Outdoor energy storage power supply data acquisition system based on low-power-consumption edge calculation

Abstract

The invention relates to the technical field of data acquisition, and discloses a low-power-consumption edge calculation outdoor energy storage power supply data acquisition system, which comprises a data analysis unit, a collaborative analysis unit, a probability analysis unit and an acquisition correction unit, and is characterized in that the data analysis unit, the collaborative analysis unit, the probability analysis unit and the acquisition correction unit are combined for analysis; intelligent dynamic regulation and control of outdoor energy storage power supply data acquisition are realized, the defect of data delay under low temperature and high load in a traditional fixed frequency acquisition mode is overcome, the data acquisition effect is ensured, low power consumption is ensured, meanwhile, the real-time recognition capability of hidden dangers such as over-discharge and internal resistance abnormity is improved, and the real-time recognition capability of the hidden dangers such as over-discharge and internal resistance abnormity is improved. And the cycle service life of the battery is effectively prolonged.

Description

Technical Field

[0001] This invention relates to the field of data acquisition technology, and more specifically to a low-power edge computing outdoor energy storage power data acquisition system. Background Technology

[0002] Currently, in the data acquisition process of outdoor energy storage batteries, the mainstream acquisition method based on low-power edge computing is fixed-frequency intermittent acquisition-sleep mode. Specifically, the edge computing module is only briefly awakened after completing the acquisition of the core parameters of the outdoor energy storage battery. After performing simple filtering, noise reduction, and format encapsulation processing on the acquired raw battery data, it immediately enters a low-power sleep mode to reduce the energy consumption of the edge computing module. At the same time, the pre-processed battery data is temporarily stored on the local edge node to avoid the additional energy consumption generated by remote data transmission, thus achieving local low-power processing and storage.

[0003] However, the above data acquisition methods still have obvious technical defects: In low-temperature outdoor environments, the electrochemical characteristics of outdoor energy storage batteries will deteriorate, and their charge and discharge loads will exhibit dynamic fluctuations. The fixed frequency control logic of the existing acquisition methods cannot dynamically adjust the operating frequency and data processing rate of the edge computing module according to the real-time charge and discharge load of the outdoor energy storage battery. When the outdoor energy storage battery is under high load discharge, the fixed low operating frequency of the edge computing module cannot quickly process the surge in battery acquisition data, resulting in battery data processing delays and affecting the acquisition of outdoor energy storage power data. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a low-power edge computing outdoor energy storage power data acquisition system, which solves the aforementioned problems.

[0005] The above-mentioned technical objective of the present invention is achieved through the following technical solution: A low-power edge computing outdoor energy storage power data acquisition system includes: The data analysis unit is used to obtain power data of the target object in real time when the target object switches between charging and discharging states based on the edge computing module of the target object, calculate the power data, and generate the charge dynamic distortion rate representing the internal electrochemical state of the target object. The collaborative analysis unit is used to acquire the surface temperature of the target object and the ambient temperature of the target object in real time, and to perform collaborative calculations on the surface temperature, ambient temperature and charge dynamic distortion rate. It generates a diffusion resistance coefficient that represents the degree of obstruction of ion migration in the electrode material inside the target object, and analyzes the relationship between the diffusion resistance coefficient and the real-time charge and discharge rate to generate a dynamic relaxation coefficient that reflects the load-bearing capacity of the target object. The probability analysis unit is used to calculate the dynamic relaxation coefficient and the battery terminal voltage in the power supply data to generate a transient response factor representing the probability of the target object experiencing operational hazards. The data acquisition and correction unit is used to determine the operational hazard index of the target object based on the transient response factor, and to dynamically adjust the sleep-wake strategy of the edge computing module based on the operational hazard index.

[0006] Furthermore, the power data is calculated to generate a charge dynamic distortion rate representing the internal electrochemical state of the target object, including: Based on power supply data, the duration of voltage rise and fall during the charging-to-discharging process is analyzed to generate an interface polarization hysteresis factor representing the inertia of electrode charge migration. The rated capacity of the target battery is obtained, and the current and rated capacity of the power supply data are calculated to obtain the real-time charge and discharge rate. The real-time charge and discharge rate is combined with the interface polarization hysteresis factor to analyze the migration resistance of lithium ion diffusion under different charge and discharge loads, and a lattice diffusion hindrance coefficient reflecting the degree of unobstructed ion transport channels inside the electrode material is generated.

[0007] Furthermore, calculations are performed on the power data to generate a charge dynamic distortion rate representing the internal electrochemical state of the target object, which also includes: Based on power supply data and lattice diffusion retardation coefficient, the amplitude of local potential distortion caused by uneven ion concentration distribution inside the electrode material is analyzed, and the charge dynamic distortion rate representing the internal electrochemical state of the target object is generated.

[0008] Furthermore, surface temperature, ambient temperature, and charge dynamic distortion rate are calculated collaboratively to generate a diffusion resistance coefficient representing the degree of obstruction to ion migration within the electrode material, including: The changes in surface temperature and ambient temperature are analyzed to generate the thermal conduction pressure difference, which represents the intensity of heat exchange between the battery casing and the outside world, and the thermal inertia drift rate, which represents the balance between heat generation and heat dissipation inside the battery.

[0009] Furthermore, by jointly calculating the surface temperature, ambient temperature, and charge dynamic distortion rate, a diffusion resistance coefficient representing the degree of obstruction to the migration of ions within the target object in the electrode material is generated, which also includes: By combining the charge dynamic distortion rate with the temperature difference conduction pressure difference and the thermal inertia drift rate, the local thermal expansion difference inside the electrode material is analyzed, and the lattice thermal expansion distortion degree, which represents the degree of thermal disturbance of the lattice structure of the electrode material, is generated. Based on real-time charge / discharge rate and lattice thermal expansion distortion, the degree of blockage of ion transport channels caused by lattice thermal expansion distortion is analyzed, and a diffusion resistance coefficient representing the degree of obstruction of ion migration within the target object in the electrode material is generated.

[0010] Furthermore, the relationship between the diffusion resistance coefficient and the real-time charge / discharge rate is analyzed to generate a dynamic relaxation coefficient that reflects the load-bearing capacity of the target object, including: The real-time charge / discharge rate is correlated with the diffusion resistance coefficient to generate a dynamic load potential coefficient that represents the impact of the load on the uniformity of ion distribution inside the electrode, and an ion concentration gradient coefficient that reflects the difference in ion concentration distribution before and after the charge / discharge switch. Based on the dynamic load potential coefficient and the ion concentration gradient coefficient, the response rate of the electrode recovering from the non-equilibrium state to the equilibrium state is analyzed, and a dynamic relaxation coefficient is generated to reflect the load-bearing capacity of the target object.

[0011] Furthermore, the dynamic relaxation coefficient and battery terminal voltage in the power supply data are calculated to generate a transient response factor representing the probability of operational hazards occurring in the target object, including: Analyze the degree of resistance and overshoot of battery terminal voltage during the switching between charging and discharging states in the power data, and generate an interface charge accumulation coefficient that represents the degree of imbalance between charge accumulation and release capabilities. The dynamic relaxation coefficient and the interfacial charge accumulation coefficient are correlated and calculated to generate a bulk-interface response phase difference that reflects the synergistic effect of bulk ion diffusion and interfacial electrochemical reaction.

[0012] Furthermore, the dynamic relaxation coefficient and battery terminal voltage in the power supply data are calculated to generate a transient response factor representing the probability of operational hazards occurring in the target object. This also includes: The phase difference between the bulk and interface responses is calculated in conjunction with the battery terminal voltage in the power supply data to generate a transient response factor representing the probability of operational hazards occurring in the target object.

[0013] Furthermore, based on the transient response factor, the operational hazard index of the target object is determined, and the sleep-wake strategy of the edge computing module is dynamically adjusted according to the operational hazard index, including: Based on the transient response factor, the degree of irreversible lattice damage induced by ion cumulative impact at the electrode interface is analyzed to obtain the operational hazard index of the target object.

[0014] Furthermore, based on the transient response factor, the operational hazard index of the target object is determined, and the sleep-wake strategy of the edge computing module is dynamically adjusted according to the operational hazard index. This also includes: When the operational risk index is 0, the edge computing module enters deep sleep mode; When 0 < operational risk index ≤ 0.5, the edge computing module enters a shallow hibernation mode; When 0.5 < operational risk index ≤ 1, the edge computing module stops hibernation and remains awake to continuously collect data.

[0015] In summary, the present invention has the following main beneficial effects: The data analysis unit generates a charge dynamic distortion rate based on power supply data, accurately reflecting the degree of distortion of the internal electrochemical state of the target object. The collaborative analysis unit integrates surface temperature and ambient temperature in real time, calculates the temperature difference conduction pressure difference and thermal inertia drift rate, and then, together with the charge dynamic distortion rate, generates the lattice thermal expansion distortion degree, ultimately obtaining the diffusion resistance coefficient, which objectively reflects the degree of ion migration resistance in the electrode material. Based on this, by analyzing the correlation between the diffusion resistance coefficient and the real-time charge and discharge rate, a dynamic load potential coefficient and ion concentration gradient coefficient are constructed to generate a dynamic relaxation coefficient, realizing a dynamic assessment of the battery load carrying capacity.

[0016] By correlating the dynamic relaxation coefficient with the battery terminal voltage depth, the interface charge accumulation coefficient and the phase difference between the bulk and interface responses are calculated, ultimately generating a transient response factor that accurately reflects the probability of operational hazards such as lithium plating and internal short circuits. The acquisition and correction unit determines the operational hazard index based on the transient response factor and dynamically adjusts the sleep-wake strategy of the edge computing module accordingly. This solution overcomes the technical defect of traditional fixed-frequency acquisition mode in being unable to dynamically adapt to load fluctuations in outdoor low-temperature environments, and avoids the lag in hazard identification caused by data processing delays during high-load discharge. By adapting the acquisition strategy, while ensuring low-power operation, it improves the real-time sensitivity and timeliness of identifying operational hazards such as over-discharge and abnormal internal resistance. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of a low-power edge computing outdoor energy storage power data acquisition system according to the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] refer to Figure 1 A low-power edge computing outdoor energy storage power data acquisition system, comprising: The data analysis unit is used to obtain power data of the target object in real time when the target object switches between charging and discharging states based on the edge computing module of the target object, calculate the power data, and generate the charge dynamic distortion rate representing the internal electrochemical state of the target object, which is an outdoor energy storage power source. Power data includes: battery terminal voltage and current; The collaborative analysis unit is used to acquire the surface temperature of the target object and the ambient temperature of the target object in real time, and to perform collaborative calculations on the surface temperature, ambient temperature and charge dynamic distortion rate. It generates a diffusion resistance coefficient that represents the degree of obstruction of ion migration in the electrode material inside the target object, and analyzes the relationship between the diffusion resistance coefficient and the real-time charge and discharge rate to generate a dynamic relaxation coefficient that reflects the load-bearing capacity of the target object. The probability analysis unit is used to calculate the dynamic relaxation coefficient and the battery terminal voltage in the power supply data to generate a transient response factor representing the probability of the target object experiencing operational hazards. The data acquisition and correction unit is used to determine the operational hazard index of the target object based on the transient response factor, and to dynamically adjust the sleep-wake strategy of the edge computing module based on the operational hazard index.

[0020] In one embodiment, the power supply data is calculated to generate a charge dynamic distortion rate representing the internal electrochemical state of the target object, including: Based on power data, the duration of voltage rise and fall during the charging-to-discharging process is analyzed to generate an interface polarization hysteresis factor representing the inertia of electrode charge migration. Specifically, at the instant when the target object switches between charging and discharging states, the response waveform of the battery terminal voltage is continuously collected at a sampling frequency of not less than 100 Hz. Specifically, data from two scenarios are collected: the first scenario is when the target object switches from the current charging state to the current discharging state instantly, and the voltage drop waveform is recorded from the instant of switching until the battery terminal voltage drops to the average voltage of all sampling points within three to five seconds after the switching. The second scenario is when the target object instantly switches from a current discharge state to a current charging state. Record the voltage rise waveform from the moment of switching until the battery terminal voltage rises to the average voltage of all sampling points within three to five seconds after the switching. For the two scenarios, calculate the voltage drop time during the charging-to-discharging process: determine the starting point of the voltage drop waveform as the voltage value at the moment of switching, and determine the ending point of the voltage drop waveform as the earliest time point corresponding to the voltage drop to the average voltage of all sampling points within the third to fifth seconds after switching. Calculate the actual time length from the starting point to the ending point, which is in seconds. Calculate the voltage rise time during the discharge-to-charge process, determine the starting point of the voltage rise waveform as the voltage value at the moment of switching, determine the ending point of the voltage rise waveform as the earliest time point corresponding to the average voltage of all sampling points within the third to fifth seconds after switching, and calculate the actual time length from the starting point to the ending point, which is also in seconds. Multiply the voltage drop time by the voltage rise time and take the square root of the result to obtain the interface polarization hysteresis factor, which represents the inertia of electrode charge migration. The interface polarization hysteresis factor is in seconds. The larger the value, the stronger the migration inertia of the electrode surface charge when switching between charging and discharging states. That is, the slower the response of charge accumulation and release, the deeper the polarization of the electrode interface.

[0021] The rated capacity of the target battery is obtained, and the current and rated capacity of the power supply data are calculated to obtain the real-time charge and discharge rate. The real-time charge and discharge rate is combined with the interface polarization hysteresis factor to analyze the migration resistance of lithium ion diffusion under different charge and discharge loads, and generate the lattice diffusion hindrance coefficient that reflects the unobstructedness of the ion transport channels inside the electrode material. Specifically, the real-time charge and discharge rate at the current moment can be obtained by dividing the absolute value of the current at the current moment by the rated capacity of the battery. During the continuous operation of the target object, a statistical window is defined as every 100 complete charge-discharge cycles. If the target object has completed less than 100 complete charge-discharge cycles, the statistical window is defined as the total number of complete charge-discharge cycles that have been completed. The interface polarization hysteresis factor calculated at each state switch within the statistical window is recorded, along with the corresponding real-time charge-discharge rate. For each pair of interface polarization hysteresis factors and real-time charge-discharge rates recorded within the statistical window, the ratio of the interface polarization hysteresis factor to the real-time charge-discharge rate in each pair of data is calculated. The arithmetic mean of all ratios within the current statistical window yields the lattice diffusion hindrance coefficient, which reflects the unobstructedness of ion transport channels within the electrode material. The larger the lattice diffusion hindrance coefficient, the greater the resistance encountered by lithium ions as they move from the electrode surface into the lattice and migrate between lattices under the same charge and discharge load conditions.

[0022] In one embodiment, calculating the power supply data to generate a charge dynamic distortion rate representing the internal electrochemical state of the target object further includes: Based on power supply data and lattice diffusion hindrance coefficient, the local potential distortion amplitude caused by uneven ion concentration distribution inside the electrode material is analyzed, and the charge dynamic distortion rate representing the internal electrochemical state of the target object is generated. Specifically, this includes: obtaining the stable voltage value reached after the most recent charge-discharge state switch; if the current operation is in the charging-to-discharge phase, the arithmetic mean of the voltages of all sampling points within the third to fifth seconds after the charge-to-discharge switch is taken as the stable voltage value; if the current operation is in the discharging-to-charging phase, the arithmetic mean of the voltages of all sampling points within the third to fifth seconds after the discharge-to-charge switch is taken as the stable voltage value. Calculate the absolute value of the difference between the current voltage and the stable voltage to obtain the voltage offset. The voltage offset mainly reflects the degree of deviation of the local potential from the equilibrium state at the current moment. Obtain the rated capacity of the target battery in ampere-hours. Multiply the lattice diffusion hindrance coefficient by the real-time charge-discharge rate to obtain the characteristic time. The characteristic time is in seconds. The characteristic time represents the dynamic response time scale corresponding to the ion diffusion hindrance under the current charge-discharge load. Dividing the voltage offset by the characteristic time yields the charge dynamic distortion rate, which represents the internal electrochemical state of the target object. The unit is volts per second. The larger the value of the charge dynamic distortion rate, the faster the voltage deviates from the equilibrium value per unit time. That is, the more severe the local potential distortion caused by uneven ion concentration distribution is, thus accurately reflecting the degree of dynamic distortion of the internal electrochemical state of the battery.

[0023] By accurately calculating power data, three core parameters are generated: interface polarization hysteresis factor, lattice diffusion hindrance coefficient, and charge dynamic distortion rate. These parameters can accurately reflect the internal electrochemical state of outdoor energy storage batteries, effectively solving the defects of existing fixed-frequency intermittent acquisition methods. They can accurately capture the voltage response characteristics when switching between charge and discharge states, reflecting the electrode charge migration inertia, ion transport resistance, and local potential distortion amplitude. This allows the edge computing module to dynamically adjust the data acquisition rate to avoid data processing delays under high load conditions.

[0024] In one embodiment, the surface temperature, ambient temperature, and charge dynamic distortion rate are calculated together to generate a diffusion resistance coefficient representing the degree of obstruction to the migration of ions within the target object in the electrode material, including: The changes in surface temperature and ambient temperature are analyzed to generate the temperature difference conduction pressure difference, which represents the intensity of heat exchange between the battery casing and the outside world, and the thermal inertia drift rate, which represents the balance between heat generation and heat dissipation inside the battery. Specifically, the surface temperature of the target object and the ambient temperature of the target object are collected in real time at a sampling frequency of not less than 1 Hz, both in degrees Celsius. At each sampling moment, the absolute value of the difference between the surface temperature and the ambient temperature at the current moment is calculated to obtain the instantaneous temperature difference. For all instantaneous temperature difference values ​​within the previous 60 seconds, the maximum value is selected as the temperature difference conduction pressure difference, which represents the intensity of heat exchange between the battery casing and the outside world, in degrees Celsius. The larger the value of the temperature difference conduction pressure difference, the stronger the temperature difference driving force on both sides of the battery casing and the greater the potential energy of heat conduction. The change in surface temperature at the current moment compared to the previous second is calculated to obtain the temperature rise rate. The arithmetic mean of all temperature rise rates in the 60 consecutive seconds preceding the current moment is calculated to obtain the average temperature rise rate. Then, the root mean square value of the temperature rise rate in the 60 consecutive seconds preceding the current moment is calculated to obtain the temperature rise rate fluctuation intensity. The temperature rise rate fluctuation intensity is divided by the absolute value of the average temperature rise rate to obtain the thermal inertia drift rate, which represents the balance between heat generation and heat dissipation inside the battery. The larger the value of the thermal inertia drift rate, the more unstable the balance between heat generation and heat dissipation inside the battery, the more violent the thermal inertia fluctuation, and the more uneven the internal thermal field distribution.

[0025] In one embodiment, the surface temperature, ambient temperature, and charge dynamic distortion rate are calculated collaboratively to generate a diffusion resistance coefficient representing the degree of obstruction to the migration of ions within the target object in the electrode material. This also includes: By combining the dynamic distortion rate of charge with the thermal conductivity pressure difference and the thermal inertia drift rate, the local thermal expansion differences within the electrode material are analyzed, generating the lattice thermal expansion distortion degree, which represents the degree of thermal disturbance to the electrode material's lattice structure. Specifically, the dynamic distortion rate of charge is multiplied by the thermal conductivity pressure difference, and then by the thermal inertia drift rate. The calculation result is then normalized to the 0-1 range to obtain the lattice thermal expansion distortion degree, which represents the degree of thermal disturbance to the electrode material's lattice structure. The larger the value of the lattice thermal expansion distortion degree, the more significant the local thermal expansion differences caused by electrochemical distortion under the combined action of thermal driving force and thermal fluctuation, and the more severe the thermal disturbance to the electrode material's lattice structure.

[0026] Based on real-time charge / discharge rate and lattice thermal expansion distortion, the degree of blockage of ion transport channels caused by lattice thermal expansion distortion is analyzed, and a diffusion resistance coefficient representing the degree of obstruction of ion migration in the electrode material inside the target object is generated. Specifically, this includes: obtaining the real-time charge / discharge rate at each state switch in the statistical window, calculating the standard deviation of this set of data, and obtaining the intensity of charge / discharge load fluctuation. Multiplying the degree of lattice thermal expansion distortion by the real-time charge / discharge rate yields the basic diffusion resistance. Then, multiplying the degree of lattice thermal expansion distortion by the intensity of charge / discharge load fluctuation yields the dynamic diffusion resistance increment. Adding the basic diffusion resistance to the dynamic diffusion resistance increment and normalizing the result to the 0-1 range yields the diffusion resistance coefficient, which represents the degree of obstruction to the migration of ions within the target object in the electrode material. The larger the value of the diffusion resistance coefficient, the more severe the blockage of ion transport channels caused by lattice thermal expansion distortion under the combined effect of the current load intensity and load fluctuation.

[0027] By collecting surface temperature and ambient temperature in real time, the system accurately reflects the thermal conductivity pressure difference and thermal inertia drift rate. Then, combined with the charge dynamic distortion rate, it generates the lattice thermal expansion distortion degree, which comprehensively reflects the degree of thermal disturbance of the electrode material lattice. At the same time, based on the real-time charge and discharge rate and the intensity of charge and discharge load fluctuations, the diffusion resistance coefficient is calculated to achieve dynamic quantification of the degree of blockage of ion transport channels. This enables the real-time capture of the thermal-electric coupling distortion characteristics inside the battery, improving the sensitivity and timeliness of identifying operational hazards such as over-discharge and abnormal internal resistance, thereby optimizing the operation and protection strategy of outdoor energy storage batteries.

[0028] In one embodiment, the relationship between the diffusion resistance coefficient and the real-time charge / discharge rate is analyzed to generate a dynamic relaxation coefficient that reflects the load-bearing capacity of the target object, including: The real-time charge / discharge rate is correlated with the diffusion resistance coefficient to generate a dynamic load potential coefficient that represents the impact of the load on the uniformity of ion distribution inside the electrode, and an ion concentration gradient coefficient that reflects the difference in ion concentration distribution before and after the charge / discharge switch. Specifically, the calculation involves: taking the current time as a reference, retrieving the real-time charge / discharge rate for each second in the previous 10 consecutive seconds, for a total of ten data points, calculating the difference between adjacent data points to obtain nine variables, taking the maximum absolute value of these nine variables as the load change amplitude, and multiplying the load change amplitude by the diffusion resistance coefficient at the current time to obtain the dynamic load potential coefficient that represents the impact of the load on the uniformity of ion distribution inside the electrode. The larger the value of the dynamic load potential coefficient, the stronger the impact of the instantaneous change of the load on the uniformity of ion distribution inside the electrode, and the more uneven the potential distribution. Using the current moment as a reference, retrieve the real-time charge / discharge rate for every second within the previous 60 consecutive seconds, for a total of 60 data points. Calculate the arithmetic mean of these 60 data points to obtain the recent average charge / discharge rate. Calculate the absolute value of the difference between the current real-time charge / discharge rate and the recent average charge / discharge rate to obtain the load deviation amplitude. Multiply the load deviation amplitude by the diffusion resistance coefficient at the current moment to obtain the ion concentration gradient coefficient, which reflects the difference in ion concentration distribution before and after the charge / discharge switch. The larger the ion concentration gradient coefficient, the greater the deviation of the current load from the recent average level, the more significant the ion concentration gradient within the electrode material, and the greater the concentration difference before and after the charge / discharge switch.

[0029] Based on the dynamic load potential coefficient and the ion concentration gradient coefficient, the response rate of the electrode recovering from the non-equilibrium state to the equilibrium state is analyzed, and a dynamic relaxation coefficient reflecting the load-bearing capacity of the target object is generated. Specifically, the dynamic load potential coefficient is multiplied by the ion concentration gradient coefficient to obtain the transient non-equilibrium disturbance intensity; the diffusion resistance coefficient is divided by the real-time charge and discharge rate to obtain the characteristic relaxation time required to reflect the migration of ions in the crystal lattice. The rated voltage of the target object is obtained, the transient non-equilibrium disturbance intensity is multiplied by the characteristic relaxation time, and then divided by the rated voltage of the target object. The calculation result is normalized to the 0-1 interval to obtain the dynamic relaxation coefficient reflecting the load-bearing capacity of the target object. The dynamic relaxation coefficient is an inverse characterization. The larger the dynamic relaxation coefficient, the slower the response rate of the electrode to recover from the current non-equilibrium state to the equilibrium state after experiencing load shock and concentration difference. That is, the weaker the battery's ability to withstand load fluctuations, and the longer the time required for the internal ion distribution to recover to uniformity.

[0030] By deeply exploring the correlation mechanism between the diffusion resistance coefficient and the real-time charge / discharge rate, a dynamic load potential coefficient and an ion concentration gradient coefficient are constructed, thereby generating a dynamic relaxation coefficient. This enables accurate dynamic assessment of the load-bearing capacity of outdoor energy storage batteries. The dynamic load potential coefficient accurately reflects the impact intensity of instantaneous load changes on the uniformity of ion distribution inside the electrode, while the ion concentration gradient coefficient effectively characterizes the difference in ion concentration distribution caused by the current load deviating from the recent average level. The dynamic relaxation coefficient generated by the fusion of the two can dynamically reflect the response rate of the electrode recovering from a non-equilibrium state to an equilibrium state. This overcomes the technical defect that cannot dynamically adapt to load fluctuations under the fixed frequency acquisition mode, and improves the real-time identification sensitivity of operational hazards such as over-discharge and abnormal internal resistance.

[0031] In one embodiment, the dynamic relaxation coefficient and the battery terminal voltage in the power supply data are calculated to generate a transient response factor representing the probability of a potential operational hazard occurring in the target object, including: Analyze the degree of resistance and overshoot of battery terminal voltage in power supply data at the moment of switching between charging and discharging states, and generate an interface charge accumulation coefficient that represents the degree of imbalance between charge accumulation and release capacity. Specifically, this includes: taking the current moment as a reference, acquiring battery terminal voltage data within 1 second before and after the moment of the most recent switching between charging and discharging states, with a sampling frequency of not less than 100 Hz. The voltage value at the moment of switching is identified as the reference voltage, and the minimum voltage value from 0.1 seconds to 0.5 seconds after switching and the maximum voltage value from 0.5 seconds to 1 second after switching are extracted respectively. The absolute value of the difference between the minimum voltage and the reference voltage is calculated to obtain the transient voltage sag depth, which mainly reflects the degree of obstruction at the moment of charge release; the absolute value of the difference between the maximum voltage and the reference voltage is calculated to obtain the transient voltage overshoot height, which reflects the degree of overshoot at the moment of charge accumulation. Adding the transient voltage dip depth to the transient voltage overshoot height yields the interface charge accumulation coefficient, which represents the degree of imbalance between charge accumulation and release capabilities.

[0032] The dynamic relaxation coefficient and the interfacial charge accumulation coefficient are correlated and calculated to generate a bulk-interface response phase difference that reflects the synergy between bulk ion diffusion and interfacial electrochemical reaction. Specifically, this involves dividing the interfacial charge accumulation coefficient by the rated voltage of the target object to obtain the interfacial accumulation index, dividing the current dynamic relaxation coefficient by the interfacial accumulation index to obtain the bulk-interfacial response ratio, calculating the arctangent of the bulk-interfacial response ratio, and normalizing the calculation result to the 0-1 range to obtain the bulk-interfacial response phase difference. The bulk-interfacial response phase difference is mainly used to reflect the temporal synergy between the bulk ion diffusion process and the interfacial charge transfer reaction. The larger the bulk-interfacial response phase difference, the greater the lag between the bulk response and the interfacial reaction. The bulk ion migration rate cannot keep up with the rapid accumulation and release of interfacial charge, resulting in intensified internal ion concentration polarization and increased potential risks to battery operation.

[0033] In one embodiment, the calculation of the dynamic relaxation coefficient and the battery terminal voltage in the power supply data to generate a transient response factor representing the probability of a potential operational hazard in the target object further includes: The phase difference between the bulk phase and the interface response is calculated together with the battery terminal voltage in the power supply data to generate a transient response factor that represents the probability of the target object having operational hazards. Specifically, this includes: calculating the absolute value of the difference between the current battery terminal voltage and the rated voltage to obtain the voltage deviation amplitude in volts; multiplying the voltage deviation amplitude by the phase difference between the bulk phase and the interface response to obtain the voltage-time cumulative offset. The voltage-time cumulative offset reflects the intensity of electrochemical imbalance accumulated due to response lag under abnormal voltage conditions. Obtain the maximum instantaneous voltage within 1 second after each state switch in the statistical window before the current time, record these voltage values, and calculate their arithmetic mean to obtain the recent average peak voltage. Obtain the minimum instantaneous voltage within 1 second after each state switch in the statistical window before the current time, record these voltage values, and calculate their arithmetic mean to obtain the recent average valley voltage. Subtract the recent average valley voltage from the recent average peak voltage to obtain the recent voltage fluctuation range in volts. Divide the voltage-time cumulative offset by the recent voltage fluctuation range and normalize the calculation result to the 0-1 range to obtain the transient response factor, which represents the probability of the target object having operational hazards. The larger the value of the transient response factor, the more the electrochemical imbalance inside the battery has significantly exceeded its normal fluctuation range in the recent operation under the combined effect of the current voltage anomaly and the bulk phase-interface response lag, and the higher the probability of operational hazards such as lithium plating and internal short circuit.

[0034] This technical solution generates an interface charge accumulation coefficient by deeply correlating the dynamic relaxation coefficient with the battery terminal voltage. This accurately reflects the degree of imbalance between charge accumulation and release capacity during charge-discharge switching. Furthermore, it calculates the phase difference between the bulk phase and the interface response, dynamically reflecting the temporal synergy between bulk ion diffusion and interface electrochemical reaction. This effectively reveals the state of intensified internal ion concentration polarization caused by bulk migration lag. Finally, it integrates the voltage deviation amplitude and the recent voltage fluctuation range to generate a transient response factor, enabling accurate assessment of the probability of operational hazards such as lithium plating and internal short circuits.

[0035] In one embodiment, the operational vulnerability index of the target object is determined based on the transient response factor, and the sleep-wake strategy of the edge computing module is dynamically adjusted based on the operational vulnerability index, including: Based on transient response factors, the degree of irreversible lattice damage induced by cumulative ion impact at the electrode interface is analyzed to obtain the operational hazard index of the target object. Specifically, this includes: collecting the N transient response factors per second at the current moment, where N is the total number of transient response factors; summing the durations corresponding to moments when the transient response factor is >0.6 to obtain the cumulative high-hazard operating time. The duration corresponding to each transient response factor that meets the above conditions is 1 second. The root mean square of the N transient response factors is calculated to obtain the overall impact intensity. The cumulative high-hazard operating time is multiplied by the overall impact intensity to obtain the cumulative total impact. Obtain the instantaneous values ​​of all transient response factors within the consecutive 24 hours prior to the current moment. Select the top five values ​​from these values ​​and calculate the arithmetic mean of these five values ​​to obtain the recent peak tolerance baseline value. The recent peak tolerance baseline value represents the highest impact level that the battery has actually withstood in the most recent day. Divide the total cumulative impact by the recent peak tolerance baseline value and normalize the calculation result to the 0-1 range to obtain the operational hazard index of the target object. The larger the operational hazard index, the more the total cumulative electrochemical impact on the electrode interface of the battery has significantly exceeded its recent actual tolerance capacity. The deeper the irreversible damage to the lattice caused by the cumulative impact of repeated ion insertion and extraction, the more severe the degradation of the battery's health status.

[0036] In one embodiment, the method of determining the operational hazard index of the target object based on the transient response factor and dynamically adjusting the sleep-wake strategy of the edge computing module based on the operational hazard index further includes: When the operational risk index is 0, the edge computing module enters deep sleep mode. In deep sleep mode, it is only woken up when the charging and discharging state of the outdoor energy storage power supply is detected to switch. When 0 < operational risk index ≤ 0.5, the edge computing module enters shallow sleep mode and wakes up every 10 seconds to collect data. When 0.5 < operational risk index ≤ 1, the edge computing module stops hibernation and remains awake to continuously collect data.

[0037] By generating an operational hazard index and dynamically adjusting the sleep-wake strategy of the edge computing module based on the operational hazard index, the operational hazard index integrates the cumulative high-hazard running time and the overall impact intensity, objectively reflecting the depth of battery health degradation and achieving dynamic adaptation between the collection frequency and the hazard level. This solution overcomes the data latency and energy waste under the fixed frequency collection mode.

[0038] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A low-power edge computing outdoor energy storage power data acquisition system, characterized in that, include: The data analysis unit is used to obtain power data of the target object in real time when the target object switches between charging and discharging states based on the edge computing module of the target object, calculate the power data, and generate the charge dynamic distortion rate representing the internal electrochemical state of the target object. The collaborative analysis unit is used to acquire the surface temperature of the target object and the ambient temperature of the target object in real time, and to perform collaborative calculations on the surface temperature, ambient temperature and charge dynamic distortion rate. It generates a diffusion resistance coefficient that represents the degree of obstruction of ion migration in the electrode material inside the target object, and analyzes the relationship between the diffusion resistance coefficient and the real-time charge and discharge rate to generate a dynamic relaxation coefficient that reflects the load-bearing capacity of the target object. The probability analysis unit is used to calculate the dynamic relaxation coefficient and the battery terminal voltage in the power supply data to generate a transient response factor representing the probability of the target object experiencing operational hazards. The data acquisition and correction unit is used to determine the operational hazard index of the target object based on the transient response factor, and to dynamically adjust the sleep-wake strategy of the edge computing module based on the operational hazard index.

2. The low-power edge computing outdoor energy storage power data acquisition system according to claim 1, characterized in that, The power data is calculated to generate a charge dynamic distortion rate representing the internal electrochemical state of the target object, including: Based on power supply data, the duration of voltage rise and fall during the charging-to-discharging process is analyzed to generate an interface polarization hysteresis factor representing the inertia of electrode charge migration. The rated capacity of the target battery is obtained, and the current and rated capacity of the power supply data are calculated to obtain the real-time charge and discharge rate. The real-time charge and discharge rate is combined with the interface polarization hysteresis factor to analyze the migration resistance of lithium ion diffusion under different charge and discharge loads, and a lattice diffusion hindrance coefficient reflecting the degree of unobstructed ion transport channels inside the electrode material is generated.

3. The low-power edge computing outdoor energy storage power data acquisition system according to claim 2, characterized in that, The calculation of power source data generates a charge dynamic distortion rate representing the internal electrochemical state of the target object, and also includes: Based on power supply data and lattice diffusion retardation coefficient, the amplitude of local potential distortion caused by uneven ion concentration distribution inside the electrode material is analyzed, and the charge dynamic distortion rate representing the internal electrochemical state of the target object is generated.

4. The low-power edge computing outdoor energy storage power data acquisition system according to claim 3, characterized in that, A diffusion resistance coefficient representing the degree of obstruction to ion migration within the electrode material is generated by jointly calculating surface temperature, ambient temperature, and charge dynamic distortion rate, including: The changes in surface temperature and ambient temperature are analyzed to generate the thermal conduction pressure difference, which represents the intensity of heat exchange between the battery casing and the outside world, and the thermal inertia drift rate, which represents the balance between heat generation and heat dissipation inside the battery.

5. The low-power edge computing outdoor energy storage power data acquisition system according to claim 4, characterized in that, The surface temperature, ambient temperature, and charge dynamic distortion rate are calculated collaboratively to generate a diffusion resistance coefficient representing the degree of obstruction to ion migration within the electrode material. This also includes: By combining the charge dynamic distortion rate with the temperature difference conduction pressure difference and the thermal inertia drift rate, the local thermal expansion difference inside the electrode material is analyzed, and the lattice thermal expansion distortion degree, which represents the degree of thermal disturbance of the lattice structure of the electrode material, is generated. Based on real-time charge / discharge rate and lattice thermal expansion distortion, the degree of blockage of ion transport channels caused by lattice thermal expansion distortion is analyzed, and a diffusion resistance coefficient representing the degree of obstruction of ion migration within the target object in the electrode material is generated.

6. The low-power edge computing outdoor energy storage power data acquisition system according to claim 5, characterized in that, The relationship between the diffusion resistance coefficient and the real-time charge / discharge rate is analyzed to generate a dynamic relaxation coefficient that reflects the load-bearing capacity of the target object, including: The real-time charge / discharge rate is correlated with the diffusion resistance coefficient to generate a dynamic load potential coefficient that represents the impact of the load on the uniformity of ion distribution inside the electrode, and an ion concentration gradient coefficient that reflects the difference in ion concentration distribution before and after the charge / discharge switch. Based on the dynamic load potential coefficient and the ion concentration gradient coefficient, the response rate of the electrode recovering from the non-equilibrium state to the equilibrium state is analyzed, and a dynamic relaxation coefficient is generated to reflect the load-bearing capacity of the target object.

7. The low-power edge computing outdoor energy storage power data acquisition system according to claim 6, characterized in that, The dynamic relaxation coefficient and battery terminal voltage in the power supply data are calculated to generate a transient response factor representing the probability of operational hazards occurring in the target object, including: Analyze the degree of resistance and overshoot of battery terminal voltage during the switching between charging and discharging states in the power data, and generate an interface charge accumulation coefficient that represents the degree of imbalance between charge accumulation and release capabilities. The dynamic relaxation coefficient and the interfacial charge accumulation coefficient are correlated and calculated to generate a bulk-interface response phase difference that reflects the synergistic effect of bulk ion diffusion and interfacial electrochemical reaction.

8. The low-power edge computing outdoor energy storage power data acquisition system according to claim 7, characterized in that, The calculation of dynamic relaxation coefficients and battery terminal voltages in power supply data generates a transient response factor representing the probability of operational hazards occurring in the target object. This also includes: The phase difference between the bulk and interface responses is calculated in conjunction with the battery terminal voltage in the power supply data to generate a transient response factor representing the probability of operational hazards occurring in the target object.

9. A low-power edge computing outdoor energy storage power data acquisition system according to claim 8, characterized in that, Based on the transient response factor, the operational hazard index of the target object is determined, and the sleep-wake strategy of the edge computing module is dynamically adjusted according to the operational hazard index, including: Based on the transient response factor, the degree of irreversible lattice damage induced by ion cumulative impact at the electrode interface is analyzed to obtain the operational hazard index of the target object.

10. A low-power edge computing outdoor energy storage power data acquisition system according to claim 9, characterized in that, Based on the transient response factor, the operational hazard index of the target object is determined, and the sleep-wake strategy of the edge computing module is dynamically adjusted according to the operational hazard index. This also includes: When the operational risk index is 0, the edge computing module enters deep sleep mode; When 0 < operational risk index ≤ 0.5, the edge computing module enters a shallow hibernation mode; When 0.5 < operational risk index ≤ 1, the edge computing module stops hibernation and remains awake to continuously collect data.

Images