002 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0108] The memory 7003 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), magnetic disk storage medium, other magnetic storage devices, or any other medium that can be used to carry or store computer programs and can be read by a computer, without limitation herein.

[0109] The memory 7003 is used to store the computer program for executing the embodiment of the present disclosure, and the execution is controlled by the processor 7001. The processor 7001 is used to execute the computer program stored in the memory 7003 to implement the steps shown in the above method embodiment.

[0110] The electronic device includes, but is not limited to, a terminal or server capable of adjusting the welding operation using the above-mentioned battery core welding method.

[0111] An embodiment of the present disclosure provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps and corresponding contents of the aforementioned method embodiment can be implemented.

[0112] It should be noted that the computer-readable storage medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0113] The embodiments of the present disclosure also provide a computer program product, including a computer program, which can implement the steps and corresponding contents of the aforementioned method embodiments when executed by a processor.

[0114] The terms "first", "second", "third", "fourth", "1", "2", etc. (if any) in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than that shown or described in the text.

[0115] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0116] It should be understood that, although the flowchart of the embodiment of the present disclosure indicates each operation step by arrows, the implementation order of these steps is not limited to the order indicated by the arrows. Unless clearly stated herein, in some implementation scenarios of the embodiment of the present disclosure, the implementation steps in each flowchart can be executed in other orders according to demand. In addition, some or all of the steps in each flowchart may include multiple sub-steps or multiple stages based on the actual implementation scenario. Some or all of these sub-steps or stages can be executed at the same time, and each sub-step or stage in these sub-steps or stages can also be executed at different times. In scenarios with different execution times, the execution order of these sub-steps or stages can be flexibly configured according to demand, and the embodiment of the present disclosure does not limit this.

[0117] The above is only an optional implementation method for some implementation scenarios of the present disclosure. It should be pointed out that for ordinary technicians in this technical field, without departing from the technical concept of the scheme of the present disclosure, other similar implementation methods based on the technical ideas of the present disclosure are also within the protection scope of the embodiments of the present disclosure.

Claims

1. A battery core welding method, characterized in that: include: Get the battery cells to be welded; Performing at least one welding operation on the battery core to be welded until the welding state of the battery core to be welded is successful; The welding operation includes: Acquire first welding state information corresponding to the current welding operation of the battery cell to be welded; the first welding state information includes welding state and welding power; Acquire a value prediction model corresponding to the current welding operation, and determine a welding energy required for the current welding operation from at least one candidate energy based on the first welding state information and the value prediction model corresponding to the current welding operation; Using the welding energy to weld the battery core to be welded, and obtaining corresponding second welding state information and a reward value; If the welding state in the second welding state information is welding failure, predicting the welding energy required for the next welding operation from at least one candidate energy through the value prediction model based on the second welding state information; updating the value prediction model based on the welding energy required for the current welding operation, the welding energy required for the next welding operation and the reward value; The second welding state information is used as the first welding state information for the next time, and the updated value prediction model is used as the value prediction model corresponding to the next welding operation.

2. The battery core welding method according to claim 1, characterized in that: The acquiring of the value prediction model corresponding to the current welding operation, and determining the welding energy required for the current welding operation from at least one candidate energy based on the first welding state information and the value prediction model corresponding to the current welding operation, comprises: Acquire a value prediction model corresponding to the current welding operation, use the welding power in the first welding state information as an input of the value prediction model, and obtain a value function corresponding to each of the at least one candidate energy; Determine the selection probability corresponding to each candidate energy based on the value function corresponding to each candidate energy and the sum of the value functions of the candidate energy; The candidate energy with the highest probability is selected from the at least one candidate energy as the welding energy required for the current welding operation.

3. The battery core welding method according to claim 2, characterized in that: The determining of the selection probability corresponding to each candidate energy based on the value function corresponding to each candidate energy and the sum of the value functions of the candidate energy includes: Determine the first probability parameter corresponding to each candidate energy based on the value function and preset temperature parameter corresponding to each candidate energy; the preset temperature parameter is used to simulate the actual temperature condition, and the temperature parameter gradually decreases as the number of welding operations increases; Based on the first probability parameter corresponding to each candidate energy and the sum of the first probability parameters corresponding to the candidate energy, the selection probability corresponding to each candidate energy is determined.

4. The battery core welding method according to claim 2, characterized in that: The updating of the value prediction model based on the welding energy required for the current welding operation, the welding energy required for the next welding operation and the reward value comprises: Determine a corresponding first value function based on the welding energy required for the current welding operation by using a preset value prediction model; Determining a corresponding second value function based on the welding energy required for the next welding operation by a preset value prediction model; The value prediction model is updated based on the first value function, the second value function and the reward value.

5. The battery core welding method according to claim 4, characterized in that: The updating of the value prediction model based on the first value function, the second value function and the reward value includes: Determine a first evaluation value corresponding to the current welding operation based on the first value function and the welding parameters corresponding to the current welding operation; Determine a second evaluation value corresponding to the next welding operation based on the second value function and the welding parameters corresponding to the next welding operation; obtain the target evaluation value based on the second evaluation value and the reward value; obtaining an error value based on a difference between the target evaluation value and the first evaluation value; The value prediction model is updated based on the error value.

6. The battery core welding method according to claim 1, characterized in that: The step of welding the battery core to be welded using the welding energy to obtain corresponding second welding state information and a reward value includes: Using the welding energy to weld the battery core to be welded, and obtaining corresponding second welding state information; A corresponding reward value is determined based on the second welding state information through a preset reward function.

7. The battery core welding method according to claim 1, characterized in that: If the welding state in the second welding state information is welding failure, the welding energy required for the next welding operation is predicted from at least one candidate energy by using the value prediction model based on the second welding state information, including: If the welding state in the second welding state information is welding failure, the welding power in the second welding state information is used as an input of the value prediction model to obtain a value function corresponding to each of the at least one candidate energy; Determine the selection probability corresponding to each candidate energy based on the value function corresponding to each candidate energy and the sum of the value functions of the candidate energy; A candidate energy with the highest probability is selected from the at least one candidate energy as the welding energy required for the next welding operation.

8. A core welding device, characterized in that: include: A battery cell acquisition module is used to acquire the battery cells to be welded; A cell welding module, used for performing at least one welding operation on the cell to be welded, until the welding state of the cell to be welded is successful; The battery core welding module is also used for: Acquire first welding state information corresponding to the current welding operation of the battery cell to be welded; the first welding state information includes welding state and welding power; Acquire a value prediction model corresponding to the current welding operation, and determine a welding energy required for the current welding operation from at least one candidate energy based on the first welding state information and the value prediction model corresponding to the current welding operation; Using the welding energy to weld the battery core to be welded, and obtaining corresponding second welding state information and a reward value; If the welding state in the second welding state information is welding failure, predicting the welding energy required for the next welding operation from at least one candidate energy through the value prediction model based on the second welding state information; updating the value prediction model based on the welding energy required for the current welding operation, the welding energy required for the next welding operation and the reward value; The second welding state information is used as the first welding state information for the next time, and the updated value prediction model is used as the value prediction model corresponding to the next welding operation.

9. An electronic device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the electric core welding method according to any one of claims 1 to 7 are implemented.

11. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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