Intelligent charging system for solid-state lead battery energy storage power station

Abstract

The invention relates to the technical field of battery intelligent charging, in particular to an intelligent charging system for a solid-state lead battery energy storage power station. The method comprises the following steps: acquiring a real-time impedance characteristic parameter set and a real-time battery operation parameter set, and 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; obtaining a renewable energy power generation data set, and based on the renewable energy power generation data set and according to the multi-dimensional battery state data set, carrying out evolution analysis on solid electrolyte impedance fluctuation, and constructing a solid electrolyte impedance evolution model; determining an adaptive battery charging strategy according to the solid electrolyte impedance evolution model; and according to the self-adaptive battery charging strategy, power station charging parameters are adjusted, and a charging adjustment report is determined and output. The passivation speed of the solid electrolyte in the solid lead battery can be slowed down under the condition of intermittent fluctuation of renewable energy power generation, and the service life of the battery is prolonged.

Description

Technical Field

[0001] The present application relates to the technical field of battery intelligent 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 unable to meet the long-term and high-reliability energy storage needs of renewable energy due to their low energy density, short cycle life, and high maintenance costs. Solid-state lead batteries, as their upgraded technology, have higher safety and longer cycle life.

[0003] However, when existing solid-state lead batteries are used in renewable energy storage power stations, they are affected by the intermittent fluctuations in the power generation of renewable energy. During the continuous non-steady-state charging and discharging process, the solid electrolyte inside the solid-state lead battery is prone to accelerated passivation, causing the battery capacity to decay faster. Summary of the invention

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

[0005] In a first aspect, the present application provides a smart charging method for a solid-state lead battery energy storage power station, the method comprising: Acquire a real-time impedance characteristic parameter set and a real-time battery operation parameter set, and determine a multi-dimensional battery status data set according to the real-time impedance characteristic parameter set and the real-time battery operation parameter set; Acquire a renewable energy power generation data set, and based on the renewable energy power generation data set and a multi-dimensional battery state data set, perform evolution analysis on solid electrolyte impedance fluctuations to construct a solid electrolyte impedance evolution model; Determining an adaptive battery charging strategy according to the solid electrolyte impedance evolution model; According to the adaptive battery charging strategy, the power station charging parameters are adjusted, and a charging adjustment report is determined and output.

[0006] Through this scheme, a multidimensional battery status data set is constructed 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 used to reflect the power generation situation, the fluctuation of the solid electrolyte impedance inside the solid-state lead battery under the influence of the change of power generation situation is analyzed, and a solid electrolyte impedance evolution model for predicting the change of solid electrolyte impedance is constructed to realize the prediction of the solid electrolyte impedance during the battery charging process. Based on the predicted change of solid electrolyte impedance, an adaptive battery charging strategy is determined to adjust the charging parameters of the power station, 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-state lead battery under the condition of intermittent fluctuations in renewable energy power generation, thereby extending the battery life.

[0007] Optionally, the real-time impedance characteristic parameter set includes charge transfer resistance, interface capacitance and real-time interface impedance; The real-time battery operating parameter set includes charging power, operating temperature and 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 to 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 cycle is triggered once every time the charge state 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 a hardware clock synchronization protocol.

[0008] Through this scheme, based on the in-situ electrochemical impedance spectroscopy detection method, combined with small current perturbations and charge state periodic triggering mechanism, the accuracy and effectiveness of the real-time impedance characteristic parameter set are improved. Through hardware-level clock synchronization, the time lag deviation between the real-time impedance characteristic parameter set with different data sources and the real-time battery operation parameter set is eliminated, providing scientific and reliable data for the subsequent construction of the solid electrolyte impedance evolution model.

[0009] Optionally, determining a multidimensional battery status data set according to the real-time impedance characteristic parameter set and the real-time battery operation parameter set includes: According to the detection cycle, downsampling the real-time battery operation parameter set, retaining data of each parameter in the real-time battery operation parameter set and each time point corresponding to the detection cycle, and determining a battery periodic operation parameter set; Using a cubic spline interpolation algorithm, all parameters in the real-time impedance characteristic set and all parameters in the battery periodic operation parameter set are aligned on a unified time axis to determine an impedance characteristic periodic fluctuation parameter set; The multi-dimensional battery status data set is constructed according to the battery periodic operation parameter set and the impedance characteristic periodic fluctuation parameter set.

[0010] Through this scheme, the real-time battery operation parameter set is downsampled based on the detection cycle, and the corresponding data in the real-time battery operation parameter set at the corresponding time points of different detection cycles are screened to obtain the battery periodic operation parameter set. Further, through the cubic spline interpolation algorithm, the real-time impedance characteristic parameter set and the battery periodic operation parameter set are unified to the same time base to obtain the impedance characteristic periodic fluctuation parameter set, so as to construct the impedance characteristic periodic fluctuation parameter set, avoid the data correlation analysis deviation caused by the difference in data order of magnitude caused by the difference in sampling frequency between the real-time battery operation parameter set and the real-time impedance characteristic set, and improve the accuracy of the subsequent solid electrolyte evolution analysis process.

[0011] Optionally, based on the renewable energy power generation data set and according to the multidimensional battery state data set, performing evolution analysis on the solid electrolyte impedance fluctuation and constructing a solid electrolyte impedance evolution model includes: Based on the ridge regression algorithm, according to the real-time interface impedance, the correlation and influence relationship between the charge transfer resistance, the interface capacitance, the charging power, the operating temperature and the state of charge at the corresponding time point on the solid electrolyte impedance is analyzed, and the resistance influence coefficient, the capacitance influence coefficient, the power influence coefficient and the charge influence coefficient are obtained by fitting; The solid electrolyte impedance evolution model is constructed according to the resistance influence coefficient, the capacitance influence coefficient, the power influence coefficient and the charging influence coefficient.

[0012] Through this scheme, the ridge regression algorithm is used to quantitatively analyze the correlation between the charge transfer resistance, interface capacitance, charging power, operating temperature and charge state at the corresponding time points under different real-time interface impedances and the solid electrolyte impedance, and the resistance influence coefficient, capacitance influence coefficient, power influence coefficient and charge influence coefficient are fitted. Then, a solid electrolyte impedance evolution model for predicting the change of solid electrolyte impedance is constructed to reduce the problem of inaccurate estimation results caused by the coupling effect between the missing parameters of the traditional least squares method.

[0013] Optionally, the solid electrolyte impedance evolution model is constructed according to the resistance influence coefficient, the capacitance influence coefficient, the power influence coefficient and the charge influence coefficient, which is specifically the following formula: ; in, 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 propagation delay of the incentive of renewable energy fluctuations to the battery, is the power influence coefficient, For time point The corresponding charging power is as follows, is the battery rated power, is the charging influence coefficient, is the temperature difference between the operating temperature and the preset reference temperature, is the state of charge.

[0014] Through this scheme, mathematical analysis methods are used to constrain the quantification process of solid electrolyte impedance according to the resistance influence coefficient, capacitance influence coefficient, power influence coefficient and charge influence coefficient, and a solid electrolyte impedance evolution model is constructed to derive the internal electrolyte impedance of the battery under different influencing factors, so as to ensure the scientificity and accuracy of the prediction of the solid electrolyte impedance process.

[0015] Optionally, the adaptive battery charging strategy includes: 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 value 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; Dynamically adjust pulse charging current, correct cutoff voltage and couple temperature and current relationship according to real-time charge transfer resistance, interface capacitance, state of charge and operating temperature; Through multi-strategy coordinated control, the impedance fluctuation amplitude and temperature rise rate of the solid electrolyte during the charging process are constrained within a preset safe evolution range.

[0016] Through this solution, impedance prediction and multi-strategy collaboration are utilized to timely limit the impedance fluctuation amplitude to a safe range, inhibit the deterioration of solid electrolyte impedance, and control the temperature rise rate at the same time to reduce the risk of thermal runaway of battery charging. The charging efficiency is guaranteed through the coordinated control of pulse charging and temperature-current coupling, and the overcharging side reaction of the battery under high charge state is reduced through dynamic cut-off voltage adjustment, thereby improving the battery cycle life.

[0017] Optionally, the adaptive pulse charging strategy includes: 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, the change trend of the charge transfer resistance under different states of charge is determined; If the increase in the current charge transfer resistance compared to the charge transfer resistance corresponding to the previous detection cycle exceeds the preset resistance increase, the pulse load reduction mode is triggered, and the pulse current peak is reduced according to the proportional coefficient corresponding to the current charge state. At the same time, the duty cycle is reduced to within a preset proportional range of the original value, and the intermittent period is extended to within a preset multiple range of the original duration, until the current charge transfer resistance falls back to within a preset fluctuation threshold range.

[0018] Through this scheme, based on the dynamic load reduction mechanism, when the increase of the current charge transfer resistance compared with the charge transfer resistance corresponding to the previous detection cycle exceeds the preset resistance increase, the pulse load reduction mode is activated to dynamically adjust the pulse current peak, duty cycle and intermittent period until the current charge transfer resistance falls back to 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 the influence of the charge transfer resistance adjustment process on the battery charging fluctuation range is reduced.

[0019] Optionally, the variable cut-off voltage control strategy includes: Determining impedance increments at different time points according to the impedance prediction values ​​corresponding to different time points; Analyze the impedance increment and the operating temperature, and when the impedance increment exceeds the reference impedance increment or the operating temperature is higher than the preset upper temperature 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, 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.

[0020] Through this scheme, the impedance increment is predicted and analyzed, and based on this, the cut-off voltage is dynamically reduced in combination with the operating temperature during the battery charging process to reduce the accumulation of solid electrolyte interface stress in the high-voltage constant-voltage stage and the fluctuation amplitude of the solid electrolyte interface impedance. This is coordinated with the adaptive pulse charging strategy to form a "current-voltage" two-dimensional control, thereby improving the inhibitory effect on the deterioration of the solid electrolyte interface impedance.

[0021] Optionally, the temperature-current coupling control strategy includes: According to the battery operation experimental data, a mapping relationship table between the operating temperature gradient and the maximum allowable charging current is established, in which the current upper limit and the temperature rise rate threshold corresponding to different temperature intervals are set; Determine the battery temperature change rate according to the operating temperature at different time points, and if the battery temperature change rate exceeds the corresponding temperature rise rate threshold, reduce the charging current according to the gradient according to the current interface capacitance; According to the reduction range of the charging current, the duration of the corresponding constant voltage charging phase is extended; When the operating temperature drops below the corresponding temperature rise rate threshold and is maintained for more than a preset number of maintenance cycles of the detection cycles, the original charging current is restored.

[0022] Through this scheme, a monitoring mechanism for the temperature rise rate is utilized to reduce the probability of thermal runaway triggering of solid-state lead batteries. At the same time, a gradient current reduction mechanism is utilized to reduce the sudden stress change at the solid electrolyte interface caused by a sudden drop in charging current, thereby reducing the fluctuation amplitude of the solid electrolyte impedance. Furthermore, a dynamic constant voltage compensation mechanism is used to reduce the extended charging time caused by charging current adjustment.

[0023] In a second aspect, the present application provides an intelligent charging system for a solid-state lead battery energy storage power station, the system comprising: A data construction module, used 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; An evolution analysis module is used to obtain a renewable energy power generation data set, and based on the renewable energy power generation data set and a multi-dimensional battery state data set, perform evolution analysis on the solid electrolyte impedance fluctuation and construct a solid electrolyte impedance evolution model; A strategy analysis module, used to determine an adaptive battery charging strategy based on the solid electrolyte impedance evolution model; The adjustment output module is used to adjust the charging parameters of the power station according to the adaptive battery charging strategy, and determine and output a charging adjustment report. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0025] Figure 1 A schematic diagram of an application scenario provided for an embodiment of the present application; Figure 2 A flow chart of a smart charging method for a solid-state lead battery energy storage power station provided in one embodiment of the present application; Figure 3A schematic structural diagram of an intelligent charging system for a solid-state lead battery energy storage power station provided in one embodiment of the present application. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0027] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article, unless otherwise specified, generally means that the associated objects before and after are in an "or" relationship.

[0028] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0029] When existing solid-state lead batteries are used in renewable energy storage power stations, they are affected by the intermittent fluctuations in renewable energy power generation. During the continuous non-steady-state charging and discharging process, the solid electrolyte inside the solid-state lead battery is prone to accelerated passivation, causing the battery capacity to decay faster.

[0030] 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 multidimensional battery status data set is constructed. On this basis, combined with the renewable energy power generation data set used to reflect the power generation situation, the fluctuation of the solid electrolyte impedance inside the solid-state lead battery under the influence of the change in power generation situation is analyzed, and a solid electrolyte impedance evolution model for predicting the change in solid electrolyte impedance is constructed to achieve the prediction of the solid electrolyte impedance during the battery charging process, and based on the predicted change in solid electrolyte impedance, an adaptive battery charging strategy is determined to adjust the charging parameters of the power station, 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-state lead battery under the condition of intermittent fluctuations in renewable energy power generation, and extend the battery life.

[0031] Figure 1 A schematic diagram of an application scenario provided by the present application. In the process of using solid-state lead batteries to store renewable energy, the method provided by the present application can reduce the passivation rate of the solid electrolyte inside the solid-state lead battery under the condition of intermittent fluctuations in renewable energy generation, thereby extending the battery life.

[0032] Specifically, the method provided in the present application is applied to any server, which communicates with an electrochemical impedance spectroscopy measuring device, a battery status sensor, and a power generation monitoring system respectively, and obtains a real-time impedance characteristic parameter set provided by the electrochemical impedance spectroscopy measuring device and a real-time battery operation parameter set provided by the battery status sensor through the server. According to the real-time impedance characteristic parameter set and the real-time battery operation parameter set, a multidimensional battery status data set is constructed. On this basis, combined with a renewable energy power generation data set provided by the power generation monitoring system to reflect the power generation situation, an evolution analysis is performed on the fluctuation of the solid electrolyte impedance 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 change in the solid electrolyte impedance is constructed to achieve the prediction of the solid electrolyte impedance during the battery charging process, and based on the predicted change in the solid electrolyte impedance, an adaptive battery charging strategy is determined to adjust the charging parameters of the power station, 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-state lead battery under the condition of intermittent fluctuations in renewable energy power generation, thereby extending the battery life.

[0033] For specific implementation methods, please refer to the following embodiments.

[0034] Figure 2 This is a flow chart of an intelligent charging system for a solid-state lead battery energy storage power station provided in an embodiment of the present application. The method of this embodiment can be applied to the server in the above scenario. Figure 2 As shown, the method includes: S201 , obtaining a real-time impedance characteristic parameter set and a real-time battery operation parameter set, and determining a multi-dimensional battery status data set according to the real-time impedance characteristic parameter set and the real-time battery operation parameter set.

[0035] The real-time impedance characteristic parameter set may be a parameter set used to characterize the dynamic characteristics of the electrochemical reaction inside the solid-state lead battery, and the real-time impedance characteristic parameter set may be obtained by an electrochemical impedance spectroscopy measurement device.

[0036] The real-time battery operating parameter set may be a set of parameters used to characterize the operating state of a solid-state lead-acid battery. The real-time battery operating parameter set may be acquired through several different types of battery state sensors, such as a temperature sensor, a load sensor, and the like.

[0037] The multi-dimensional battery status data set may be a data set obtained by performing time alignment processing on the real-time impedance characteristic parameter set and the real-time battery operation parameter set.

[0038] Specifically, the fundamental difference between solid-state lead batteries and traditional lead-acid batteries is that solid-state lead batteries use solid materials as electrolytes, such as high molecular polymers or composite solid materials, to replace the liquid sulfuric acid electrolyte in traditional lead-acid batteries. Solid-state batteries have higher safety and energy density than lead-acid batteries. However, when solid-state lead batteries are used in renewable energy storage, due to the intermittent fluctuations in renewable energy generation (such as wind power, solar energy, etc., affected by climate and time cycles), the charging power in the renewable energy storage process has intermittent fluctuations, which in turn causes the solid electrolyte impedance inside the solid-state lead battery to continue to fluctuate intermittently, which will cause the solid electrolyte interface impedance passivation problem after a long time, resulting in accelerated battery capacity decay and shortened battery life. The existing technology usually reduces the volatility of charging power by limiting the charging power within a fixed range. Although this can slow down the solid electrolyte interface impedance passivation rate to a certain extent, it is not flexible enough, easily causes significant energy loss, and is difficult to smooth the change amplitude of charging power. By collecting the real-time impedance characteristic parameter set and the real-time battery operation status parameter set during the battery operation process, the real-time impedance characteristic parameter set and the real-time battery operation status parameter set are uniformly time-aligned to obtain a multi-dimensional battery status data set, which provides a data basis for analyzing the correlation between the real-time operation status of the battery and the battery impedance characteristics in the subsequent charging process when the charging power fluctuates, so as to realize the prediction of the solid electrolyte impedance change during the real-time operation of the battery.

[0039] S202, obtaining a renewable energy power generation data set, and based on the renewable energy power generation data set and the multi-dimensional battery state data set, performing an evolution analysis on the solid electrolyte impedance fluctuation, and constructing a solid electrolyte impedance evolution model.

[0040] The renewable energy power generation data set may be a data set used to characterize the real-time power generation status and power generation trend changes of renewable energy. The renewable energy power generation data set may be obtained through a power generation monitoring system integrated in a renewable energy power station.

[0041] A solid electrolyte may be a solid material used as a battery electrolyte inside a solid-state lead battery.

[0042] The solid electrolyte impedance fluctuation can be the changing trend of the solid electrolyte impedance inside the solid lead battery during the process of storing renewable energy.

[0043] The solid electrolyte impedance evolution model can be a mathematical model used to characterize the influence of various parameters in a multidimensional battery state data set on the change of solid electrolyte impedance during battery operation.

[0044] Specifically, through mathematical analysis, based on the renewable energy power generation data set and according to the multi-dimensional battery status data set, a correlation model between renewable energy power generation and battery internal impedance changes is established to achieve cross-scale analysis of external energy input characteristics and battery state evolution. Through the solid electrolyte impedance oxidation model, it is possible to evaluate the changing trend of the solid electrolyte impedance inside the battery based on the input of external renewable energy and the battery operating status, which serves as the core reference for subsequent charging strategy conversion.

[0045] S203. Determine an adaptive battery charging strategy based on a solid electrolyte impedance evolution model.

[0046] The adaptive battery charging strategy can be a multi-strategy collaborative charging control solution that integrates current control, voltage control, and temperature control.

[0047] Specifically, based on the solid electrolyte impedance evolution model, the change of solid electrolyte impedance is predicted according to the renewable energy power generation and battery state changes. With the goal of reducing the fluctuation amplitude of the solid electrolyte, corresponding charging parameter adjustment strategies are formulated for different battery charging stages from the perspectives of current control, voltage control and temperature control. Then, an adaptive battery charging strategy is constructed to reduce the fluctuation amplitude of the solid electrolyte impedance and extend the battery life while ensuring the energy storage efficiency of the solid-state lead battery.

[0048] S204: Adjust the charging parameters of the power station according to the adaptive battery charging strategy, and determine and output a charging adjustment report.

[0049] The power station charging parameters may be parameters that affect the battery charging state, such as charging current and charging voltage.

[0050] The charge regulation report can be a comprehensive document containing the charge change parameters.

[0051] Specifically, in the process of adjusting the charging parameters of the power station, the IoT devices are used to collect the changing data of the charging process in real time, and the visualization engine is used to generate a multi-dimensional analysis report containing visualization elements such as impedance spectrum, temperature rise cloud map, efficiency thermodynamic map, etc. The report output frequency can be configured to 10-30 minutes / time, and the report output object is the power station staff, so that the power station staff can understand the charging situation in real time.

[0052] Through this scheme, a multidimensional battery status data set is constructed 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 used to reflect the power generation situation, the fluctuation of the solid electrolyte impedance inside the solid-state lead battery under the influence of the change of power generation situation is analyzed, and a solid electrolyte impedance evolution model for predicting the change of solid electrolyte impedance is constructed to realize the prediction of the solid electrolyte impedance during the battery charging process. Based on the predicted change of solid electrolyte impedance, an adaptive battery charging strategy is determined to adjust the charging parameters of the power station, 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-state lead battery under the condition of intermittent fluctuations in renewable energy power generation, thereby extending the battery life.

[0053] In some embodiments, the real-time impedance characteristic parameter set includes charge transfer resistance, interface capacitance and real-time interface 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, limiting the detection frequency range within a preset detection frequency range, limiting the amplitude corresponding to the excitation signal to be less than the amplitude of the battery rated current within a preset amplitude range, and the detection cycle is triggered once for each change in the state of charge by a preset charge change amplitude; all parameters in the real-time impedance characteristic parameter set and the real-time battery operation parameter set use a hardware clock synchronization protocol.

[0054] Charge transfer resistance can be the impedance component generated when charge is transferred at the electrode / electrolyte interface during the charge and discharge process of a solid-state lead battery. It reflects the kinetic characteristics of the electrochemical reaction and is extracted through the real impedance value in the medium and low frequency bands (0.1Hz-10kHz) in the in-situ electrochemical impedance spectroscopy detection. Its source is the electrochemical polarization process inside the battery.

[0055] The interface capacitance can be a parameter that characterizes the capacitance characteristics of the double-layer structure of the electrode and the solid electrolyte interface. It is directly related to the interface stability and is obtained by calculating the inverse of the imaginary impedance in the high frequency band (1kHz-100kHz) of the electrochemical impedance spectroscopy. The source is the interface charge accumulation effect.

[0056] The charging power can be the current rate at which the battery inputs electrical energy, which is collected in real time by the power sensor built into the bidirectional charge and discharge controller.

[0057] Real-time interfacial impedance can be a comprehensive parameter reflecting the internal ion migration resistance and grain boundary impedance of the current solid electrolyte. It is obtained by separating the diffusion impedance component in the low-frequency band (<1Hz) of the impedance spectrum, and its source is the microstructural characteristics of the solid electrolyte.

[0058] The operating temperature may be the temperature value of the internal core area of ​​the battery during operation.

[0059] The state of charge can be a percentage value used to characterize the remaining power of the battery and the rated capacity, which is calculated by the Coulomb integration method combined with the open circuit voltage correction model.

[0060] In-situ electrochemical impedance spectroscopy detection can be an online battery detection technology using a three-electrode system, in which the working electrode and the reference electrode are connected to the positive and negative electrodes of the battery respectively, and the auxiliary electrode is used for current balance. The detection process does not interrupt the battery operation.

[0061] The detection frequency range may be a signal frequency interval used in an in-situ electrochemical impedance spectroscopy detection process.

[0062] The preset detection frequency range may be a preset detection signal frequency interval for solid-state lead batteries, and the preset detection frequency range may be 0.1 Hz-100 kHz.

[0063] The amplitude corresponding to the excitation signal may be the amplitude of change corresponding to the current signal applied to the battery during the in-situ electrochemical impedance spectroscopy detection process.

[0064] The preset amplitude range may be a preset limit range for the amplitude of the excitation signal, and the preset amplitude range may be set to <5% of the rated current of the battery.

[0065] The detection cycle may be a unit time period for obtaining a real-time impedance characteristic parameter set by an in-situ electrochemical impedance spectroscopy detection method. Each time a detection cycle corresponds to a time, the current real-time impedance characteristic parameter set is obtained.

[0066] The preset charge change amplitude may be a preset state change amplitude for triggering in-situ electrochemical impedance spectroscopy detection, and the preset charge change amplitude may be set to ±2%.

[0067] The hardware clock synchronization protocol may be a protocol for ensuring clock consistency between data collected by multiple hardware devices, and the hardware clock synchronization protocol may adopt the PTP protocol (IEEE 1588 Precision Clock Synchronization Protocol, a precision clock synchronization protocol standard for network measurement and control systems).

[0068] Specifically, compared with liquid batteries, the change of interfacial impedance of solid-state batteries is the dominant factor in their performance degradation. The direct contact between the solid electrolyte and the electrode causes a significant increase in the interfacial impedance. The charge transfer resistance directly reflects the lithium ion deintercalation barrier, and the change of interfacial capacitance can warn of the growth of the interfacial layer. For example, when the interfacial capacitance drops by 20%, it indicates that the solid electrolyte interface has irreversibly thickened and the charging strategy needs to be adjusted immediately. Traditional DC internal resistance detection cannot distinguish between the contributions of bulk and interfacial impedance. By dividing the detection frequency band from 0.1Hz to 100kHz, the bulk charge transfer impedance (1Hz-1kHz) and interfacial capacitance characteristics (>1kHz) can be decoupled to provide multi-scale information for impedance evolution analysis. Solid-state lead batteries are sensitive to overcharge. Flow sensitivity, through amplitude limitation, while ensuring the signal-to-noise ratio and avoiding accelerated aging, the upper limit of the frequency range is set to 100kHz, which can effectively suppress the phase distortion caused by the lead inductance during the in-situ electrochemical impedance spectroscopy detection process; the solid electrolyte inside the solid-state lead battery is prone to electrolyte grain boundary cracking when the state of charge fluctuates significantly. By setting a ±2% state of charge change trigger threshold, the impedance data can be densely sampled at the critical point of phase change; charging power transients will cause instantaneous shifts in the impedance spectrum. If there is a direct time deviation between the real-time impedance characteristic parameter set and the real-time battery operation parameter set, it will cause subsequent prediction and analysis of the solid electrolyte impedance to be distorted. After adopting the hardware clock synchronization protocol, the multi-parameter time alignment error is reduced to the microsecond level.

[0069] Through this scheme, based on the in-situ electrochemical impedance spectroscopy detection method, combined with small current perturbations and charge state periodic triggering mechanism, the accuracy and effectiveness of the real-time impedance characteristic parameter set are improved. Through hardware-level clock synchronization, the time lag deviation between the real-time impedance characteristic parameter set with different data sources and the real-time battery operation parameter set is eliminated, providing scientific and reliable data for the subsequent construction of the solid electrolyte impedance evolution model.

[0070] In some embodiments, the real-time battery operating parameter set is downsampled according to the detection cycle, and the data of each parameter in the real-time battery operating parameter set and the corresponding time point of each detection cycle are retained to determine the battery periodic operating parameter set; a cubic spline interpolation algorithm is used to align all parameters in the real-time impedance characteristic set with all parameters in the battery periodic operating parameter set on a unified time axis to determine the impedance characteristic periodic fluctuation parameter set; a multidimensional battery status data set is constructed based on the battery periodic operating parameter set and the impedance characteristic periodic fluctuation parameter set.

[0071] Downsampling can be a data processing process that filters data points of real-time battery operating parameters (such as second-level temperature data) collected at high frequency according to the detection cycle, and retains part of the data that is aligned with the impedance detection time point.

[0072] The battery periodic operation parameter set may be a set of parameters corresponding to different detection cycle time points in the real-time battery operation parameter set.

[0073] The cubic spline interpolation algorithm may be a mathematical algorithm for achieving smooth curve fitting through a piecewise cubic polynomial function.

[0074] The unified time axis alignment process may be a process of mapping impedance parameters and operating parameters of different sampling frequencies to the same time coordinate system through interpolation processing.

[0075] The impedance characteristic periodic fluctuation parameter set may be a set of parameters corresponding to different detection period time points in the real-time impedance characteristic parameter set.

[0076] Specifically, there is a difference in sampling frequency between the real-time battery operation parameter set and the real-time impedance characteristic set, which leads to a significant difference in the number of parameters in the real-time battery operation parameter set and the real-time impedance characteristic set. Even if the parameters in the real-time battery operation 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 operation parameters that cannot establish an effective connection with the implementation impedance characteristic parameters (the number of time points does not correspond); taking the time points corresponding to different detection cycles as the benchmark, the key parameters synchronized with the detection cycle in the real-time battery operation parameter set are retained by downsampling to obtain the battery periodic operation parameter set; on this basis, the real-time impedance characteristic parameter set (low-frequency discrete data) and the battery periodic operation parameter set are unified to the same time base by using the cubic spline interpolation algorithm and the corresponding piecewise cubic polynomial, and the impedance characteristic periodic fluctuation parameter set is obtained. The impedance characteristic periodic fluctuation parameter set is constructed by integrating the battery periodic operation parameter set and the impedance characteristic periodic fluctuation parameter set.

[0077] Through this scheme, the real-time battery operation parameter set is downsampled based on the detection cycle, and the corresponding data in the real-time battery operation parameter set at the corresponding time points of different detection cycles are screened to obtain the battery periodic operation parameter set. Further, through the cubic spline interpolation algorithm, the real-time impedance characteristic parameter set and the battery periodic operation parameter set are unified to the same time base to obtain the impedance characteristic periodic fluctuation parameter set, so as to construct the impedance characteristic periodic fluctuation parameter set, avoid the data correlation analysis deviation caused by the difference in data order of magnitude caused by the difference in sampling frequency between the real-time battery operation parameter set and the real-time impedance characteristic set, and improve the accuracy of the subsequent solid electrolyte evolution analysis process.

[0078] In some embodiments, based on the ridge regression algorithm, the correlation and influence relationship between the charge transfer resistance, interface capacitance, charging power, operating temperature and charge state at the corresponding time point on the solid electrolyte impedance is analyzed, and the resistance influence coefficient, capacitance influence coefficient, power influence coefficient and charge influence coefficient are fitted; according to the resistance influence coefficient, capacitance influence coefficient, power influence coefficient and charge influence coefficient, a solid electrolyte impedance evolution model is constructed.

[0079] The ridge regression algorithm can be an improved least squares estimation method. It solves the multicollinearity problem by introducing the L2 regularization term. Its technical source is statistical learning theory, which is used to deal with the model instability problem caused by variable correlation in high-dimensional data.

[0080] The resistance influence coefficient may be a value used to quantify the degree of influence of a change in charge transfer resistance on a change in solid electrolyte impedance.

[0081] The capacitance influence coefficient may be a value used to quantify the degree of influence of the change in interface capacitance on the change in solid electrolyte impedance.

[0082] The power influence coefficient may be a numerical value used to quantify the influence of charging power fluctuation on the change of solid electrolyte impedance.

[0083] The charge influence coefficient may be a numerical value used to quantify the degree of influence of a change in the charge state on a change in the impedance of the solid electrolyte.

[0084] Specifically, the change in solid electrolyte impedance is the result of the combined effects of electrochemistry, thermodynamics, and electrical behavior. For example, an increase in charging power will intensify the ion concentration gradient (electrical factor), leading to an increase in solid electrolyte impedance; while an increase in temperature (thermal factor) can temporarily improve ion mobility and partially offset the power effect. A model that only considers a single factor cannot capture the coupling effect of multiple factors. Furthermore, due to the correlation between different parameters, such as the strong correlation between charge transfer resistance and interface capacitance, the traditional least squares method will lead to distorted coefficient estimation. By introducing the regularized constraint algorithm of ridge regression, with real-time interface impedance as the dependent variable and 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 charge influence coefficient are fitted to quantify the associated effects of different parameters on solid electrolyte impedance. On this basis, a solid electrolyte impedance evolution model for predicting changes in solid electrolyte impedance is constructed.

[0085] Through this scheme, the ridge regression algorithm is used to quantitatively analyze the correlation between the charge transfer resistance, interface capacitance, charging power, operating temperature and charge state at the corresponding time points under different real-time interface impedances and the solid electrolyte impedance, and the resistance influence coefficient, capacitance influence coefficient, power influence coefficient and charge influence coefficient are fitted. Then, a solid electrolyte impedance evolution model for predicting the change of solid electrolyte impedance is constructed to reduce the problem of inaccurate estimation results caused by the coupling effect between the missing parameters of the traditional least squares method.

[0086] In some embodiments, a solid electrolyte impedance evolution model is constructed based on the resistance influence coefficient, the capacitance influence coefficient, the power influence coefficient and the charge influence coefficient, which is specifically the following formula (1): (1) in, 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 propagation delay of the incentive of renewable energy fluctuations to the battery, is the power influence coefficient, For time point The corresponding charging power is: is the battery rated power, is the charging influence coefficient, is the temperature difference between the operating temperature and the preset reference temperature, The state of charge.

[0087] The excitation propagation delay can be the lag time required for a change in renewable energy generation power to be transmitted to the battery.

[0088] The battery rated power may be a rated charging power of the battery.

[0089] Specifically, through formula (1) Describe the effect of changes in charge transfer resistance on the impedance of solid electrolytes; Describe the effect of interface capacitance changes on solid electrolyte impedance; The dynamic effect of power fluctuations from renewable energy sources on the battery state of charge has a propagation delay, through The effect of fluctuations in renewable energy power generation on the impedance of solid electrolytes is described by introducing propagation delays. The coupling effect of state of charge and temperature mainly affects the kinetics of chemical reactions. The influence of the coupling of charge state and temperature on the impedance of the solid electrolyte is described, and the contribution of the above influences is comprehensively considered to quantify the corresponding quantitative value of the solid electrolyte impedance.

[0090] Through this scheme, mathematical analysis methods are used to constrain the quantification process of solid electrolyte impedance according to the resistance influence coefficient, capacitance influence coefficient, power influence coefficient and charge influence coefficient, and a solid electrolyte impedance evolution model is constructed to derive the internal electrolyte impedance of the battery under different influencing factors, so as to ensure the scientificity and accuracy of the prediction of the solid electrolyte impedance process.

[0091] In some embodiments, based on 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 cutoff voltage control strategy and a temperature-current coupling control strategy are integrated; through multi-strategy collaborative control, the pulse charging current is dynamically adjusted, the cutoff voltage is corrected and the relationship between temperature and current is coupled according to the real-time charge transfer resistance, interface capacitance, state of charge and operating temperature, 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 range.

[0092] The impedance prediction value may be a subsequent solid electrolyte impedance value predicted according to a solid electrolyte impedance evolution model.

[0093] The adaptive pulse charging strategy may be a pulse charging strategy that dynamically adjusts pulse parameters according to the real-time electrochemical state of the battery. Pulse charging may be a process of charging the battery by sending a series of short high-voltage pulse currents.

[0094] The variable cut-off voltage control strategy may be a voltage regulation strategy for dynamically modifying the charge termination voltage.

[0095] The temperature-current coupling control strategy may be a regulation strategy for adjusting the maximum allowable charging current according to changes in battery temperature.

[0096] The pulse charging current may be the current applied to the battery during the pulse charging process, which appears in the form of periodic pulses.

[0097] The cut-off voltage may be the highest safe voltage value that the battery charging process can reach. When the battery voltage reaches this value, the charging process will automatically stop.

[0098] The temperature-current relationship may be a mapping relationship between a change in charging current and a change in battery operating temperature.

[0099] Specifically, the future impedance trend is predicted by the solid electrolyte impedance evolution model, and the corresponding solid electrolyte impedance values ​​at different time points in the subsequent fixed time period are estimated, so as to identify potential risks (such as sudden impedance increase) in advance and provide a forward-looking basis for strategy adjustment. During the battery charging process, a single adjustment strategy for a single-dimensional parameter (such as adjusting only the current or voltage) is difficult to simultaneously cope with the synergistic effects of solid electrolyte impedance growth and temperature rise. It requires the mutual coordination of multi-dimensional collaborative control strategies to balance battery life and battery charging efficiency. The charging strategy of this scheme is collaboratively composed of a pulse charging strategy, a variable cutoff voltage control strategy, and a temperature-current coupling strategy. The pulse charging strategy utilizes intermittent high current pulses to reduce the polarization effect, and combines the analysis of the charge transfer resistance change to suppress the fluctuation amplitude of the solid electrolyte impedance; the variable cutoff voltage control strategy is used to dynamically adjust the cutoff voltage during the battery charging process to timely suppress the possible deterioration of the solid electrolyte impedance; the temperature-current coupling strategy is used to associate the battery temperature rise rate with the upper limit of the charging current to prevent thermal runaway that may occur during the battery charging process from a thermodynamic level.

[0100] Through this solution, impedance prediction and multi-strategy collaboration are utilized to timely limit the impedance fluctuation amplitude to a safe range, inhibit the deterioration of solid electrolyte impedance, and control the temperature rise rate at the same time to reduce the risk of thermal runaway of battery charging. The charging efficiency is guaranteed through the coordinated control of pulse charging and temperature-current coupling, and the overcharging side reaction of the battery under high charge state is reduced through dynamic cut-off voltage adjustment, thereby improving the battery cycle life.

[0101] In some embodiments, during the constant current charging stage, the changing trend of the charge transfer resistance under different charge states is determined based on the correlation curve between the real-time value of the charge transfer resistance and the charge state; if the increase in the current charge transfer resistance compared with the charge transfer resistance corresponding to the previous detection cycle exceeds the preset resistance increase, the pulse load reduction mode is triggered, and the pulse current peak is reduced according to the proportional coefficient corresponding to the current charge state. At the same time, the duty cycle is reduced to a preset proportional range of the original value, and the intermittent period is extended to within a preset multiple range of the original duration, until the current charge transfer resistance falls back to within a preset fluctuation threshold range.

[0102] The constant current charging stage may be a stage in the battery charging process in which the charging current is kept at a relatively constant state.

[0103] The correlation curve may be a time series data curve for describing the nonlinear relationship between the charge transfer resistance and the state of charge.

[0104] The proportionality coefficient may be a ratio conversion coefficient used to characterize the change in pulse current peak value and charge transfer resistance at different charge states. The proportionality coefficient may be obtained by fitting battery experimental data.

[0105] The pulse current peak value may be the maximum current value allowed within the pulse period of the constant current charging stage.

[0106] The duty cycle can be the ratio of the pulse current application time to the entire pulse period.

[0107] The pause period may be the time interval between two adjacent pulse periods.

[0108] The preset resistance increase amplitude may be a charge transfer resistance relative increase threshold for triggering the pulse load reduction mode.

[0109] The pulse load reduction mode can be a protection mechanism that automatically reduces the current peak, shortens the duty cycle, and prolongs the intermittent period when the charge transfer resistance increases abnormally.

[0110] The preset ratio interval may be a preset reduction ratio (eg, 50%-70%) of the duty cycle in the load reduction mode.

[0111] The preset ratio range may be a multiple of the intermittent period extension in the load reduction mode and a range of multiples of the intermittent period extension (eg, 1.5-2 times).

[0112] The preset fluctuation threshold range may be a maximum allowable fluctuation range of the charge transfer resistance.

[0113] Specifically, at the initial stage of constant current charging, the charge transfer resistance value is recorded at intervals of every 5% change in the state of charge, and an initial correlation curve is generated. When each detection cycle is updated, the new resistance value is compared with the historical value under the same state of charge in the curve, and the deviation is calculated to obtain the correlation curve between the charge transfer resistance and the state of charge. The curve reflects a series of change characteristics of the charge transfer resistance under different states of charge. According to this series of change characteristics, the growth trend of the charge transfer resistance at different time points is analyzed. If the increase in the current charge transfer resistance compared with the corresponding charge transfer resistance of the previous detection cycle exceeds the preset resistance increase, it means that the growth trend of the current charge transfer resistance in the current detection cycle is abnormal, and the pulse load reduction mode is triggered at this time. During the operation of the pulse load reduction mode, the three parameters of the pulse current peak value, the duty cycle and the intermittent period are mainly adjusted. The pulse current peak value is reduced according to the proportional coefficient corresponding to the current state of charge, and the duty cycle is reduced to the preset proportional range of the original value, and the intermittent period is extended to the preset multiple range of the original duration until the current charge transfer resistance falls back to the preset fluctuation threshold range, and the charge transfer resistance is gradually adjusted to reduce the impact on the battery charging fluctuation amplitude.

[0114] Through this scheme, based on the dynamic load reduction mechanism, when the increase of the current charge transfer resistance compared with the charge transfer resistance corresponding to the previous detection cycle exceeds the preset resistance increase, the pulse load reduction mode is activated to dynamically adjust the pulse current peak, duty cycle and intermittent period until the current charge transfer resistance falls back to 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 the influence of the charge transfer resistance adjustment process on the battery charging fluctuation range is reduced.

[0115] In some embodiments, the impedance increment at different time points is determined based on the impedance prediction values ​​corresponding to different time points; the impedance increment and the operating temperature are analyzed, and when the impedance increment exceeds the reference impedance increment or the operating temperature is higher than the preset temperature upper limit, the cut-off voltage reduction amount is analyzed 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, 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.

[0116] The impedance increment may be the difference between the current impedance prediction value and the corresponding previous impedance value.

[0117] The reference impedance increment may be a maximum increment value allowed for the solid electrolyte impedance within one detection cycle.

[0118] The preset upper temperature limit may be the highest safe temperature value that the battery can reach during the charging process.

[0119] The temperature-impedance coupling coefficient can be used to characterize the effect of the coupling effect of temperature and electrolyte impedance on voltage. The temperature-impedance coupling coefficient can be obtained by fitting battery experimental data.

[0120] The cut-off voltage reduction amount may be the cut-off voltage reduction amplitude required to suppress interface degradation due to increased impedance of the solid electrolyte.

[0121] The reduction range may be an adjustment range of the cut-off voltage.

[0122] The dynamic voltage limiting module may be a hardware control unit that adjusts the voltage threshold of the charging circuit in real time.

[0123] Specifically, during the charging process of solid-state lead batteries, abnormal fluctuations in electrolyte interface impedance are the core cause of battery performance degradation or even thermal runaway. As the charging voltage approaches or reaches the cut-off voltage, the electrochemical reaction inside the battery accelerates, which will lead to an increase in solid electrolyte interface impedance. This is because the electrolyte is subjected to adverse reactions, such as electrolyte decomposition or the formation of an interface 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 real-time changes in the battery state. For example, the solid electrolyte interface reaction intensifies at high temperatures. If charging is still performed based on the cut-off voltage, the electrolyte interface layer will accelerate thickening, resulting in an increase in charge transfer resistance, and then A vicious cycle of rising impedance is triggered; the cut-off voltage needs to be flexibly adjusted according to the charging state of the battery, and the impedance increment at different time points is determined according to the impedance prediction values ​​corresponding to different time points. When the impedance increment exceeds the benchmark impedance increment or the operating temperature is higher than the preset temperature upper limit, it indicates that the current battery charging state has tended to be abnormal. According to the temperature-impedance coupling coefficient, combined with the impedance difference between the current impedance increment and the benchmark impedance increment, the cut-off voltage reduction amount is determined according to the product of the coupling coefficient and the impedance difference. The cut-off voltage reduction amount is used 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.

[0124] Through this scheme, the impedance increment is predicted and analyzed, and based on this, the cut-off voltage is dynamically reduced in combination with the operating temperature during the battery charging process to reduce the accumulation of solid electrolyte interface stress in the high-voltage constant-voltage stage and the fluctuation amplitude of the solid electrolyte interface impedance. This is coordinated with the adaptive pulse charging strategy to form a "current-voltage" two-dimensional control, thereby improving the inhibitory effect on the deterioration of the solid electrolyte interface impedance.

[0125] In some embodiments, a mapping relationship table between the operating temperature gradient and the maximum allowable charging current is established based on the battery operation experimental data, and the current upper limit and the temperature rise rate threshold corresponding to different temperature intervals are set in the mapping relationship table; the battery temperature change rate is determined according to the operating temperature at different time points. If the battery temperature change rate exceeds the corresponding temperature rise rate threshold, the charging current is reduced according to the gradient based on the current interface capacitance; according to the reduction amplitude of the charging current, the corresponding constant voltage charging stage duration is extended; when the operating temperature drops below the corresponding temperature rise rate threshold and is maintained for more than a preset number of maintenance cycle detection cycles, the original charging current is restored.

[0126] The mapping relationship table may be a data table associating different battery operating temperature ranges (such as 0-25°C, 25-45°C, >45°C) with corresponding current upper limits and temperature rise rates.

[0127] The temperature rise rate threshold may be a maximum temperature rise rate allowed during the battery charging process.

[0128] The battery temperature change rate may be a change value of the battery operating temperature per unit time.

[0129] The preset temperature change rate may be a maximum value of the battery temperature change allowed within a preset unit time.

[0130] The preset charge ratio may be a preset maximum load state reached during the battery charging process.

[0131] Constant voltage charging phase duration Charging time in constant voltage mode.

[0132] The preset steady-state interval may be a temperature range that allows the original charging current to be restored (eg, falls below 40° C. and maintains a fluctuation of ±2° C.).

[0133] The preset number of maintenance cycles may be the number of detection cycles that the temperature needs to be maintained stable.

[0134] Specifically, the temperature control strategy for solid-state lead batteries in the prior art only cuts off charging when the temperature exceeds an absolute threshold (such as 60°C), but the thermal runaway of solid-state lead batteries is often caused by an abnormal temperature rise rate (such as a rise of 15°C within 5 minutes). At this time, the absolute temperature may not have reached the corresponding threshold, but irreversible solid electrolyte interface impedance deterioration has occurred, and directly shutting down or significantly reducing the current is likely to cause a sudden increase in solid electrolyte interface stress, causing its impedance to fluctuate greatly; by performing charge and discharge cycle experiments on the solid-state lead battery pack within the corresponding operating temperature range, recording the maximum allowable current and the corresponding temperature rise rate threshold in different temperature ranges, 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 means 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, the current reduction amplitude is increased by 10% until the temperature rise rate is lower than the threshold, thereby realizing a gradient current reduction adjustment of the charging current, and the extension of the charging time caused by the current reduction is compensated by dynamically extending the constant voltage stage.

[0135] Through this scheme, a monitoring mechanism for the temperature rise rate is utilized to reduce the probability of thermal runaway triggering of solid-state lead batteries. At the same time, a gradient current reduction mechanism is utilized to reduce the sudden stress change at the solid electrolyte interface caused by a sudden drop in charging current, thereby reducing the fluctuation amplitude of the solid electrolyte impedance. Furthermore, a dynamic constant voltage compensation mechanism is used to reduce the extended charging time caused by charging current adjustment.

[0136] Figure 3 A schematic diagram of a smart charging system for a solid-state lead battery energy storage power station provided in one embodiment of the present application is shown in FIG. Figure 3As shown, a smart 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 .

[0137] A data construction module 301 is used 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; An evolution analysis module 302 is used to obtain a renewable energy power generation data set, and based on the renewable energy power generation data set and a multi-dimensional battery state data set, perform evolution analysis on the solid electrolyte impedance fluctuation and construct a solid electrolyte impedance evolution model; A strategy analysis module 303, used to determine an adaptive battery charging strategy according to the solid electrolyte impedance evolution model; The adjustment output module 304 is used to adjust the charging parameters of the power station according to the adaptive battery charging strategy, and determine and output a charging adjustment report.

[0138] Optionally, in the data construction module 301, the real-time impedance characteristic parameter set includes charge transfer resistance, interface capacitance and real-time interface 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 to 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 cycle 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 a hardware clock synchronization protocol.

[0139] Optionally, the data construction module 301 is specifically used for: According to the detection cycle, the real-time battery operation parameter set is downsampled, and the data of each parameter in the real-time battery operation parameter set and the corresponding time point of each detection cycle are retained to determine the battery periodic operation parameter set; using a cubic spline interpolation algorithm, all parameters in the real-time impedance feature set and all parameters in the battery periodic operation parameter set are aligned on a unified time axis to determine the impedance feature periodic fluctuation parameter set; according to the battery periodic operation parameter set and the impedance feature periodic fluctuation parameter set, the multidimensional battery status data set is constructed.

[0140] Optionally, the evolution analysis module 302 is specifically used to: Based on the ridge regression algorithm, according to the real-time interface impedance, the correlation and influence relationship between the charge transfer resistance, the interface capacitance, the charging power, the operating temperature and the charge state at the corresponding time point on the solid electrolyte impedance is analyzed, and the resistance influence coefficient, the capacitance influence coefficient, the power influence coefficient and the charge influence coefficient are fitted; according to the resistance influence coefficient, the capacitance influence coefficient, the power influence coefficient and the charge influence coefficient, the solid electrolyte impedance evolution model is constructed.

[0141] Optionally, 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, which is specifically the following formula: ; in, 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 propagation delay of the incentive of renewable energy fluctuations to the battery, is the power influence coefficient, For time point The corresponding charging power is as follows, is the battery rated power, is the charging influence coefficient, is the temperature difference between the operating temperature and the preset reference temperature, is the state of charge.

[0142] Optionally, the strategy analysis module 303 is specifically used to: 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 value output by the solid electrolyte impedance evolution model and the multi-dimensional battery state data set, an adaptive pulse charging strategy, a variable cutoff voltage control strategy and a temperature-current coupling control strategy are integrated; according to the real-time charge transfer resistance, interface capacitance, state of charge and operating temperature, the pulse charging current is dynamically adjusted, the cutoff voltage is corrected and the relationship between temperature and current is coupled; through multi-strategy coordinated control, the solid electrolyte impedance fluctuation amplitude and temperature rise rate during the charging process are constrained within a preset safe evolution range.

[0143] Optionally, the adaptive pulse charging strategy in the strategy analysis module 303 is specifically used for: In the constant current charging stage, the change trend of the charge transfer resistance under different charge states is determined according to the correlation curve between the real-time value of the charge transfer resistance and the charge state; if the increase of the current charge transfer resistance compared with the charge transfer resistance corresponding to the previous detection cycle exceeds the preset resistance increase, the pulse load reduction mode is triggered, and the pulse current peak is reduced according to the proportional coefficient corresponding to the current charge state, and the duty cycle is reduced to within the preset proportional range of the original value, and the intermittent period is extended to within the preset multiple range of the original duration, until the current charge transfer resistance falls back to within the preset fluctuation threshold range.

[0144] Optionally, the variable cut-off voltage control strategy in the strategy analysis module 303 is specifically used for: According to the impedance prediction values ​​corresponding to different time points, the impedance increment at different time points is determined; the impedance increment and the operating temperature are analyzed, and when the impedance increment exceeds the reference impedance increment or the operating temperature is higher than the preset temperature upper limit, the cut-off voltage reduction amount is analyzed 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, 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.

[0145] Optionally, the temperature-current coupling control strategy in the strategy analysis module 303 is specifically used for: According to the battery operation experimental data, a mapping relationship table between the operating temperature gradient and the maximum allowable charging current is established, in which the current upper limit and the temperature rise rate threshold corresponding to different temperature intervals are set; according to the operating temperature at different time points, the battery temperature change rate is determined, if the battery temperature change rate exceeds the corresponding temperature rise rate threshold, the charging current is reduced according to the gradient according to the current interface capacitance; according to the reduction amplitude of the charging current, the corresponding constant voltage charging stage duration is extended; when the operating temperature drops back to below the corresponding temperature rise rate threshold and is maintained for more than a preset number of maintenance cycles of the detection cycle, the original charging current is restored.

[0146] The system of this embodiment can be used to execute the method of any of the above embodiments. The implementation principles and technical effects are similar and will not be described in detail here.

Claims

1. An intelligent charging system for a solid-state lead battery energy storage power station, characterized in that: include: Acquire a real-time impedance characteristic parameter set and a real-time battery operation parameter set, and determine a multi-dimensional battery status data set according to the real-time impedance characteristic parameter set and the real-time battery operation parameter set; Acquire a renewable energy power generation data set, and based on the renewable energy power generation data set and a multi-dimensional battery state data set, perform evolution analysis on solid electrolyte impedance fluctuations to construct a solid electrolyte impedance evolution model; Determining an adaptive battery charging strategy according to the solid electrolyte impedance evolution model; According to the adaptive battery charging strategy, the power station charging parameters are adjusted, and a charging adjustment report is determined and output.

2. The system according to claim 1, characterized in that The real-time impedance characteristic parameter set includes charge transfer resistance, interface capacitance and real-time interface impedance; The real-time battery operating parameter set includes charging power, operating temperature and 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 to 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 cycle is triggered once every time the charge state 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 a hardware clock synchronization protocol.

3. The system according to claim 2, characterized in that Determining a multidimensional battery status data set according to the real-time impedance characteristic parameter set and the real-time battery operation parameter set includes: According to the detection cycle, downsampling the real-time battery operation parameter set, retaining data of each parameter in the real-time battery operation parameter set and each time point corresponding to the detection cycle, and determining a battery periodic operation parameter set; Using a cubic spline interpolation algorithm, all parameters in the real-time impedance characteristic set and all parameters in the battery periodic operation parameter set are aligned on a unified time axis to determine an impedance characteristic periodic fluctuation parameter set; The multi-dimensional battery status data set is constructed according to the battery periodic operation parameter set and the impedance characteristic periodic fluctuation parameter set.

4. The system according to claim 2, characterized in that Based on the renewable energy power generation data set and the multi-dimensional battery state data set, the solid electrolyte impedance fluctuation is subjected to evolution analysis to construct a solid electrolyte impedance evolution model, including: Based on the ridge regression algorithm, according to the real-time interface impedance, the correlation and influence relationship between the charge transfer resistance, the interface capacitance, the charging power, the operating temperature and the state of charge at the corresponding time point on the solid electrolyte impedance is analyzed, and the resistance influence coefficient, the capacitance influence coefficient, the power influence coefficient and the charge influence coefficient are obtained by fitting; The solid electrolyte impedance evolution model is constructed according to the resistance influence coefficient, the capacitance influence coefficient, the power influence coefficient and the charging influence coefficient.

5. The system according to claim 4, characterized in that The solid electrolyte impedance evolution model is constructed according to the resistance influence coefficient, the capacitance influence coefficient, the power influence coefficient and the charge influence coefficient, which is specifically the following formula: ； in, 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 propagation delay of the incentive of renewable energy fluctuations to the battery, is the power influence coefficient, For time point The corresponding charging power is as follows, is the battery rated power, is the charging influence coefficient, is the temperature difference between the operating temperature and the preset reference temperature, is the state of charge.

6. The system according to claim 5, characterized in that The adaptive battery charging strategy includes: 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 value 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; Dynamically adjust pulse charging current, correct cutoff voltage and couple temperature and current relationship according to real-time charge transfer resistance, interface capacitance, state of charge and operating temperature; Through multi-strategy coordinated control, the impedance fluctuation amplitude and temperature rise rate of the solid electrolyte during the charging process are constrained within a preset safe evolution range.

7. The system according to claim 6, characterized in that The adaptive pulse charging strategy includes: 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, the change trend of the charge transfer resistance under different states of charge is determined; If the increase in the current charge transfer resistance compared to the charge transfer resistance corresponding to the previous detection cycle exceeds the preset resistance increase, the pulse load reduction mode is triggered, and the pulse current peak is reduced according to the proportional coefficient corresponding to the current charge state. At the same time, the duty cycle is reduced to within a preset proportional range of the original value, and the intermittent period is extended to within a preset multiple range of the original duration, until the current charge transfer resistance falls back to within a preset fluctuation threshold range.

8. The system according to claim 7, characterized in that The variable cut-off voltage control strategy includes: Determining impedance increments at different time points according to the impedance prediction values ​​corresponding to different time points; Analyze the impedance increment and the operating temperature, and when the impedance increment exceeds the reference impedance increment or the operating temperature is higher than the preset upper temperature 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, 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.

9. The system according to claim 8, characterized in that The temperature-current coupling control strategy includes: According to the battery operation experimental data, a mapping relationship table between the operating temperature gradient and the maximum allowable charging current is established, in which the current upper limit and the temperature rise rate threshold corresponding to different temperature intervals are set; Determine the battery temperature change rate according to the operating temperature at different time points, and if the battery temperature change rate exceeds the corresponding temperature rise rate threshold, reduce the charging current according to the gradient according to the current interface capacitance; According to the reduction range of the charging current, the duration of the corresponding constant voltage charging phase is extended; When the operating temperature drops below the corresponding temperature rise rate threshold and is maintained for more than a preset number of maintenance cycles of the detection cycles, the original charging current is restored.

10. The system according to claim 1, characterized in that include: A data construction module, used 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; An evolution analysis module is used to obtain a renewable energy power generation data set, and based on the renewable energy power generation data set and a multi-dimensional battery state data set, perform evolution analysis on the solid electrolyte impedance fluctuation and construct a solid electrolyte impedance evolution model; A strategy analysis module, used to determine an adaptive battery charging strategy based on the solid electrolyte impedance evolution model; The adjustment output module is used to adjust the charging parameters of the power station according to the adaptive battery charging strategy, and determine and output a charging adjustment report.

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