Digital twin drive life prolonging system and method for lithium battery aging process

Abstract

The invention discloses a digital twin drive life prolonging system and method in a lithium battery aging process, the system comprises a sensor detection module, an AI control module and a regulation and control module, the sensor detection module detects pressure, temperature, gas components, SEI film conditions and voltage data between a conductive layer and an anode, and inputs the data into the AI control module; the AI control module performs electrochemical-thermodynamic-mechanics multi-physics field coupling modeling by applying a digital twinborn technology, performs data analysis and processing in combination with cross-working-condition model migration, screens out an optimal regulation and control scheme according to a mirage optimization algorithm, sends an instruction to the regulation and control module, and controls the regulation and control module according to the instruction of the AI control module. Parameters in the energy storage cabin are adjusted, the SEI film state is maintained, and lithium dendrite precipitation is controlled, so that the service life of the lithium battery is prolonged; according to the method, the digital twinborn model is utilized to identify battery capacity attenuation inflection points, trigger lithium supplement or charge strategy optimization and other regulation measures, irreversible damage is avoided, and the replacement frequency is reduced.

Description

Technical Field

[0001] The present invention relates to a system and method for extending the lifespan, and particularly to a digital twin-driven lifespan extension system and method for the aging process of lithium batteries. Background Art

[0002] Due to advantages such as high energy density, long cycle life, and low self-discharge rate, lithium batteries are widely used in fields such as electric vehicles, energy storage systems, and consumer electronics. However, during long-term use, lithium batteries will naturally age, which causes the battery capacity to gradually decrease and the internal resistance to increase. This not only affects the performance of the device but also increases the safety risks during use. Therefore, it is particularly important to develop effective technical means to extend the service life of lithium batteries so that they can maintain efficient operation for a longer time.

[0003] Traditional methods usually rely on regular maintenance and replacement of new batteries, but this cannot fundamentally solve the problems caused by battery aging. In addition, these methods cannot effectively predict the specific state of lithium batteries at a certain future moment, resulting in the inability to carry out timely and effective management. In recent years, as an emerging technical means, digital twin technology has the ability to correspond physical objects with virtual models and achieve comprehensive monitoring and accurate simulation of the state of objects. Summary of the Invention

[0004] Object of the Invention: The object of the present invention is to provide a digital twin-driven lifespan extension system for the aging process of lithium batteries to monitor, regulate, and optimize the state of lithium batteries in the energy storage bin, thereby extending the lifespan of lithium batteries. On the other hand, a digital twin-driven lifespan extension method for the aging process of lithium batteries is provided.

[0005] Technical Solution: The digital twin-driven lifespan extension system described in the present invention includes:

[0006] A sensor detection module, including a micro sensor and a conductive layer voltage sensor. The micro sensor is used to collect data on pressure, temperature, gas composition, and SEI film conditions; the conductive layer voltage sensor is used to monitor the voltage between the conductive layer and the anode. When the conductive layer is penetrated by dendrites, the voltage drops suddenly, and an alarm is given through the conductive layer voltage sensor.

[0007] The AI control module is used to perform coupled modeling of electrochemistry-thermodynamics-mechanics multi-physical fields by using digital twin technology. The coupled modeling establishes a joint solver for a pseudo-two-dimensional electrochemical model, a heat conduction equation, and a dendritic growth phase field model through COMSOL Multiphysics, and realizes field parameter coupling through a Jacobian matrix. Data analysis and processing are carried out in combination with cross-condition model migration. The cross-condition model migration uses a domain adversarial neural network to extract the common characteristics of battery degradation at different charge and discharge rates and temperatures, and aligns the source domain and target domain feature distributions through the maximum mean discrepancy. A battery life prediction and regulation function is constructed in combination with the data collected by the sensor detection module, and the mirage algorithm is used to calculate the optimal battery life prediction and regulation scheme.

[0008] The regulation module is used to regulate the parameters in the energy storage cabin according to the calculation results of the AI control module, maintain the state of the SEI film and control the precipitation of lithium dendrites, and realize the optimization of the charging regulation strategy.

[0009] Preferably, the calculation parameters of the mirage algorithm include a battery capacity attenuation calculation model, an SEI film state regulation model, a lithium dendrite inhibition regulation strategy, and a life extension regulation function.

[0010] Preferably, the battery life prediction and regulation function is as follows:

[0011]

[0012] E(t) = ω1T(t) + ω2P(t) + ω3G(t) + ω4|V cell (t) - V nom |;

[0013]

[0014] Constraint conditions:

[0015] Among them, L(t) represents the remaining battery life, η cap (t) represents the capacity attenuation correction factor, η sei (t) represents the health of the SEI film, η dendnite (t) represents the lithium dendrite inhibition factor, e -αE(t) represents the environmental stress attenuation term, α represents the environmental stress attenuation coefficient, C achual (t) represents the actual capacity, C0 represents the initial capacity, ΔT(τ) represents the temperature fluctuation intensity, β represents the capacity attenuation rate coefficient, V threshold Critical voltage, V sei represents the SEI film voltage, γ represents the SEI film failure risk coefficient, J dendnite represents the dendritic growth rate, J cntrepresents the critical threshold, δ represents the dendrite inhibition coefficient, T(t) represents the temperature, P(t) represents the pressure, G(t) represents the gas component concentration, ω1, ω2, ω3 represent the weight coefficients, C(t) represents the actual capacity, C0 represents the initial capacity, T opt represents the optimal operating temperature, V crit represents the SEI film rupture threshold voltage, λ represents the temperature sensitivity coefficient, u(t) represents the dynamic regulation input signal, I(t) represents the real-time charge and discharge current, I max represents the maximum allowable current, V system (t) represents the system terminal voltage, V max represents the upper limit of the safety voltage, I opt represents the optimal operating current, V nom represents the nominal voltage, V cell (t) represents the real-time single cell voltage, I charge (t) represents the real-time charging current.

[0016] Preferably, the regulation module includes a temperature control component, a pressure regulating valve, a lithium salt replenishing device, and an MCU microcontroller. The temperature control component is used to regulate the temperature range. The pressure regulating valve is used to achieve dynamic balance of the energy storage bin pressure. The lithium salt replenishing device is used to supplement the electrolyte through ultrasonic atomization. The MCU microcontroller is used to execute the firmware program, process sensor data, and trigger control instructions to optimize the charging regulation strategy.

[0017] Preferably, the mirage algorithm is as follows:

[0018] (1) In the initialization stage, based on the data collected by the sensor detection module, candidate solutions are generated by random initialization to form an initial population. A range threshold and a maximum number of iterations are set. Each candidate solution corresponds to a set of regulation parameters;

[0019] The regulation parameters include the remaining battery life, the capacity attenuation correction factor, the SEI film health, the lithium dendrite inhibition factor, the environmental stress attenuation term, and the dynamic regulation input signal;

[0020] (2) In the global exploration stage, the refraction path of light in different meteorological conditions in the mirage is simulated, and the updated position of the individual is deduced according to the refraction principle. Each individual corresponds to the current regulation parameters;

[0021] (3) In the local development stage, the neighborhood of the current solution is searched. If the current individual is not the optimal individual in the population, its position update will refer to the information of the better individual in the population and move closer to the better area. If the current individual is the optimal individual in the population, local perturbations will be carried out within a small range to further optimize its own position and find the optimal solution;

[0022] (4) Iterative update stage. In each iteration, after updating the individual positions according to the global exploration stage and the local development stage, calculate the fitness value of each individual, and update the optimal solution of the population. If the numerical value of the objective function reaches a certain range threshold or the maximum number of iterations is reached, then terminate the iterative update and output the final result to determine the control parameters.

[0023] Preferably, the initialization formula in step 1 is as follows:

[0024] X i,j = X min,j + rand(0,1)×(X max,j - X min,j );

[0025] Among them, X i,j represents the j-th dimension position of the i-th individual, X max,j , X min,j represent the maximum and minimum boundaries of the j-th dimension, and rand(0,1) represents a random number;

[0026] The position update formula in step 2 is as follows:

[0027]

[0028] Among them, represents the position of the i-th individual in the t-th generation, represents the position of the optimal individual in the t-th generation population, α represents the step size factor, and f represents the fitness function;

[0029] The formula for finding the optimal solution in step 3 is as follows:

[0030]

[0031] Among them, represents the mean value of the positions of the individuals in the t-th generation population, and β, γ represent the control parameters.

[0032] Preferably, it further includes enhancing the global search ability by introducing Gaussian mutation as a strategy of the mirage algorithm, and mutating each individual with a probability p. The formula is as follows:

[0033]

[0034] Among them, N(0,σ) represents Gaussian random noise.

[0035] A digital twin-driven method for extending the lifespan of a lithium battery during the aging process according to the present invention includes the following steps:

[0036] S1. The sensor detection module collects data on pressure, temperature, gas composition, voltage between the conductive layer and the anode, and the SEI film condition;

[0037] S2. Input the collected data into the AI control module, establish a joint solver for a pseudo-two-dimensional electrochemical model, a heat conduction equation, and a dendritic growth phase field model through COMSOL Multiphysics, and realize parameter coupling between multiple physical fields of electrochemistry-thermodynamics-mechanics through the Jacobian matrix;

[0038] S3. Use a domain adversarial neural network to extract the common characteristics of battery degradation at different charge and discharge rates and temperatures, align the source domain and target domain feature distributions through the maximum mean discrepancy, construct a battery life prediction and regulation function based on the data collected by the sensor detection module, calculate the optimal solution for life prediction and regulation using the mirage algorithm, and send it to the regulation module;

[0039] S4. The regulation module adjusts the parameters in the energy storage cabin according to the instructions sent by the AI control module, maintains the state of the SEI film and controls the precipitation of lithium dendrites. After the regulation is completed, the sensor detection module continues to monitor the battery state to form a closed-loop feedback.

[0040] Beneficial effects: Compared with the prior art, the present invention has the following remarkable advantages: 1. By introducing the digital twin technology, real-time fusion of multi-physical field coupling modeling data of electrochemistry, thermodynamics, and structural mechanics is carried out to identify the changes in the internal microstructure of the battery, realizing dynamic visualization monitoring of the aging process, and using the mirage algorithm for calculation to obtain the best parameters for equipment regulation, thus prolonging the life of the lithium battery; 2. By extending the service cycle and optimizing the charge and discharge management, combined with the battery performance tracking function of the digital twin platform, the risk of heavy metal pollution is effectively alleviated, contributing to the realization of the carbon neutral goal, and the comprehensive economic and environmental benefits are remarkable; 3. It can not only accurately predict the degradation trend of the battery, but also dynamically adjust the usage strategy according to different working conditions, significantly improving the overall operation efficiency and reliability of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a schematic structural framework diagram of the present invention;

[0042] Figure 2 It is a schematic flow diagram of the mirage algorithm of the present invention;

[0043] Figure 3 It is a schematic working process diagram of the present invention;

[0044] Figure 4 It is a schematic system flow diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings.

[0046] A digital twin-driven system for prolonging the life of a lithium battery during the aging process, such asFigure 1 As shown in the figure, it includes:

[0047] A sensor detection module, including a micro sensor and a conductive layer voltage sensor. The micro sensor is used to collect data on pressure, temperature, gas composition, and SEI film conditions; the conductive layer voltage sensor is used to monitor the voltage between the conductive layer and the anode. When the conductive layer is penetrated by dendrites, the voltage drops suddenly, and an alarm is triggered through the conductive layer voltage sensor.

[0048] An AI control module, which is used to perform multi - physical field coupling modeling of electrochemistry - thermodynamics - mechanics using digital twin technology. The coupling modeling establishes a joint solver for a pseudo - two - dimensional electrochemical model, a heat conduction equation, and a dendrite growth phase - field model through COMSOL Multiphysics, and realizes field - to - field parameter coupling through the Jacobian matrix; combined with cross - operating condition model migration for data analysis and processing. The cross - operating condition model migration uses a domain - adversarial neural network to extract the common characteristics of battery degradation at different charge - discharge rates and temperatures, and aligns the source domain and target domain feature distributions through the maximum mean discrepancy. Combining the data collected by the sensor detection module, a battery life prediction and regulation function is constructed, and the mirage algorithm is used to calculate the optimal battery life prediction and regulation scheme.

[0049] A regulation module, which is used to regulate the parameters in the energy storage cabin according to the calculation results of the AI control module, maintain the SEI film state and control the precipitation of lithium dendrites, and realize the optimization of the charging regulation strategy.

[0050] As Figures 3 - 4 shown, a method for extending the life of a lithium - ion battery driven by digital twin during the aging process includes the following steps:

[0051] The micro sensor collects data on pressure, temperature, and gas composition to detect the SEI film conditions; a porous conductive layer is embedded inside the battery, and the voltage between the conductive layer and the anode is monitored through the conductive layer voltage sensor. When dendrites penetrate the conductive layer, the sudden voltage drop triggers an alarm.

[0052] The data is input into the AI control module, and the AI control module uses digital twin technology to perform multi - physical field coupling modeling of electrochemistry - thermodynamics - mechanics, specifically including:

[0053] Establish a joint solver for a pseudo - two - dimensional electrochemical model, a heat conduction equation, and a dendrite growth phase - field model through COMSOL Multiphysics, and realize field - to - field parameter coupling through the Jacobian matrix.

[0054] Combined with cross-operating condition model migration for data analysis and processing, the cross-operating condition model migration uses a domain adversarial neural network to extract the common characteristics of battery degradation at different charge and discharge rates and temperatures, aligns the feature distributions of the source domain and the target domain through the maximum mean discrepancy, constructs a battery life prediction and regulation function based on the data collected by the sensor detection module, and calculates the optimal solution for life prediction and regulation using the mirage algorithm;

[0055] The battery life prediction and regulation function is as follows:

[0056]

[0057] E(t) = ω1T(t) + ω2P(t) + ω3G(t) + ω4|V cell (t) - V nom |;

[0058]

[0059] Constraints:

[0060] Among them, L(t) represents the remaining battery life, η cap (t) represents the capacity attenuation correction factor, η sei (t) represents the SEI film health, η dendnite (t) represents the lithium dendrite inhibition factor, e -αE(t) represents the environmental stress attenuation term, α represents the environmental stress attenuation coefficient, C achual (t) represents the actual capacity, C0 represents the initial capacity, ΔT(τ) represents the temperature fluctuation intensity, β represents the capacity attenuation rate coefficient, V threshold critical voltage, V sei represents the SEI film voltage, γ represents the SEI film failure risk coefficient, J dendnite represents the dendrite growth rate, J cnt represents the critical threshold, δ represents the dendrite inhibition coefficient, T(t) represents the temperature, P(t) represents the pressure, G(t) represents the gas component concentration, ω1, ω2, ω3 represent the weight coefficients, C(t) represents the actual capacity, C0 represents the initial capacity, T opt represents the optimal operating temperature, V crit represents the SEI film rupture threshold voltage, λ represents the temperature sensitivity coefficient, u(t) represents the dynamic regulation input signal, I(t) represents the real-time charge and discharge current, I max represents the maximum allowable current, V system (t) represents the system terminal voltage, V max represents the upper safety voltage limit, I opt represents the optimal operating current, V nom represents the nominal voltage, V cell(t) represents the real-time monomer voltage, I charge (t) represents the real-time charging current.

[0061] When calculating the most accurate value of life prediction, the maximum value of life extension, and each parameter value in the function using the multi-objective optimization algorithm, the mirage algorithm is selected for calculation. As Figure 2 shown, the mirage algorithm is as follows:

[0062] (1) In the initialization stage, based on the data collected by the sensors, candidate solutions are generated by random initialization to form an initial population. A range threshold and a maximum number of iterations are set. Each candidate solution corresponds to a set of control parameters; the control parameters include the remaining battery life L(t), the capacity attenuation correction factor η cap (t), the SEI film health η cap (t), the lithium dendrite inhibition factor η dendnite (t), the environmental stress attenuation term η dendnite (t), and the dynamic control input signal u(t);

[0063] X i,j = X min,j + rand(0,1)×(X max,j - X min,j );

[0064] Among them, X i,j represents the j-th dimension position of the i-th individual, X max,j , X min,j represent the maximum and minimum boundaries of the j-th dimension, and rand(0,1) represents a random number;

[0065] (2) In the global exploration stage, simulating the refraction path of light in different meteorological conditions in a mirage, the position update formula of the individual is derived according to the refraction principle to achieve global exploration and avoid falling into a local optimal solution. Each individual corresponds to the current control parameters, and its position update formula is:

[0066]

[0067] Among them, represents the position of the i-th individual in the t-th generation, represents the position of the optimal individual in the t-th generation population, α represents the step size factor, and f represents the fitness function.

[0068] (3) In the local development stage, a fine search is conducted in the neighborhood of the current solution to improve the quality of the solution. If the current individual is not the optimal individual in the population, its position update will refer to the information of better individuals in the population and move closer to the better region. If the current individual is the optimal individual in the population, a local perturbation will be performed within a small range to further optimize its own position in order to find a more accurate optimal solution. The formulas for the current individual not being the optimal individual and being the optimal individual in the population are as follows:

[0069]

[0070] Among them, represents the mean of the positions of the individuals in the t-th generation population, and β and γ represent control parameters.

[0071] (4) In the iterative update stage, in each iteration, after updating the individual positions according to the global exploration stage and the local development stage, calculate the fitness value of each individual and update the optimal solution of the population. If the numerical value of the objective function reaches a certain range threshold or the maximum number of iterations is reached, the iterative update will be terminated and the final result will be output to determine the control parameters.

[0072] (5) By introducing Gaussian mutation as a strategy of the mirage algorithm to enhance the global search ability, each individual is mutated with a probability p, and the formula is as follows:

[0073]

[0074] Among them, N(0,σ) represents Gaussian random noise.

Claims

1. A digital twin-driven lifespan extension system for the aging process of a lithium battery, characterized in that, It includes: A sensor detection module, including a micro-sensor and a conductive layer voltage sensor. The micro-sensor is used to collect data on pressure, temperature, gas composition, and SEI film conditions; The conductive layer voltage sensor is used to monitor the voltage between the conductive layer and the anode. When the conductive layer is penetrated by dendrites, the voltage drops suddenly, and an alarm is given through the conductive layer voltage sensor; An AI control module, which is used to perform multi-physics field coupling modeling of electrochemistry-thermodynamics-mechanics by using digital twin technology. The coupling modeling establishes a joint solver for a pseudo-two-dimensional electrochemical model, a heat conduction equation, and a dendrite growth phase field model through COMSOL Multiphysics, and realizes field parameter coupling through a Jacobian matrix; combines cross-condition model migration for data analysis and processing. The cross-condition model migration uses a domain adversarial neural network to extract the common characteristics of battery degradation at different charge and discharge rates and temperatures, and aligns the source domain and target domain feature distributions through the maximum mean discrepancy. Combines the data collected by the sensor detection module to construct a battery life prediction and regulation function, and calculates the optimal solution for battery life prediction and regulation by using the mirage algorithm; A regulation module, which is used to regulate the parameters in the energy storage cabin according to the calculation results of the AI control module, maintain the state of the SEI film and control the precipitation of lithium dendrites, and realize the optimization of the charging regulation strategy.

2. The extended lifespan system according to claim 1, wherein The calculation parameters of the mirage algorithm include a battery capacity attenuation calculation model, an SEI film state regulation model, a lithium dendrite inhibition regulation strategy, and a life extension regulation function.

3. The extended lifespan system according to claim 1, wherein The battery life prediction and regulation function is as follows: E(t) = ω1T(t) + ω2P(t) + ω3G(t) + ω4|V cell (t) - V nom |; Constraints: Among them, L(t) represents the remaining battery life, and η cap (t) represents the capacity attenuation correction factor, and η sei (t) represents the SEI film health, and η dendnite (t) represents the lithium dendrite inhibition factor, and e -αE(t) represents the environmental stress attenuation term, α represents the environmental stress attenuation coefficient, and C achual (t) represents the actual capacity, C0 represents the initial capacity, ΔT(τ) represents the temperature fluctuation intensity, β represents the capacity attenuation rate coefficient, and V threshold critical voltage, and V sei represents the SEI film voltage, γ represents the SEI film failure risk coefficient, and J dendnite represents the dendrite growth rate, and J cnt represents the critical threshold, δ represents the dendrite inhibition coefficient, T(t) represents the temperature, P(t) represents the pressure, G(t) represents the gas component concentration, ω1, ω2, ω3 represent the weight coefficients, C(t) represents the actual capacity, C0 represents the initial capacity, and T opt represents the optimal operating temperature, and V crit represents the SEI film rupture threshold voltage, λ represents the temperature sensitivity coefficient, u(t) represents the dynamic regulation input signal, I(t) represents the real-time charge and discharge current, and I max represents the maximum allowable current, and V system (t) represents the system terminal voltage, and V max represents the upper limit of the safety voltage, and I opt represents the optimal operating current, and V nom represents the nominal voltage, and V cell (t) represents the real-time single-cell voltage, and I charge (t) represents the real-time charging current.

4. The extended lifespan system according to claim 3, wherein The regulation module includes a temperature control component, a pressure regulating valve, a lithium salt replenishing device, and an MCU microcontroller. The temperature control component is used to regulate the temperature range, the pressure regulating valve is used to achieve dynamic balance of the pressure in the energy storage bin, the lithium salt replenishing device is used to replenish the electrolyte through ultrasonic atomization, and the MCU microcontroller is used to execute the firmware program, process sensor data, and trigger control instructions to realize the optimization of the charging regulation strategy.

5. The extended lifespan system according to claim 1, characterized in that, The mirage algorithm is as follows: (1) In the initialization stage, based on the data collected by the sensor detection module, candidate solutions are generated in a random initialization manner to form an initial population. A range threshold and a maximum number of iterations are set, and each candidate solution corresponds to a set of regulation parameters; The regulation parameters include the remaining battery life, a capacity attenuation correction factor, the health of the SEI film, a lithium dendrite inhibition factor, an environmental stress attenuation term, and a dynamic regulation input signal; (2) In the global exploration stage, simulate the refraction path of light in different meteorological conditions in a mirage, and deduce the updated position of an individual according to the refraction principle. Each individual corresponds to the current regulation parameters; (3) In the local development stage, search the neighborhood of the current solution. If the current individual is not the optimal individual in the population, its position update will refer to the information of a better individual in the population and move closer to the better area. If the current individual is the optimal individual in the population, a local perturbation will be performed within a small range to further optimize its own position and find the optimal solution; (4) Iterative update stage. In each iteration, after updating the individual positions according to the global exploration stage and the local development stage, calculate the fitness value of each individual, and update the optimal solution of the population. If the target function value reaches a certain range threshold or the maximum number of iterations is reached, terminate the iterative update, output the final result, and determine the control parameters.

6. The extended lifespan system according to claim 5, characterized in that, The initialization formula described in step 1 is as follows: X i,j = X min,j + rand(0,1) × (X max,j - X min,j ); Among them, X i,j represents the j-th dimensional position of the i-th individual, X max,j , X min,j represent the maximum and minimum boundaries of the j-th dimension, and rand(0,1) represents a random number; The position update formula described in step 2 is as follows: Among them, represents the position of the i-th individual in the t-th generation, represents the position of the optimal individual in the t-th generation population, α represents the step size factor, and f represents the fitness function; The formula for finding the optimal solution described in step 3 is as follows: Among them, represents the mean value of the individual positions of the t-th generation population, and β and γ represent control parameters.

7. The extended lifespan system according to claim 1, characterized in that, It also includes enhancing the global search ability by introducing Gaussian mutation as a strategy of the mirage algorithm. Each individual is mutated with probability p, and the formula is as follows: Among them, N(0,σ) represents Gaussian random noise.

8. A method for extending the lifespan of a lithium battery during the aging process driven by digital twin, characterized in that, It includes the following steps: S1. The sensor detection module collects data on pressure, temperature, gas composition, voltage between the conductive layer and the anode, and the SEI film condition. S2. Input the collected data into the AI control module. Establish a joint solver for the pseudo-two-dimensional electrochemical model, heat conduction equation, and dendrite growth phase field model through COMSOL Multiphysics, and realize parameter coupling between electrochemistry-thermodynamics-mechanics multi-physical fields through the Jacobian matrix. S3. Use the domain adversarial neural network to extract the common characteristics of battery degradation at different charge and discharge rates and temperatures, and align the source domain and target domain feature distributions through the maximum mean discrepancy. Construct a battery life prediction and regulation function based on the data collected by the sensor detection module, calculate the optimal life prediction and regulation scheme using the mirage algorithm, and send it to the regulation module. S4. The regulation module adjusts the parameters in the energy storage cabin according to the instructions sent by the AI control module, maintains the SEI film state and controls the precipitation of lithium dendrites. After the regulation is completed, the sensor detection module continues to monitor the battery state to form a closed-loop feedback.

9. An electronic device, comprising: Processor; And a memory arranged to store computer-executable instructions, the executable instructions, when executed, cause the processor to execute the method according to claim 8.

10. A computer-readable storage medium, the computer-readable storage medium stores one or more programs, and when the one or more programs are executed by an electronic device including a plurality of application programs, the electronic device executes the method according to claim 8.

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