Charging control method and device for high-frequency intelligent charger

Abstract

The invention discloses a charging control method and device for a high-frequency intelligent charger, and relates to the technical field of charging control, and the method comprises the steps: obtaining the state data of a rechargeable battery, carrying out the multi-granularity analysis, and constructing a battery feature portrait; a standard control curve of the high-frequency intelligent charger is established, conversion node labeling is carried out in the standard control curve, the standard control curve has a control parameter strategy, and control parameters comprise voltage, current, frequency and temperature; carrying out matching analysis on the battery characteristic portrait and the standard control curve, and identifying an overlapping curve and a difference curve; and performing compensation regulation and control on the difference curve, and performing smooth fitting on the difference curve and the overlapping curve to obtain a charging adaptive control strategy which is used for performing charging control on the current target rechargeable battery. The technical problem that charging control is not intelligent enough and is difficult to adapt to different battery characteristics in the prior art is solved, and the technical effect of intelligent and accurate charging control is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of charging control, and particularly relates to a charging control method and device for a high-frequency intelligent charger. BACKGROUND

[0002] When the high-frequency intelligent charger charges different types of batteries, due to the differences in capacity, internal resistance, temperature characteristics and charging response of each battery, the traditional charging control method adopts fixed voltage, current or frequency parameters for control, which cannot dynamically adjust according to the real-time state of the battery, and is prone to cause problems such as low charging efficiency, excessive temperature rise or shortened battery life, and is difficult to meet the efficient and safe charging needs of diversified batteries. SUMMARY

[0003] The present application provides a charging control method and device for a high-frequency intelligent charger, which is used to solve the technical problems that the charging control in the prior art is not intelligent enough and is difficult to adapt to different battery characteristics.

[0004] In view of the above problems, the present application provides a charging control method and device for a high-frequency intelligent charger.

[0005] In a first aspect of the present application, a charging control method for a high-frequency intelligent charger is provided, and the method comprises: obtaining state data of a charging battery, performing multi-granularity analysis, and constructing a battery characteristic portrait; establishing a standard control curve of a high-frequency intelligent charger, and performing conversion node labeling in the standard control curve, wherein the standard control curve has a control parameter strategy, and the standard control curve includes voltage, current, frequency and temperature; matching and analyzing the battery characteristic portrait and the standard control curve, identifying an overlapping curve and a difference curve; compensating and regulating the difference curve, and performing smooth fitting with the overlapping curve to obtain a charging adaptive control strategy, which is used for charging control of a current target charging battery.

[0006] In a second aspect of the present application, a charging control device for a high-frequency intelligent charger is provided, and the device comprises: The system includes a multi-granularity analysis module for acquiring battery state data, performing multi-granularity analysis, and constructing a battery feature profile; a node annotation module for establishing a standard control curve for a high-frequency smart charger and annotating conversion nodes within the standard control curve, wherein the standard control curve contains control parameter strategies and includes voltage, current, frequency, and temperature; a matching analysis module for matching the battery feature profile with the standard control curve to identify overlapping and differential curves; and a charging control module for compensating and adjusting the differential curves and smoothly fitting them with the overlapping curves to obtain an adaptive charging control strategy for controlling the charging of the current target battery.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: This application acquires the state data of a rechargeable battery, performs multi-granularity analysis, and constructs a battery feature profile. It establishes a standard control curve for a high-frequency intelligent charger and annotates conversion nodes within the standard control curve. The standard control curve includes control parameter strategies, encompassing voltage, current, frequency, and temperature. The application matches and analyzes the battery feature profile with the standard control curve to identify overlapping and differential curves. It compensates and adjusts the differential curves and smoothly fits them to the overlapping curves to obtain an adaptive charging control strategy for controlling the charging of the current target battery. This invention addresses the technical problems of insufficient intelligence and difficulty in adapting to different battery characteristics in existing technologies. By constructing a battery feature profile and adaptively adjusting the charging control strategy, it achieves intelligent and precise charging control. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 A schematic flowchart of a charging control method for a high-frequency smart charger provided in an embodiment of this application; Figure 2 This is a schematic diagram of a charging control device for a high-frequency smart charger, provided as an embodiment of this application.

[0010] Figure labeling: Multi-granularity parsing module 11, node labeling module 12, matching parsing module 13, charging control module 14. Detailed Implementation

[0011] This application provides a charging control method and apparatus for a high-frequency smart charger, which addresses the technical problems of insufficient intelligence in charging control and difficulty in adapting to different battery characteristics in the prior art. By constructing a battery feature profile and adaptively adjusting the charging control strategy, it achieves the technical effect of intelligent and precise charging control.

[0012] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0013] It should be noted that any variation of the terms "comprising" and "having" is intended to cover non-exclusive inclusion, for example, a process, method, apparatus, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such process, method, product, or apparatus.

[0014] Example 1, as Figure 1 As shown, this application provides a charging control method for a high-frequency smart charger, the method comprising: Step S100: Obtain the state data of the rechargeable battery, perform multi-granularity analysis, and construct a battery feature profile.

[0015] In this embodiment, the state data of the rechargeable battery is first acquired in real time. Then, the state data is analyzed hierarchically from multiple dimensions, combining wide-range feature analysis with fine-grained feature extraction. Finally, the battery feature profile is constructed by combining the results of both wide-range and fine-grained analysis.

[0016] Furthermore, the method provided in the application embodiments, which involves obtaining the state data of the rechargeable battery, performing multi-granularity analysis, and constructing a battery feature profile, also includes: Real-time acquisition of rechargeable battery status data, including battery voltage, current, temperature, battery health status, remaining capacity, internal resistance, charging cycle, charging rate, and charging history data; hierarchical analysis of the rechargeable battery status data from multiple dimensions, at least divided into wide-range analysis and fine-grained analysis; and construction of the battery feature profile based on the analysis results of each granularity.

[0017] In this embodiment, multi-dimensional operating data of the rechargeable battery is first collected in real time through a high-precision voltage sampling circuit, a current sampling circuit, a temperature sensor, and a BMS communication interface, forming state data of the rechargeable battery including battery voltage, battery current, battery temperature, state of health (SOH), remaining charge (SOC), battery internal resistance, charging cycle, charging rate, and charging history data.

[0018] Subsequently, the state data of the rechargeable battery was analyzed from multiple dimensions and in a hierarchical manner, including both wide-range and fine-grained analysis. Wide-range analysis, by analyzing historical charge-discharge data and combining parameters such as internal resistance changes, capacity degradation trends, and charging speed, assesses the battery's health status and creates a battery health profile. It also analyzes the battery's charge-discharge performance under different temperature, humidity, and load conditions to evaluate its adaptability to various environments and establish an environmental adaptability profile. Then, based on the battery type, its charging characteristics, energy loss, and matching degree with charger control parameters are evaluated to construct a battery type profile. Fine-grained analysis analyzes the charging response information of the rechargeable battery in different voltage ranges, assessing the battery's charging efficiency, current changes, losses, and charging stability in each range, extracting voltage range response characteristics. Simultaneously, it combines charging data from different current ranges to assess battery temperature rise changes and charging capacity, identifying potential overheating risks and forming current range response characteristics. Furthermore, it analyzes the battery's charging response in different frequency ranges, comparing charging efficiency, temperature changes, and losses to establish frequency range response characteristics, thereby obtaining the battery's dynamic performance in each operating range.

[0019] After completing the wide-range and fine-grained analysis, the battery health profile, environmental adaptability profile, battery type profile, response characteristics of each voltage range, response characteristics of each current range, and response characteristics of each frequency range obtained above are integrated in a unified manner. Data from different sources are normalized and summarized so that each feature can be expressed in the same system. The integrated result is then used as the battery feature profile.

[0020] Furthermore, in the method provided in the application embodiments, the wide-range parsing includes: By analyzing historical charge and discharge data of rechargeable batteries, combined with information on internal resistance, capacity degradation, and charging speed, the health status of the batteries is verified and evaluated, and a health profile of the batteries is constructed. The charging and discharging performance of rechargeable batteries under different environmental conditions is analyzed to evaluate the adaptability of rechargeable batteries under various external conditions and to establish an environmental adaptability profile. Based on the type of rechargeable battery, the charging characteristics, loss situation, and control strategies matched with the charger are evaluated to establish a battery type profile.

[0021] In this embodiment, by analyzing historical charge and discharge data of the rechargeable battery, the voltage change curves, discharge capacity records, charging time distribution, and internal resistance measurements over multiple charge and discharge cycles are compiled and compared to comprehensively evaluate capacity degradation, internal resistance change trends, and charging speed information. First, the discharge capacity at different cycle counts is statistically analyzed to calculate the capacity degradation rate. Then, the degree of degradation in battery conductivity is analyzed using internal resistance detection results. Finally, the time ratio and trend of the constant current and constant voltage stages are compared in conjunction with charging speed information. For example, at the same charging rate, if the battery's discharge capacity decreases from 3000 mAh to 2700 mAh, the internal resistance increases from 5 milliohms to 9 milliohms, and the charging speed decreases by 15%, it can be determined that its performance has significantly degraded. Through the above data analysis, the battery's health status is verified and evaluated, constructing a health profile of the battery.

[0022] When analyzing the charging and discharging performance of rechargeable batteries under different environmental conditions, voltage, current, temperature, and time data are collected and categorized under low-temperature, normal-temperature, and high-temperature environments. Energy conversion efficiency, discharge plateau voltage, temperature rise changes, and charging speed information are compared under different conditions. The impact of the environment on battery performance is assessed by calculating the differences in capacity degradation rate and charging time under each environment. For example, in low-temperature environments, the battery charging time increases from 90 minutes at normal temperature to 130 minutes, and the capacity degradation rate increases; while in high-temperature environments, the internal resistance change increases, the temperature rise accelerates, and although the charging speed increases, energy loss intensifies. By comparing data from different environments, the battery's adaptability under various external conditions is evaluated, and an environmental adaptability profile is established.

[0023] Finally, based on the type of rechargeable battery, the charging characteristics, energy loss, and control strategies matched with the charger are analyzed. Tests are conducted under constant current / constant voltage charging, staged current-limiting charging, and high-frequency charging modes, based on the battery's rated voltage, capacity, maximum allowable charging current, and operating temperature range. Charging speed, energy loss, and temperature changes are recorded under different currents and frequencies, and the trends in internal resistance and capacity degradation are compared. For example, during high-rate charging, if the battery's internal resistance is stable, capacity retention is high, charging speed is fast, and temperature rise is small, it indicates that it is suitable for a high-power charging strategy. However, under the same conditions, if internal resistance increases, energy loss is significant, and charging efficiency decreases, control parameters need to be adjusted to match the optimal charging mode. Through the above data comparison and evaluation, the correspondence between charging characteristics and charger output parameters is determined, and a battery type characteristic profile is established.

[0024] Furthermore, in the method provided in the application embodiments, the fine-grained analysis further includes: Based on the charging response information of the rechargeable battery in different voltage ranges, the charging efficiency, current changes, losses, and charging stability of the battery are analyzed to obtain the response characteristics of each voltage range; the charging efficiency and temperature rise changes of the rechargeable battery in different current ranges are analyzed, and the charging capacity and overheating risk of the battery are evaluated through data analysis to establish the response characteristics of each current range; the response degree of the rechargeable battery to the charging process in different frequency ranges, including charging efficiency, temperature changes, and battery losses, is analyzed to establish the response characteristics of each frequency range.

[0025] In this embodiment, based on the charging response information of the rechargeable battery in different voltage ranges, the voltage, current, and time data of the battery at each stage of the charging process are first collected in real time, and the entire charging curve is divided into multiple voltage ranges, for example, segmented in steps of 0.1V or 0.2V from the initial voltage to the cutoff voltage. By calculating the current change curve, energy input, and temperature rise rate in each voltage range, the charging efficiency, current fluctuation, and energy loss of the battery in different voltage ranges are analyzed to determine its charging stability. For example, in the low voltage range, the current is larger and the temperature rise is slower, resulting in higher charging efficiency; while in the high voltage range, the current gradually decreases, the temperature rise increases, and the efficiency decreases, indicating enhanced polarization. By comparing and calculating these characteristic data, the response characteristics of each voltage range are obtained, characterizing the charging behavior of the battery in different voltage ranges.

[0026] Subsequently, when analyzing the charging efficiency and temperature rise changes of the rechargeable battery in different current ranges, based on the collected current, voltage, and temperature data, the charging process was divided into multiple current ranges according to current intensity, and the energy input, temperature rise rate, and voltage stability per unit time were calculated for each range. Through data analysis, the charging capacity and overheating risk of the battery in different current ranges were assessed. For example, in the high current range, if the temperature rise rate increases significantly and the charging efficiency decreases, it indicates that the battery is prone to overheating; while in the medium current range, the battery temperature rise is relatively gradual and the energy conversion efficiency is high, indicating that the charging conditions are more ideal. Through the above comparisons, the response characteristics of each current range were established to reflect the performance differences of the battery under different charging currents.

[0027] Finally, when analyzing the battery's response to the charging process in different frequency ranges, the charging efficiency, temperature changes, and energy loss of the battery were recorded and calculated in real time by changing the output frequency of the high-frequency smart charger. Different frequency ranges, such as 10kHz, 20kHz, and 30kHz, were used as test ranges to compare and analyze the battery's charging stability and energy utilization when the frequency changed. For example, if the charging efficiency improves and the temperature change remains stable when the frequency increases, it indicates that energy transfer is more efficient at that frequency; conversely, if the efficiency decreases or the temperature rises too quickly, it indicates that the battery's response to that frequency is poor. By performing data fitting and trend analysis on the charging behavior at different frequencies, the response characteristics of each frequency range were established, and the dynamic response law of the battery under multi-frequency operating conditions was determined.

[0028] Step S200: Establish a standard control curve for the high-frequency smart charger and mark the conversion nodes in the standard control curve. The standard control curve contains a control parameter strategy, and the control parameters include voltage, current, frequency, and temperature.

[0029] In this embodiment, when establishing the standard control curve of the high-frequency intelligent charger, the rechargeable battery is first tested multiple times under controlled experimental conditions to complete the charging process. Voltage, current, frequency, and temperature data are continuously collected during the charging process, and the changes in each stage are recorded in the same coordinate system according to the time sequence to obtain the standard control curve reflecting the operating characteristics of the entire charging process. Then, based on the collected trends of voltage increase over time and current decrease over time, the constant current stage with a large current in the early stage of charging, the constant voltage stage with the current gradually decreasing after the voltage approaches the target voltage, and the trickle maintenance stage when necessary are identified. The voltage control value, current control value, frequency control value, and temperature control threshold corresponding to each stage are set as the control parameter strategy in the standard control curve, so that the established standard control curve simultaneously includes four types of parameters: voltage control, current control, frequency control, and temperature control.

[0030] The conversion nodes are then marked in the standard control curve. During this process, based on voltage changes, frequency adjustments, and temperature feedback characteristics during charging, constant current to constant voltage conversion nodes, high-frequency to low-frequency conversion nodes, and temperature threshold nodes are sequentially set. The constant current to constant voltage conversion node indicates that when the battery voltage approaches the target voltage, the charger automatically switches from constant current mode to constant voltage mode to ensure voltage stability and prevent overcharging. The high-frequency to low-frequency conversion node maintains a high frequency in the early stages of charging to improve energy transfer efficiency, while reducing the frequency as the battery approaches full charge to reduce heat accumulation and improve charging stability. The temperature threshold node triggers the charger to adjust the charging current or pause charging when the battery temperature exceeds a safe temperature threshold to prevent overheating risks.

[0031] Furthermore, in the method provided in the application embodiments, the step of annotating the transformation nodes in the standard control curve further includes: The charger is marked with a constant current to constant voltage conversion node, where the charger automatically switches from constant current mode to constant voltage mode when the battery voltage approaches the target voltage; a high frequency to low frequency conversion node is marked, where high frequency is used in the initial stage of battery charging to improve charging efficiency, and switches to low frequency mode to reduce heat and improve stability when the battery is close to being fully charged; and a temperature threshold node is marked, where the charger automatically adjusts the charging current or stops charging when the battery temperature exceeds the set safe temperature threshold.

[0032] In this embodiment, when marking the constant current to constant voltage transition node, the voltage and current changes of the rechargeable battery throughout the entire charging process are first monitored, and voltage-time and current-time curves are plotted. The changing trends of the voltage rise rate and current decay rate are then calculated. When the battery voltage gradually approaches the target voltage and the current decay rate increases while the output power tends to stabilize, this moment is determined as the constant current to constant voltage transition node. This node is used to identify the critical point where the charging process transitions from the constant current stage to the constant voltage stage. By marking this node on the standard control curve, the charger can automatically switch its operating mode when the battery voltage approaches the set value, thereby maintaining voltage stability, suppressing current fluctuations, and preventing overcharging and battery overheating.

[0033] When marking the high-frequency to low-frequency transition node, statistical analysis of frequency output, energy conversion efficiency, and temperature rise changes during the initial, middle, and later stages of charging is used to identify the inflection point of charging efficiency and temperature change. In the initial charging stage, the charger operates at a higher frequency to improve energy transfer rate and reduce polarization effects. When the battery's state of charge reaches a set threshold, charging efficiency decreases, and the temperature rise rate increases, this point is identified as the high-frequency to low-frequency transition node. By marking this node on the standard control curve, the charger can automatically reduce its output frequency in subsequent stages, thereby reducing energy loss and heat accumulation, and maintaining thermal balance and stable operation during the charging process.

[0034] When marking temperature threshold nodes, the battery temperature change curve is monitored in real time during charging and compared with a preset safe temperature threshold. When the detected temperature approaches or exceeds the safe temperature threshold, that moment is designated as the temperature threshold node. This node is used to trigger a temperature protection mechanism, causing the charger to automatically reduce the charging current or suspend charging when the temperature is too high, thereby preventing risks such as electrolyte decomposition, electrode deformation, or safety failure caused by overheating.

[0035] Step S300: Match and analyze the battery feature profile with the standard control curve to identify overlapping curves and difference curves.

[0036] In this embodiment, when matching and analyzing the battery feature profile with the standard control curve, a hierarchical strategy iterative search is first performed based on the feature granularity in the battery feature profile. Each search strategy is then comprehensively evaluated based on the optimization weights of different granularity levels to determine the target charging control curve. The target charging control curve includes charging control parameters and the corresponding battery feature response relationship. Subsequently, the target charging control curve is aligned and compared with the standard control curve to identify overlapping and differing curves. Overlapping curves represent curve control regions where the charging control parameter range is completely consistent with the strategy, while differing curves represent curve control regions where the charging control parameter range or strategy differs.

[0037] Furthermore, in the method provided in the application embodiments, the method of matching and analyzing the battery feature profile with the standard control curve to identify overlapping curves and difference curves also includes: Based on the battery feature profile, a hierarchical strategy iterative search is performed according to feature granularity. The search strategy is evaluated based on the optimization weights of each level of granularity to determine the target charging control curve. The target charging control curve is the control strategy with the best evaluation result, including charging control parameters and the corresponding battery feature response relationship. The target charging control curve is aligned and compared with the standard control curve to identify overlapping curves and difference curves. The overlapping curve is the curve control region where the charging control parameter range and strategy completely overlap, and the difference curve is the curve control region where the charging control parameter range and / or strategy differ.

[0038] In this embodiment, based on the battery feature profile, a hierarchical strategy iterative search is performed according to feature granularity. When evaluating the search strategy based on the optimization weights of each level of granularity, the charging parameter response relationship is first analyzed at both coarse and fine granular levels based on the battery feature profile. During this process, reward response relationships that have a positive impact on charging performance and antagonistic response relationships that may lead to battery damage or temperature rise are identified. Subsequently, using these response relationships as basic conditions, the charging parameters are iteratively optimized in multiple dimensions through the hierarchical strategy search method, with the goal of maximizing rewards and minimizing antagonistic losses. This yields the charging strategy that best matches the battery feature profile at each level, which includes the charging cycle and its corresponding charging control parameters such as voltage, current, frequency, and temperature. Next, based on the optimization weights of each level of granularity, the charging strategies obtained at different levels are fused and corrected to ensure that the control parameters between strategies maintain consistency and continuity at both the global and local levels. Finally, a target charging control curve is established using the fused and corrected charging strategy. This target charging control curve includes the optimal charging control parameters and their corresponding battery feature response relationships.

[0039] Subsequently, when aligning and comparing the target charging control curve with the standard control curve, the two curves are compared segment by segment on the same time axis, and the comparison is performed segment by segment along the four control parameter dimensions of voltage, current, frequency, and temperature. When the target charging control curve and the standard control curve are completely identical in the charging control parameter range and control strategy within the same control stage, that stage is identified as an overlapping curve, corresponding to the curve control region where the charging control parameter range and strategy completely overlap. When the comparison reveals that the target charging control curve limits the current, prematurely reduces the frequency, or lowers the temperature threshold in a certain stage, while the standard control curve maintains its original setting, that stage is identified as a difference curve.

[0040] Furthermore, in the method provided in the application embodiments, based on the battery feature profile, a hierarchical strategy iterative search is performed according to the feature granularity, and the search strategy is evaluated based on the optimization weights of each level of granularity to determine the target charging control curve, further comprising: Based on the battery feature profile, charging parameter response relationships are analyzed at both coarse and fine granular levels to determine reward and resistance response relationships. Charging parameter strategy search is then performed based on these relationships, with the goal of maximizing reward and minimizing resistance loss. The optimal charging strategy for each level, satisfying the feature profile response objective, is obtained, including charging cycle and charging control parameters. The obtained charging strategies are then fused and corrected according to the optimization weights at each level. Using the fused charging strategy, the target charging control curve is established.

[0041] In this embodiment, based on the battery characteristic profile, a coarse-scale analysis of charging parameter response relationships is first performed. Time series analysis is used to model historical charge / discharge voltage data, charge / discharge current data, temperature change data, and performance change data derived from internal resistance and capacity decay. This determines which voltage control ranges, current control ranges, frequency usage ranges, and temperature constraints can maintain stable battery operation during the overall charging process. Control conditions that result in high battery capacity retention, slow internal resistance growth, and stable temperature changes are identified as reward responses, while control conditions that lead to accelerated capacity decay, excessive internal resistance increase, or temperature rise deviating from the safe range are identified as conflict responses. Subsequently, a fine-grained analysis of charging parameter response relationships is performed. A segmented interval feature analysis method is used to segmentally calculate the battery's charging efficiency in different voltage ranges, temperature rise changes in different current ranges, and energy loss and stability in different frequency ranges. Control conditions that improve energy utilization, maintain smooth current changes, and suppress heat accumulation caused by frequency switching during local charging stages are identified as reward responses, while control conditions that cause increased temperature rise rate, aggravated voltage fluctuations, or increased energy loss during local stages are identified as conflict responses.

[0042] Subsequently, when searching for charging parameter strategies based on reward-response and resistance-response relationships, a multi-objective parameter optimization method is employed at the coarse-range level. Voltage control values, current control values, frequency control values, and temperature control thresholds are used as variables to be optimized. Parameter combinations corresponding to reward-response relationships are used as the preferred direction, and parameter combinations corresponding to resistance-response relationships are used as the constraint direction. Under the premise of ensuring charging safety and long-term performance, a charging strategy meeting the requirements of the coarse-range level is searched. This strategy includes the division of charging cycles and the control parameters within each charging cycle. Then, at the fine-grained level, the same multi-objective parameter optimization method is used. The response characteristics of each voltage range, current range, and frequency range are used as the evaluation criteria. Control conditions for improving local efficiency and stabilizing thermal characteristics are used as optimization objectives, while control conditions for local energy loss and temperature anomalies are used as constraint objectives. A charging strategy meeting the requirements of the fine-grained level is searched. This strategy also includes the charging cycle and its corresponding voltage, current, frequency, and temperature control parameters. Through the above search process, charging strategies that satisfy the feature profile response objectives are obtained at two levels. The optimal result for each level is the strategy that maximizes the reward and minimizes the resistance loss.

[0043] Finally, when fusing and correcting the obtained charging strategies based on the optimization weights of each granularity level, a weighted fusion method is adopted. The charging strategy obtained at the coarse-range level is used as the global baseline strategy, and the charging strategy obtained at the fine-grained level is used as the local correction strategy. Based on the optimization weights corresponding to the wide-range analysis and the fine-grained analysis, the voltage control parameters, current control parameters, frequency control parameters, and temperature control thresholds within the same time period are linearly weighted or sequentially prioritized for synthesis. This ensures that the overall charging control meets both macroscopic safety and matching requirements, as well as segmented efficiency and thermal management requirements. After fusion, the time series of control parameters is continuously verified and gradient smoothed to maintain an executable slope for parameter changes between conversion cycle nodes. Using the fused and corrected charging strategy, a target charging control curve is established, which simultaneously includes the charging control parameters and their corresponding battery characteristic response relationships.

[0044] Furthermore, the method provided in the application embodiments also includes: Obtain the target charging duration constraint and the battery loss tolerance threshold, wherein the battery loss tolerance threshold is associated with the target charging duration and the battery feature profile; use the target charging duration constraint and the battery loss tolerance threshold as constraints to adjust and correct the target charging control curve.

[0045] In this embodiment, when obtaining the charging target duration constraint and the battery loss tolerance threshold, the user-set charging time is first used as the starting condition. Information such as battery capacity, current remaining charge, allowable charging current range, allowable operating temperature range, internal resistance variation, and charging efficiency from the battery feature profile is read. The average charging power required to charge the battery from its current charge to the target charge within the set time, along with the corresponding combination of charging current, voltage, and frequency, is calculated. This determines the charging target duration constraint, i.e., the amount of energy input that must be completed within that time period. Subsequently, based on the internal resistance increase trend, capacity decay rate, and temperature rise reflected in the battery feature profile, the potential performance losses under different charging intensities such as high current, high frequency, and near full voltage are calculated. The maximum acceptable loss range for the battery in the current state is given, and this range is determined as the battery loss tolerance threshold. This threshold is then correlated with the user-set charging target duration. If the set time is short, the battery loss tolerance threshold is set more tightly to prevent excessive loss due to pursuit of speed; if the set time is long, the battery loss tolerance threshold can be appropriately relaxed to improve overall charging efficiency.

[0046] After obtaining the target charging duration constraint and the battery loss tolerance threshold, the generated target charging control curve is checked and adjusted segment by segment. In this process, firstly, the charging current and voltage within each conversion cycle are checked according to the time axis to ensure energy input is completed within the user-set time. If insufficient, the current or frequency is increased in the initial segment. Secondly, according to the battery loss tolerance threshold, the increased current and frequency are checked to see if the temperature exceeds the safe range, or if the internal resistance change or capacity degradation exceeds the allowable range. If so, the current slope is reduced, the constant voltage stage time is extended, or the high-frequency segment is switched to a low-frequency segment in the corresponding interval to reduce the charging intensity within the tolerance range. After completing these two rounds of constraints, the corrected charging cycles, voltage control parameters, current control parameters, frequency control parameters, and temperature control threshold are rearranged on the same time axis to obtain the target charging control curve that simultaneously satisfies the target charging duration constraint and the battery loss tolerance threshold.

[0047] Step S400: Compensate and adjust the difference curve and smoothly fit it with the overlapping curve to obtain a charging adaptive control strategy for charging control of the current target battery.

[0048] In this embodiment, when compensating and adjusting the difference curve and smoothly fitting it with the overlapping curve, the target charging control parameters for the difference interval are first determined based on the difference curve. The difference interval is then compensated and adjusted to ensure that its control parameters, such as voltage, current, and frequency, remain consistent with the standard control region. Subsequently, the compensated and adjusted difference interval is compared with the adjacent overlapping curve to determine the control parameter gradient. When the fluctuation threshold of the charging battery meets the set standard, the curves are directly stitched together to form a continuous control curve. When the fluctuation threshold is not met, an interpolation method is used to smooth the difference curve and the standard control curve, ensuring that the current, voltage, and frequency control parameters change continuously and have stable gradients within the transition range. Finally, a seamless connection between the difference interval and the overlapping curve is achieved, resulting in a charging adaptive control strategy.

[0049] Finally, based on the obtained adaptive charging control strategy, the charging control of the current target battery is performed.

[0050] Furthermore, in the method provided in the application embodiments, the method of compensating and adjusting the difference curve and smoothly fitting it with the overlapping curve to obtain a charging adaptive control strategy further includes: Based on the difference curve, the target charging control parameters for the difference range are located, and the difference range is compensated and regulated using the target charging control parameters. The control parameter gradient is determined by comparing the compensated and regulated difference range with the overlapping curve in the neighborhood. When the fluctuation threshold of the charging battery is met, the curves are stitched together to obtain the charging adaptive control strategy. When the fluctuation threshold of the charging battery is not met, an interpolation method is used to smooth the compensated difference curve and the standard control curve to ensure that the continuity and change gradient of the current, voltage, and frequency control parameters during charging meet the fluctuation threshold. The smoothed difference range is then seamlessly connected with the overlapping curve in the neighborhood to obtain the charging adaptive control strategy.

[0051] In this embodiment, when locating the target charging control parameters for the difference interval based on the difference curve, the segments in the difference curve where the voltage control parameters, current control parameters, and frequency control parameters deviate are first identified, and these segments are determined as the difference interval. Then, within this difference interval, the target charging control parameters of the charging battery under the same operating stage are extracted, including the charging voltage setpoint, charging current amplitude, frequency switching node, and temperature adjustment threshold. These target charging control parameters are then applied to the difference interval to ensure that the control parameters in this segment are consistent with the standard control requirements, thereby compensating for and regulating the difference interval.

[0052] After completing the compensation and control of the difference interval, the control parameters of the compensation interval and the adjacent overlapping curve are judged by gradient. In this judgment process, the rate of change of the voltage control parameter gradient, current control parameter gradient, and frequency control parameter gradient are calculated, and their change amplitudes are compared to see if they are within the fluctuation threshold range set by the charging battery. When the change gradients of the above control parameters all meet the fluctuation threshold requirements, it indicates that the compensation interval and the overlapping curve have continuity in parameter change, and the curves can be directly spliced ​​to obtain a continuous and stable charging control curve, thereby forming a charging adaptive control strategy.

[0053] When the gradient change of the control parameters exceeds the fluctuation threshold, an interpolation method is used to smooth the transition section between the compensated difference curve and the standard control curve to maintain the continuity and smoothness of the control parameters during charging. During this smoothing process, interpolation sequences that continuously change along the time axis are generated for the voltage, current, and frequency control parameters, ensuring that the control parameters change smoothly and with controllable gradients within the transition range, while meeting the fluctuation threshold requirements. After smoothing, the corrected difference interval is seamlessly connected to adjacent overlapping curves, forming a charging curve where the voltage, current, and frequency control parameters are continuous and their changes are limited throughout the entire process. This ultimately yields a charging adaptive control strategy for controlling the charging of the current target battery.

[0054] In summary, the embodiments of this application have at least the following technical effects: This application acquires the state data of a rechargeable battery, performs multi-granularity analysis, and constructs a battery feature profile. It establishes a standard control curve for a high-frequency intelligent charger and annotates conversion nodes within the standard control curve. The standard control curve includes control parameter strategies, encompassing voltage, current, frequency, and temperature. The application matches and analyzes the battery feature profile with the standard control curve to identify overlapping and differential curves. It compensates and adjusts the differential curves and smoothly fits them to the overlapping curves to obtain an adaptive charging control strategy for controlling the charging of the current target battery. This invention addresses the technical problems of insufficient intelligence and difficulty in adapting to different battery characteristics in existing technologies. By constructing a battery feature profile and adaptively adjusting the charging control strategy, it achieves intelligent and precise charging control.

[0055] Example 2, based on the same inventive concept as the charging control method for a high-frequency smart charger in the foregoing examples, such as... Figure 2 As shown, this application provides a charging control device for a high-frequency smart charger. The device and method embodiments in this application are based on the same inventive concept. The device includes: The multi-granularity analysis module 11 is used to acquire the state data of the rechargeable battery, perform multi-granularity analysis, and construct a battery feature profile; the node annotation module 12 is used to establish a standard control curve for the high-frequency smart charger and perform conversion node annotation on the standard control curve, wherein the standard control curve contains a control parameter strategy, and the standard control curve includes voltage, current, frequency, and temperature; the matching analysis module 13 is used to perform matching analysis between the battery feature profile and the standard control curve to identify overlapping curves and difference curves; the charging control module 14 is used to compensate and adjust the difference curves and perform smooth fitting with the overlapping curves to obtain a charging adaptive control strategy for charging control of the current target rechargeable battery.

[0056] Furthermore, the device is also used to perform the following functions: Real-time acquisition of rechargeable battery status data, including battery voltage, current, temperature, battery health status, remaining capacity, internal resistance, charging cycle, charging rate, and charging history data; hierarchical analysis of the rechargeable battery status data from multiple dimensions, at least divided into wide-range analysis and fine-grained analysis; and construction of the battery feature profile based on the analysis results of each granularity.

[0057] Furthermore, the device is also used to perform the following functions: By analyzing historical charge and discharge data of rechargeable batteries, combined with information on internal resistance, capacity degradation, and charging speed, the health status of the batteries is verified and evaluated, and a health profile of the batteries is constructed. The charging and discharging performance of rechargeable batteries under different environmental conditions is analyzed to evaluate the adaptability of rechargeable batteries under various external conditions and to establish an environmental adaptability profile. Based on the type of rechargeable battery, the charging characteristics, loss situation, and control strategies matched with the charger are evaluated to establish a battery type profile.

[0058] Furthermore, the device is also used to perform the following functions: Based on the charging response information of the rechargeable battery in different voltage ranges, the charging efficiency, current changes, losses, and charging stability of the battery are analyzed to obtain the response characteristics of each voltage range; the charging efficiency and temperature rise changes of the rechargeable battery in different current ranges are analyzed, and the charging capacity and overheating risk of the battery are evaluated through data analysis to establish the response characteristics of each current range; the response degree of the rechargeable battery to the charging process in different frequency ranges, including charging efficiency, temperature changes, and battery losses, is analyzed to establish the response characteristics of each frequency range.

[0059] Furthermore, the device is also used to perform the following functions: The charger is marked with a constant current to constant voltage conversion node, where the charger automatically switches from constant current mode to constant voltage mode when the battery voltage approaches the target voltage; a high frequency to low frequency conversion node is marked, where high frequency is used in the initial stage of battery charging to improve charging efficiency, and switches to low frequency mode to reduce heat and improve stability when the battery is close to being fully charged; and a temperature threshold node is marked, where the charger automatically adjusts the charging current or stops charging when the battery temperature exceeds the set safe temperature threshold.

[0060] Furthermore, the device is also used to perform the following functions: Based on the battery feature profile, a hierarchical strategy iterative search is performed according to feature granularity. The search strategy is evaluated based on the optimization weights of each level of granularity to determine the target charging control curve. The target charging control curve is the control strategy with the best evaluation result, including charging control parameters and the corresponding battery feature response relationship. The target charging control curve is aligned and compared with the standard control curve to identify overlapping curves and difference curves. The overlapping curve is the curve control region where the charging control parameter range and strategy completely overlap, and the difference curve is the curve control region where the charging control parameter range and / or strategy differ.

[0061] Furthermore, the device is also used to perform the following functions: Based on the battery feature profile, charging parameter response relationships are analyzed at both coarse and fine granular levels to determine reward and resistance response relationships. Charging parameter strategy search is then performed based on these relationships, with the goal of maximizing reward and minimizing resistance loss. The optimal charging strategy for each level, satisfying the feature profile response objective, is obtained, including charging cycle and charging control parameters. The obtained charging strategies are then fused and corrected according to the optimization weights at each level. Using the fused charging strategy, the target charging control curve is established.

[0062] Furthermore, the device is also used to perform the following functions: Obtain the target charging duration constraint and the battery loss tolerance threshold, wherein the battery loss tolerance threshold is associated with the target charging duration and the battery feature profile; use the target charging duration constraint and the battery loss tolerance threshold as constraints to adjust and correct the target charging control curve.

[0063] Furthermore, the device is also used to perform the following functions: Based on the difference curve, the target charging control parameters for the difference range are located, and the difference range is compensated and regulated using the target charging control parameters. The control parameter gradient is determined by comparing the compensated and regulated difference range with the overlapping curve in the neighborhood. When the fluctuation threshold of the charging battery is met, the curves are stitched together to obtain the charging adaptive control strategy. When the fluctuation threshold of the charging battery is not met, an interpolation method is used to smooth the compensated difference curve and the standard control curve to ensure that the continuity and change gradient of the current, voltage, and frequency control parameters during charging meet the fluctuation threshold. The smoothed difference range is then seamlessly connected with the overlapping curve in the neighborhood to obtain the charging adaptive control strategy.

[0064] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0065] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0066] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A charging control method for a high-frequency smart charger, characterized in that, include: Obtain the state data of the rechargeable battery, perform multi-granularity analysis, and construct a battery feature profile; Establish a standard control curve for a high-frequency intelligent charger and mark the conversion nodes in the standard control curve. The standard control curve contains a control parameter strategy and includes voltage, current, frequency, and temperature. The battery feature profile is matched and analyzed with the standard control curve to identify overlapping and difference curves. The difference curve is compensated and adjusted, and then smoothly fitted with the overlapping curve to obtain a charging adaptive control strategy for charging control of the current target battery.

2. The charging control method for a high-frequency intelligent charger according to claim 1, characterized in that, Acquire the state data of the rechargeable battery, perform multi-granularity analysis, and construct a battery feature profile, including: Real-time acquisition of rechargeable battery status data, including battery voltage, current, temperature, battery health status, remaining capacity, internal resistance, charging cycle, charging rate, and charging history data; The state data of the rechargeable battery is analyzed in a multi-dimensional hierarchical manner, which is at least divided into two parts: wide-range analysis and fine-grained analysis. Based on the analysis results at each granularity, the battery feature profile is constructed.

3. The charging control method for a high-frequency intelligent charger according to claim 2, characterized in that, The wide-range parsing includes: By analyzing historical charge and discharge data of rechargeable batteries, and combining information on internal resistance, capacity degradation, and charging speed, the health status of the battery is verified and evaluated, and a health profile of the battery is constructed. Analyze the charging and discharging performance of rechargeable batteries under different environmental conditions, evaluate the adaptability of rechargeable batteries under various external conditions, and establish an environmental adaptability profile. Based on the type of rechargeable battery, evaluate its charging characteristics, loss status, and control strategies that match the charger to establish a characteristic profile of the battery type.

4. The charging control method for a high-frequency intelligent charger according to claim 3, characterized in that, The fine-grained analysis includes: Based on the charging response information of the rechargeable battery in different voltage ranges, the charging efficiency, current change, loss and charging stability of the battery are analyzed to obtain the response characteristics of each voltage range. The charging efficiency and temperature rise of rechargeable batteries in different current ranges were analyzed, and the charging capacity and overheating risk of batteries were evaluated through data analysis to establish the response characteristics of each current range. The response of rechargeable batteries to the charging process in different frequency ranges was analyzed, including charging efficiency, temperature changes, and battery wear, and the response characteristics of each frequency range were established.

5. The charging control method for a high-frequency intelligent charger according to claim 1, characterized in that, In the standard control curve, the transformation node is labeled, including: The constant current to constant voltage conversion node is marked, where the charger automatically switches from constant current mode to constant voltage mode when the battery voltage approaches the target voltage; The high-frequency to low-frequency conversion nodes are marked. In the initial stage of battery charging, high frequency is used to improve charging efficiency, and when the battery is close to being fully charged, it switches to low-frequency mode to reduce heat and improve stability. The temperature threshold node is marked. When the battery temperature exceeds the set safe temperature threshold, the charger automatically adjusts the charging current or stops charging.

6. The charging control method for a high-frequency intelligent charger according to claim 4, characterized in that, The battery feature profile is matched and analyzed with the standard control curve to identify overlapping and difference curves, including: Based on the battery feature profile, a hierarchical strategy iterative search is performed according to the feature granularity. The search strategy is evaluated based on the optimization weight of each level of granularity to determine the target charging control curve. The target charging control curve is the control strategy with the best evaluation result, including the charging control parameters and the corresponding battery feature response relationship. By aligning and comparing the target charging control curve with the standard control curve, overlapping curves and differential curves are identified. The overlapping curve is the curve control region where the charging control parameter range and strategy completely overlap, and the differential curve is the curve control region where the charging control parameter range and / or strategy differ.

7. The charging control method for a high-frequency intelligent charger according to claim 6, characterized in that, Based on the battery feature profile, a hierarchical strategy iterative search is performed according to feature granularity. The search strategy is evaluated based on the optimization weights of each level of granularity to determine the target charging control curve, including: Based on the battery feature profile, the charging parameter response relationship is analyzed at two levels: coarse range and fine granularity, to determine the reward response relationship and the resistance response relationship. Based on the reward response relationship and the resistance response relationship, a charging parameter strategy search is performed. The goal is to maximize the reward and minimize the resistance loss, and to obtain the best charging strategy for each level that satisfies the feature profile response target, including the charging cycle and charging control parameters. The obtained charging strategy is fused and corrected based on the optimization weights of each level of granularity, and the target charging control curve is established using the fused charging strategy.

8. The charging control method for a high-frequency intelligent charger according to claim 6, characterized in that, Also includes: Obtain the charging target duration constraint and the battery loss tolerance threshold, wherein the battery loss tolerance threshold is associated with the charging target duration and the battery feature profile; The target charging duration constraint and the battery loss tolerance threshold are used as constraints to adjust and correct the target charging control curve.

9. The charging control method for a high-frequency intelligent charger according to claim 1, characterized in that, The difference curve is compensated and adjusted, and then smoothly fitted with the overlapping curve to obtain a charging adaptive control strategy, including: Based on the difference curve, the target charging control parameters for the difference range are located, and the difference range is compensated and adjusted using the target charging control parameters. The differential range of the compensation regulation is compared with the overlapping curve in the neighborhood to determine the control parameter gradient. When the fluctuation threshold of the charging battery is met, the curves are stitched together to obtain the charging adaptive control strategy. When the fluctuation threshold of the rechargeable battery is not met, an interpolation method is used to smooth the compensated difference curve and the standard control curve to ensure that the continuity and change gradient of the current, voltage and frequency control parameters during the charging process meet the fluctuation threshold. The smoothed difference interval is seamlessly connected with the neighboring overlapping curve to obtain the charging adaptive control strategy.

10. A charging control device for a high-frequency intelligent charger, characterized in that, The apparatus is used to perform a charging control method for a high-frequency smart charger as described in any one of claims 1-9, the apparatus comprising: The multi-granularity parsing module is used to acquire the state data of the rechargeable battery, perform multi-granularity parsing, and build a battery feature profile. The node annotation module is used to establish the standard control curve of the high-frequency smart charger and to perform conversion node annotation in the standard control curve. The standard control curve contains control parameter strategies, including voltage, current, frequency, and temperature. The matching and parsing module is used to match and parse the battery feature profile with the standard control curve to identify overlapping curves and difference curves. The charging control module is used to compensate and adjust the difference curve and smoothly fit it with the overlapping curve to obtain a charging adaptive control strategy for charging control of the current target battery.

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