A method and system for optimizing sodium battery charging

Abstract

This invention relates to the field of electrochemical energy storage technology, and discloses a method and system for optimizing sodium battery charging. The method includes: based on a preset multi-stage charging strategy, real-time acquisition of voltage and current data of the sodium battery as raw operating parameters; obtaining real-time internal state parameters through dynamic parameter identification; mapping these parameters to a pre-set rule base and health state thresholds to generate strategy adjustment decisions, thereby adaptively adjusting the constant current value, constant voltage value, and transition conditions of the charging stage to form optimized charging instructions; driving the charging execution unit according to the instructions, acquiring the real-time charging status, and iteratively judging the internal parameters and termination conditions to finally output the charging completion status; this invention can improve the efficiency of sodium battery charging optimization.

Description

Technical Field

[0001] This invention relates to the field of electrochemical energy storage technology, and in particular to a method and system for optimizing the charging of sodium batteries. Background Technology

[0002] Sodium batteries, with their readily available raw materials and low cost, have become an important research direction in the field of electrochemical energy storage. The scientific nature of their charging control strategy directly affects the battery's charging efficiency, cycle life, and safety. Currently, sodium battery charging mostly adopts a multi-stage charging strategy with preset fixed parameters. This strategy lacks a linkage mechanism with the real-time operating status of the battery, and only executes charging operations according to fixed constant current values, constant voltage values, and stage transition conditions. It cannot dynamically adjust charging parameters according to the actual operating status of the battery, making it difficult to adapt to the charging needs of sodium batteries in different usage stages and under different operating conditions.

[0003] Current sodium battery charging technologies suffer from insufficient timeliness and accuracy in identifying internal battery state parameters. They often employ offline or quasi-online identification methods, failing to obtain core internal state parameters such as ohmic resistance and polarization voltage in real time using voltage and current data. Furthermore, they lack a corresponding decision-making system to provide precise basis for adjusting charging parameters, easily leading to a mismatch between the charging process and the battery's actual health state. This not only reduces the charging efficiency of sodium batteries but may also accelerate internal aging and shorten battery cycle life due to overcharging or undercharging. Simultaneously, existing technologies lack a comprehensive charging execution status monitoring and iteration termination judgment mechanism, with relatively simplistic determination of charging termination conditions, making it difficult to guarantee the stability and safety of the charging process. Summary of the Invention

[0004] This invention provides a method and system for optimizing sodium battery charging to solve the problems mentioned in the background section.

[0005] To achieve the above objectives, the present invention provides a sodium battery charging optimization method, comprising: S1. Based on a preset multi-stage charging strategy, the voltage and current data of the sodium battery are collected in real time to obtain the original operating parameters of the sodium battery; S2. Perform dynamic parameter identification on the original operating parameters to obtain the real-time internal state parameters of the sodium battery; S3. Based on a preset decision rule base, the real-time internal state parameters are mapped to preset health state thresholds to obtain the adjustment decision of the multi-stage charging strategy. S4. Based on the adjustment decision, the constant current value, constant voltage value and stage transition conditions of the multi-stage charging strategy are adaptively adjusted to obtain the optimized charging command for the sodium battery. S5. Based on the optimized charging command, drive the charging execution unit to obtain the charging execution state of the sodium battery; S6. Based on the charging execution state, the real-time internal state parameters and the charging termination condition are iteratively terminated to obtain the charging completion state of the sodium battery.

[0006] In a preferred embodiment, the step of acquiring voltage and current data of the sodium battery in real time based on a preset multi-stage charging strategy to obtain the original operating parameters of the sodium battery includes: Based on the voltage and current sensors connected to the sodium battery, the analog signals of the sodium battery are synchronously acquired to obtain the analog voltage signal and analog current signal of the sodium battery. The analog voltage signal and analog current signal are converted from analog to digital to obtain the synchronous digital voltage data and digital current data of the sodium battery; The digital voltage data and digital current data are time-series encapsulated to obtain the original operating parameters of the sodium battery.

[0007] In a preferred embodiment, the step of performing dynamic parameter identification on the original operating parameters to obtain the real-time internal state parameters of the sodium battery includes: Feature extraction is performed on the original operating parameters to obtain the sampling time terminal voltage data and load current data of the sodium battery; A parameter identification model based on recursive least squares method is constructed, and the terminal voltage data and the load current data are used as the input sequence of the parameter identification model. The input sequence is recursively calculated online to obtain the real-time model parameter vector of the parameter identification model; The real-time model parameter vector is analyzed by parameter mapping to obtain the real-time ohmic internal resistance and real-time polarization voltage of the sodium battery. The real-time internal resistance and the real-time polarization voltage are tensor-combined to obtain the real-time internal state parameters of the sodium battery.

[0008] In a preferred embodiment, the step of performing online recursive calculation on the input sequence to obtain the real-time model parameter vector of the parameter identification model includes: Initialize the covariance matrix and parameter vector required for the recursive calculation; Based on the terminal voltage data and load current data at the sampling time, the data vector of the parameter identification model at the sampling time is constructed; Based on the data vector, the covariance matrix and forgetting factor of the previous sampling time, calculate the gain vector of the current sampling time; The residual between the gain vector and the parameter vector at the previous sampling time is used to generate the current parameter vector estimation error of the parameter identification model. Based on the estimation error between the gain vector and the parameter vector, the parameter vector at the previous sampling time is recursively corrected to obtain the real-time model parameter vector of the parameter identification model.

[0009] In a preferred embodiment, the step of mapping the real-time internal state parameters to preset health state thresholds based on a pre-set decision rule base to obtain the adjustment decision of the multi-stage charging strategy includes: Based on the real-time internal state parameters, a parallel threshold comparison is performed on the preset health state threshold to obtain a comparison result set of the real-time internal state parameters and the preset health state threshold. Based on the comparison result set, rule matching is performed on the preset decision rule base to obtain the target adjustment rule of the multi-stage charging strategy. Based on the target adjustment rule, the current stage parameters of the multi-stage charging strategy are used to derive the adjustment decision of the multi-stage charging strategy.

[0010] In a preferred embodiment, the step of adaptively adjusting the constant current value, constant voltage value, and stage transition conditions of the multi-stage charging strategy based on the adjustment decision to obtain the optimized charging command for the sodium battery includes: The adjustment decision is analyzed in a structured manner to obtain the adjustment requirements of the multi-stage charging strategy; The constant current value of the multi-stage charging strategy is corrected to obtain the optimized constant current value of the multi-stage charging strategy. The constant voltage value in the multi-stage charging strategy is corrected to obtain the optimized constant voltage value of the multi-stage charging strategy. The stage transition conditions in the multi-stage charging strategy are reset to obtain the optimized stage transition conditions of the multi-stage charging strategy. The optimized constant current value, the optimized constant voltage value, and the optimized stage transition conditions are combined to generate the optimized charging command for the sodium battery.

[0011] In a preferred embodiment, the step of correcting the constant current value of the multi-stage charging strategy to obtain the optimized constant current value of the multi-stage charging strategy includes: The adjustment requirement is subjected to current characteristic separation to obtain the current adjustment flag and current adjustment range related to the charging current; The current adjustment flag is mapped and matched with a predefined current adjustment action library to obtain the specific adjustment operation type of the constant current value; Based on the adjustment operation type and the current adjustment range, the constant current value is adjusted in a directional manner to obtain the intermediate current value of the constant current value. The intermediate current value is compared with the preset absolute current safety boundary to determine if it exceeds the boundary, thereby obtaining the optimized constant current value of the multi-stage charging strategy.

[0012] In a preferred embodiment, the step of driving the charging execution unit with instructions based on the optimized charging command to obtain the charging execution state of the sodium battery includes: The optimized charging command is converted into a protocol to obtain the drive command for the charging execution unit; Based on the driving instructions of the charging execution unit, the charging execution unit is driven by instructions; The execution status of the driving instruction is verified to obtain an execution confirmation signal for the driving instruction; Based on the execution confirmation signal, the voltage and current response during the sodium battery charging process are monitored in real time to obtain the charging execution status of the sodium battery.

[0013] In a preferred embodiment, the step of iteratively determining the charging completion state of the sodium battery by comparing the real-time internal state parameters with the charging termination condition based on the charging execution state includes: The real-time internal state parameters are compared with the preset charging termination conditions one by one to obtain the preliminary termination determination result of the sodium battery. The consistency of the charging execution state with the preset charging process stability conditions is verified to obtain the process stability determination result of the sodium battery. The preliminary termination determination result and the process stability determination result are logically fused to obtain the comprehensive termination determination signal of the sodium battery. Based on the comprehensive termination determination signal, the charging process is updated to obtain the charging completion status of the sodium battery.

[0014] To address the above problems, the present invention also provides a sodium battery charging optimization system, the system comprising: The data acquisition module is used to acquire the voltage and current data of the sodium battery in real time based on a preset multi-stage charging strategy, so as to obtain the original operating parameters of the sodium battery. The online parameter identification module is used to perform dynamic parameter identification on the original operating parameters to obtain the real-time internal state parameters of the sodium battery. The decision generation module is used to perform rule mapping between the real-time internal state parameters and the preset health state threshold based on a preset decision rule library, so as to obtain the adjustment decision of the multi-stage charging strategy. The parameter adaptive module is used to adaptively adjust the constant current value, constant voltage value and stage transition conditions of the multi-stage charging strategy based on the adjustment decision, so as to obtain the optimized charging command of the sodium battery. The instruction driving module is used to drive the charging execution unit based on the optimized charging instruction to obtain the charging execution state of the sodium battery. The status output module is used to iteratively terminate the charging by comparing the real-time internal status parameters with the charging termination conditions based on the charging execution status, so as to obtain the charging completion status of the sodium battery.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. The sodium battery charging optimization method of the present invention forms original operating parameters by collecting battery voltage and current data in real time. It achieves dynamic parameter identification by relying on the parameter identification model constructed by recursive least squares method. It can accurately obtain the battery's real-time ohmic internal resistance, polarization voltage and other core internal state parameters. Combined with the pre-set decision rule library, it completes accurate rule mapping and adjustment decision generation. It can adaptively adjust the constant current value, constant voltage value and stage transition conditions of multi-stage charging strategy. Moreover, the current value correction link will combine the absolute current safety boundary to make over-limit judgment. It realizes the dynamic, accurate and safe optimization configuration of charging parameters, which greatly improves the efficiency of sodium battery charging optimization. At the same time, it makes the charging command highly compatible with the actual internal state of the battery, and improves the scientificity and rationality of charging parameter adjustment.

[0016] 2. The optimized method and system of this invention realize intelligent, closed-loop control of the entire process from battery operation data acquisition to charging completion status output. The instruction-driven link ensures the accurate response of the charging execution unit through protocol conversion and status verification, and can also monitor the battery charging execution status in real time. The charging termination judgment link integrates the preliminary termination judgment result and the process stability judgment result for logical analysis, realizing iterative and accurate determination of charging termination, improving the accuracy of charging completion status judgment, ensuring the stability of sodium battery charging process, and at the same time, the precise control and dynamic optimization of the entire process can make the charging process more consistent with the actual health state of the battery, effectively optimize the overall charging effect, improve the safety and reliability of the charging process, and help maintain the stability of the battery's internal state. Attached Figure Description

[0017] Figure 1 This is a schematic flowchart of a sodium battery charging optimization method according to an embodiment of the present invention; Figure 2This is a functional block diagram of a sodium battery charging optimization system provided in an embodiment of the present invention; The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0019] This application provides a sodium battery charging optimization method. The executing entity of this sodium battery charging optimization method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application embodiment: a server, a terminal, etc. In other words, the sodium battery charging optimization method can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0020] Reference Figure 1 The diagram shown is a flowchart illustrating a sodium battery charging optimization method according to an embodiment of the present invention. In this embodiment, the sodium battery charging optimization method includes: S1. Based on a preset multi-stage charging strategy, the voltage and current data of the sodium battery are collected in real time to obtain the original operating parameters of the sodium battery; In this embodiment of the invention, the real-time acquisition of voltage and current data of the sodium battery based on a preset multi-stage charging strategy to obtain the original operating parameters of the sodium battery includes: Based on the voltage and current sensors connected to the sodium battery, the analog signals of the sodium battery are synchronously acquired to obtain the analog voltage signal and analog current signal of the sodium battery. The analog voltage signal and analog current signal are converted from analog to digital to obtain the synchronous digital voltage data and digital current data of the sodium battery; The digital voltage data and digital current data are time-series encapsulated to obtain the original operating parameters of the sodium battery.

[0021] Connect the voltage sensor's detection terminal to the positive and negative terminals of the sodium battery respectively, ensuring a secure connection between the sensor terminals and the sodium battery electrode terminals, without any poor contact or looseness. Simultaneously, connect the current sensor in series into the main charging and discharging circuit of the sodium battery, ensuring that all current generated during charging and discharging can flow completely through the current sensor. After activating the preset multi-stage charging strategy, immediately send a synchronous acquisition command to the voltage and current sensors, controlling both sensors to start analog signal acquisition at the same time. The response time between sending the acquisition command and the sensor starting acquisition is controlled within 5ms, thereby achieving synchronous acquisition of analog signals. The voltage sensor captures the potential difference change between the positive and negative terminals of the sodium battery in real time, converting this potential difference change into a continuously changing analog voltage signal and outputting it in real time. The current sensor captures the magnitude and direction changes of the current in the main charging and discharging circuit in real time, converting this current change into a continuously changing analog current signal and outputting it in real time. Finally, the analog voltage and analog current signals of the sodium battery are obtained.

[0022] The analog voltage signal output from the voltage sensor and the analog current signal output from the current sensor are respectively connected to two independent signal input terminals of the same analog-to-digital converter (ADC) module through shielded transmission lines. The shielded transmission lines are used to prevent external electromagnetic interference from affecting the analog signals and to ensure that the analog signals are transmitted without distortion. After the ADC module is started, it first acquires and processes the analog voltage signal. The internal signal sampling unit captures the instantaneous amplitude of the analog voltage signal at a fixed acquisition frequency. Then, the internal signal quantization unit converts each captured instantaneous amplitude into a corresponding digital quantity, which is the digitized voltage data. At the same time, the ADC module acquires and processes the analog current signal at the same acquisition frequency. Similarly, the signal sampling unit captures the instantaneous amplitude of the analog current signal, and the signal quantization unit converts it into a corresponding digital quantity, which is the digitized current data. Since the two analog signals use the same ADC module and the acquisition frequency and processing sequence are completely consistent, the synchronous digitized voltage data and digitized current data of the sodium battery are finally obtained.

[0023] The synchronous digital voltage and digital current data output from the analog-to-digital conversion module are transmitted to the data processing unit for timing encapsulation. The data processing unit first records the acquisition time point corresponding to each set of synchronous digital voltage and digital current data. This acquisition time point is consistent with the time when the sensor acquires analog signals and performs analog-to-digital conversion processing. Then, the digital voltage and digital current data corresponding to the same acquisition time point are bound together to form a complete data set containing the acquisition time, digital voltage data, and digital current data. Subsequently, according to the order of the acquisition time points, all the bound complete data sets are arranged sequentially to form an ordered dataset. This ordered dataset is the original operating parameter of the sodium battery. During the timing encapsulation process, it is ensured that the acquisition time point of each set of data is not repeated, the data binding is not deviated, and the arrangement order is not disordered, so as to ensure that the original operating parameters can accurately reflect the real-time operating status of the sodium battery under the multi-stage charging strategy.

[0024] S2. Perform dynamic parameter identification on the original operating parameters to obtain the real-time internal state parameters of the sodium battery; In this embodiment of the invention, the step of performing dynamic parameter identification on the original operating parameters to obtain the real-time internal state parameters of the sodium battery includes: Feature extraction is performed on the original operating parameters to obtain the sampling time terminal voltage data and load current data of the sodium battery; A parameter identification model based on recursive least squares method is constructed, and the terminal voltage data and the load current data are used as the input sequence of the parameter identification model. The input sequence is recursively calculated online to obtain the real-time model parameter vector of the parameter identification model; The real-time model parameter vector is analyzed by parameter mapping to obtain the real-time ohmic internal resistance and real-time polarization voltage of the sodium battery. The real-time internal resistance and the real-time polarization voltage are tensor-combined to obtain the real-time internal state parameters of the sodium battery.

[0025] The step of performing online recursive calculation on the input sequence to obtain the real-time model parameter vector of the parameter identification model includes: Initialize the covariance matrix and parameter vector required for the recursive calculation; Based on the terminal voltage data and load current data at the sampling time, the data vector of the parameter identification model at the sampling time is constructed; Based on the data vector, the covariance matrix and forgetting factor of the previous sampling time, calculate the gain vector of the current sampling time; The residual between the gain vector and the parameter vector at the previous sampling time is used to generate the current parameter vector estimation error of the parameter identification model. Based on the estimation error between the gain vector and the parameter vector, the parameter vector at the previous sampling time is recursively corrected to obtain the real-time model parameter vector of the parameter identification model.

[0026] The collected raw operating parameters of the sodium battery are transmitted to the feature extraction unit. The feature extraction unit analyzes each set of raw operating parameters one by one, accurately extracting the corresponding digital voltage data from each set of data. This digital voltage data is directly determined as the sampling time terminal voltage data of the sodium battery at the corresponding sampling time. At the same time, the corresponding digital current data is accurately extracted from each set of data. The digital current data is defined according to the charging and discharging state of the sodium battery. When the sodium battery is in the discharging state, the digital current data is the positive load current data, and when the sodium battery is in the charging state, the digital current data is the negative load current data. During the extraction process, it is ensured that the sampling time terminal voltage data and load current data at each sampling time correspond one-to-one and are consistent with the acquisition time in the raw operating parameters, without data omission, misalignment, or misjudgment of charging and discharging state. Finally, the sampling time terminal voltage data and load current data of the sodium battery are obtained.

[0027] The hardware foundation for the parameter identification model is established by selecting a processor adapted for sodium battery parameter identification as the core of the model. The model's output and input terms are determined. The output term is the terminal voltage data collected at each sampling time. The input term includes the load current data collected at each sampling time and a polarization history term. The polarization history term is constructed using current and historical load current data. Specifically, it is constructed by selecting the load current data from the current sampling time and the previous 10 consecutive sampling times. A preset range of 10 consecutive times is defined as the historical data, determined based on the dynamic response period of the sodium battery polarization effect, which aligns with the actual operating characteristics of the sodium battery. These load current data are then arranged chronologically by sampling time and their arithmetic mean is taken. This average value is the current sampling value. The polarization history term at each time step is used to characterize the dynamic process of the polarization effect. Then, two components that need to be identified in real time in the model are determined. The first component directly corresponds to the real-time ohmic internal resistance of the sodium battery, and the second component is related to the time constant of the sodium battery circuit and is used to derive the real-time polarization voltage. At the same time, it is clarified that the modeling error in the model includes minor interference factors that were not considered in the modeling process and slight measurement deviations when collecting voltage and current. In this way, the parameter identification model based on the recursive least squares method is constructed. After the model is constructed, the terminal voltage data of all sampling time steps are used as the output sequence of the model, and the load current data of all sampling time steps and the corresponding constructed polarization history term are used as the input sequence of the model. This ensures that the sampling time of the input sequence and the output sequence correspond one-to-one, without misalignment or missing.

[0028] The parameter identification model is as follows: In the formula, represents the first... The terminal voltage data collected at each sampling time. Indicates the first Each sampling time The collected load current data shows positive values ​​for discharging and negative values ​​for charging. This represents a polarization history term constructed based on current and historical load current data, used to characterize the dynamic process of polarization effects. , That is, the model parameter vector to be identified in real time. The amount. Directly corresponds to the real-time ohmic internal resistance R Related to the circuit time constant, used to derive or calculate the real-time polarization voltage. . : Indicates unmodeled factors such as modeling errors and measurement noise.

[0029] The online recursive calculation function of the parameter identification model is activated. First, the initial parameter vector of the model is set. Both components of the initial parameter vector are selected from the standard values ​​calibrated at the factory of the sodium battery. The component corresponding to the real-time ohmic internal resistance is initially set to the standard ohmic internal resistance calibrated at the factory of the sodium battery, and the component related to the circuit time constant is initially set to the value corresponding to the standard circuit time constant calibrated at the factory of the sodium battery. Then, the input and output sequences at the first sampling time are selected and substituted into the constructed parameter identification model. The theoretical terminal voltage data at that sampling time is calculated through the model. The theoretical terminal voltage data is compared with the actual collected terminal voltage data at the sampling time, and the deviation value is calculated. When the deviation value is greater than 0.01V, the two components of the initial parameter vector are adjusted. The alignment process aims to bring the theoretical terminal voltage data closer to the actual terminal voltage data at the sampling time until the deviation is less than or equal to 0.01V. At this point, the parameter vector for the first sampling time is obtained. Subsequently, the input and output sequences for the second sampling time are selected, and the parameter vector obtained from the previous sampling time is used as the initial value. The above process of theoretical calculation, deviation comparison, and parameter adjustment is repeated until the parameter vector for the second sampling time is obtained. This process is repeated for all the input and output sequences at all sampling times. The parameter vector for each sampling time is adjusted based on the parameter vector of the previous time to ensure that the parameter vector of each sampling time can accurately match the input and output data of the current time. Finally, the real-time model parameter vector corresponding to all sampling times is obtained.

[0030] The real-time model parameter vector obtained from online recursive calculation at each sampling moment is transmitted to the parameter parsing unit. The parameter parsing unit decomposes the real-time model parameter vector at each sampling moment. This vector contains two components. The first component directly corresponds to the real-time ohmic internal resistance of the sodium battery and does not require additional calculation. The value of this component is directly used as the real-time ohmic internal resistance of the sodium battery at the current sampling moment, ensuring that this value is completely consistent with the value of the first component of the real-time model parameter vector. The second component is related to the time constant of the sodium battery circuit. After decomposing this component, the value of the second component is multiplied by the fixed structural parameters of the sodium battery circuit. The result is the real-time polarization voltage of the sodium battery at the current sampling moment. During the derivation process, the calculation steps are ensured to be clear, the numerical transmission is accurate, and there are no deviations. Finally, through the decomposition, mapping, and derivation of the real-time model parameter vector at each sampling moment, the real-time ohmic internal resistance and real-time polarization voltage of the sodium battery at each sampling moment are obtained. Both parameters correspond one-to-one with the real-time model parameter vector at the corresponding sampling moment, which can accurately reflect the current internal operating state of the sodium battery.

[0031] The real-time ohmic resistance and real-time polarization voltage obtained from parameter mapping analysis at each sampling time are transmitted to the data merging unit for tensor merging processing. First, the tensor dimensions of the two parameters are determined. The real-time ohmic resistance at each sampling time is set as a 1×1 dimension tensor, and the real-time polarization voltage at each sampling time is also set as a 1×1 dimension tensor. Then, according to the order of sampling times, the real-time ohmic resistance tensor and the real-time polarization voltage tensor at the same sampling time are horizontally concatenated, forming a 1×2 dimension two-dimensional tensor. This two-dimensional tensor contains the sodium at the current sampling time. The real-time ohmic internal resistance and real-time polarization voltage information of the battery are then combined with the two-dimensional tensor formed by splicing all the sampling times and arranged in the order of sampling time to form a three-dimensional tensor. This three-dimensional tensor completely contains the real-time ohmic internal resistance and real-time polarization voltage data of the sodium battery at all sampling times, and can comprehensively reflect the internal state of the sodium battery at different sampling times. This three-dimensional tensor is the real-time internal state parameter of the sodium battery. During the merging process, it is ensured that the splicing of the two parameter tensors at each sampling time is accurate, and the arrangement order of all two-dimensional tensors is consistent with the sampling time, without any errors, omissions or repetitions.

[0032] Before initiating the online recursive calculation, the initialization of the covariance matrix and parameter vector required for the recursive calculation is completed. The covariance matrix is ​​initialized using a 2×2 identity matrix, with all diagonal elements set to 1 and all off-diagonal elements set to 0. This initial value is chosen based on the dispersion characteristics of the sodium battery's parameters at the factory, ensuring the initial convergence speed of the recursive calculation. The parameter vector is initialized using the standard parameter values ​​calibrated at the sodium battery's factory. This parameter vector contains two components: the first component is initially set to the standard ohmic internal resistance value calibrated at the sodium battery's factory, and the second component is initially set to the corresponding value of the standard circuit time constant calibrated at the sodium battery's factory. During initialization, it is ensured that the dimensions and values ​​of the covariance matrix are set accurately, and that the two components of the parameter vector are completely consistent with the standard parameters of the sodium battery without deviation. After initialization, this covariance matrix is ​​used as the covariance matrix for the 0th sampling time, and this parameter vector is used as the parameter vector for the 0th sampling time, providing the basis for the subsequent recursive calculation at the first sampling time. Finally, the initialization of the covariance matrix and parameter vector required for the recursive calculation is completed.

[0033] For each sampling time, based on the extracted terminal voltage and load current data at that sampling time, a data vector for the parameter identification model at that sampling time is constructed. During the construction process, the dimension of the data vector is first determined to be 2×1, which matches the dimension of the parameter vector to ensure that subsequent calculations can proceed normally. Then, the load current data at that sampling time is selected as the first element of the data vector, and the polarization history term that has been constructed at that sampling time is selected as the second element of the data vector. The construction method of the polarization history term is the same as before, that is, the load current data of the current sampling time and the previous 10 consecutive sampling times are selected, arranged in chronological order of collection time, and the arithmetic mean is taken. The two elements are filled into the 2×1 dimension vector in a fixed order of "first element first, second element last". During the filling process, it is ensured that the element values ​​are completely consistent with the load current data and polarization history term of the corresponding sampling time, without any misalignment or incorrect filling. The construction of the corresponding data vector is completed separately for each sampling time, and finally the regression data vector corresponding to the parameter identification model at each sampling time is obtained.

[0034] For each sampling time, the gain vector at that time is calculated. Before the calculation, a preset forgetting factor value is determined. Considering the rate of change of sodium battery parameters, and given that the internal parameters of the sodium battery change gradually during charging and discharging, the forgetting factor is fixed at 0.98. This value balances the real-time performance and stability of the recursive calculation, and avoids excessive interference from historical data in the calculation of current parameters. Simultaneously, the covariance matrix updated at the previous sampling time and the regression data vector constructed at the current sampling time are retrieved. First, the covariance matrix from the previous sampling time is multiplied by the regression data vector at the current sampling time to obtain a 2×1 intermediate vector. Then, the regression data vector at the current sampling time is transposed. The transposed result is... The 1×2 dimension vector is first used. Then, the transposed regression data vector is multiplied by the covariance matrix of the previous sampling time to obtain a 1×2 dimension intermediate matrix. This intermediate matrix is ​​then multiplied by the untransposed regression data vector of the current sampling time to obtain a single numerical scalar. The preset forgetting factor value is added to this scalar to obtain the denominator required for the gain vector calculation. Finally, the previously obtained 2×1 dimension intermediate vector is divided by this denominator. During the division operation, each element is divided by the corresponding denominator value. The resulting 2×1 dimension vector is the recursive gain vector calculated at the current sampling time. During the calculation process, the order of each operation and the value transfer are ensured to be accurate, with no calculation deviation. Finally, the gain vector corresponding to each sampling time is obtained.

[0035] The formula for calculating the gain vector is as follows: In the formula, This represents the recursive gain vector calculated at the k-th sampling time. This represents the covariance matrix updated at the (k-1)th sampling time. This represents the regression data vector constructed at the k-th sampling time. This represents the preset forgetting factor. Represents regression data vector Transpose of; The gain vector at the current sampling time, the parameter vector at the previous sampling time, the sampling time terminal voltage data, and the regression data vector at the current sampling time are retrieved. A residual generation operation is then performed to obtain the current parameter vector estimation error of the parameter identification model. During the generation process, the parameter vector at the previous sampling time is first multiplied with the regression data vector at the current sampling time. The two components of the parameter vector are multiplied with the two elements of the regression data vector respectively. The two multiplication results are then added together to obtain a single theoretical calculated value. Subsequently, the theoretical calculated value is subtracted from the actual sampling time terminal voltage data collected at the current sampling time. The difference obtained is the residual, which is directly used as the current parameter vector estimation error of the parameter identification model. During the calculation process, the numerical accuracy of the subtraction operation is ensured. Both the positive and negative values ​​of the difference are retained without additional correction. Finally, the parameter vector estimation error corresponding to each sampling time is obtained.

[0036] Based on the gain vector and parameter vector estimation error calculated at the current sampling time, the parameter vector at the previous sampling time is recursively corrected. Before correction, the gain vector at the current sampling time is multiplied by the parameter vector estimation error. The two elements of the gain vector are multiplied by the parameter vector estimation error to obtain a 2×1 dimension correction vector. Then, the parameter vector at the previous sampling time is added to the correction vector. Each component of the parameter vector is added to the corresponding element of the correction vector. The resulting vector is the real-time model parameter vector of the parameter identification model at the current sampling time. During the correction process, it is ensured that the addition and subtraction operations of each component are accurate and that there are no numerical omissions or calculation errors. The above correction process is repeated for each sampling time based on the parameter vector corrected at the previous time to ensure that the real-time model parameter vector can accurately match the input data at the current sampling time. Finally, the real-time model parameter vector corresponding to the parameter identification model at each sampling time is obtained.

[0037] S3. Based on a preset decision rule base, the real-time internal state parameters are mapped to preset health state thresholds to obtain the adjustment decision of the multi-stage charging strategy. In this embodiment of the invention, the step of mapping the real-time internal state parameters to a preset health state threshold based on a pre-set decision rule base to obtain the adjustment decision of the multi-stage charging strategy includes: Based on the real-time internal state parameters, a parallel threshold comparison is performed on the preset health state threshold to obtain a comparison result set of the real-time internal state parameters and the preset health state threshold. Based on the comparison result set, rule matching is performed on the preset decision rule base to obtain the target adjustment rule of the multi-stage charging strategy. Based on the target adjustment rule, the current stage parameters of the multi-stage charging strategy are used to derive the adjustment decision of the multi-stage charging strategy.

[0038] Pre-set health state thresholds are stored in the threshold storage unit and are divided into two categories, corresponding to the real-time internal resistance and real-time polarization voltage in the real-time internal state parameters, respectively. The preset health state threshold for the real-time internal resistance is set to 1.2 times the factory-calibrated internal resistance of the sodium battery. This threshold is determined based on the safety operation standards of sodium batteries to prevent overheating and damage caused by excessive internal resistance. The preset health state threshold for the real-time polarization voltage is set to 0.1V. This threshold is determined based on the safe range of the polarization effect of sodium batteries to prevent damage to the active materials inside the battery caused by excessive polarization voltage. When the parallel threshold comparison operation is initiated, the real-time internal state parameters at the same sampling time are simultaneously received by two independent comparison units. One comparison unit retrieves the real-time internal resistance and the corresponding preset health state threshold, comparing the value of the real-time internal resistance with 120mΩ one by one to clearly record whether the real-time internal resistance is greater than 120mΩ. The current resistance is less than or equal to 120mΩ. Another comparison unit synchronously retrieves the real-time polarization voltage and the corresponding preset health state threshold, comparing the real-time polarization voltage value with 0.1V one by one to clearly record whether the real-time polarization voltage is greater than 0.1V or less than or equal to 0.1V. The comparison operations of the two comparison units start and complete synchronously, without any order. After the comparison is completed, the two comparison results at the same sampling time are bound together to form a complete set of comparison information containing the real-time internal resistance comparison result and the real-time polarization voltage comparison result. Then, according to the order of the sampling time, the complete comparison information of all sampling times is arranged in sequence to form an ordered set. This ordered set is the comparison result set of the real-time internal state parameter and the preset health state threshold. The whole process ensures synchronous comparison, accurate recording, no omissions, and no misjudgments. The comparison result at each sampling time can accurately reflect the correspondence between the real-time internal state parameter and the threshold.

[0039] A pre-set decision rule base is stored in the decision rule storage unit. The rule base contains four fixed rules, which can cover all possible combinations of comparison results in the comparison result set. Each rule corresponds to a unique adjustment direction. The specific rules are set as follows: Rule 1: When the real-time ohmic internal resistance is ≤120mΩ and the real-time polarization voltage is ≤0.1V, the current parameters of the multi-stage charging strategy are not adjusted; Rule 2: When the real-time ohmic internal resistance is >120mΩ and the real-time polarization voltage is ≤0.1V, the charging current of the current stage of the multi-stage charging strategy is reduced; Rule 3: When the real-time ohmic internal resistance is ≤120mΩ and the real-time polarization voltage is >0.1V, the charging time of the current stage of the multi-stage charging strategy is shortened; Rule 4: When the real-time ohmic internal resistance is >120mΩ and the real-time polarization voltage is >0.1V, the current stage of the multi-stage charging strategy is switched to the next charging stage, and the current charging current is reduced. When the charging current is reduced to the next stage, during rule matching, the complete comparison information for each sampling moment in the comparison result set is retrieved. Each comparison information is compared with the four rules in the decision rule base one by one. During the comparison, it is ensured that the comparison results of real-time internal resistance and real-time polarization voltage are completely consistent with the conditions in the rules, and there are no partial matches. If the comparison information at a certain sampling moment is completely consistent with the conditions of the first rule, then the target adjustment rule corresponding to that sampling moment is the first rule; if it is completely consistent with the conditions of the second rule, then it corresponds to the second rule, and so on. Only one rule is matched at each sampling moment, and there are no cases of multiple rule matching or no rule matching. After the matching is completed, the rule corresponding to each sampling moment is recorded. This rule is the target adjustment rule of the multi-stage charging strategy. It is ensured that the target adjustment rule is completely consistent with the comparison results and the corresponding rules in the decision rule base, without any deviation.

[0040] The target adjustment rule for each sampling moment is retrieved, along with the specific parameters of the current stage of the multi-stage charging strategy. These parameters include charging current, charging time, and charging stage type. The initial charging current is set to 2A, the initial charging time to 30 minutes, and the stage type is divided into constant current charging and constant voltage charging, performed sequentially according to a preset order. Decision derivation is conducted based on the target adjustment rule, strictly adhering to the adjustment direction corresponding to the rule, combined with a fixed adjustment range. This range is set based on the operating characteristics of the sodium battery to ensure no damage to the battery after adjustment. The charging current adjustment range is fixed at 0.2A, and the charging time adjustment range is fixed at 5 minutes. If the target adjustment rule is not to adjust the current parameters of the multi-stage charging strategy, the adjustment decision is directly derived as "keeping all parameters of the current stage of the multi-stage charging strategy unchanged." If the target adjustment rule is to reduce the charging current of the current stage, the current charging current value is retrieved, and 0.2A is subtracted from it to obtain the adjusted charging current value. The adjustment decision is then derived as "reducing the charging current of the current stage of the multi-stage charging strategy." The current is adjusted from its original value to the corresponding new value. If the target adjustment rule is to shorten the charging time of the current stage, the charging time value of the current stage is retrieved, and 5 minutes are subtracted from the current charging time to obtain the adjusted charging time value. The adjustment decision is derived as "adjusting the charging time of the current stage of the multi-stage charging strategy from its original value to the corresponding new value". If the target adjustment rule is to switch the current stage to the next charging stage and reduce the charging current of the next stage, the type of the next stage of the current charging stage is first determined, and the charging current of the next stage is set to the initial charging current minus 0.2A. The adjustment decision is derived as "switching the current charging stage of the multi-stage charging strategy to the corresponding next charging stage and adjusting the charging current of the next stage to the corresponding new value". During the derivation process, it is ensured that the adjustment range is fixed, the numerical calculation is accurate, and the adjustment decision clearly includes the adjustment direction and parameter information before and after the adjustment. The target adjustment rule at each sampling time corresponds to a unique adjustment decision. Finally, the adjustment decision of the multi-stage charging strategy is obtained, and the adjustment decision is completely matched with the target adjustment rule, which meets the requirements for safe operation of sodium batteries.

[0041] S4. Based on the adjustment decision, the constant current value, constant voltage value and stage transition conditions of the multi-stage charging strategy are adaptively adjusted to obtain the optimized charging command for the sodium battery. In this embodiment of the invention, the step of adaptively adjusting the constant current value, constant voltage value, and stage transition conditions of the multi-stage charging strategy based on the adjustment decision to obtain the optimized charging command for the sodium battery includes: The adjustment decision is analyzed in a structured manner to obtain the adjustment requirements of the multi-stage charging strategy; The constant current value of the multi-stage charging strategy is corrected to obtain the optimized constant current value of the multi-stage charging strategy. The constant voltage value in the multi-stage charging strategy is corrected to obtain the optimized constant voltage value of the multi-stage charging strategy. The stage transition conditions in the multi-stage charging strategy are reset to obtain the optimized stage transition conditions of the multi-stage charging strategy. The optimized constant current value, the optimized constant voltage value, and the optimized stage transition conditions are combined to generate the optimized charging command for the sodium battery.

[0042] The step of correcting the constant current value of the multi-stage charging strategy to obtain the optimized constant current value of the multi-stage charging strategy includes: The adjustment requirement is subjected to current characteristic separation to obtain the current adjustment flag and current adjustment range related to the charging current; The current adjustment flag is mapped and matched with a predefined current adjustment action library to obtain the specific adjustment operation type of the constant current value; Based on the adjustment operation type and the current adjustment range, the constant current value is adjusted in a directional manner to obtain the intermediate current value of the constant current value. The intermediate current value is compared with the preset absolute current safety boundary to determine if it exceeds the boundary, thereby obtaining the optimized constant current value of the multi-stage charging strategy.

[0043] The adjustment decisions of the multi-stage charging strategy are transmitted to the decision analysis unit. The adjustment decisions are then structured and analyzed to obtain the adjustment requirements. During the analysis, the adjustment decisions are first decomposed into three types of core information according to the adjustment object: information related to constant current value adjustment, information related to constant voltage value adjustment, and information related to stage transition condition adjustment. During the decomposition process, the adjustment direction, adjustment object, and specific adjustment range of each adjustment decision are extracted one by one. The adjustment range adopts the fixed values ​​preset above, namely, the charging current adjustment range is 0.2A, the charging voltage adjustment range is 0.05V, and the stage transition condition adjustment range corresponds to a current threshold of 0.1A and a time threshold of 5 minutes. After the decomposition is completed, the adjustment direction and adjustment range of the same adjustment object are bound together. Invalid information is eliminated, and all the bound information is integrated in a fixed order of "constant current value adjustment, constant voltage value adjustment, stage transition condition adjustment" to form a set of clear and unambiguous adjustment requirements. These adjustment requirements are the adjustment requirements of the multi-stage charging strategy. During the analysis process, it is ensured that the decomposition is comprehensive and the integration is orderly. Every item in the adjustment requirements corresponds completely to the adjustment decision, without omissions or misalignments, and can accurately guide subsequent correction operations.

[0044] The process retrieves the current constant current value of the multi-stage charging strategy and the adjustment requirements for the constant current value in the adjustment requests. The constant current value is then corrected to obtain an optimized value. Before correction, the safe range of the sodium battery charging current is defined, with a preset minimum constant current value of 1A and a maximum constant current value of 3A. This range is determined based on the tolerance characteristics of the sodium battery electrode material, preventing damage to the electrodes from excessive current and low charging efficiency from excessive current. If the adjustment request requires maintaining the constant current value, the current constant current value is directly used as the optimized constant current value without additional correction. If the adjustment request requires reducing the constant current value, the current constant current value is subtracted from the preset adjustment range of 0.2A to obtain the corrected current value. After correction, it is necessary to verify whether this value is within the safe range of 1A to 3A. If it is within the range, this value is the optimized constant current value; if it is below 1A, the optimized constant current value is fixed at 1A. During the correction process, the numerical calculation is ensured to be accurate, and the adjustment range strictly follows the preset requirements without arbitrary changes. Finally, the optimized constant current value of the multi-stage charging strategy is obtained, and this value complies with the sodium battery safety charging standards.

[0045] The current constant voltage value of the multi-stage charging strategy and the adjustment requirements for the constant voltage value in the adjustment needs are retrieved. The constant voltage value is corrected to obtain the optimized constant voltage value. Before correction, the safe range of sodium battery charging voltage is defined. The preset minimum constant voltage value is 3.7V and the maximum constant voltage value is 3.9V. This range is determined based on the stability characteristics of sodium battery electrolyte, which can avoid electrolyte decomposition due to excessive voltage and insufficient charging due to excessive voltage. If the adjustment needs to keep the constant voltage value unchanged, the current constant voltage value is directly used as the optimized constant voltage value without additional correction. If the adjustment requirement calls for adjusting the constant voltage value, the current constant voltage value is corrected by a preset adjustment range of 0.05V. When switching to the constant voltage stage, the voltage is increased by 0.05V, and when switching to the constant current stage, the voltage is decreased by 0.05V. After correction, it is necessary to verify whether the value is within the safe range of 3.7V to 3.9V. If it is within the range, the value is the optimized constant voltage value. If it is outside the range, the corresponding safe boundary value is taken as the optimized constant voltage value. During the correction process, it is ensured that the numerical calculation is accurate and that the adjustment logic matches the stage type, so as to finally obtain the optimized constant voltage value of the multi-stage charging strategy.

[0046] The current stage transition conditions of the multi-stage charging strategy are retrieved: the initial settings are a current threshold of 0.5A for the transition from the constant current stage to the constant voltage stage, a time threshold of 20 minutes for the transition from the constant voltage stage to the next stage, and adjustment requirements regarding stage transition conditions in the adjustment requests. The stage transition conditions are reset to obtain optimized stage transition conditions. The stage transition conditions only include two categories: current threshold and time threshold. During the reset, the safe range of the two types of thresholds is first defined. The preset range of the current threshold is 0.3A to 0.8A, and the preset range of the time threshold is 10 minutes to 30 minutes. This range is determined based on the change cycle of the internal state parameters of the sodium battery, which can ensure accurate stage transition timing. If the adjustment requests require that the stage transition conditions remain unchanged, then the current current threshold and time threshold are directly used as the optimized ones. The optimized stage transition conditions are as follows: If the adjustment requirements call for shortening the charging time, the time threshold is reduced by 5 minutes. If a stage switching is required, the current threshold is adjusted accordingly. The current threshold for switching from constant current to constant voltage is reduced by 0.1A, and the time threshold for switching from constant voltage to the next stage is reduced by 5 minutes. After resetting, it is verified whether the current threshold is within the range of 0.3A to 0.8A and the time threshold is within the range of 10 minutes to 30 minutes. If both are within the range, the combination of the current threshold and the time threshold is the optimized stage transition condition. If they are outside the range, the corresponding safety boundary value is taken as the reset threshold. During the reset process, it is ensured that the adjustment of the two types of thresholds is synchronized and completely matches the stage adjustment requirements in the adjustment requirements, with no logical deviation. Finally, the optimized stage transition conditions of the multi-stage charging strategy are obtained.

[0047] The optimized constant current value, optimized constant voltage value, and optimized stage transition conditions are transmitted to the instruction synthesis unit for instruction synthesis to generate the optimized charging instruction for the sodium battery. Before synthesis, the fixed format of the optimized charging instruction is determined, which is "charging stage type + optimized constant current value + optimized constant voltage value + optimized stage transition condition". The charging stage type is determined according to the stage switching requirements in the adjustment needs. During synthesis, the current charging stage type is filled in first, followed by the optimized constant current value and optimized constant voltage value, and finally the optimized stage transition condition is specified. After filling in the information, the instruction content is verified to check whether the values ​​of the optimized constant current value, optimized constant voltage value, and optimized stage transition condition are completely consistent with the results after correction and reset. The instruction format is also checked to ensure that it is standardized and without missing information. After verification, the complete instruction content is encapsulated. The standardized instruction formed by encapsulation is the optimized charging instruction for the sodium battery. During the synthesis process, it is ensured that the parameters are filled in accurately and the format is standardized. The instruction content can be directly used to control the charging and discharging execution unit of the sodium battery, realizing the adaptive implementation of the multi-stage charging strategy.

[0048] The adjustment requirements of the multi-stage charging strategy obtained above are transmitted to the current feature separation unit. The adjustment requirements are separated by current features to obtain the current adjustment flag and the current adjustment amplitude. Before the separation operation, the core information related to the constant current value in the adjustment requirements is identified, and the preset flag is "current adjustment". During the separation, each item in the adjustment requirements is screened one by one, and irrelevant information related to constant voltage value adjustment and stage transition condition adjustment is removed. Only the adjustment content related to the constant current value is retained. Then, the current adjustment flag and the current adjustment amplitude are extracted from the retained content. The current adjustment flag contains only two fixed types, namely "keep unchanged" and "reduce", which correspond to the adjustment direction of the constant current value in the adjustment decision. The current adjustment amplitude adopts the preset fixed value of 0.2A. This amplitude is determined based on the tolerance characteristics of sodium battery electrode material, which can avoid the current adjustment amplitude being too large to damage the electrode and too small to adapt to the internal state change quickly. During the separation process, it is ensured that only information related to the charging current is extracted, without omission or misalignment. After extraction, the current adjustment flag and the current adjustment amplitude are bound to ensure that the correspondence between the two is unique, and finally the current adjustment flag and the current adjustment amplitude related to the charging current are obtained.

[0049] A predefined current adjustment action library is stored in the action library storage unit. This action library contains only two specific adjustment operation types that correspond one-to-one with the current adjustment flag: "keep the constant current value unchanged" and "reduce the constant current value by a fixed amount". Both operation types have clearly defined execution logic without ambiguity. During mapping and matching, the current adjustment flag obtained by separating the current characteristics is transmitted to the matching unit. The current adjustment flag is compared with the two operation types in the current adjustment action library one by one. If the current adjustment flag is "keep unchanged", it is a complete match with the operation type "keep the constant current value unchanged" in the action library. If the current adjustment flag is "reduce", it is a complete match with the operation type "reduce the constant current value by a fixed amount" in the action library. During the matching process, it is ensured that the flag and the operation type correspond one-to-one, and there are no cases of multiple matches or mismatches. After the matching is completed, the successfully matched operation type is recorded. The recorded operation type is the specific adjustment operation type of the constant current value.

[0050] The system retrieves the current constant current value, specific adjustment operation type, and current adjustment range of the multi-stage charging strategy. Based on these three factors, it performs a directional numerical adjustment of the constant current value to obtain an intermediate current value. Before adjustment, the specific value of the current constant current is confirmed to ensure accuracy. If the specific adjustment operation type is "keep the constant current value unchanged," no changes are needed to the current constant current value; it is directly used as the intermediate current value after directional numerical adjustment, ensuring complete consistency between the intermediate and current constant current values. If the specific adjustment operation type is "reduce the constant current value by a fixed range," the current constant current value is subtracted by the current adjustment range of 0.2A. During the subtraction operation, the numerical calculation is ensured to be accurate and without deviation. The resulting difference is the intermediate current value after directional numerical adjustment. The adjustment process strictly follows the execution logic of the adjustment operation type, keeping the adjustment range fixed and not arbitrarily changed, ultimately yielding the intermediate current value of the constant current.

[0051] The preset absolute current safety boundaries are stored in the safety boundary storage unit in advance, and are explicitly set as a minimum constant current value of 1A and a maximum constant current value of 3A. These safety boundaries are determined based on the tolerance characteristics of the sodium battery electrode materials and charging efficiency requirements. 1A is the minimum safe current; below this value, the sodium battery charging efficiency will be too low and unable to meet normal charging needs. 3A is the maximum safe current; above this value, the sodium battery electrode will overheat and the active material will be depleted. When an out-of-bounds judgment is made, the obtained intermediate current value is transmitted to the out-of-bounds judgment unit, which compares the intermediate current value with 1A and 3A respectively. If the intermediate current value is greater than or equal to 1A... If the intermediate current value is less than or equal to 3A, it means that the intermediate current value is within the absolute current safety boundary range, and no additional adjustment is required. This intermediate current value can be directly used as the optimized constant current value of the multi-stage charging strategy. If the intermediate current value is less than 1A, it means that the intermediate current value is lower than the safety boundary. In this case, the optimized constant current value is fixed at 1A to ensure that the sodium battery is charged normally. If the intermediate current value is greater than 3A, it means that the intermediate current value is higher than the safety boundary. In this case, the optimized constant current value is fixed at 3A to avoid damaging the sodium battery electrodes. During the judgment process, it is ensured that the comparison is accurate, the boundary is clear, and there is no ambiguity in the judgment. Finally, the optimized constant current value of the multi-stage charging strategy is obtained.

[0052] S5. Based on the optimized charging command, drive the charging execution unit to obtain the charging execution state of the sodium battery; In this embodiment of the invention, the step of driving the charging execution unit with instructions based on the optimized charging instructions to obtain the charging execution state of the sodium battery includes: The optimized charging command is converted into a protocol to obtain the drive command for the charging execution unit; Based on the driving instructions of the charging execution unit, the charging execution unit is driven by instructions; The execution status of the driving instruction is verified to obtain an execution confirmation signal for the driving instruction; Based on the execution confirmation signal, the voltage and current response during the sodium battery charging process are monitored in real time to obtain the charging execution status of the sodium battery.

[0053] The optimized charging command for the sodium battery generated earlier is transmitted to the protocol conversion unit. The optimized charging command is then converted to a protocol to obtain the drive command for the charging execution unit. The format of the optimized charging command is "charging stage type + optimized constant current value + optimized constant voltage value + optimized stage transition condition". The protocol conversion unit first parses the command, extracting the charging stage type, optimized constant current value, optimized constant voltage value, and optimized stage transition condition one by one, confirming that all information is complete and accurate. Then, the parsed command information is converted into the RS485 communication protocol format preset by the charging execution unit. The protocol is a dedicated communication protocol calibrated by the charging execution unit at the factory. During the conversion process, it is encapsulated according to the fixed frame format of the RS485 protocol. The frame header is fixed at 0x0, and the data segment is filled with the parsed information in sequence. The charging stage type is represented by 0x02 for constant current stage and 0x03 for constant voltage stage. The current, voltage and threshold values ​​are converted into hexadecimal data according to the encoding rule that 1A corresponds to 0x0A, 1V corresponds to 0x0A, and 1 minute corresponds to 0x01. The frame tail is fixed at 0xFF. After encapsulation, the frame format and encoding accuracy are checked to ensure that the driving command can be accurately recognized by the charging execution unit, and finally the driving command of the charging execution unit is obtained.

[0054] The drive command obtained from the protocol conversion is sent to the control port of the charging execution unit through a shielded signal transmission line. The shielded transmission line can isolate external electromagnetic interference and avoid data distortion or loss during command transmission. After receiving the drive command, the control port of the charging execution unit starts the internal command parsing module to decompose the RS485 protocol frame of the drive command, extract the frame header, data segment, and frame tail information, and verify that the frame header is 0x01 and the frame tail is 0xFF, confirming that the command is a valid charging drive command. Then, the hexadecimal code in the data segment is converted into decimal actual operation parameters to clarify the charging stage type, the optimized constant current value, and the optimized constant current... The charging execution unit starts the internal power module according to the actual operating parameters based on the specific values ​​of the voltage value and the optimized stage transition conditions. If it is a constant current charging stage, the power module is controlled to output a stable optimized constant current value, and the output voltage is adjusted to the initial value adapted to the current to ensure that the current output fluctuation does not exceed ±0.05A. If it is a constant voltage charging stage, the power module is controlled to output a stable optimized constant voltage value, and the current is adjusted to adapt to the voltage requirement to ensure that the voltage output fluctuation does not exceed ±0.01V. During the stage transition, the charging parameters are detected in real time, and the stage switching is strictly performed in accordance with the optimized stage transition conditions to complete the instruction drive of the charging execution unit.

[0055] While driving the charging execution unit with commands, a status verification unit is activated to verify the execution status of the driving commands and obtain an execution confirmation signal. The status verification unit establishes real-time communication with the charging execution unit through a feedback line. After the charging execution unit starts the charging action, its internal feedback module immediately generates a response signal. This response signal is encapsulated according to the RS485 protocol, with a frame header of 0x04, a data segment containing the hexadecimal code 0x616374697665 corresponding to "Command received successfully, start execution", and a frame tail of 0xFE. The feedback signal is transmitted to [the relevant authority / organization] through the feedback line. The status verification unit disassembles the response signal, extracts the data segment encoding, and converts it into text information. The converted text information is compared one by one with the preset execution confirmation signal standard content. At the same time, it checks whether the frame header and frame tail of the response signal conform to the preset format. If the text information is completely consistent with the standard content, the frame format is error-free, and there is no data loss or encoding deviation, it confirms that the driving instruction has been successfully received and started by the charging execution unit. At this time, a signal containing the verification time, verification result, and initial state of the charging execution unit is generated. This signal is the execution confirmation signal of the driving instruction.

[0056] Upon receiving the execution confirmation signal from the status verification, the real-time acquisition function of the voltage and current sensors is immediately activated. The acquisition frequency is fixed at once per second, determined based on the parameter change rate during sodium battery charging. This frequency accurately captures the real-time response changes of voltage and current. The voltage sensor continuously acquires voltage response data between the positive and negative electrodes of the sodium battery, ensuring that the deviation between the acquired data and the actual voltage of the sodium battery does not exceed ±0.01V. The current sensor continuously acquires current response data in the main charging circuit, ensuring that the deviation between the acquired data and the actual current does not exceed ±0.05A. During the acquisition process, the acquisition timing of the two sensors is synchronized, and the voltage and current response data at each acquisition moment are recorded. According to the one-to-one correspondence, the monitoring unit compares the collected voltage response data with the optimized constant voltage value and the current response data with the optimized constant current value in real time. It verifies whether the voltage response data is within ±0.01V of the optimized constant voltage value and whether the current response data is within ±0.05A of the optimized constant current value. At the same time, it monitors whether the charging stage transition strictly follows the optimized stage transition conditions. It integrates the voltage response data, current response data, stage transition execution status and parameter comparison results of all collected moments to form a complete set containing charging parameters and execution status at each moment. This set is the charging execution status of the sodium battery, which can comprehensively reflect the current charging operation status of the sodium battery.

[0057] S6. Based on the charging execution state, the real-time internal state parameters and the charging termination condition are iteratively terminated to obtain the charging completion state of the sodium battery.

[0058] In this embodiment of the invention, the step of iteratively determining the charging completion state of the sodium battery by comparing the real-time internal state parameters with the charging termination condition based on the charging execution state includes: The real-time internal state parameters are compared with the preset charging termination conditions one by one to obtain the preliminary termination determination result of the sodium battery. The consistency of the charging execution state with the preset charging process stability conditions is verified to obtain the process stability determination result of the sodium battery. The preliminary termination determination result and the process stability determination result are logically fused to obtain the comprehensive termination determination signal of the sodium battery. Based on the comprehensive termination determination signal, the charging process is updated to obtain the charging completion status of the sodium battery.

[0059] Pre-defined charging termination conditions are stored in the termination condition storage unit, explicitly including two fixed judgment indicators, corresponding to the real-time internal resistance and real-time polarization voltage in the real-time internal state parameters. The termination threshold corresponding to the real-time internal resistance is set to the factory-calibrated internal resistance of the sodium battery, and the termination threshold corresponding to the real-time polarization voltage is set to 0.05V. These two thresholds are determined based on the internal parameter characteristics of the sodium battery under full charge conditions, accurately determining whether the battery has reached the full charge standard. During item-by-item comparison, the real-time internal state parameters of the sodium battery obtained above are transmitted to the comparison unit, and the real-time internal resistance value and real-time polarization voltage value at each sampling time are extracted one by one. First, the real-time internal resistance value is compared with 100mΩ, and this is clearly recorded. The value is determined by comparing the real-time polarization voltage value at the same sampling time with 0.05V. The comparison is then recorded to determine whether the value is greater than or less than 0.05V. These two comparisons are performed simultaneously, without any specific order. After the comparisons are completed, the results are linked. If both conditions are met ("real-time internal resistance ≤ 100mΩ, real-time polarization voltage ≤ 0.05V"), the preliminary termination result for that sampling time is "charging termination condition met." If either condition is not met, the preliminary termination result is "charging termination condition not met." Each sampling time undergoes separate comparison and determination, ultimately yielding the preliminary termination result for the sodium battery.

[0060] Pre-defined stable charging conditions are stored in a stable condition storage unit, explicitly including three fixed verification indicators: charging current fluctuation range, charging voltage fluctuation range, and stage transition execution accuracy. The charging current fluctuation range is set to ±0.05A, the charging voltage fluctuation range to ±0.01V, and the stage transition execution accuracy is set to "the deviation between the stage transition timing and the optimized stage transition conditions does not exceed 1 minute." These three indicators are determined based on the operational requirements of stable charging of sodium batteries, avoiding misjudgments due to fluctuations in charging parameters. During consistency verification, the sodium battery charging execution status obtained earlier is retrieved, and each indicator is checked sequentially. The current response data and voltage response data at each sampling moment are collected, and the current difference and voltage difference between two adjacent sampling moments are calculated. If all current differences are within ±0.05A and all voltage differences are within ±0.01V, and the time deviation between the stage transition execution time and the time specified by the optimized stage transition conditions does not exceed 1 minute, then the process stability judgment result for that sampling moment is "charging process is stable". If any indicator is not met, the process stability judgment result is "charging process is unstable". During the verification process, it is ensured that all data at each sampling moment are checked without omission or misjudgment, and the final process stability judgment result of the sodium battery is obtained.

[0061] A fixed logical fusion rule is preset, clearly defining the logical correspondence. It includes only two fusion scenarios: The first is where the initial termination judgment result is "charging termination condition met" and the process stability judgment result is "charging process stable," with the corresponding fused comprehensive termination judgment signal being "charging terminated." The second is where the initial termination judgment result is "charging termination condition not met" or the process stability judgment result is "charging process unstable," with the corresponding fused comprehensive termination judgment signal being "continue charging." This logical rule is determined based on the dual judgment requirements of the sodium battery's fully charged state, avoiding false termination or overcharging issues caused by a single judgment indicator. During logical fusion, it simultaneously adjusts... The preliminary termination judgment result and the process stability judgment result at the same sampling time are taken, and the two results are compared one by one with the preset logic fusion rules to confirm the fusion status of the two results. If the first fusion status is met, a signal containing the sampling time, fusion basis, and termination instruction identifier is generated. This signal is the comprehensive termination judgment signal of the sodium battery. If the second fusion status is met, a signal containing the sampling time, fusion basis, and continue instruction identifier is generated. This signal is the comprehensive termination judgment signal of the sodium battery. During the fusion process, the logic correspondence is ensured to be accurate, with no multiple signals or signal loss. Finally, the comprehensive termination judgment signal of the sodium battery is obtained.

[0062] The obtained comprehensive termination judgment signal is transmitted to the charging state update unit. Based on this signal, the charging process state is updated to obtain the charging completion state of the sodium battery. Before updating, the specific type of the comprehensive termination judgment signal is confirmed. If the comprehensive termination judgment signal is "charging terminated", a stop drive command is immediately sent to the charging execution unit to control the charging execution unit to turn off the power output and stop the charging operation of the sodium battery. At the same time, the current sampling time, the current real-time internal state parameters of the sodium battery, and the charging execution state data are recorded. The charging process state is updated to "charging complete", and the charging completion time and the real-time ohmic internal resistance in the fully charged state are clearly marked. Together with the real-time polarization voltage value, a complete charging completion status record is formed. If the comprehensive termination judgment signal is "continue charging", no stop command is sent to the charging execution unit. The original optimized charging command drive state is maintained, and the charging process of the sodium battery continues. The charging process status is updated to "charging in progress". The real-time internal state parameters and charging execution status data at the current sampling time are recorded for the iterative termination judgment at the next sampling time. During the update process, it is ensured that the status identifier is clear, the record is complete, and it corresponds completely with the comprehensive termination judgment signal. It is also connected to the drive logic of the charging execution unit mentioned above, and finally the charging completion status of the sodium battery is obtained.

[0063] like Figure 2 The diagram shown is a functional block diagram of a sodium battery charging optimization system provided in an embodiment of the present invention.

[0064] The sodium battery charging optimization system 100 described in this invention can be installed in an electronic device. Depending on the functions implemented, the sodium battery charging optimization system 100 may include a data acquisition module 101, an online parameter identification module 102, a decision generation module 103, a parameter adaptation module 104, an instruction driving module 105, and a status output module 106. The module described in this invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and are stored in the memory of the electronic device.

[0065] In this embodiment, the functions of each module / unit are as follows: The data acquisition module 101 is used to acquire the voltage and current data of the sodium battery in real time based on a preset multi-stage charging strategy, so as to obtain the original operating parameters of the sodium battery. The online parameter identification module 102 is used to perform dynamic parameter identification on the original operating parameters to obtain the real-time internal state parameters of the sodium battery. The decision generation module 103 is used to perform rule mapping between the real-time internal state parameters and the preset health state threshold based on a preset decision rule library, so as to obtain the adjustment decision of the multi-stage charging strategy. The parameter adaptive module 104 is used to adaptively adjust the constant current value, constant voltage value and stage transition conditions of the multi-stage charging strategy based on the adjustment decision, so as to obtain the optimized charging command of the sodium battery. The instruction driving module 105 is used to drive the charging execution unit based on the optimized charging instruction to obtain the charging execution state of the sodium battery. The status output module 106 is used to perform iterative termination judgment on the real-time internal status parameters and charging termination conditions based on the charging execution status to obtain the charging completion status of the sodium battery.

[0066] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0067] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0068] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0069] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0070] This application embodiment can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0071] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for optimizing the charging of sodium batteries, characterized in that, The method includes: S1. Based on a preset multi-stage charging strategy, the voltage and current data of the sodium battery are collected in real time to obtain the original operating parameters of the sodium battery; S2. Perform dynamic parameter identification on the original operating parameters to obtain the real-time internal state parameters of the sodium battery; S3. Based on a preset decision rule base, the real-time internal state parameters are mapped to preset health state thresholds to obtain the adjustment decision of the multi-stage charging strategy. S4. Based on the adjustment decision, the constant current value, constant voltage value and stage transition conditions of the multi-stage charging strategy are adaptively adjusted to obtain the optimized charging command for the sodium battery. S5. Based on the optimized charging command, drive the charging execution unit to obtain the charging execution state of the sodium battery; S6. Based on the charging execution state, the real-time internal state parameters and the charging termination condition are iteratively terminated to obtain the charging completion state of the sodium battery.

2. The sodium battery charging optimization method as described in claim 1, characterized in that, The preset multi-stage charging strategy collects voltage and current data of the sodium battery in real time to obtain the original operating parameters of the sodium battery, including: Based on the voltage and current sensors connected to the sodium battery, the analog signals of the sodium battery are synchronously acquired to obtain the analog voltage signal and analog current signal of the sodium battery. The analog voltage signal and analog current signal are converted from analog to digital to obtain the synchronous digital voltage data and digital current data of the sodium battery. The digital voltage data and digital current data are time-series encapsulated to obtain the original operating parameters of the sodium battery.

3. The sodium battery charging optimization method as described in claim 1, characterized in that, The step of performing dynamic parameter identification on the original operating parameters to obtain the real-time internal state parameters of the sodium battery includes: Feature extraction is performed on the original operating parameters to obtain the sampling time terminal voltage data and load current data of the sodium battery; A parameter identification model based on recursive least squares method is constructed, and the terminal voltage data and the load current data are used as the input sequence of the parameter identification model. The input sequence is recursively calculated online to obtain the real-time model parameter vector of the parameter identification model; The real-time model parameter vector is analyzed by parameter mapping to obtain the real-time ohmic internal resistance and real-time polarization voltage of the sodium battery. The real-time internal resistance and the real-time polarization voltage are tensor-combined to obtain the real-time internal state parameters of the sodium battery.

4. The sodium battery charging optimization method as described in claim 3, characterized in that, The step of performing online recursive calculation on the input sequence to obtain the real-time model parameter vector of the parameter identification model includes: Initialize the covariance matrix and parameter vector required for the recursive calculation; Based on the terminal voltage data and load current data at the sampling time, the data vector of the parameter identification model at the sampling time is constructed; Based on the data vector, the covariance matrix and forgetting factor of the previous sampling time, calculate the gain vector of the current sampling time; The residual between the gain vector and the parameter vector at the previous sampling time is used to generate the current parameter vector estimation error of the parameter identification model. Based on the estimation error between the gain vector and the parameter vector, the parameter vector at the previous sampling time is recursively corrected to obtain the real-time model parameter vector of the parameter identification model.

5. The sodium battery charging optimization method as described in claim 1, characterized in that, The method, based on a pre-set decision rule base, maps the real-time internal state parameters to preset health state thresholds using rules to obtain adjustment decisions for the multi-stage charging strategy, including: Based on the real-time internal state parameters, a parallel threshold comparison is performed on the preset health state threshold to obtain a comparison result set of the real-time internal state parameters and the preset health state threshold. Based on the comparison result set, rule matching is performed on the preset decision rule base to obtain the target adjustment rule of the multi-stage charging strategy. Based on the target adjustment rule, the current stage parameters of the multi-stage charging strategy are used to derive the adjustment decision of the multi-stage charging strategy.

6. The sodium battery charging optimization method as described in claim 1, characterized in that, Based on the adjustment decision, the constant current value, constant voltage value, and stage transition conditions of the multi-stage charging strategy are adaptively adjusted to obtain the optimized charging command for the sodium battery, including: The adjustment decision is analyzed in a structured manner to obtain the adjustment requirements of the multi-stage charging strategy; The constant current value of the multi-stage charging strategy is corrected to obtain the optimized constant current value of the multi-stage charging strategy. The constant voltage value in the multi-stage charging strategy is corrected to obtain the optimized constant voltage value of the multi-stage charging strategy. The stage transition conditions in the multi-stage charging strategy are reset to obtain the optimized stage transition conditions of the multi-stage charging strategy. The optimized constant current value, the optimized constant voltage value, and the optimized stage transition conditions are combined to generate the optimized charging command for the sodium battery.

7. The sodium battery charging optimization method as described in claim 6, characterized in that, The step of correcting the constant current value of the multi-stage charging strategy to obtain the optimized constant current value of the multi-stage charging strategy includes: The adjustment requirement is subjected to current characteristic separation to obtain the current adjustment flag and current adjustment range related to the charging current; The current adjustment flag is mapped and matched with a predefined current adjustment action library to obtain the specific adjustment operation type of the constant current value; Based on the adjustment operation type and the current adjustment range, the constant current value is adjusted in a directional manner to obtain the intermediate current value of the constant current value. The intermediate current value is compared with the preset absolute current safety boundary to determine if it exceeds the boundary, thereby obtaining the optimized constant current value of the multi-stage charging strategy.

8. The sodium battery charging optimization method as described in claim 1, characterized in that, The step of driving the charging execution unit with instructions based on the optimized charging instructions to obtain the charging execution state of the sodium battery includes: The optimized charging command is converted into a protocol to obtain the drive command for the charging execution unit; Based on the driving instructions of the charging execution unit, the charging execution unit is driven by instructions; The execution status of the driving instruction is verified to obtain an execution confirmation signal for the driving instruction; Based on the execution confirmation signal, the voltage and current response during the sodium battery charging process are monitored in real time to obtain the charging execution status of the sodium battery.

9. The sodium battery charging optimization method as described in claim 1, characterized in that, The step of iteratively determining the charging completion status of the sodium battery by comparing the real-time internal state parameters with the charging termination condition based on the charging execution state includes: The real-time internal state parameters are compared with the preset charging termination conditions one by one to obtain the preliminary termination determination result of the sodium battery. The consistency of the charging execution state with the preset charging process stability conditions is verified to obtain the process stability determination result of the sodium battery. The preliminary termination determination result and the process stability determination result are logically fused to obtain the comprehensive termination determination signal of the sodium battery. Based on the comprehensive termination determination signal, the charging process is updated to obtain the charging completion status of the sodium battery.

10. A sodium battery charging optimization system, characterized in that, The system for implementing the sodium battery charging optimization method according to claim 1 includes: The data acquisition module is used to acquire the voltage and current data of the sodium battery in real time based on a preset multi-stage charging strategy, so as to obtain the original operating parameters of the sodium battery. The online parameter identification module is used to perform dynamic parameter identification on the original operating parameters to obtain the real-time internal state parameters of the sodium battery. The decision generation module is used to perform rule mapping between the real-time internal state parameters and the preset health state threshold based on a preset decision rule library, so as to obtain the adjustment decision of the multi-stage charging strategy. The parameter adaptive module is used to adaptively adjust the constant current value, constant voltage value and stage transition conditions of the multi-stage charging strategy based on the adjustment decision, so as to obtain the optimized charging command of the sodium battery. The instruction driving module is used to drive the charging execution unit based on the optimized charging instruction to obtain the charging execution state of the sodium battery. The status output module is used to iteratively terminate the charging by comparing the real-time internal status parameters with the charging termination conditions based on the charging execution status, so as to obtain the charging completion status of the sodium battery.

Images