An intelligent charging system for a solid-state lead battery energy storage power station

Abstract

This application relates to the field of battery intelligent charging technology, and particularly to an intelligent charging system for a solid-state lead battery energy storage power station. The system includes: obtaining a set of real-time impedance characteristic parameters and a set of real-time battery operation parameters, and determining a multi-dimensional battery state data set according to the set of real-time impedance characteristic parameters and the set of real-time battery operation parameters; obtaining a renewable energy power generation data set, and based on the renewable energy power generation data set, according to the multi-dimensional battery state data set, performing an evolution analysis on the solid electrolyte impedance fluctuation to construct a solid electrolyte impedance evolution model; determining an adaptive battery charging strategy according to the solid electrolyte impedance evolution model; adjusting the charging parameters of the power station according to the adaptive battery charging strategy, and determining and outputting a charging adjustment report. This application can slow down the passivation rate of the solid electrolyte inside the solid-state lead battery under the condition of intermittent fluctuations in renewable energy power generation and extend the service life of the battery.

Description

Technical Field

[0001] This application relates to the technical field of intelligent battery charging, and in particular to an intelligent charging system for a solid-state lead battery energy storage power station. Background Art

[0002] With the large-scale development and grid connection of renewable energy, traditional lead-acid batteries are difficult to meet the long-cycle and high-reliability energy storage requirements of renewable energy due to problems such as low energy density, short cycle life, and high maintenance costs. As an upgraded technology, solid-state lead batteries have higher safety and longer cycle life.

[0003] However, when existing solid-state lead batteries are applied to renewable energy storage power stations, affected by the intermittent fluctuations of renewable energy power generation, during the continuous non-steady charging and discharging process, the solid electrolyte inside the solid-state lead battery is prone to the problem of accelerated passivation, resulting in an accelerated decline in battery capacity. Summary of the Invention

[0004] This application provides an intelligent charging system for a solid-state lead battery energy storage power station to solve the above technical problems.

[0005] In a first aspect, this application provides an intelligent charging method for a solid-state lead battery energy storage power station, and the method includes:

[0006] Obtain a set of real-time impedance characteristic parameters and a set of real-time battery operating parameters, and determine a multi-dimensional battery state data set according to the set of real-time impedance characteristic parameters and the set of real-time battery operating parameters;

[0007] Obtain a renewable energy power generation data set, and based on the renewable energy power generation data set, perform an evolution analysis on the impedance fluctuation of the solid electrolyte according to the multi-dimensional battery state data set, and construct a solid electrolyte impedance evolution model;

[0008] Determine an adaptive battery charging strategy according to the solid electrolyte impedance evolution model;

[0009] Adjust the charging parameters of the power station according to the adaptive battery charging strategy, and determine and output a charging adjustment report.

[0010] Through this solution, based on the real-time impedance characteristic parameter set and the real-time battery operation parameter set, a multi-dimensional battery state data set is constructed. On this basis, combined with the renewable energy power generation data set used to reflect the power generation situation, an evolutionary analysis is carried out on the fluctuations of the internal solid electrolyte impedance of the solid lead battery under the influence of changes in the power generation situation, and a solid electrolyte impedance evolution model for predicting the changes in the solid electrolyte impedance is constructed to realize the prediction of the solid electrolyte impedance during the battery charging process. Based on the predicted changes in the solid electrolyte impedance, an adaptive battery charging strategy is determined, and the charging parameters of the power station are adjusted accordingly, and the corresponding charging adjustment report is provided to the power station staff to slow down the passivation rate of the solid electrolyte inside the solid lead battery under the intermittent fluctuations of renewable energy power generation and extend the battery service life.

[0011] Optionally, the real-time impedance characteristic parameter set includes charge transfer resistance, interface capacitance, and real-time interface impedance;

[0012] The real-time battery operation parameter set includes charging power, operating temperature, and state of charge;

[0013] The real-time impedance characteristic parameter set is obtained through in-situ electrochemical impedance spectroscopy detection. The detection frequency range is limited within a preset detection frequency range, the amplitude corresponding to the excitation signal is limited to be less than the amplitude of the battery rated current within a preset amplitude range, and the detection period is triggered once for every preset charge change amplitude of the state of charge.

[0014] All parameters in the real-time impedance characteristic parameter set and the real-time battery operation parameter set adopt the hardware clock synchronization protocol.

[0015] Through this solution, based on the in-situ electrochemical impedance spectroscopy detection method, combined with the small-amplitude current perturbation and the state-of-charge periodic triggering mechanism, the accuracy and effectiveness of the real-time impedance characteristic parameter set are improved. The time-delay deviation between the real-time impedance characteristic parameter set and the real-time battery operation parameter set with different data sources is eliminated through hardware-level clock synchronization, providing scientific and reliable data for the subsequent construction of the solid electrolyte impedance evolution model.

[0016] Optionally, determining the multi-dimensional battery state data set according to the real-time impedance characteristic parameter set and the real-time battery operation parameter set includes:

[0017] According to the detection period, downsample the real-time battery operation parameter set, retain the data of each parameter in the real-time battery operation parameter set at the time points corresponding to each detection period, and determine the battery periodic operation parameter set;

[0018] Using the cubic spline interpolation algorithm, perform unified time-axis alignment processing on all parameters in the real-time impedance feature set and all parameters in the battery periodic operation parameter set to determine the impedance feature periodic fluctuation parameter set;

[0019] Construct the multi-dimensional battery state data set according to the battery periodic operation parameter set and the impedance feature periodic fluctuation parameter set.

[0020] Through this solution, taking the detection period as the benchmark, perform downsampling processing on the real-time battery operation parameter set, screen and obtain the corresponding data in the real-time battery operation parameter set at the corresponding time points corresponding to different detection periods, and obtain the battery periodic operation parameter set. Further, through the cubic spline interpolation algorithm, unify the real-time impedance feature parameter set and the battery periodic operation parameter set to the same time reference to obtain the impedance feature periodic fluctuation parameter set, so as to construct the impedance feature periodic fluctuation parameter set, avoiding the data correlation analysis deviation caused by the difference in the data order of magnitude brought about by the sampling frequency difference between the real-time battery operation parameter set and the real-time impedance feature set, and improving the accuracy of the subsequent solid electrolyte evolution analysis process.

[0021] Optionally, based on the renewable energy power generation data set, perform evolutionary analysis on the impedance fluctuation of the solid electrolyte according to the multi-dimensional battery state data set, and construct a solid electrolyte impedance evolution model, including:

[0022] Based on the ridge regression algorithm, analyze the correlation influence relationship of the charge transfer resistance, the interface capacitance, the charging power, the operating temperature, and the state of charge on the solid electrolyte impedance at the corresponding time points according to the real-time interface impedance, and fit to obtain the resistance influence coefficient, the capacitance influence coefficient, the power influence coefficient, and the charge influence coefficient;

[0023] Construct the solid electrolyte impedance evolution model according to the resistance influence coefficient, the capacitance influence coefficient, the power influence coefficient, and the charge influence coefficient.

[0024] Through this solution, using the ridge regression algorithm, perform quantitative analysis on the correlation influence between the charge transfer resistance, the interface capacitance, the charging power, the operating temperature, and the state of charge and the solid electrolyte impedance at the corresponding time points under different real-time interface impedances, fit to obtain the resistance influence coefficient, the capacitance influence coefficient, the power influence coefficient, and the charge influence coefficient, and then construct a solid electrolyte impedance evolution model for predicting the change of the solid electrolyte impedance, so as to reduce the problem that the estimation result is inaccurate due to the loss of the coupling effect between parameters by the traditional least squares method.

[0025] Optionally, the constructing the solid electrolyte impedance evolution model according to the resistance influence coefficient, the capacitance influence coefficient, the power influence coefficient, and the charge influence coefficient is specifically the following formula:

[0026] ;

[0027] Wherein, is the impedance of the solid electrolyte, is the charge transfer resistance, is the resistance influence coefficient, is the interface capacitance, is the capacitance influence coefficient, is the excitation propagation delay of the renewable energy fluctuation on the battery, is the power influence coefficient, is the time point corresponding to the charging power at this time point, is the rated power of the battery, is the state-of-charge influence coefficient, is the temperature difference between the operating temperature and the preset reference temperature, is the state of charge.

[0028] Through this solution, by means of mathematical analysis, according to the resistance influence coefficient, capacitance influence coefficient, power influence coefficient and state-of-charge influence coefficient, the quantization process of the solid electrolyte impedance is constrained, and a solid electrolyte impedance evolution model for deriving the internal electrolyte impedance of the battery under different influencing factors is constructed to ensure the scientificity and accuracy of the process of predicting the solid electrolyte impedance.

[0029] Optionally, the adaptive battery charging strategy includes:

[0030] Predict the development trend of the solid electrolyte impedance according to the solid electrolyte impedance evolution model, and determine the impedance prediction values corresponding to different time points;

[0031] Based on the impedance prediction values output by the solid electrolyte impedance evolution model and the multi-dimensional battery state data set, integrate the adaptive pulse charging strategy, variable cut-off voltage control strategy and temperature-current coupling control strategy;

[0032] According to the real-time charge transfer resistance, interface capacitance, state of charge and operating temperature, dynamically adjust the pulse charging current, correct the cut-off voltage and couple the temperature and current relationship;

[0033] Through multi-strategy collaborative control, the fluctuation amplitude of the solid electrolyte impedance and the temperature rise rate during the charging process are constrained within a preset safe evolution interval.

[0034] Through this solution, by using impedance prediction and multi-strategy coordination, the impedance fluctuation amplitude is timely limited within a safe range, the deterioration of the solid electrolyte impedance is suppressed, and at the same time, the temperature rise rate is controlled to reduce the risk of thermal runaway during battery charging. Through the coordinated control of pulse charging and temperature-current coupling, the charging efficiency is guaranteed, and the overcharge side reaction in the high state of charge of the battery is reduced by adjusting the dynamic cut-off voltage, thereby improving the battery cycle life.

[0035] Optionally, the adaptive pulse charging strategy includes:

[0036] In the constant current charging stage, according to the correlation curve between the real-time value of the charge transfer resistance and the state of charge, determine the change trend of the charge transfer resistance at different states of charge;

[0037] If the growth amplitude of the current charge transfer resistance compared to the charge transfer resistance in the previous detection period exceeds the preset resistance growth amplitude, trigger the pulse load reduction mode, reduce the peak pulse current according to the proportionality coefficient corresponding to the current state of charge, and at the same time reduce the duty cycle to within the preset proportionality interval of the original value, and extend the intermittent period to within the preset multiple range of the original duration until the current charge transfer resistance falls back within the preset fluctuation threshold range.

[0038] Through this solution, based on the dynamic load reduction mechanism, when the growth amplitude of the current charge transfer resistance compared to the charge transfer resistance in the previous detection period exceeds the preset resistance growth amplitude, by activating the pulse load reduction mode, the peak pulse current, duty cycle and intermittent period are dynamically adjusted until the current charge transfer resistance falls back within the preset fluctuation threshold range. Through the progressive charge transfer resistance adjustment strategy, the abnormal growth of the charge transfer resistance is suppressed in the initial stage, and at the same time, the impact of the charge transfer resistance adjustment process on the battery charging fluctuation amplitude is reduced.

[0039] Optionally, the variable cut-off voltage control strategy includes:

[0040] According to the impedance prediction values corresponding to different time points, determine the impedance increments at different time points;

[0041] Analyze the impedance increment and the operating temperature. When the impedance increment exceeds the reference impedance increment or the operating temperature is higher than the preset temperature upper limit, based on the temperature-impedance coupling coefficient, analyze and obtain the cut-off voltage reduction amount;

[0042] The cut-off voltage reduction amount satisfies the following relationship: the reduction amplitude is linearly positively correlated with the impedance increment;

[0043] According to the cut-off voltage reduction amount, correct the cut-off voltage in the current charging stage, and the corrected cut-off voltage acts on the charging circuit in real time through the dynamic voltage limiting module.

[0044] Through this solution, the impedance increment is predicted and analyzed. Based on this, combined with the operating temperature during the battery charging process, the cut-off voltage is dynamically adjusted downward to reduce the accumulation of solid electrolyte interface stress in the constant high-voltage stage, and reduce the fluctuation range of the solid electrolyte interface impedance. It cooperates with the adaptive pulse charging strategy to form a "current-voltage" two-dimensional control, improving the suppression effect on the deterioration of the solid electrolyte interface impedance.

[0045] Optionally, the temperature-current coupling control strategy includes:

[0046] According to the battery operation experimental data, establish a mapping relationship table between the operating temperature gradient and the maximum allowable charging current. In the mapping relationship table, set the current upper limit and the temperature rise rate threshold corresponding to different temperature intervals;

[0047] According to the operating temperature at different time points, determine the battery temperature change rate. If the battery temperature change rate exceeds the corresponding temperature rise rate threshold, then according to the current interface capacitance, reduce the charging current in a gradient manner;

[0048] According to the reduction amplitude of the charging current, extend the duration of the corresponding constant voltage charging stage;

[0049] When the operating temperature drops below the corresponding temperature rise rate threshold and remains above the preset maintenance cycle number of detection cycles, restore the original charging current.

[0050] Through this solution, using the monitoring mechanism for the temperature rise rate, the triggering probability of thermal runaway of the solid lead battery is reduced. At the same time, using the gradient current reduction mechanism, the sudden change of the solid electrolyte interface stress caused by the sudden drop of the charging current is reduced to reduce the fluctuation range of the solid electrolyte impedance. Further, through the dynamic constant voltage compensation mechanism, the charging extension time caused by the adjustment of the charging current is reduced.

[0051] In a second aspect, the present application provides an intelligent charging system for a solid lead battery energy storage power station. The system includes:

[0052] A data construction module, configured to obtain a real-time impedance characteristic parameter set and a real-time battery operation parameter set, and determine a multi-dimensional battery state data set according to the real-time impedance characteristic parameter set and the real-time battery operation parameter set;

[0053] An evolution analysis module, configured to obtain a renewable energy power generation data set, and based on the renewable energy power generation data set, perform an evolution analysis on the solid electrolyte impedance fluctuation according to the multi-dimensional battery state data set, and construct a solid electrolyte impedance evolution model;

[0054] A strategy analysis module, configured to determine an adaptive battery charging strategy according to the solid electrolyte impedance evolution model;

[0055] An adjustment output module, configured to adjust the charging parameters of the power station according to the adaptive battery charging strategy, determine and output a charging adjustment report. Description of the Drawings

[0056] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0057] Figure 1 A schematic diagram of an application scenario provided by an embodiment of the present application;

[0058] Figure 2 A flowchart of an intelligent charging method for a solid-state lead battery energy storage power station provided by an embodiment of the present application;

[0059] Figure 3 A schematic structural diagram of an intelligent charging system for a solid-state lead battery energy storage power station provided by an embodiment of the present application. Detailed Embodiments

[0060] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0061] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after, unless otherwise specified.

[0062] The following will further describe the embodiments of the present application in detail with reference to the drawings of the specification.

[0063] When existing solid-state lead batteries are applied to renewable energy storage power stations, affected by the intermittent fluctuations of renewable energy power generation, during the continuous non-steady charging and discharging process, the solid electrolyte inside the solid-state lead battery is prone to the problem of accelerated passivation, resulting in a faster decline in battery capacity.

[0064] Based on this, the present application provides an intelligent charging system for a solid-state lead battery energy storage power station. According to the real-time impedance characteristic parameter set and the real-time battery operation parameter set, a multi-dimensional battery state data set is constructed. On this basis, combined with the renewable energy power generation data set used to reflect the power generation situation, an evolutionary analysis is carried out on the fluctuations of the impedance of the solid electrolyte inside the solid-state lead battery under the influence of changes in the power generation situation, and a solid electrolyte impedance evolution model for predicting the changes in the solid electrolyte impedance is constructed to achieve the prediction of the solid electrolyte impedance during the battery charging process. Taking the predicted changes in the solid electrolyte impedance as a benchmark, an adaptive battery charging strategy is determined, thereby adjusting the charging parameters of the power station and providing the corresponding charging adjustment report to the power station staff to slow down the passivation rate of the solid electrolyte inside the solid-state lead battery under the intermittent fluctuations of renewable energy power generation and extend the service life of the battery.

[0065] Figure 1 FIG. 4 is a schematic diagram of an application scenario provided by the present application. During the process of using a solid-state lead battery to store renewable energy, by the method provided by the present application, the passivation rate of the solid electrolyte inside the solid-state lead battery is slowed down under the intermittent fluctuations of renewable energy power generation, and the service life of the battery is extended.

[0066] Specifically, the method provided by the present application is applied to any server, and the server communicates with an electrochemical impedance spectroscopy measurement device, a battery state sensor, and a power generation monitoring system respectively. The server obtains the real-time impedance characteristic parameter set provided by the electrochemical impedance spectroscopy measurement device and the real-time battery operation parameter set provided by the battery state sensor, constructs a multi-dimensional battery state data set according to the real-time impedance characteristic parameter set and the real-time battery operation parameter set. On this basis, combined with the renewable energy power generation data set provided by the power generation monitoring system used to reflect the power generation situation, an evolutionary analysis is carried out on the fluctuations of the impedance of the solid electrolyte inside the solid-state lead battery under the influence of changes in the power generation situation, constructs a solid electrolyte impedance evolution model for predicting the changes in the solid electrolyte impedance to achieve the prediction of the solid electrolyte impedance during the battery charging process. Taking the predicted changes in the solid electrolyte impedance as a benchmark, an adaptive battery charging strategy is determined, thereby adjusting the charging parameters of the power station and providing the corresponding charging adjustment report to the power station staff to slow down the passivation rate of the solid electrolyte inside the solid-state lead battery under the intermittent fluctuations of renewable energy power generation and extend the service life of the battery.

[0067] Specific implementation manners can refer to the following embodiments.

[0068] Figure 2 FIG. 5 is a flowchart of an intelligent charging system for a solid-state lead battery energy storage power station provided by an embodiment of the present application. The method of this embodiment can be applied to the server in the above scenario. As Figure 2 shown, the method includes:

[0069] S201. Obtain a real-time impedance feature parameter set and a real-time battery operating parameter set, and determine a multi-dimensional battery state data set according to the real-time impedance feature parameter set and the real-time battery operating parameter set.

[0070] The real-time impedance feature parameter set can be a parameter set used to characterize the dynamic characteristics of the internal electrochemical reaction of the solid-state lead battery, and the real-time impedance feature parameter set can be obtained through an electrochemical impedance spectroscopy measurement device.

[0071] The real-time battery operating parameter set can be a parameter set used to characterize the operating state of the solid-state lead battery, and the real-time battery operating parameter set can be obtained through several different types of battery state sensors, such as temperature sensors, load sensors, etc.

[0072] The multi-dimensional battery state data set can be a data set obtained after performing time series alignment processing on the real-time impedance feature parameter set and the real-time battery operating parameter set.

[0073] Specifically, the fundamental difference between the solid-state lead battery and the traditional lead-acid battery lies in that the solid-state lead battery uses solid materials as electrolytes, such as polymer or composite solid materials, to replace the liquid sulfuric acid electrolyte in the traditional lead-acid battery. The solid-state battery has higher safety and energy density compared to the lead-acid battery. However, when the solid-state lead battery is applied to the process of renewable energy storage, due to the intermittent and fluctuating characteristics of renewable energy generation (such as wind energy, solar energy, etc., affected by climate and time cycles), the charging power in the process of renewable energy storage has intermittent fluctuations, which in turn causes the impedance of the solid electrolyte inside the solid-state lead battery to continuously and intermittently fluctuate. After a long time, it leads to the passivation problem of the impedance at the solid electrolyte interface, resulting in an accelerated battery capacity decay rate and a shortened battery service life. The existing technology usually reduces the charging power volatility by limiting the charging power within a fixed range. Although this can slow down the passivation speed of the solid electrolyte interface impedance to a certain extent, its flexibility is insufficient, which is likely to cause significant energy losses and is difficult to smooth the change range of the charging power. By collecting the real-time impedance feature parameter set and the real-time battery operating state parameter set during the battery operation process, performing unified time series alignment processing on the real-time impedance feature parameter set and the real-time battery operating state parameter set, and obtaining a multi-dimensional battery state data set, it provides a data basis for analyzing the correlation between the real-time operating state of the battery and the battery impedance characteristics under the condition of charging power fluctuations during the subsequent charging process, so as to realize the prediction of the change of the solid electrolyte impedance during the real-time operation of the battery.

[0074] S202. Obtain a renewable energy generation data set, and based on the renewable energy generation data set, perform an evolutionary analysis on the solid electrolyte impedance fluctuation according to the multi-dimensional battery state data set, and construct a solid electrolyte impedance evolution model.

[0075] A renewable energy power generation dataset can be a set of data used to characterize the real-time power generation status and the changing trend of power generation of renewable energy. The renewable energy power generation dataset can be obtained through the power generation monitoring system integrated in the renewable energy power station.

[0076] The solid electrolyte can be a solid material used as the battery electrolyte inside a solid-state lead battery.

[0077] The solid electrolyte impedance fluctuation can be the changing trend of the internal solid electrolyte impedance during the process of the solid-state lead battery storing energy from renewable energy.

[0078] The solid electrolyte impedance evolution model can be a mathematical model used to characterize the influence of each parameter in the multi-dimensional battery state dataset on the change of the solid electrolyte impedance during the battery operation process.

[0079] Specifically, through mathematical analysis means, based on the renewable energy power generation dataset, according to the multi-dimensional battery state dataset, an association model between the renewable energy power generation situation and the internal impedance change of the battery is established to realize the cross-scale analysis of the external energy input characteristics and the battery state evolution. Through the solid electrolyte impedance oxidation model, the changing trend of the internal solid electrolyte impedance of the battery is evaluated according to the input situation of the external renewable energy and combined with the battery operation state, so as to serve as the core reference basis for the subsequent conversion of the charging strategy.

[0080] S203. Determine the adaptive battery charging strategy according to the solid electrolyte impedance evolution model.

[0081] The adaptive battery charging strategy can be a multi-strategy collaborative charging control scheme integrating current control, voltage control, temperature control and other directions.

[0082] Specifically, based on the solid electrolyte impedance evolution model, according to the renewable energy power generation situation and the battery state change, the change of the solid electrolyte impedance is predicted. Taking the reduction of the solid electrolyte fluctuation amplitude as the goal, starting from the directions of current control, voltage control and temperature control, the corresponding charging parameter adjustment strategies for different battery charging stages are formulated, and then the adaptive battery charging strategy is constructed to reduce the solid electrolyte impedance fluctuation amplitude while ensuring the energy storage efficiency of the solid-state lead battery and prolong the battery service life.

[0083] S204. Adjust the power station charging parameters according to the adaptive battery charging strategy, and determine and output the charging adjustment report.

[0084] The power station charging parameters can be parameters affecting the battery charging state, such as charging current and charging voltage.

[0085] The charging adjustment report can be a comprehensive document containing the charging change parameters.

[0086] Specifically, during the process of adjusting the charging parameters of the power station, the changing data of the charging process is collected in real time through Internet of Things devices, and a multi-dimensional analysis report containing visualization elements such as impedance spectrograms, temperature rise cloud maps, and efficiency heat maps is generated through a visualization engine. The report output frequency can be configured to be once every 10 - 30 minutes, and the report output object is the power station staff, so that the power station staff can grasp the charging situation in real time.

[0087] Through this solution, based on the real-time impedance characteristic parameter set and the real-time battery operation parameter set, a multi-dimensional battery state data set is constructed. On this basis, combined with the renewable energy power generation data set used to reflect the power generation situation, an evolutionary analysis of the fluctuations of the internal solid electrolyte impedance of the solid lead battery under the influence of changes in the power generation situation is carried out, and a solid electrolyte impedance evolution model for predicting the changes in the solid electrolyte impedance is constructed to achieve the prediction of the solid electrolyte impedance during the battery charging process. Based on the predicted changes in the solid electrolyte impedance, an adaptive battery charging strategy is determined, and the charging parameters of the power station are adjusted accordingly, and the corresponding charging adjustment report is provided to the power station staff to slow down the passivation rate of the internal solid electrolyte of the solid lead battery under the intermittent fluctuations of renewable energy power generation and extend the battery life.

[0088] In some embodiments, the real-time impedance characteristic parameter set includes charge transfer resistance, interfacial capacitance, and real-time interfacial impedance; the real-time battery operation parameter set includes charging power, operating temperature, and state of charge; the real-time impedance characteristic parameter set is obtained through in-situ electrochemical impedance spectroscopy detection, the detection frequency range is limited within a preset detection frequency range, the amplitude corresponding to the excitation signal is limited to be less than the amplitude of the battery rated current within a preset amplitude range, and the detection period is triggered once every time the state of charge changes by a preset charge change amplitude; all parameters in the real-time impedance characteristic parameter set and the real-time battery operation parameter set adopt the hardware clock synchronization protocol.

[0089] The charge transfer resistance can be the impedance component generated when charges transfer at the electrode / electrolyte interface during the charge and discharge process of the solid lead battery, reflecting the kinetic characteristics of the electrochemical reaction, and is extracted from the real part impedance value in the middle and low frequency bands (0.1 Hz - 10 kHz) in in-situ electrochemical impedance spectroscopy detection, and its source is the internal electrochemical polarization process of the battery.

[0090] The interfacial capacitance can be a parameter characterizing the capacitance characteristics of the double-layer structure at the interface between the electrode and the solid electrolyte, which is directly related to the interface stability, and is calculated through the reciprocal of the imaginary part impedance in the high frequency band (1 kHz - 100 kHz) of electrochemical impedance spectroscopy detection, and its source is the interfacial charge accumulation effect.

[0091] The charging power can be the rate at which the battery currently inputs electrical energy, and is collected in real time through the power sensor built in the bidirectional charge and discharge controller.

[0092] The real-time interfacial impedance can be a comprehensive parameter reflecting the current ion migration resistance and grain boundary impedance inside the solid electrolyte, which is obtained by separating the diffusion impedance component in the low-frequency range (<1Hz) of the impedance spectrum, and its source is the microstructural characteristics of the solid electrolyte.

[0093] The operating temperature can be the temperature value of the internal core area of the battery during its operation.

[0094] The state of charge can be the percentage value used to characterize the remaining battery charge and the rated capacity, which is obtained by calculating using the Coulomb integration method combined with the open-circuit voltage correction model.

[0095] In-situ electrochemical impedance spectroscopy detection can be an on-line battery detection technology using a three-electrode system. The working electrode and the reference electrode are respectively connected to the positive and negative electrodes of the battery, and the auxiliary electrode is used for current balance. The battery operation is not interrupted during the detection process.

[0096] The detection frequency range can be the signal frequency interval used during the in-situ electrochemical impedance spectroscopy detection process.

[0097] The preset detection frequency range can be the preset signal frequency interval for the solid lead battery. The preset detection frequency range can be taken as 0.1Hz - 100kHz.

[0098] The amplitude corresponding to the excitation signal can be the change amplitude of the current signal applied to the battery during the in-situ electrochemical impedance spectroscopy detection process.

[0099] The preset amplitude range can be the preset limit range for the amplitude of the excitation signal. The preset amplitude range can be set to <5% of the battery rated current.

[0100] The detection period can be the unit time period for obtaining the real-time impedance characteristic parameter set by means of in-situ electrochemical impedance spectroscopy detection. Every time the time corresponding to one detection period passes, the current real-time impedance characteristic parameter set is obtained.

[0101] The preset state of charge change amplitude can be the preset state change amplitude used to trigger the in-situ electrochemical impedance spectroscopy detection. The preset state of charge change amplitude can be set to ±2%.

[0102] The hardware clock synchronization protocol can be a protocol used to ensure the clock consistency between the data collected by multiple hardware devices. The hardware clock synchronization protocol can adopt the PTP protocol (IEEE 1588 Precision Clock Synchronization Protocol, the precision clock synchronization protocol standard for network measurement and control systems).

[0103] Specifically, compared with liquid batteries, the change in interfacial impedance of solid-state batteries is the dominant factor in their performance degradation. The direct contact between the solid electrolyte and the electrode leads to a significant increase in interfacial impedance. The charge transfer resistance directly reflects the lithium-ion insertion / extraction barrier, and the change in interfacial capacitance can warn of the growth of the interfacial layer. For example, when the interfacial capacitance drops by 20%, it indicates irreversible thickening of the solid electrolyte interface, and the charging strategy needs to be adjusted immediately. Traditional DC internal resistance detection cannot distinguish the contributions of the bulk and interfacial impedances. By dividing the detection frequency band into 0.1 Hz - 100 kHz, the bulk charge transfer impedance (1 Hz - 1 kHz) and interfacial capacitance characteristics (>1 kHz) can be decoupled, providing multi-scale information for impedance evolution analysis. Solid-state lead batteries are sensitive to overcurrent. By limiting the amplitude, while ensuring the signal-to-noise ratio, acceleration of aging can be avoided. Setting the upper limit of the frequency range to 100 kHz can effectively suppress the phase distortion caused by lead inductance during in-situ electrochemical impedance spectroscopy detection. The solid electrolyte inside the solid-state lead battery is prone to electrolyte grain boundary cracking when the state of charge fluctuates significantly. Setting a ±2% state of charge change trigger threshold can densely sample impedance data at the phase transition critical point. Charging power transients will cause instantaneous shifts in the impedance spectrum. If there is a time deviation between the real-time impedance characteristic parameter set and the real-time battery operating parameter set, it will lead to distortion in the subsequent predictive analysis of the solid electrolyte impedance. After adopting the hardware clock synchronization protocol, the multi-parameter time alignment error is reduced to the microsecond level.

[0104] Through this solution, based on the in-situ electrochemical impedance spectroscopy detection method, combined with a small-amplitude current perturbation and a state-of-charge periodic triggering mechanism, the accuracy and effectiveness of the real-time impedance characteristic parameter set are improved. By eliminating the time lag deviation between the real-time impedance characteristic parameter set from different data sources and the real-time battery operating parameter set through hardware-level clock synchronization, scientific and reliable data is provided for the subsequent construction of the solid electrolyte impedance evolution model.

[0105] In some embodiments, according to the detection period, downsampling is performed on the real-time battery operating parameter set, retaining the data of each parameter in the real-time battery operating parameter set corresponding to the time points of each detection period to determine the battery periodic operating parameter set. The cubic spline interpolation algorithm is used to perform unified time axis alignment processing on all parameters in the real-time impedance characteristic set and all parameters in the battery periodic operating parameter set to determine the impedance characteristic periodic fluctuation parameter set. Based on the battery periodic operating parameter set and the impedance characteristic periodic fluctuation parameter set, a multi-dimensional battery state data set is constructed.

[0106] Downsampling can be a data processing process of screening data points of real-time battery operating parameters collected at high frequencies (such as second-level temperature data) according to the detection period and retaining only the part of the data aligned with the impedance detection time points.

[0107] The battery periodic operating parameter set can be a set of various parameters corresponding to different detection period time points in the real-time battery operating parameter set.

[0108] The cubic spline interpolation algorithm can be a mathematical algorithm that realizes curve smooth fitting through piecewise cubic polynomial functions.

[0109] The unified time axis alignment process can be a process of mapping impedance parameters and operating parameters with different sampling frequencies to the same time coordinate system through interpolation processing.

[0110] The impedance characteristic period fluctuation parameter set can be a set of parameters corresponding to different detection period time points within the real-time impedance characteristic parameter set.

[0111] Specifically, there are differences in the sampling frequencies between the real-time battery operating parameter set and the real-time impedance characteristic set, resulting in significant differences in the number of parameters directly between the real-time battery operating parameter set and the real-time impedance characteristic set. Even if the parameters in the real-time battery operating parameter set and the real-time impedance characteristic set are in the same time coordinate system through the hardware clock synchronization protocol, there will still be some real-time battery operating parameters that cannot establish an effective connection with the real-time impedance characteristic parameters (the number of time points does not correspond); based on the time points corresponding to different detection periods, by downsampling to retain the key parameters synchronized with the detection period in the real-time battery operating parameter set, the battery periodic operating parameter set is obtained; on this basis, using the cubic spline interpolation algorithm, through the corresponding piecewise cubic polynomial, the real-time impedance characteristic parameter set (low-frequency discrete data) and the battery periodic operating parameter set are unified to the same time reference to obtain the impedance characteristic period fluctuation parameter set. By integrating the battery periodic operating parameter set and the impedance characteristic period fluctuation parameter set, the impedance characteristic period fluctuation parameter set is constructed.

[0112] Through this solution, based on the detection period, the real-time battery operating parameter set is downsampled, and the corresponding data in the real-time battery operating parameter set at the time points corresponding to different detection periods are screened to obtain the battery periodic operating parameter set. Further, through the cubic spline interpolation algorithm, the real-time impedance characteristic parameter set and the battery periodic operating parameter set are unified to the same time reference to obtain the impedance characteristic period fluctuation parameter set, so as to construct the impedance characteristic period fluctuation parameter set, avoiding the data correlation analysis deviation caused by the difference in the order of magnitude of data brought by the sampling frequency difference between the real-time battery operating parameter set and the real-time impedance characteristic set, and improving the accuracy of the subsequent solid electrolyte evolution analysis process.

[0113] In some embodiments, based on the ridge regression algorithm, analyze the correlation influence relationship of charge transfer resistance, interface capacitance, charging power, operating temperature, and state of charge on the solid electrolyte impedance at the corresponding time points, and fit to obtain the resistance influence coefficient, capacitance influence coefficient, power influence coefficient, and charge influence coefficient; according to the resistance influence coefficient, capacitance influence coefficient, power influence coefficient, and charge influence coefficient, construct a solid electrolyte impedance evolution model.

[0114] The ridge regression algorithm can be an improved least squares estimation method that solves the multicollinearity problem by introducing an L2 regularization term. Its technical origin is statistical learning theory and it is used to handle the instability problem of the model caused by variable correlation in high-dimensional data.

[0115] The resistance influence coefficient can be a numerical value used to quantify the degree of influence of the change in charge transfer resistance on the change in the impedance of the solid electrolyte.

[0116] The capacitance influence coefficient can be a numerical value used to quantify the degree of influence of the change in interface capacitance on the change in the impedance of the solid electrolyte.

[0117] The power influence coefficient can be a numerical value used to quantify the degree of influence of the charging power fluctuation on the change in the impedance of the solid electrolyte.

[0118] The state-of-charge influence coefficient can be a numerical value used to quantify the degree of influence of the change in the state of charge on the change in the impedance of the solid electrolyte.

[0119] Specifically, the change in the impedance of the solid electrolyte is the result of the combined action of electrochemistry, thermodynamics, and electrical behavior. For example, an increase in charging power will exacerbate the ion concentration gradient (electrical factor), resulting in an increase in the impedance of the solid electrolyte; while an increase in temperature (thermal factor) can temporarily improve the ion mobility and partially offset the power influence. A model that only considers a single factor cannot capture this coupling effect under the combined action of multiple factors; furthermore, due to the correlation between different parameters, such as a strong correlation between charge transfer resistance and interface capacitance, the traditional least squares method will lead to distorted coefficient estimation. By introducing a regularized constraint algorithm of ridge regression, with the real-time interface impedance as the dependent variable and the charge transfer resistance, interface capacitance, charging power, operating temperature, and state of charge at the corresponding time point as independent variables, the resistance influence coefficient, capacitance influence coefficient, power influence coefficient, and state-of-charge influence coefficient are obtained by fitting, so as to quantify the associated influence of different parameters on the impedance of the solid electrolyte. On this basis, a solid electrolyte impedance evolution model for predicting the change in the impedance of the solid electrolyte is constructed.

[0120] Through this solution, the ridge regression algorithm is used to quantitatively analyze the associated influence between the charge transfer resistance, interface capacitance, charging power, operating temperature, and state of charge at the corresponding time point under different real-time interface impedances and the impedance of the solid electrolyte, and the resistance influence coefficient, capacitance influence coefficient, power influence coefficient, and state-of-charge influence coefficient are obtained by fitting. Furthermore, a solid electrolyte impedance evolution model for predicting the change in the impedance of the solid electrolyte is constructed to reduce the problem that the estimation result is inaccurate due to the loss of the coupling effect between parameters by the traditional least squares method.

[0121] In some embodiments, a solid electrolyte impedance evolution model is constructed according to the resistance influence coefficient, capacitance influence coefficient, power influence coefficient, and charge influence coefficient, specifically as the following formula (1):

[0122] (1)

[0123] Wherein, is the solid electrolyte impedance, is the charge transfer resistance, is the resistance influence coefficient, is the interface capacitance, is the capacitance influence coefficient, is the excitation propagation delay of the renewable energy fluctuation to the battery, is the power influence coefficient, is the time point the corresponding charging power at, is the battery rated power, is the charge influence coefficient, is the temperature difference between the operating temperature and the preset reference temperature, is the state of charge.

[0124] The excitation propagation delay can be the lag time required for the change in the renewable energy generation power to be conducted to the battery.

[0125] The battery rated power can be the rated charging power of the battery.

[0126] Specifically, the influence of the change in the charge transfer resistance on the solid electrolyte impedance is described by in formula (1); the influence of the change in the interface capacitance on the solid electrolyte impedance is described by ; there is a propagation delay in the dynamic influence of the power fluctuation of the renewable energy on the battery charging state. The influence of the power fluctuation of the renewable energy generation on the solid electrolyte impedance is described by under the condition of introducing the propagation delay; the coupling effect of the state of charge and temperature mainly affects the chemical reaction kinetics. The influence on the solid electrolyte impedance under the coupling effect of the state of charge and temperature is described by and the corresponding quantitative value of the solid electrolyte impedance is obtained by quantifying the contributions generated by the above-mentioned influences.

[0127] Through this solution, by using mathematical analysis means, according to the resistance influence coefficient, capacitance influence coefficient, power influence coefficient, and charge influence coefficient, the quantification process of the solid electrolyte impedance is constrained, and a solid electrolyte impedance evolution model for deriving the internal electrolyte impedance of the battery under different influencing factors is constructed to ensure the scientificity and accuracy of the process of predicting the solid electrolyte impedance.

[0128] In some embodiments, according to the solid electrolyte impedance evolution model, the development trend of the solid electrolyte impedance is predicted to determine the corresponding impedance prediction values at different time points; based on the impedance prediction values output by the solid electrolyte impedance evolution model and the multi-dimensional battery state data set, an adaptive pulse charging strategy, a variable cut-off voltage control strategy, and a temperature-current coupling control strategy are integrated; through the collaborative control of multiple strategies, according to the real-time charge transfer resistance, interface capacitance, state of charge, and operating temperature, the pulse charging current is dynamically adjusted, the cut-off voltage is corrected, and the relationship between temperature and current is coupled, so that the fluctuation amplitude of the solid electrolyte impedance and the battery temperature rise rate during the charging process are constrained within a preset safe evolution interval.

[0129] The impedance prediction value can be the subsequent solid electrolyte impedance value predicted according to the solid electrolyte impedance evolution model.

[0130] The adaptive pulse charging strategy can be a pulse charging strategy that dynamically adjusts the pulse parameters according to the real-time electrochemical state of the battery. Pulse charging can be a process of charging the battery by sending a series of short high-voltage pulse currents.

[0131] The variable cut-off voltage control strategy can be a voltage regulation strategy for dynamically correcting the charging termination voltage.

[0132] The temperature-current coupling control strategy can be a regulation strategy for adjusting the maximum allowable charging current according to the change in the battery temperature.

[0133] The pulse charging current can be the current applied to the battery during the pulse charging process, which appears in the form of periodic pulses.

[0134] The cut-off voltage can be the highest safe voltage value that the battery can reach during the charging process. When the battery voltage reaches this value, the charging process will automatically stop.

[0135] The relationship between temperature and current can be the mapping relationship between the change in the charging current and the change in the battery operating temperature.

[0136] Specifically, the impedance evolution model of the solid electrolyte is used to predict the future impedance trend, estimate the corresponding solid electrolyte impedance values at different time points within a subsequent fixed time period, and identify potential risks (such as sudden impedance increase) in advance, providing a forward-looking basis for strategy adjustment. During the battery charging process, a single adjustment strategy for a single-dimensional parameter (such as only adjusting the current or voltage) is difficult to simultaneously address the combined effects of the growth of the solid electrolyte impedance and temperature rise. Multidimensional collaborative control strategies need to cooperate with each other to balance battery life and battery charging efficiency. The charging strategy of this solution is composed of a pulse charging strategy, a variable cut-off voltage control strategy, and a temperature-current coupling strategy. Through the pulse charging strategy, intermittent high-current pulses are used to reduce the polarization effect. Combining the analysis of the change in charge transfer resistance, the fluctuation amplitude of the solid electrolyte impedance is suppressed. Through the variable cut-off voltage control strategy, the cut-off voltage during the battery charging process is dynamically adjusted to promptly suppress the possible deterioration trend of the solid electrolyte impedance. Through the temperature-current coupling strategy, the battery temperature rise rate is associated with the upper limit of the charging current to prevent possible thermal runaway during the battery charging process from a thermodynamic perspective.

[0137] Through this solution, by using impedance prediction and multi-strategy collaboration, the impedance fluctuation amplitude is timely limited within a safe range, the deterioration of the solid electrolyte impedance is suppressed, and at the same time, the temperature rise rate is controlled to reduce the risk of battery charging thermal runaway. Through the collaborative control of pulse charging and temperature-current coupling, the charging efficiency is guaranteed. By dynamically adjusting the cut-off voltage, the overcharge side reaction under the high state of charge of the battery is reduced, and the battery cycle life is improved.

[0138] In some embodiments, during the constant current charging stage, according to the correlation curve between the real-time value of the charge transfer resistance and the state of charge, the change trend of the charge transfer resistance at different states of charge is determined; if the growth amplitude of the current charge transfer resistance compared to the corresponding charge transfer resistance in the previous detection cycle exceeds the preset resistance growth amplitude, the pulse load reduction mode is triggered, the peak value of the pulse current is reduced according to the proportionality coefficient corresponding to the current state of charge, and at the same time, the duty cycle is reduced to a preset proportionality interval of the original value, and the intermittent period is extended to a preset multiple range of the original duration until the current charge transfer resistance drops back within the preset fluctuation threshold range.

[0139] The constant current charging stage can be the stage during the battery charging process where the charging current remains in a relatively constant state.

[0140] The correlation curve can be a time series data curve used to describe the non-linear relationship between the charge transfer resistance and the state of charge.

[0141] The proportionality coefficient can be a proportional conversion coefficient used to characterize the change in the peak value of the pulse current and the change in the charge transfer resistance at different states of charge. The proportionality coefficient can be obtained by fitting battery experimental data.

[0142] The peak pulse current can be the maximum current value allowed within the pulse period of the constant-current charging stage.

[0143] The duty cycle can be the ratio of the pulse current application time to the entire pulse period.

[0144] The intermittent period can be the time interval between two adjacent pulse periods.

[0145] The preset resistance growth amplitude can be the relative growth threshold of the charge transfer resistance that triggers the pulse load-dropping mode.

[0146] The pulse load-dropping mode can be a protection mechanism that automatically reduces the current peak, reduces the duty cycle, and extends the intermittent period when the charge transfer resistance abnormally increases.

[0147] The preset ratio range can be the preset reduction ratio of the duty cycle in the load-dropping mode (such as 50%-70%).

[0148] The preset multiple range can be the extension multiple of the intermittent period and the range interval of the extension multiple of the intermittent period in the load-dropping mode (such as 1.5-2 times).

[0149] The preset fluctuation threshold range can be the maximum allowable fluctuation range of the charge transfer resistance.

[0150] Specifically, at the initial stage of constant-current charging, at intervals of every 5% state of charge change, record the charge transfer resistance value to generate an initial correlation curve. When each detection cycle is updated, compare the new resistance value with the historical value at the same state of charge in the curve, calculate the deviation degree, and obtain the correlation curve between the charge transfer resistance and the state of charge. This curve reflects a series of change characteristics of the charge transfer resistance under different states of charge. According to this series of change characteristics, analyze the growth trend of the charge transfer resistance at different time points. If the growth amplitude of the current charge transfer resistance compared with the corresponding charge transfer resistance in the previous detection cycle exceeds the preset resistance growth amplitude, it indicates that the growth trend of the current charge transfer resistance has become abnormal during the current detection cycle. At this time, trigger the pulse load-dropping mode. During the operation of the pulse load-dropping mode, mainly adjust three parameters: the peak pulse current, the duty cycle, and the intermittent period. Reduce the peak pulse current according to the proportional coefficient corresponding to the current state of charge, and at the same time reduce the duty cycle to within the preset ratio range of the original value, and extend the intermittent period to within the preset multiple range of the original duration until the current charge transfer resistance falls back within the preset fluctuation threshold range, gradually adjusting the charge transfer resistance to reduce the impact on the charging fluctuation amplitude of the battery.

[0151] Through this solution, based on the dynamic load reduction mechanism, when the growth rate of the current charge transfer resistance exceeds the preset resistance growth rate compared to the charge transfer resistance corresponding to the previous detection period, by activating the pulse load reduction mode, the peak value of the pulse current, the duty cycle, and the intermittent period are dynamically adjusted until the current charge transfer resistance drops back within the preset fluctuation threshold range. Through the progressive charge transfer resistance adjustment strategy, the abnormal growth of the charge transfer resistance is suppressed in the initial stage, and at the same time, the impact of the charge transfer resistance adjustment process on the battery charging fluctuation amplitude is reduced.

[0152] In some embodiments, according to the impedance prediction values corresponding to different time points, the impedance increments at different time points are determined; the impedance increment and the operating temperature are analyzed. When the impedance increment exceeds the reference impedance increment or the operating temperature is higher than the preset temperature upper limit, based on the temperature-impedance coupling coefficient, the cut-off voltage reduction amount is analyzed and obtained; the cut-off voltage reduction amount satisfies the following relationship: the reduction amplitude is linearly and positively correlated with the impedance increment; according to the cut-off voltage reduction amount, the cut-off voltage in the current charging stage is corrected, and the corrected cut-off voltage acts on the charging circuit in real time through the dynamic voltage limiting module.

[0153] The impedance increment can be the difference between the current impedance prediction value and the corresponding previous impedance value.

[0154] The reference impedance increment can be the maximum increment value allowed for the solid electrolyte impedance within a detection period.

[0155] The preset temperature upper limit can be the highest safe temperature value reached by the battery during charging.

[0156] The temperature-impedance coupling coefficient can be used to characterize the influence of the coupling effect of temperature and electrolyte impedance on voltage, and the temperature-impedance coupling coefficient can be obtained by fitting battery experimental data.

[0157] The cut-off voltage reduction amount can be the cut-off voltage amplitude required to suppress the interface deterioration of the solid electrolyte due to increased impedance.

[0158] The reduction amplitude can be the adjustment amplitude of the cut-off voltage.

[0159] The dynamic voltage limiting module can be a hardware control unit that adjusts the voltage threshold of the charging circuit in real time.

[0160] Specifically, during the charging process of a solid-state lead battery, abnormal fluctuations in the electrolyte interface impedance are the core cause leading to battery performance degradation and even thermal runaway. As the charging voltage approaches or reaches the cut-off voltage, the electrochemical reactions inside the battery accelerate, which will cause an increase in the solid electrolyte interface impedance. This is because the electrolyte suffers adverse reactions, such as the decomposition of the electrolyte or the formation of an interfacial film. In the prior art, the cut-off voltage is usually set to a fixed value, and the fixed cut-off voltage cannot respond to the real-time changes in the battery state. For example, at high temperatures, the solid electrolyte interface reaction intensifies. If charging is still carried out based on the cut-off voltage as a reference, it will accelerate the thickening of the electrolyte interface layer, resulting in an increase in the charge transfer resistance, and then triggering a vicious cycle of increasing impedance. It is necessary to flexibly adjust the cut-off voltage according to the charging state of the battery. According to the impedance prediction values corresponding to different time points, determine the impedance increment at different time points. When the impedance increment exceeds the reference impedance increment or the operating temperature is higher than the preset temperature upper limit, it indicates that the current battery charging state tends to be abnormal. According to the temperature-impedance coupling coefficient, combined with the impedance difference between the current impedance increment and the reference impedance increment, determine the cut-off voltage reduction amount based on the product of the coupling coefficient and the impedance difference, and use the cut-off voltage reduction amount as a data reference to correct the cut-off voltage in the current charging stage to reduce the fluctuation amplitude of the solid electrolyte interface impedance.

[0161] Through this solution, predict and analyze the impedance increment. Based on this, combined with the operating temperature during the battery charging process, dynamically reduce the cut-off voltage, reduce the accumulation of solid electrolyte interface stress in the high-voltage constant-voltage stage, reduce the fluctuation amplitude of the solid electrolyte interface impedance, cooperate with the adaptive pulse charging strategy, form a "current-voltage" two-dimensional control, and improve the inhibitory effect on the deterioration of the solid electrolyte interface impedance.

[0162] In some embodiments, according to the battery operation experimental data, establish a mapping relationship table between the operating temperature gradient and the maximum allowable charging current. In the mapping relationship table, set the current upper limit and the temperature rise rate threshold corresponding to different temperature intervals; according to the operating temperature at different time points, determine the battery temperature change rate. If the battery temperature change rate exceeds the corresponding temperature rise rate threshold, then reduce the charging current according to the current interface capacitance in a gradient manner; according to the reduction amplitude of the charging current, extend the duration of the corresponding constant-voltage charging stage; when the operating temperature drops below the corresponding temperature rise rate threshold and remains for more than the preset number of maintenance cycle detection periods, restore the original charging current.

[0163] The mapping relationship table can be an associated data table of different battery operating temperature intervals (such as 0 - 25°C, 25 - 45°C, >45°C) and the corresponding current upper limit and temperature rise rate.

[0164] The temperature rise rate threshold can be the maximum temperature rise rate allowed during the battery charging process.

[0165] The battery temperature change rate can be the change value of the battery operating temperature per unit time.

[0166] The preset temperature change rate can be the maximum value allowed for the battery temperature change per unit time preset.

[0167] The preset state of charge ratio can be the maximum load state reached during the charging process of the operating battery preset.

[0168] The duration of the constant voltage charging stage is the charging time in the constant voltage mode.

[0169] The preset steady state interval can be the temperature range allowing the restoration of the original charging current (such as falling below 40°C and maintaining a fluctuation of ±2°C).

[0170] The preset number of maintenance cycles can be the number of detection cycles required for the temperature to remain stable.

[0171] Specifically, the existing technology for the temperature control strategy of solid lead batteries only cuts off the charging when the temperature exceeds the absolute threshold (such as 60°C). However, the thermal runaway of solid lead batteries is often triggered by abnormal temperature rise rates (such as a 15°C rise within 5 minutes). At this time, the absolute temperature may not have reached the corresponding threshold yet, but irreversible deterioration of the solid electrolyte interface impedance has occurred. And directly turning off or significantly reducing the current easily causes a sudden increase in the stress of the solid electrolyte interface, resulting in a large fluctuation in its impedance. By conducting charge and discharge cycle experiments on the solid lead battery pack within the corresponding operating temperature range, recording the maximum allowable current and the corresponding temperature rise rate threshold in different temperature intervals, and constructing a mapping relationship table. If the current battery temperature change rate exceeds the corresponding temperature rise rate threshold in the mapping relationship table, it indicates that the current temperature rise rate is too fast. When the temperature rise rate exceeds the limit for the first time, the charging current is reduced by 20%. If it still exceeds the limit in the next cycle, an additional 10% reduction in the current is added until the temperature rise rate is lower than the threshold, realizing the gradient reduction adjustment of the charging current. The extension of the charging time caused by the current reduction is compensated by dynamically extending the constant voltage stage.

[0172] Through this solution, by using the monitoring mechanism for the temperature rise rate, the triggering probability of thermal runaway of solid lead batteries is reduced. At the same time, by using the gradient current reduction mechanism, the sudden change in the stress of the solid electrolyte interface caused by the sudden drop of the charging current is reduced, so as to reduce the fluctuation amplitude of the solid electrolyte impedance. Further, through the dynamic constant voltage compensation mechanism, the extended charging time caused by the adjustment of the charging current is reduced.

[0173] Figure 3 FIG. is a schematic structural diagram of an intelligent charging system for a solid lead battery energy storage power station provided by an embodiment of the present application, as Figure 3As shown in the figure, an intelligent charging system 300 for a solid-state lead battery energy storage power station in this embodiment includes: a data construction module 301, an evolution analysis module 302, a strategy analysis module 303, and an adjustment output module 304.

[0174] The data construction module 301 is configured to obtain a real-time impedance characteristic parameter set and a real-time battery operation parameter set, and determine a multi-dimensional battery state data set according to the real-time impedance characteristic parameter set and the real-time battery operation parameter set;

[0175] The evolution analysis module 302 is configured to obtain a renewable energy power generation data set, and based on the renewable energy power generation data set, perform an evolution analysis on the impedance fluctuation of the solid electrolyte according to the multi-dimensional battery state data set, and construct a solid electrolyte impedance evolution model;

[0176] The strategy analysis module 303 is configured to determine an adaptive battery charging strategy according to the solid electrolyte impedance evolution model;

[0177] The adjustment output module 304 is configured to adjust the charging parameters of the power station according to the adaptive battery charging strategy, and determine and output a charging adjustment report.

[0178] Optionally, in the data construction module 301, the real-time impedance characteristic parameter set includes a charge transfer resistance, an interfacial capacitance, and a real-time interfacial impedance; the real-time battery operation parameter set includes a charging power, an operating temperature, and a state of charge; the real-time impedance characteristic parameter set is obtained by in-situ electrochemical impedance spectroscopy detection, the detection frequency range is limited within a preset detection frequency range, the amplitude corresponding to the excitation signal is limited to be less than the amplitude of the battery rated current within a preset amplitude range, and the detection period is triggered once every time the state of charge changes by a preset state of charge change amplitude; all parameters in the real-time impedance characteristic parameter set and the real-time battery operation parameter set adopt a hardware clock synchronization protocol.

[0179] Optionally, the data construction module 301 is specifically configured to:

[0180] According to the detection period, downsample the real-time battery operation parameter set, retain the data of each parameter in the real-time battery operation parameter set corresponding to each time point of each detection period, and determine a battery periodic operation parameter set; use a cubic spline interpolation algorithm to perform unified time axis alignment processing on all parameters in the real-time impedance characteristic set and all parameters in the battery periodic operation parameter set, and determine an impedance characteristic periodic fluctuation parameter set; construct the multi-dimensional battery state data set according to the battery periodic operation parameter set and the impedance characteristic periodic fluctuation parameter set.

[0181] Optionally, the evolution analysis module 302 is specifically configured to:

[0182] Based on the ridge regression algorithm, according to the real-time interfacial impedance, analyze the correlation influence relationship of the charge transfer resistance, the interfacial capacitance, the charging power, the operating temperature, and the state of charge on the solid electrolyte impedance at the corresponding time point, and fit to obtain the resistance influence coefficient, the capacitance influence coefficient, the power influence coefficient, and the charge influence coefficient; according to the resistance influence coefficient, the capacitance influence coefficient, the power influence coefficient, and the charge influence coefficient, construct the solid electrolyte impedance evolution model.

[0183] Optionally, when the evolution analysis module 302 constructs the solid electrolyte impedance evolution model according to the resistance influence coefficient, the capacitance influence coefficient, the power influence coefficient, and the charge influence coefficient, the specific formula is as follows:

[0184] ;

[0185] Where, is the solid electrolyte impedance, is the charge transfer resistance, is the resistance influence coefficient, is the interfacial capacitance, is the capacitance influence coefficient, is the excitation propagation delay of the renewable energy fluctuation to the battery, is the power influence coefficient, is the time point is the corresponding charging power at the time point, is the rated power of the battery, is the charge influence coefficient, is the temperature difference between the operating temperature and the preset reference temperature, is the state of charge.

[0186] Optionally, the strategy analysis module 303 is specifically used for:

[0187] According to the solid electrolyte impedance evolution model, predict the development trend of the solid electrolyte impedance, and determine the corresponding impedance prediction values at different time points; based on the impedance prediction values output by the solid electrolyte impedance evolution model and the multi-dimensional battery state data set, integrate the adaptive pulse charging strategy, the variable cut-off voltage control strategy, and the temperature-current coupling control strategy; according to the real-time charge transfer resistance, interfacial capacitance, state of charge, and operating temperature, dynamically adjust the pulse charging current, correct the cut-off voltage, and couple the temperature and current relationship; through multi-strategy collaborative control, constrain the fluctuation amplitude and temperature rise rate of the solid electrolyte impedance during the charging process within the preset safe evolution interval.

[0188] Optionally, the adaptive pulse charging strategy in the policy analysis module 303 is specifically configured to:

[0189] In the constant current charging stage, according to the correlation curve between the real-time value of the charge transfer resistance and the state of charge, determine the change trend of the charge transfer resistance at different states of charge; if the growth rate of the current charge transfer resistance exceeds the preset resistance growth rate compared to the charge transfer resistance corresponding to the previous detection period, trigger the pulse down-regulation mode, reduce the peak pulse current according to the proportionality coefficient corresponding to the current state of charge, and at the same time reduce the duty cycle to a preset proportion range of the original value, and extend the intermittent period to a preset multiple range of the original duration until the current charge transfer resistance falls back within the preset fluctuation threshold range.

[0190] Optionally, the variable cut-off voltage control strategy in the policy analysis module 303 is specifically configured to:

[0191] According to the impedance prediction values corresponding to different time points, determine the impedance increments at different time points; analyze the impedance increments and the operating temperature. When the impedance increment exceeds the reference impedance increment or the operating temperature is higher than the preset temperature upper limit, based on the temperature-impedance coupling coefficient, analyze and obtain the cut-off voltage reduction amount; the cut-off voltage reduction amount satisfies the following relationship: the reduction amplitude is linearly positively correlated with the impedance increment; according to the cut-off voltage reduction amount, correct the cut-off voltage in the current charging stage, and the corrected cut-off voltage acts on the charging circuit in real time through the dynamic voltage limiting module.

[0192] Optionally, the temperature-current coupling control strategy in the policy analysis module 303 is specifically configured to:

[0193] According to the battery operation experimental data, establish a mapping relationship table between the operating temperature gradient and the maximum allowable charging current. The mapping relationship table sets the current upper limit and the temperature rise rate threshold corresponding to different temperature intervals; according to the operating temperature at different time points, determine the battery temperature change rate. If the battery temperature change rate exceeds the corresponding temperature rise rate threshold, reduce the charging current according to the current interface capacitance in a gradient manner; according to the reduction amplitude of the charging current, extend the corresponding constant voltage charging stage duration; when the operating temperature drops below the corresponding temperature rise rate threshold and remains for more than the preset number of maintenance periods of the detection period, restore the original charging current.

[0194] The system of this embodiment can be used to execute the method of any of the above embodiments, and its implementation principle and technical effects are similar, so they will not be elaborated here.

Claims

1. An intelligent charging system for a solid-state lead battery energy storage power station, characterized in that, Including: Obtain a real-time impedance characteristic parameter set and a real-time battery operation parameter set, and determine a multi-dimensional battery state data set according to the real-time impedance characteristic parameter set and the real-time battery operation parameter set; Obtain a renewable energy power generation data set, and based on the renewable energy power generation data set, perform an evolution analysis on the impedance fluctuation of the solid electrolyte according to the multi-dimensional battery state data set, and construct a solid electrolyte impedance evolution model; Determine an adaptive battery charging strategy according to the solid electrolyte impedance evolution model; Adjust the charging parameters of the power station according to the adaptive battery charging strategy, and determine and output a charging adjustment report; The real-time impedance characteristic parameter set includes charge transfer resistance, interfacial capacitance, and real-time interfacial impedance; The real-time battery operation parameter set includes charging power, operating temperature, and state of charge; The real-time impedance characteristic parameter set is obtained through in-situ electrochemical impedance spectroscopy detection. The detection frequency range is limited within a preset detection frequency range, the amplitude corresponding to the excitation signal is limited to be less than the amplitude of the battery rated current within a preset amplitude range, and the detection period is triggered once for each change of the state of charge by a preset state-of-charge change amplitude; All parameters in the real-time impedance characteristic parameter set and the real-time battery operation parameter set adopt a hardware clock synchronization protocol; The performing an evolution analysis on the impedance fluctuation of the solid electrolyte according to the multi-dimensional battery state data set, and constructing a solid electrolyte impedance evolution model based on the renewable energy power generation data set includes: Based on the ridge regression algorithm, analyze the correlation influence relationship of the charge transfer resistance, the interfacial capacitance, the charging power, the operating temperature, and the state of charge on the solid electrolyte impedance at the corresponding time point according to the real-time interfacial impedance, and fit to obtain a resistance influence coefficient, a capacitance influence coefficient, a power influence coefficient, and a state-of-charge influence coefficient; Construct the solid electrolyte impedance evolution model according to the resistance influence coefficient, the capacitance influence coefficient, the power influence coefficient, and the state-of-charge influence coefficient.

2. The system according to claim 1, wherein The determining a multi-dimensional battery state data set according to the real-time impedance characteristic parameter set and the real-time battery operation parameter set includes: Downsample the real-time battery operation parameter set according to the detection period, retain the data of each parameter in the real-time battery operation parameter set corresponding to each time point of each detection period, and determine a battery periodic operation parameter set; Use the cubic spline interpolation algorithm to perform unified time axis alignment processing on all parameters in the real-time impedance characteristic set and all parameters in the battery periodic operation parameter set, and determine an impedance characteristic periodic fluctuation parameter set; Construct the multi-dimensional battery state data set according to the battery periodic operation parameter set and the impedance characteristic periodic fluctuation parameter set.

3. The system according to claim 1, wherein The constructing the solid electrolyte impedance evolution model according to the resistance influence coefficient, the capacitance influence coefficient, the power influence coefficient, and the state-of-charge influence coefficient is specifically the following formula: ； Among them, is the impedance of the solid electrolyte, is the charge transfer resistance, is the resistance influence coefficient, is the interface capacitance, is the capacitance influence coefficient, is the excitation propagation delay of the renewable energy fluctuation to the battery, is the power influence coefficient, is the time point is the corresponding charging power at is the rated power of the battery, is the charge influence coefficient, is the temperature difference between the operating temperature and the preset reference temperature, is the state of charge.

4. The system according to claim 3, wherein The adaptive battery charging strategy includes: Predict the development trend of the solid electrolyte impedance according to the solid electrolyte impedance evolution model, and determine the corresponding impedance prediction values at different time points; Integrate the adaptive pulse charging strategy, variable cut-off voltage control strategy, and temperature-current coupling control strategy based on the impedance prediction value output by the solid-state electrolyte impedance evolution model and the multi-dimensional battery state dataset; Dynamically adjust the pulse charging current, correct the cut-off voltage, and couple the temperature-current relationship according to the real-time charge transfer resistance, interface capacitance, state of charge, and operating temperature; Through the collaborative control of multiple strategies, the fluctuation amplitude of the solid-state electrolyte impedance and the temperature rise rate during the charging process are constrained within a preset safe evolution interval.

5. The system according to claim 4, characterized in that, The adaptive pulse charging strategy includes: In the constant current charging stage, determine the change trend of the charge transfer resistance at different states of charge according to the correlation curve between the real-time value of the charge transfer resistance and the state of charge; If the growth rate of the current charge transfer resistance exceeds the preset resistance growth rate compared to the charge transfer resistance corresponding to the previous detection period, trigger the pulse load reduction mode, reduce the peak pulse current according to the proportional coefficient corresponding to the current state of charge, at the same time reduce the duty cycle to within a preset proportional interval of the original value, and extend the intermittent period to within a preset multiple range of the original duration until the current charge transfer resistance falls back within the preset fluctuation threshold range.

6. The system according to claim 5, wherein The variable cut-off voltage control strategy includes: Determine the impedance increment at different time points according to the impedance prediction value corresponding to different time points; Analyze the impedance increment and the operating temperature. When the impedance increment exceeds the reference impedance increment or the operating temperature is higher than the preset temperature upper limit, analyze and obtain the cut-off voltage reduction amount based on the temperature-impedance coupling coefficient; The cut-off voltage reduction amount satisfies the following relationship: the reduction amplitude is linearly positively correlated with the impedance increment; According to the cut-off voltage reduction amount, correct the cut-off voltage in the current charging stage, and the corrected cut-off voltage acts on the charging circuit in real time through the dynamic voltage limiting module.

7. The system according to claim 6, wherein The temperature-current coupling control strategy includes: Based on the battery operation experimental data, establish a mapping relationship table between the operating temperature gradient and the maximum allowable charging current, and set the current upper limit and temperature rise rate threshold corresponding to different temperature intervals in the mapping relationship table; Determine the battery temperature change rate according to the operating temperature at different time points. If the battery temperature change rate exceeds the corresponding temperature rise rate threshold, reduce the charging current according to the current interface capacitance in a gradient manner; Prolong the duration of the corresponding constant voltage charging stage according to the reduction amplitude of the charging current; When the operating temperature drops below the corresponding temperature rise rate threshold and remains above the preset maintenance cycle number of detection periods, restore the original charging current.

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