Energy storage charging strategy implementation method and system based on super-capacity coupling lithium battery

Abstract

The invention relates to the technical field of charging regulation and control, and discloses an energy storage charging strategy implementation method and system based on a super-capacity coupling lithium battery, and the method comprises the steps: collecting current and temperature data, and carrying out the preliminary analysis; establishing a temperature prediction model, and optimizing parameters of the temperature prediction model; calculating an accumulation item parameter of the temperature according to an accumulation effect of the temperature; and performing temperature monitoring and adjustment according to a prediction result, and dynamically adjusting a charging strategy. According to the method, a temperature prediction model is constructed, a nonlinear term and an integral term are utilized, so that the model can more accurately describe the trend that the temperature changes along with current, the battery temperature is monitored in real time according to a prediction result, a charging strategy is dynamically adjusted, and a charging method is optimized. When the temperature is close to the upper limit, overheating is avoided by reducing the charging current, so that the battery is protected, and the service life of the battery is prolonged.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage charging strategy control, and in particular to a method and system for realizing an energy storage charging strategy based on an ultra-capacitor coupled lithium battery. Background Art

[0002] For thermal power units, long-term frequency regulation will lead to increased coal consumption, reduced reliability, and shortened operating life. On the other hand, high-quality and efficient frequency regulation power sources are scarce. At present, coal-fired thermal power units are still the main frequency regulation power source. In addition, the demand for large-scale new energy grid connection, environmental protection pressure restricts the regulation capacity of units, and the problem of "heat-based electricity" for heating units has further increased the demand for power frequency regulation. However, the performance of auxiliary frequency regulation and energy storage equipment currently supporting thermal power plants cannot meet the efficiency and reliability requirements of the units themselves, affecting the acquisition of grid subsidy income and low economic benefits.

[0003] In response to these problems, the prior art proposes an energy storage system with a supercapacitor coupled lithium battery. The generator set is a rotating large-capacity active and reactive source, while the energy storage system can be regarded as a relatively small-capacity static power generator. The main difference between the two lies in their output range and response characteristics. The former has a large output range but a slow response speed, while the latter has a relatively small capacity but a fast response speed. The coordinated operation between the two can significantly improve the execution effect of the thermal power unit on the AGC instructions of the power grid. By utilizing the ability of the energy storage system to quickly adjust the output power, the thermal power unit is used as the basic unit to respond to the AGC instructions, and the energy storage system is used as a supplementary fast response unit. The unit output and the energy storage system output are combined and sent to the power grid as the total system output, which can achieve the purpose of improving the unit AGC performance. Single lithium battery energy storage or single supercapacitor energy storage.

[0004] It is impossible to simultaneously meet the high power density required by the primary frequency regulation of thermal power and the high energy density required by the secondary frequency regulation. The hybrid energy storage technology that combines the two can achieve complementary advantages and improve system performance. In the control strategy of the hybrid energy storage system, the method of calling supercapacitors first is adopted. For small instructions, the supercapacitor responds completely; for large instructions, the supercapacitor responds at full power, and the lithium battery is used as a supplement. In this way, the depth of charge and discharge of the lithium battery can be made shallower, which is more conducive to extending the life.

[0005] However, the charging time and temperature of lithium batteries are two conflicting goals. Shortening the charging time can reduce the response time of the energy storage system, but it requires increasing the charging current, which will also cause the battery temperature to rise. Excessive temperature rise will cause battery safety issues, while excessive energy loss will reduce the efficiency of power utilization and increase costs. Summary of the invention

[0006] In view of the above-mentioned problems, the present invention is proposed.

[0007] Therefore, the technical problem solved by the present invention is: how to shorten the charging time while ensuring the temperature is as low as possible.

[0008] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for implementing an energy storage charging strategy based on an ultra-capacitor coupled lithium battery, comprising: Collect current and temperature data and conduct preliminary analysis; Establish a temperature prediction model and optimize the parameters of the temperature prediction model; Obtain the cumulative term parameters of temperature according to the cumulative effect of temperature; Monitor and adjust the temperature based on the prediction results, and dynamically adjust the charging strategy.

[0009] As a preferred solution of the energy storage charging strategy implementation method based on the supercapacitor coupled lithium battery described in the present invention, the preliminary analysis includes: assuming that the lithium battery needs to be charged, the size of the charging current is As charging progresses, the temperature naturally rises. The relationship between current and temperature is expressed as follows: , in, Indicates the temperature of the lithium-ion battery surface; Indicates battery quality; Indicates the thermal capacity of the battery; It indicates the temperature rise coefficient of the battery open circuit voltage. Experiments have shown that the temperature rise coefficient is almost unaffected by temperature. Indicates current; Indicates internal resistance; , represents the voltage coefficient; represents the heat transfer coefficient; represents a constant to be determined; Indicates operating temperature; The prediction formula for optimizing the relationship between current and temperature is expressed as: , in, represents a constant to be determined; represents the integral index; Indicates the amount of current that changes with time.

[0010] As a preferred solution of the method for implementing the energy storage charging strategy based on the ultra-capacitor coupled lithium battery described in the present invention, wherein: the temperature prediction model parameters include: confirming and According to the existing data of lithium battery detection current and temperature, select the current value and temperature value corresponding to each other. Group data; the temperature prediction model formula is expressed as: , Randomly generate 200 groups , each group Bundle The group current is input into the formula; we get The calculated temperature value corresponding to the group; the root mean square prediction error RMSE between the calculated temperature value and the actual temperature value is expressed as: , in, represents the sample size, and Respectively indicate time The actual and predicted values ​​of ; find The group [AB C] corresponds to the group with the smallest RMSE. Assume that the jth group The value of ], the corresponding RMSE is the smallest, then select [ ],right[ ] for further parameter optimization.

[0011] As a preferred solution of the method for implementing the energy storage charging strategy based on the ultra-capacitor coupled lithium battery described in the present invention, wherein: the optimization operation includes: ] parameters for further optimization; , Among them, Sigmoid() and tanh() represent activation functions; , in, Indicates the number of cycles, initial ; After the optimization is completed, use the new [ ] Determine whether RMSE is reduced by 30%. If not, perform the second step of optimization, define RMSE as the loss function, and perform optimization iteration until the parameter RMSE is reduced to the required threshold.

[0012] As a preferred solution of the energy storage charging strategy implementation method based on the ultra-capacitor coupled lithium battery described in the present invention, wherein: the cumulative parameter for obtaining the temperature includes, D and The source is the temperature accumulation item caused by the cumulative effect of temperature. During the charging process, the current changes from Evenly rise to , the temperature changes from ;arrive ;Record 100 points where the current and temperature correspond one to one in this process, expressed as ; The time collection interval is ; Using optimization to complete [ ]Bundle , , , ..., , substitute into the temperature prediction model formula In this way, the corresponding , , , ..., ; Actual value , , , ..., and , , , ..., The difference is expressed as , then the difference is caused by the cumulative effect of temperature.

[0013] As a preferred solution of the method for implementing the energy storage charging strategy based on the ultra-capacitor coupled lithium battery of the present invention, wherein: the temperature monitoring and adjustment according to the prediction results includes: , , Where b represents the rate of increase of current. Using the cumulative effect formula, we can solve a set of D and The relationship formula is expressed as: , exist Find the minimum value in ,set up Corresponding , according to the minimum value calculation formula: , Can be solved ,make , solve all ;right Find the mean, which is the final Mean.

[0014] As a preferred solution of the energy storage charging strategy implementation method based on the super-capacitor coupled lithium battery described in the present invention, the dynamic adjustment charging strategy includes: assuming that charging starts now, the change of the charging current with time is I=bt, and the upper limit of the battery temperature is Tmax; according to the current size and temperature relationship prediction formula, the formula is deduced as follows: , Analyze when the temperature reaches Tmax, and calculate it according to this formula When the time reaches Tmax, The charging current is automatically reduced at all times. The formula for confirming δt is expressed as: , At this time, the charging current is , becomes ,in, Represents a coefficient that controls the current decay.

[0015] A system for implementing an energy storage charging strategy based on a supercapacitor coupled lithium battery using any method described in the present invention, comprising: The data analysis module collects the current and temperature data of the lithium battery during the charging process, analyzes the relationship between the current and temperature during the charging process, and determines the preliminary parameter range for the temperature prediction model; Prediction model module, which builds a temperature prediction model, fits the model using the collected data, and optimizes the parameters; The parameter obtaining module records the data of the charging process, calculates the temperature accumulation item, and obtains the accumulation effect parameter; The charging strategy adjustment module monitors the temperature of the lithium battery in real time to ensure that it does not exceed the set temperature limit. When the predicted temperature is about to be reached, the charging current is adjusted in advance.

[0016] A computer device comprises: a memory and a processor; the memory stores a computer program, comprising: the steps of implementing any one of the methods of the present invention when the processor executes the computer program.

[0017] A computer-readable storage medium stores a computer program, comprising: when the computer program is executed by a processor, the steps of implementing any one of the methods of the present invention are implemented.

[0018] The beneficial effects of the present invention are as follows: a temperature prediction model is constructed, and nonlinear terms and integral terms are used to enable the model to more accurately describe the trend of temperature changes with current. The battery temperature is monitored in real time according to the prediction results, and the charging strategy is dynamically adjusted to optimize the charging method. When the temperature approaches the upper limit, overheating is avoided by reducing the charging current, thereby protecting the battery and extending the battery life. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them: Figure 1 An overall flow chart of a method for implementing an energy storage charging strategy based on a supercapacitor coupled lithium battery provided in the first embodiment of the present invention; Figure 2 A schematic diagram of charging control in a method for implementing an energy storage charging strategy based on a supercapacitor coupled lithium battery provided in a second embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0021] Example 1, reference Figure 1 , is an embodiment of the present invention, and provides a method for implementing an energy storage charging strategy based on a supercapacitor coupled lithium battery, comprising: S1: Collect current and temperature data and perform preliminary analysis.

[0022] Furthermore, the current and temperature data are collected and analyzed initially. By establishing a formula for the relationship between current and temperature, basic data can be provided for subsequent optimization.

[0023] Furthermore, preliminary analysis can reveal the preliminary relationship between current and temperature and help identify the influencing factors. The importance of this step is that it provides direction for subsequent optimization and determines the parameters that need to be focused on.

[0024] Furthermore, the preliminary analysis includes that, assuming that the lithium battery needs to be charged, the magnitude of the charging current is As charging progresses, the temperature naturally rises. The relationship between current and temperature is expressed as follows: , in, Indicates the temperature of the lithium-ion battery surface; Indicates battery quality; Indicates the thermal capacity of the battery; It indicates the temperature rise coefficient of the battery open circuit voltage. Experiments have shown that the temperature rise coefficient is almost unaffected by temperature. Indicates current; Indicates internal resistance; , represents the voltage coefficient; represents the heat transfer coefficient; represents a constant to be determined; Indicates operating temperature.

[0025] Furthermore, the prediction formula for optimizing the relationship between current and temperature is expressed as: , in, represents a constant to be determined; represents the integral index; Indicates the amount of current that changes with time.

[0026] It should be noted that by collecting current and temperature data, the optimization model is used to accurately predict the temperature changes of lithium-ion batteries. The key steps in this process include preliminary data analysis and the application of optimized prediction formulas. Through scientific and reasonable parameter settings, the accuracy of temperature prediction can be significantly improved, thereby achieving safer and more efficient battery charging management.

[0027] S2: Establish a temperature prediction model and optimize the temperature prediction model parameters.

[0028] Furthermore, the temperature prediction model parameters include confirming ABCD and , based on the existing data of lithium battery detection current and temperature, 1000 sets of data with one-to-one correspondence between current value and temperature value are selected; the temperature prediction model formula is expressed as: , Furthermore, 200 groups [ABC] are randomly generated, and each group [ABC] inputs 1000 groups of current into the formula; 1000 groups of corresponding calculated temperature values ​​are obtained; the root mean square prediction error RMSE between the calculated temperature value and the actual temperature value is expressed as: , in, represents the sample size, and Respectively indicate time The actual value and predicted value of; find the group with the smallest RMSE among the 200 groups [AB C], assuming that the value of the jth group [ABC] is [ ], the corresponding RMSE is the smallest, then select [ ],right[ ] for further parameter optimization.

[0029] Furthermore, the optimization operation includes, ] parameters for further optimization.

[0030] , Among them, Sigmoid() and tanh() represent activation functions.

[0031] , in, Represents the number of cycles, initially k=1.

[0032] Furthermore, after the optimization is completed, the new [ ] Determine whether RMSE is reduced by 30%. If not, perform the second step of optimization, define RMSE as the loss function, and perform optimization iterations until the parameters meet the RMSE reduction to the required threshold.

[0033] Furthermore, by randomly generating the initial parameter group, calculating the RMSE to select the optimal parameter group, and gradually optimizing the parameters, the high-precision prediction of the lithium battery temperature was successfully achieved. Each step not only considers the nonlinear relationship between the parameters, but also uses activation functions and smooth optimization methods to ensure the stability and effectiveness of parameter adjustment. These innovative optimization steps make the temperature prediction model more accurate and reliable, and can provide accurate temperature prediction in practical applications, effectively prevent battery overheating, and improve battery safety and life.

[0034] It should be noted that by randomly generating initial parameters, the parameter space can be extensively searched to avoid falling into the local optimum. Calculating RMSE can evaluate the prediction accuracy of each set of parameters to ensure the selection of the optimal parameter set. Improving the diversity of model initialization increases the possibility of finding the global optimal solution.

[0035] S3: Obtain the cumulative term parameters of temperature based on the cumulative effect of temperature.

[0036] Furthermore, the cumulative parameters for obtaining temperature include D and The source is the temperature accumulation item caused by the cumulative effect of temperature. During the charging process, the current changes from Evenly rise to , the temperature changes from ;arrive ;Record 100 points where the current and temperature correspond one to one in this process, expressed as .

[0037] Furthermore, the time collection interval is ; Using optimization to complete [ ]Bundle , , , ..., , substitute into the temperature prediction model formula In this way, the corresponding , , , ..., ; Actual value , , , ..., and , , , ..., The difference is expressed as , then the difference is caused by the cumulative effect of temperature.

[0038] It should be noted that the problem of temperature control during lithium battery charging is solved through accurate temperature prediction and dynamic adjustment of charging strategy. The combination of the cumulative effect formula and the dynamic adjustment strategy makes the charging process safer and more efficient. Traditional charging methods are difficult to monitor temperature changes in real time, which can easily lead to battery overheating. The present invention provides an accurate temperature control method by obtaining temperature cumulative term parameters and predicting temperature changes in real time, which significantly reduces the risk of overheating and improves charging efficiency.

[0039] S4: Monitor and adjust the temperature according to the prediction results, and dynamically adjust the charging strategy.

[0040] Furthermore, the temperature monitoring and adjustment based on the prediction results includes obtaining the temperature accumulation parameter and establishing a relationship formula between the temperature accumulation parameter and the current, so as to more accurately describe the temperature change during the charging process. The formula is expressed as follows: , , Where b represents the rate of increase of current. Using the cumulative effect formula, we can solve a set of D and The relationship formula is expressed as: , Furthermore, in Find the minimum value in ,set up Corresponding , according to the minimum value calculation formula: , Furthermore, we can solve ,make , solve all ;right Find the mean, which is the final Mean.

[0041] Furthermore, the dynamic adjustment of the charging strategy includes: assuming that charging starts now, the change of the charging current over time is I=bt, and the upper limit of the battery temperature is Tmax; the prediction formula based on the relationship between current and temperature is derived, and the formula is expressed as: , Furthermore, we analyze when the temperature reaches Tmax. If the temperature reaches Tmax according to this formula, then the charging current is automatically reduced at tmax-δt. The formula for determining δt is: , Furthermore, the charging current is , becomes ,in, Represents a coefficient that controls the current decay.

[0042] It should be noted that the existing technology usually adopts fixed current or simple temperature monitoring means, which is difficult to cope with the complex temperature changes during the charging process. The present invention uses the cumulative effect and dynamic adjustment strategy to provide a more intelligent and accurate temperature control method to ensure the safety and efficiency of the charging process.

[0043] On the other hand, this embodiment also provides an energy storage charging strategy implementation system based on an ultra-capacitor coupled lithium battery, which includes: The data analysis module collects the current and temperature data of the lithium battery during the charging process, analyzes the relationship between the current and temperature during the charging process, and determines the preliminary parameter range for the temperature prediction model.

[0044] The prediction model module builds a temperature prediction model, fits the model using the collected data, and performs parameter optimization.

[0045] The parameter obtaining module records the point data generated during the charging process, calculates the temperature accumulation item, and obtains the accumulation effect parameters.

[0046] The charging strategy adjustment module monitors the temperature of the lithium battery in real time to ensure that it does not exceed the set temperature limit. When the predicted temperature is about to be reached, the charging current is adjusted in advance.

[0047] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

[0048] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0049] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0050] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0051] Example 2, reference Figure 2 , which is an embodiment of the present invention, provides a method for implementing an energy storage charging strategy based on a super-capacitor coupled lithium battery. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0052] Test preparation and implementation process. This test aims to verify the collection, analysis, model establishment and optimization of current and temperature data, obtain the temperature accumulation effect parameters, and monitor and adjust the temperature according to the predicted results. The temperature and current control curve is as follows Figure 2 As shown, the charging strategy can be effectively adjusted dynamically.

[0053] The test used a certain brand of lithium-ion battery module, the charging device was an intelligent charger, the collection device was a high-precision data collector, and the test object was a lithium-ion battery module.

[0054] Equipment: Intelligent charger: set different charging current rates, high-precision data logger: used to record current and temperature data. Temperature sensor: attached to the surface of the battery, real-time temperature monitoring.

[0055] Test environment: The room temperature is controlled at 25°C and maintained constant temperature.

[0056] Data Collection and Preliminary Analysis,At the beginning of the experiment, the smart charger charged at the preset charging current rate,bbb, and the data logger recorded the charging current and surface temperature of the battery every 30 seconds,,recording a total of 1000 sets of data points.

[0057] Table 1 Charging effect comparison table , It can be seen from Table 1 that the present invention has significant improvements in charging speed and temperature control compared with the prior art solutions.

[0058] Table 2 Comparison of some experimental data , By analyzing the table data, it can be seen that the present invention has significant advantages in temperature prediction and control during battery charging. The difference ΔT between the actual temperature and the predicted temperature mostly fluctuates within the range of ±0.1°C, indicating that the temperature prediction model has a high accuracy.

[0059] The temperature prediction model established using the collected current and temperature data can accurately predict the temperature changes on the battery surface with minimal error, proving the reliability and practicality of the model. The optimized parameter D enables an accurate description of the temperature accumulation effect, making temperature control more precise and avoiding battery damage caused by a sharp rise in temperature. Based on the temperature prediction results, the charging current can be adjusted in time to prevent the battery temperature from exceeding the safety upper limit Tmax. This dynamic adjustment charging strategy effectively improves the safety and efficiency of the charging process.

[0060] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for implementing an energy storage charging strategy based on a supercapacitor coupled lithium battery, characterized in that: include: Collect current and temperature data and conduct preliminary analysis; Establish a temperature prediction model and optimize the parameters of the temperature prediction model; Obtain the cumulative term parameters of temperature according to the cumulative effect of temperature; Monitor and adjust the temperature based on the prediction results, and dynamically adjust the charging strategy.

2. The method for implementing the energy storage charging strategy based on the supercapacitor coupled lithium battery according to claim 1, characterized in that: The preliminary analysis includes: assuming that the lithium battery needs to be charged, the magnitude of the charging current is As charging progresses, the temperature naturally rises. The relationship between current and temperature is expressed as follows: ， in, Indicates the temperature of the lithium-ion battery surface; Indicates battery quality; Indicates the thermal capacity of the battery; It indicates the temperature rise coefficient of the battery open circuit voltage. Experiments have shown that the temperature rise coefficient is almost unaffected by temperature. Indicates current; Indicates internal resistance; , represents the voltage coefficient; represents the heat transfer coefficient; represents a constant to be determined; Indicates operating temperature; The prediction formula for optimizing the relationship between current and temperature is expressed as: ， in, represents a constant to be determined; represents the integral index; Indicates the amount of current that changes with time.

3. The method for implementing the energy storage charging strategy based on the supercapacitor coupled lithium battery according to claim 2, characterized in that: The temperature prediction model parameters include confirming ABCD and According to the existing data of lithium battery detection current and temperature, select the current value and temperature value corresponding to each other. Group data; the temperature prediction model formula is expressed as: ， Random Generation Group , each group Bundle The group current is input into the formula; we get The calculated temperature value corresponding to the group; the root mean square prediction error RMSE between the calculated temperature value and the actual temperature value is expressed as: ， in, represents the sample size, and Respectively indicate time The actual and predicted values ​​of ; find Group The corresponding group is the one with the smallest RMSE. Assume that group j The value of ], the corresponding RMSE is the smallest, then select [ ],right[ ] for further parameter optimization.

4. The method for implementing the energy storage charging strategy based on the supercapacitor coupled lithium battery according to claim 3, characterized in that: The optimization operation includes: ] parameters for further optimization; ， Among them, Sigmoid() and tanh() represent activation functions; ， in, Indicates the number of cycles, initial ; After the optimization is completed, use the new [ ] Determine whether RMSE is reduced by 30%. If not, perform the second step of optimization, define RMSE as the loss function, and perform optimization iterations until the parameters meet the RMSE reduction to the required threshold.

5. The method for implementing the energy storage charging strategy based on the supercapacitor coupled lithium battery according to claim 4, characterized in that: The cumulative parameters for obtaining the temperature include D and The source is the temperature accumulation item caused by the cumulative effect of temperature. During the charging process, the current changes from Evenly rise to , the temperature changes from ; arrive ;Record 100 points where the current and temperature correspond one to one in this process, expressed as ; The time collection interval is ; Using the optimization completed ]Bundle , , , ..., , substitute into the temperature prediction model formula In this way, the corresponding , , , ..., ; Actual value , , , ..., and , , , ..., The difference is expressed as , then the difference is caused by the cumulative effect of temperature.

6. The method for implementing the energy storage charging strategy based on the supercapacitor coupled lithium battery according to claim 5, characterized in that: The temperature monitoring and adjustment according to the prediction result includes obtaining the temperature accumulation item parameter of the accumulation effect according to the difference formula generated by the accumulation effect of the temperature, and the difference formula is expressed as: ， ， Where b represents the rate of increase of current. Using the cumulative effect formula, we can solve a set of D and The relationship formula is expressed as: ， exist Find the minimum value in ,set up Corresponding , according to the minimum value calculation formula: ， Can be solved ,make , solve all ;right Find the mean, which is the final Mean.

7. The method for implementing the energy storage charging strategy based on the supercapacitor coupled lithium battery according to claim 6, characterized in that: The dynamic adjustment charging strategy includes: assuming that charging starts now, the change of charging current over time is I=bt, and the upper limit of battery temperature is Tmax; deducing according to the prediction formula of the relationship between current size and temperature, the formula is expressed as: ， Analyze when the temperature reaches , suppose we calculate it according to this formula Time to arrive , then in The charging current is automatically reduced at all times. The formula for determining δt is expressed as: ， At this time, the charging current is , becomes ,in, Represents a coefficient that controls the current decay.

8. A system for implementing energy storage charging strategy based on supercapacitor coupled lithium battery using the method according to any one of claims 1 to 7, characterized in that: The data analysis module collects the current and temperature data of the lithium battery during the charging process, analyzes the relationship between the current and temperature during the charging process, and determines the preliminary parameter range for the temperature prediction model; Prediction model module, which builds a temperature prediction model, fits the model using the collected data, and optimizes the parameters; The parameter obtaining module records the data of the charging process, calculates the temperature accumulation item, and obtains the accumulation effect parameter; The charging strategy adjustment module monitors the temperature of the lithium battery in real time to ensure that it does not exceed the set temperature limit. When the predicted temperature is about to be reached, the charging current is adjusted in advance.

9. A computer device comprising: A memory and a processor; the memory stores a computer program, characterized in that: when the processor executes the computer program, the steps of the method for implementing the energy storage charging strategy based on the super-capacitor coupled lithium battery as described in any one of claims 1-7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the steps of the method for implementing the energy storage charging strategy based on the super-capacitor coupled lithium battery as described in any one of claims 1 to 7 are implemented.

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