Method, device, medium and program product for obtaining battery life extension strategy

Abstract

The present application provides a method, device, medium and program product for obtaining a battery life extension strategy, which relates to the technical field of battery management. The method for obtaining the battery life extension strategy includes: extracting aging features according to the obtained battery parameters, and obtaining an aging mode recognition result corresponding to the aging features; inputting the aging mode recognition result into a life prediction model, and generating a battery life extension strategy according to the prediction result of the life prediction model. The life prediction model is established based on historical aging features and historical aging mode recognition results, and the life extension strategy includes charge and discharge current values. The embodiments of the present application can formulate corresponding life extension strategies according to the actual state of the battery, quickly realize the matching of the life extension strategy and the battery state, improve the life extension effect, and reduce the battery usage cost.

Description

Technical Field

[0001] The present application relates to the field of battery management technology, and in particular to a method, device, medium and program product for obtaining a battery life extension strategy. Background Art

[0002] Lithium-ion battery is a secondary battery (rechargeable battery) that mainly relies on the movement of lithium ions between the positive electrode and the negative electrode to work. During the charge and discharge process, Li+ is intercalated and deintercalated between the two electrodes: when charging, Li+ is deintercalated from the positive electrode and intercalated into the negative electrode through the electrolyte, and the negative electrode is in a lithium-rich state; the opposite is true during discharge.

[0003] Lithium-ion batteries are widely used in electric vehicles (EVs) and portable electronic devices because of their high energy density, high average output voltage, and low self-discharge. Lithium-ion batteries provide the energy required for electric vehicles, portable electronic devices, and other power-consuming devices to work. As these devices are used, lithium-ion batteries need to be continuously charged and discharged, and a series of irreversible electrochemical reactions will occur inside the batteries, causing the remaining battery life to continue to shorten.

[0004] In the prior art, in order to extend the remaining service life of the battery, a fixed life extension strategy is often used to manage the battery to extend the remaining service life. However, batteries in different states require different life extension strategies, and the method of managing the battery using a fixed life extension strategy is difficult to match the specific state of the battery, resulting in poor life extension effect. Summary of the invention

[0005] The embodiments of the present application provide a battery life extension strategy acquisition method, device, medium and program product, which can solve the problem that the existing batteries use the same life extension strategy, resulting in poor life extension effect. To achieve this purpose, the embodiments of the present application provide the following solutions.

[0006] According to one aspect of an embodiment of the present application, a method for acquiring a battery life extension strategy is provided, the method comprising:

[0007] Extracting aging features according to the acquired battery parameters, and obtaining aging pattern recognition results corresponding to the aging features;

[0008] The aging pattern recognition result is input into a life prediction model, and a battery life extension strategy is generated according to the prediction result of the life prediction model. The life prediction model is established based on historical aging characteristics and historical aging pattern recognition results, and the life extension strategy includes charging and discharging current values.

[0009] In a possible implementation, extracting aging characteristics according to the acquired battery parameters includes:

[0010] Determine that it is currently in the process of using the battery, and extract battery parameters, where the battery parameters include voltage, current, temperature, and number of charge and discharge cycles;

[0011] Obtain a first parameter corresponding to the aging characteristic according to the battery parameters, and determine the aging characteristic based on the first parameter, where the aging characteristic includes battery internal resistance.

[0012] In a possible implementation, the obtaining the aging mode recognition result corresponding to the aging characteristic includes:

[0013] Input the aging characteristic into an aging mode recognition model, and determine the aging mode recognition result according to the output information of the aging mode recognition model. The aging mode recognition model is established based on a machine learning algorithm, and the aging mode includes any one of chemical aging, mechanical aging, capacity attenuation, and voltage attenuation.

[0014] In a possible implementation, the machine learning algorithm is a support vector machine, and the classification decision function of the support vector machine is:

[0015] ;

[0016] where x is the input aging characteristic, and b are model parameters, is the label of the i-th training sample, K is the kernel function, is the aging mode recognition result, and n is the total number of training samples.

[0017] In a possible implementation, the prediction result includes the battery health state, and the generating a battery life extension strategy according to the prediction result of the life prediction model includes:

[0018] Input the data of the battery health state into a first preset formula, and obtain the charge and discharge current value according to the calculation result of the first preset formula;

[0019] The first preset formula is:

[0020] ；

[0021] where, is the adjusted charge and discharge current value, is the original charge and discharge current value, e is the natural constant, α is the adjustment coefficient, is the battery health state, is the battery health state threshold.

[0022] In a possible implementation, the method further includes:

[0023] Send the lifespan extension strategy to the battery management system corresponding to the battery to adjust the operating state of the battery by using the battery management system;

[0024] Obtain the feedback information of the battery management system, and adjust the parameters of the lifespan prediction model based on the feedback information, where the feedback information is generated by the battery management system based on the implementation result of the lifespan extension strategy.

[0025] In a possible implementation manner, adjusting the parameters of the lifespan prediction model based on the feedback information includes:

[0026] Obtain the gradient of the loss function according to the feedback information, and calculate the parameters of the lifespan prediction model based on the gradient by using a second preset formula;

[0027] Train the lifespan prediction model by using the parameters;

[0028] The second preset formula is:

[0029] = - J ;

[0030] Wherein, is the parameter of the lifespan prediction model when training the lifespan prediction model for the t-th time, is the learning rate, and J is the loss function of the lifespan prediction model when training the lifespan model for the t-th time, is the gradient of the loss function J with respect to the parameter.

[0031] According to one aspect of the embodiments of the present application, an electronic device is provided, including a memory, a processor, and a computer program stored on the memory, and the processor executes the computer program to implement the steps of the method as described above.

[0032] According to one aspect of the embodiments of the present application, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method as described above are implemented.

[0033] According to one aspect of the embodiments of the present application, a computer program product is provided, which includes a computer program, and when the computer program is executed by a processor, the steps of the method as described above are implemented.

[0034] The beneficial effects brought by the technical solution provided by the embodiments of the present application are:

[0035] The present application provides a method for obtaining a battery life extension strategy. Aging characteristics are extracted based on the obtained battery parameters, and an aging mode recognition result corresponding to the aging characteristics is obtained. The aging mode recognition result is input into a life prediction model established based on historical aging characteristics and historical aging mode recognition results. A battery life extension strategy is generated according to the prediction result of the life prediction model. Therefore, in the embodiments of the present application, the aging characteristics of the battery are obtained, the prediction result of the life prediction model is obtained according to the aging characteristics, and a battery life extension strategy is generated based on the prediction result. Therefore, the embodiments of the present application can formulate corresponding life extension strategies according to the actual state of the battery, quickly realize the matching of the life extension strategy and the battery state, improve the life extension effect, and reduce the battery usage cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for the description in the embodiments of the present application.

[0037] Figure 1 It is a flowchart of the method for obtaining a battery life extension strategy provided by the embodiments of the present application;

[0038] Figure 2 It is a working flowchart of the method for obtaining a battery life extension strategy provided by the embodiments of the present application;

[0039] Figure 3 It is a structural diagram of an electronic device provided by the embodiments of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0040] The embodiments of the present application will be described below with reference to the drawings in the present application. It should be understood that the embodiments described below in conjunction with the drawings are exemplary descriptions for explaining the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions of the embodiments of the present application.

[0041] Those skilled in the art can understand that, unless specifically stated otherwise, the singular forms "a", "an", "the", and "said" used herein may also include the plural forms. It should be further understood that the terms "comprising" and "including" used in the embodiments of the present application mean 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 of other features, information, data, steps, operations, elements, components, and / or their combinations supported by the technical field of the present application. It should be understood that when we say an element is "connected" or "coupled" to another element, the element can be directly connected or coupled to the other element, or it can mean that the element and the other element establish a connection relationship through an intermediate element. In addition, the "connection" or "coupling" used herein can include wireless connection or wireless coupling. The term "and / or" used herein indicates at least one of the items defined by the term. For example, "A and / or B" indicates the implementation as "A", or the implementation as "A", or the implementation as "A and B".

[0042] To make the objectives, technical solutions, and advantages of the present application clearer, the following will further describe the embodiments of the present application in detail with reference to the accompanying drawings.

[0043] The technical solutions of the embodiments of the present application and the technical effects produced by the technical solutions of the present application will be described below by describing several exemplary embodiments. It should be noted that the following embodiments can be referenced, learned from, or combined with each other. For the same terms, similar features, and similar implementation steps in different embodiments, they will not be described repeatedly.

[0044] The battery life extension strategy acquisition method, device, medium, and program product provided by the present application are intended to solve at least one technical problem existing in the prior art.

[0045] An embodiment of the present application provides a battery life extension strategy acquisition method, which can be used in mobile phones, electric vehicles, tablet computers, laptop computers, smart speakers, and other devices that can be powered by lithium-ion batteries.

[0046] As Figure 1 - Figure 2 shown, the battery life extension strategy acquisition method includes:

[0047] S101: Extract aging features according to the acquired battery parameters, and obtain the aging mode recognition result corresponding to the aging features.

[0048] Optionally, extracting aging features according to the acquired battery parameters includes: determining that the battery is currently in use, extracting battery parameters, where the battery parameters include voltage, current, temperature, and number of charge and discharge cycles; obtaining the first parameter corresponding to the aging features according to the battery parameters, and determining the aging features based on the first parameter, where the aging features include battery internal resistance.

[0049] Optionally, the battery parameters may further include battery usage time, usage frequency, charge and discharge curve, battery model, and other parameters related to battery performance. Among them, the aging characteristics may further include at least one of battery capacity retention rate, physical deformation (change in the external shape of the battery), charge and discharge performance, self-discharge rate, etc. The battery parameters to be collected change accordingly according to the type of aging characteristics.

[0050] In one embodiment, the aging characteristic is the internal resistance of the battery. During the use of the battery, battery parameters are collected in real time. The battery parameters include voltage (V), current (I), temperature (T), and number of charge and discharge cycles (N); a first parameter related to battery aging (here the first parameter includes voltage and current) is extracted from the collected battery parameters, and the internal resistance of the battery is calculated based on the voltage and current.

[0051] Optionally, obtaining the aging mode recognition result corresponding to the aging characteristic includes: inputting the aging characteristic into the aging mode recognition model, and determining the aging mode recognition result according to the output information of the aging mode recognition model. The aging mode recognition model is established based on a machine learning algorithm, and the aging mode includes any one of chemical aging, mechanical aging, capacity attenuation, and voltage attenuation.

[0052] Optionally, the machine learning algorithm includes any one of support vector machine and neural network algorithm. The aging mode recognition model is obtained through model training based on the machine learning algorithm and training samples. The historical data of the battery can be used as the training samples, and the historical data may include historical aging characteristics and the aging modes corresponding to the historical aging characteristics.

[0053] In one embodiment, the machine learning algorithm is a support vector machine, and the classification decision function of the support vector machine is: ;

[0054] where x is the input aging characteristic, and b are model parameters, is the model parameter corresponding to the i-th training sample, is the label of the i-th training sample, K is the kernel function, is the aging mode recognition result, and n is the total number of training samples. The aging mode recognition result corresponding to each aging characteristic is obtained based on this classification decision function.

[0055] S102: Input the aging mode recognition result into the life prediction model, and generate a battery life extension strategy according to the prediction result of the life prediction model.

[0056] Optionally, the life prediction model is established based on historical aging characteristics and historical aging mode recognition results, and the life extension strategy includes the charge and discharge current value.

[0057] In one embodiment, lifespan information (such as remaining usage time) corresponding to the historical aging pattern recognition result can be obtained, and training samples for the lifespan prediction model are established based on the lifespan information and the historical aging pattern recognition result. The lifespan prediction model is established according to the training samples and a predetermined machine learning algorithm.

[0058] Optionally, the prediction result includes the battery health state, and the lifespan extension strategy is adjusted based on this health state. Among them, the prediction result can also include the aging trend of the battery and other data related to the battery lifespan.

[0059] Optionally, to improve the matching degree between the lifespan prediction model and the battery, after the lifespan prediction model is established, the lifespan prediction model is trained and predicted according to the collection frequency of battery parameters. If the battery parameters can be collected in real time or at a high frequency (greater than the predetermined collection frequency), the update frequency of the lifespan prediction model is correspondingly increased.

[0060] Optionally, the aging rate of the battery can also be obtained according to the battery parameters and the aging pattern recognition result, and the update frequency of the lifespan prediction model is increased based on this aging rate (such as setting the update frequency to be positively correlated with the aging rate).

[0061] In one embodiment, the calculation formula of the lifespan prediction model is:

[0062] = =tanh =tanh . Among them, is the hidden state at time step t (the hidden state represents the internal memory of the model at the current time step, and it is the prediction result of the lifespan prediction model), is the activation function, is the aging pattern recognition result input at time step t, is the weight matrix used to map the hidden state at the previous time step ( ) and to a new hidden state, is the bias term corresponding to , which adds a constant offset to the calculation of the hidden state, represents the candidate memory unit at time step t1, represents the weight matrix connected to the cell state calculation layer, is the bias vector corresponding to , is the state of the output gate at time step t1, σ is the sigmoid function (activation function), is the weight matrix connected to the output layer, and is the bias of the output layer; is the actual observed value.

[0063] Optionally, the prediction result includes the battery health state. A battery life extension strategy is generated according to the prediction result of the life prediction model, including: inputting the data of the battery health state into a first preset formula, and obtaining a charge and discharge current value according to the calculation result of the first preset formula; the first preset formula is:

[0064] ；

[0065] where is the adjusted charge and discharge current value, is the original charge and discharge current value, e is the natural constant, α is the adjustment coefficient, is the battery health state, is the battery health state threshold. The charge and discharge current value of the battery is obtained through this first preset formula, and the charge and discharge of the battery are controlled based on this charge and discharge current value.

[0066] Optionally, corresponding life extension strategies can also be pre-stored for different life prediction results. After obtaining the data output by the life prediction model, the corresponding life extension strategy is obtained, and this life extension strategy is used as the life extension strategy currently used by the battery.

[0067] Optionally, the life extension strategy can also include charge and discharge management information, operating point adjustment information, battery voltage adjustment information, device operating state adjustment information (such as brightness adjustment, operating mode adjustment, charge and discharge frequency, etc., measures that can extend the battery life), and other strategies that can adjust the working conditions of the battery or optimize the charge and discharge strategy. Through this strategy, deep discharge or overcharging of the battery is avoided, thereby extending the service life of the battery and reducing maintenance costs.

[0068] Optionally, after obtaining the life extension strategy, it can also be sent to the corresponding battery management system of the battery based on the life extension strategy to adjust the working state of the battery by using the battery management system; obtain the feedback information of the battery management system, and adjust the parameters of the life prediction model based on the feedback information. The feedback information is generated by the battery management system based on the implementation result of the life extension strategy. This feedback information can include information such as the actual aging mode of the battery, battery parameters, and the actual health state and aging trend of the battery. Based on this information, the parameters of the life prediction model are continuously optimized and the life extension strategy is adjusted.

[0069] Optionally, the parameters of the life prediction model can be optimized by the gradient descent algorithm, and the parameters of the life prediction model can be adjusted based on the feedback information, including: obtaining the gradient of the loss function according to the feedback information, and calculating the parameters of the life prediction model based on the gradient using the second preset formula; training the life prediction model with the parameters; the second preset formula is:

[0070] = - J ;

[0071] Wherein, is the parameter of the life prediction model when the life prediction model is trained for the t-th time, is the learning rate, J is the loss function of the life prediction model when the life model is trained for the t-th time, is the loss function J with respect to the gradient of the parameter.

[0072] Optionally, to achieve more comprehensive battery performance optimization, the object that executes the battery life extension strategy acquisition method can be inherited in the battery management system, so as to collaboratively implement the acquisition of the battery life extension strategy, the charge and discharge control of the battery, thermal management, and other battery management-related functions. Moreover, the battery life extension strategy acquisition method of the present application can perform real-time analysis of the aging mode and life prediction during the use of the battery, without the need to transmit data to an external server or system for processing, avoiding the impact of network signal transmission on battery management, reducing the information transmission cost and information transmission delay.

[0073] The following combines Figure 2 to illustrate the battery life extension strategy acquisition method of the present application.

[0074] In one embodiment, it is determined that the battery is in use, battery parameters are collected in real time during the use of the battery, and the aging characteristics of the battery are obtained from the battery parameters. The aging characteristics are input into an aging mode recognition model constructed based on a machine learning algorithm, and the aging mode of the battery is determined according to the output result of the model. A life prediction model is established using the extracted aging characteristics and the aging mode recognition result, and a life extension strategy is determined according to the prediction result of the life prediction model. The life extension strategy is applied to the battery management system. According to the feedback of the implementation result of the battery management system, the algorithm parameters and the life extension strategy are continuously optimized.

[0075] The method for obtaining the battery life extension strategy in this application extracts the aging characteristics based on the obtained battery parameters, and obtains the aging mode recognition result corresponding to the aging characteristics; inputs the aging mode recognition result into the life prediction model established based on the historical aging characteristics and historical aging mode recognition results; generates the battery life extension strategy according to the prediction result of the life prediction model. Therefore, the embodiments of this application obtain the aging characteristics of the battery, obtain the prediction result of the life prediction model according to the aging characteristics, and generate the battery life extension strategy based on the prediction result. Therefore, the embodiments of this application can formulate corresponding life extension strategies according to the actual state of the battery, quickly realize the matching of the life extension strategy and the battery state, improve the life extension effect, and reduce the battery usage cost.

[0076] In an alternative embodiment, an electronic device is provided. In an alternative embodiment, an electronic device is provided, as Figure 3 shown Figure 3 The electronic device 4000 shown includes: a processor 4001 and a memory 4003. Among them, the processor 4001 and the memory 4003 are connected, such as connected through a bus 4002. Optionally, the electronic device 4000 may further include a transceiver 4004, and the transceiver 4004 can be used for data interaction between this electronic device and other electronic devices, such as data sending and / or data receiving, etc. It should be noted that in actual applications, the transceiver 4004 is not limited to one, and the structure of the electronic device 4000 does not constitute a limitation to the embodiments of this application.

[0077] The processor 4001 may be a CPU (Central Processing Unit, central processor), a general-purpose processor, a DSP (Digital Signal Processor, data signal processor), an ASIC (Application Specific Integrated Circuit, application-specific integrated circuit), an FPGA (Field Programmable Gate Array, field programmable gate array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It can implement or execute various exemplary logic blocks, modules and circuits described in connection with the disclosure of this application. The processor 4001 may also be a combination for implementing computing functions, such as a combination including one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0078] The bus 4002 may include a path for transmitting information between the above components. The bus 4002 can be a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus 4002 can be divided into an address bus, a data bus, a control bus, etc. For the sake of simplicity in illustration, only a thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0079] The memory 4003 can be a ROM (ReadOnly 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. It can also be an EEPROM (Electrically Erasable Programmable ReadOnly Memory), a CD-ROM (Compact Disc ReadOnly Memory), or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media, 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, which is not limited herein.

[0080] The memory 4003 is used to store the computer program for implementing the embodiments of the present application, and is controlled by the processor 4001 for execution. The processor 4001 is used to execute the computer program stored in the memory 4003 to implement the steps shown in the foregoing method embodiments.

[0081] Among them, the electronic device can be any kind of electronic product that can perform human-computer interaction with an object. For example, a personal computer, a tablet computer, a smart phone, a personal digital assistant (PDA), a game console, an Internet Protocol Television (IPTV), a smart wearable device, etc.

[0082] The electronic device may further include a network device and / or an object device. Among them, the network device includes, but is not limited to, a single network server, a server group composed of multiple network servers, or a cloud composed of a large number of hosts or network servers based on cloud computing.

[0083] The network where the electronic device is located includes, but is not limited to, the Internet, wide area network, metropolitan area network, local area network, virtual private network (VPN), etc.

[0084] An embodiment of the present application provides a computer-readable storage medium storing a computer program, which when executed by a processor can implement the steps and corresponding contents of the foregoing method embodiment.

[0085] An embodiment of the present application further provides a computer program product including a computer program, which when executed by a processor can implement the steps and corresponding contents of the foregoing method embodiment.

[0086] The terms "first", "second", "third", "fourth", "1", "2", etc. (if any) in the specification, claims and above-mentioned drawings of the present application are used to distinguish similar objects and do not necessarily describe a specific order or sequence. It should be understood that such used data can be interchanged under appropriate circumstances so that the embodiments of the present application described herein can be implemented in an order other than that shown or described in words.

[0087] It should be understood that although the flowchart of the embodiment of the present application indicates each operation step by an arrow, the execution order of these steps is not limited to the order indicated by the arrow. Unless otherwise clearly stated in this document, in some implementation scenarios of the embodiment of the present application, the implementation steps in each flowchart can be executed in other orders according to requirements. 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 of these sub-steps or stages can also be executed at different times respectively. In the scenario where the execution times are different, the execution order of these sub-steps or stages can be flexibly configured according to requirements, and the embodiment of the present application does not limit this.

[0088] The above are only optional implementation manners of some implementation scenarios of the present application. It should be noted that for those of ordinary skill in the art in the technical field, without departing from the technical concept of the solution of the present application, other similar implementation means based on the technical idea of the present application also belong to the protection scope of the embodiments of the present application.

Claims

1. A method for obtaining a battery life extension strategy, characterized in that, The method includes: Extracting aging characteristics based on the obtained battery parameters, and obtaining an aging mode recognition result corresponding to the aging characteristics. The aging mode recognition result includes any one of chemical aging, mechanical aging, capacity attenuation, and voltage attenuation. The aging characteristics include at least one of battery internal resistance, battery capacity retention rate, battery physical deformation, charge and discharge performance, and self-discharge rate; Inputting the aging mode recognition result into a life prediction model, and generating a battery life extension strategy according to the prediction result of the life prediction model. The life prediction model is established based on historical aging characteristics and historical aging mode recognition results. The update frequency of the life prediction model is positively correlated with the aging speed. The life extension strategy includes charge and discharge current values; Sending the life extension strategy to the battery management system corresponding to the battery to use the battery management system to adjust the working state of the battery, including: controlling the battery to charge and discharge according to the charge and discharge current values; Obtaining feedback information of the battery management system. The feedback information is generated by the battery management system based on the implementation result of the life extension strategy. The feedback information includes the actual aging mode of the battery, battery parameters, and the actual health state and aging trend of the battery; Adjusting the parameters of the life prediction model based on the feedback information, including: Obtaining the gradient of the loss function according to the feedback information, and calculating the parameters of the life prediction model based on the gradient using a second preset formula; Training the life prediction model using the parameters; The second preset formula is: = - J ； Among them, are the parameters of the life prediction model when training the life prediction model for the t-th time, is the learning rate, J is the loss function of the life prediction model when training the life model for the t-th time, is the loss function J is the gradient of the parameter.

2. The method for obtaining a battery life extension strategy according to claim 1, wherein The extracting aging characteristics based on the obtained battery parameters includes: Determining that it is currently in the process of using the battery, and extracting battery parameters. The battery parameters include voltage, current, temperature, and charge and discharge cycle numbers; Obtaining a first parameter corresponding to the aging characteristics according to the battery parameters, and determining the aging characteristics based on the first parameter.

3. The method for obtaining a battery life extension strategy according to claim 1, wherein The obtaining an aging mode recognition result corresponding to the aging characteristics includes: Inputting the aging characteristics into an aging mode recognition model, and determining the aging mode recognition result according to the output information of the aging mode recognition model. The aging mode recognition model is established based on a machine learning algorithm.

4. The method for obtaining a battery life extension strategy according to claim 3, characterized in that The machine learning algorithm is a support vector machine, and the classification decision function of the support vector machine is: ； where x is the input aging feature, and b are model parameters, is the label of the i-th training sample, K is the kernel function, is the aging pattern recognition result, and n is the total number of training samples.

5. The method for obtaining a battery life extension strategy according to claim 1, wherein, The prediction result includes the battery health state. The generating a battery life extension strategy according to the prediction result of the life prediction model includes: Inputting the data of the battery health state into a first preset formula, and obtaining the charge and discharge current values according to the calculation result of the first preset formula; The first preset formula is: ； Among them, is the adjusted charge and discharge current value, is the original charge and discharge current value, e is the natural constant, and α is the adjustment coefficient. is the state of health of the battery, is the threshold of the state of health of the battery.

6. An electronic device, comprising a memory, a processor, and a computer program stored on the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1-5.

7. A computer-readable storage medium, having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-5.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-5.

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