Charging adapter dual-mode switching control method

Abstract

The invention belongs to the technical field of charging control, and discloses a dual-mode switching control method of a charging adapter. The method comprises the following steps: identifying a battery type, and automatically matching a switching parameter set; continuously collecting charging state data to form a state data set, and carrying out trend analysis calculation to obtain a change rate set; dynamically evaluating the charging state of the battery based on the change rate set and the real-time state data in the state data set; dynamically adjusting the switching parameter set to generate a switching threshold set; fusing the change rate set, the battery charging state and the real-time state data, carrying out multi-dimensional comparative analysis with a switching threshold value set, and judging whether mode switching is carried out or not; if yes, calculating an optimal transition curve, and gradually adjusting the charging current in the charging state data; according to the invention, the applicability and performance of the charging adapter in a battery charging scene can be effectively improved, so that the service life of the battery is prolonged and the charging experience is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of charging control, and more particularly to a dual-mode switching control method for a charging adapter. Background Art

[0002] As mobile terminal devices, wearable devices, power tools, etc. put forward higher requirements for charging performance in terms of speed, safety and high efficiency, the intelligent control of charging adapters has gradually become a key direction of technology research and development and engineering application. In the existing technology, charging adapters generally adopt a dual-mode charging strategy that combines constant current and constant voltage, in which the switching of the dual modes is usually based on whether the battery terminal voltage reaches a preset threshold as a control basis; that is, when the battery voltage is monitored to reach the switching threshold from constant current to constant voltage during the charging process, the charging control logic immediately completes the mode switching; for example, the patent with announcement number CN108365654B discloses a wireless charger suitable for any lithium battery; for another example, the patent with publication number CN116169754A discloses a control method, device and computer equipment for a dual active bridge converter.

[0003] Although this control method based on a single voltage condition is simple to implement and easy to integrate, it has the following problems in practical applications:

[0004] (1) The switching point is fixed and lacks dynamic adjustment capability: Different types of batteries (such as lithium-ion, lithium iron phosphate, and high-voltage cells) have different requirements for charging strategies, and a fixed voltage threshold cannot adapt to diverse battery characteristics;

[0005] (2) Affected by large external environmental interference: battery temperature, load status, voltage fluctuations and other factors may affect the accuracy of voltage detection, resulting in false switching or switching lag;

[0006] (3) Lack of comprehensive judgment of multi-dimensional parameters (such as current trend, charging efficiency, etc.): Relying only on voltage as the basis for switching, it is easy to switch to the constant voltage stage prematurely before the battery reaches the optimal charging state, affecting the charging efficiency.

[0007] Therefore, an intelligent dual-mode switching control method is urgently needed to achieve better matching of different charging scenarios and battery types, and improve charging efficiency, reliability and safety.

[0008] In view of this, the present invention proposes a dual-mode switching control method for a charging adapter to solve the above-mentioned problem. Summary of the Invention

[0009] In order to overcome the above-mentioned defects of the prior art and achieve the above-mentioned objectives, the present invention provides the following technical solutions: a charging adapter dual-mode switching control method, comprising:

[0010] Step S1: obtaining a battery response characteristic curve using a pulse charging test method, identifying the battery type based on the battery response characteristic curve, and automatically matching a switching parameter set based on the battery type;

[0011] Step S2: continuously collecting charging status data according to a preset sampling interval to form a status data set;

[0012] Step S3: performing trend analysis and calculation on the state data set to obtain a set of change rates, which includes a voltage change rate and a current change rate;

[0013] Step S4: constructing a nonlinear weight fusion model to dynamically evaluate the battery charging state based on the rate of change set and the real-time state data in the state data set;

[0014] Step S5: Acquire the ambient temperature, and dynamically adjust the switching parameter set in combination with the real-time status data to generate a switching threshold set;

[0015] Step S6: integrating the current change rate, battery charging state and real-time state data, and performing multi-dimensional comparative analysis with the switching threshold set to determine whether to perform mode switching;

[0016] Step S7: If mode switching is performed, a gradient transition strategy is used to calculate an optimal transition curve, and the charging current in the charging state data is gradually adjusted based on the optimal transition curve to complete the charging mode switching.

[0017] Furthermore, the method for identifying the battery type according to the battery response characteristic curve includes:

[0018] According to the battery response characteristic curve, a multi-physical quantity coupling model is constructed; the multi-physical quantity coupling model includes a sub-models, where a is the number of curves in the battery response characteristic curve; the battery response characteristic curve is input into the multi-physical quantity coupling model, and the multi-objective optimization method is used to fit the multi-physical quantity coupling model to obtain the model inverse parameters; a type mapping matrix is ​​preset, and the type mapping matrix includes parameter ranges corresponding to different battery types, and the parameter range includes the numerical range corresponding to each parameter in the model inverse parameters. The model inverse parameters are compared with each parameter range in the type mapping matrix respectively, and the parameter range in which each parameter in the model inverse parameters is within the corresponding numerical range is screened out and marked as a matching range, and the battery type corresponding to the matching range is obtained.

[0019] Furthermore, the method for obtaining the current change rate includes:

[0020] The charging currents in the state data set are sorted in ascending order according to the corresponding acquisition time to generate a current sequence; the adjacent change rates between each two adjacent charging currents in the current sequence are calculated in sequence to generate a change rate sequence; the window length is dynamically set, and the change rate sequence is divided into windows based on the window length to obtain c change rate windows, each of which includes d adjacent change rates, where d is the window length; the adjacent change rates in each change rate window are fitted with a linear regression method to obtain the corresponding current characteristic line, and the slope of the current characteristic line is used as the current change rate;

[0021] The method for obtaining the voltage change rate is the same as the method for obtaining the current change rate.

[0022] Furthermore, the step of dynamically setting the window length includes:

[0023] Step S301: Preset a type factor mapping table, which includes different battery types and corresponding response speeds; obtain the corresponding response speed from the type factor mapping table according to the identified battery type;

[0024] Step S302: Calculate the corresponding standard deviation based on all adjacent change rates and mark it as the degree of fluctuation;

[0025] Step S303: construct multiple fuzzy sets for response speed and fluctuation degree respectively;

[0026] Step S304: converting the response speed and the fluctuation degree into the membership degree of each corresponding fuzzy set through fuzzification technology;

[0027] Step S305: Input all the membership degrees into the trained membership analysis model to predict the corresponding membership set; wherein the membership set includes the membership degree of each window level;

[0028] Step S306: setting a corresponding level interval for each window level and calculating the corresponding interval mean; based on the membership degree and interval mean of each window level, dynamically calculating the window length using the centroid method.

[0029] Furthermore, the method for dynamically evaluating the battery charging state includes:

[0030] Different digital labels are assigned to different battery types and marked as type labels. The ambient temperature and charging time are obtained, and the type label, ambient temperature, and charging time are used as analysis data. The analysis data is input into a trained weight distribution model to predict a corresponding weight set. The weight set includes a weight coefficient corresponding to each data point in the battery status data, which includes the voltage change rate, current change rate, and charging status data.

[0031] Acquire real-time status data from the status data set, where the real-time status data is the charging status data corresponding to the current moment; acquire the voltage change rate and current change rate corresponding to the real-time status data from the change rate set, and use them together with the real-time status data as real-time data; calculate the corresponding real-time change rate for each data in the real-time data, and calculate the total value of the change rate by summing them up; based on the real-time change rate and the total value of the change rate, correct the weight coefficient corresponding to each data in the real-time data to obtain the corrected weight coefficient; preset a weight fusion function, and substitute the real-time data and the corresponding corrected weight coefficient into the weight fusion function in sequence to calculate the battery charging status.

[0032] Furthermore, the step of generating a switching threshold set includes:

[0033] Step S501: Preset a threshold range, and calculate a parameter adjustment range based on the threshold range and a switching parameter set;

[0034] Step S502: constructing n adjustment sets based on the parameter adjustment range, where n is an integer greater than 1;

[0035] Step S503: randomly selecting m groups of adjustment sets from the n groups of adjustment sets, and using all the m groups of adjustment sets as particles to construct a particle population, and setting the number of iterations to 0;

[0036] Step S504: constructing a potential function and calculating the potential value corresponding to each particle in the particle population;

[0037] Step S505: dividing the particle population into a plurality of particle sub-populations based on the potential value;

[0038] Step S506: setting a corresponding update mechanism for each particle sub-population;

[0039] Step S507: updating the particles in each particle sub-population according to the update mechanism;

[0040] Step S508: Compare the number of iterations with a preset iteration threshold. If the number of iterations is less than the iteration threshold, merge all particles in the particle sub-populations into the particle population, increase the number of iterations by one, and return to step S504. If the number of iterations is greater than or equal to the iteration threshold, proceed to step S509.

[0041] Step S509: obtaining the optimal particle in the particle group, marking the adjustment set corresponding to the optimal particle as the optimal set, dynamically adjusting the switching parameter set based on the optimal set, and generating a switching threshold set.

[0042] Furthermore, in step S505, the method of dividing the particle population into a plurality of particle sub-populations includes:

[0043] The particles in the particle population are sorted from large to small according to the corresponding electric potential values ​​to generate a particle sequence; a division standard is preset, and the division standard includes two increasing division coefficients; the product of the two division coefficients and m is respectively used as the first division number and the second division number; according to the positive order of the particle sequence, each particle is assigned a corresponding serial number; according to the serial number of each particle, the first particle and the second particle are determined; the particle that is arranged in front of the first particle and the first particle are divided into a high-quality population; the particle that is arranged in front of the second particle and behind the first particle and the second particle are divided into an ordinary population; and the particle that is arranged behind the second particle is divided into an inferior population.

[0044] Furthermore, the method for determining whether to switch modes includes:

[0045] The current change rate in the real-time data is marked as the real-time electric change rate, and the real-time electric change rate is compared with the change rate threshold in the switching threshold set; if the real-time electric change rate is less than the change rate threshold, the change duration is statistically calculated based on the change rate set; if the real-time electric change rate is greater than or equal to the change rate threshold, the mode switching is not performed; the change duration, the battery charging status, and the charging voltage and battery surface temperature in the real-time status data are used as judgment data, and each data in the judgment data is compared with the corresponding switching threshold in the switching threshold set; if the battery surface temperature is less than the corresponding switching threshold, and each remaining data in the judgment data is greater than or equal to the corresponding switching threshold, the mode switching is performed; if the battery surface temperature is greater than or equal to the corresponding switching threshold, or there is remaining data in the judgment data that is less than the corresponding switching threshold, the mode switching is not performed.

[0046] Furthermore, the method of calculating the change duration based on the set of change rates includes:

[0047] According to the positive order of the change rate sequence, the current change rates in the change rate set are sorted to generate an electric change sequence. The real-time electric change rate in the electric change sequence is selected as the starting point and the electric change sequence is traversed in reverse. If the current change rate is less than the change rate threshold, it is added to the pre-built pass set. If the current change rate is greater than or equal to the change rate threshold, the reverse traversal of the electric change sequence is terminated. The number of current change rates in the pass set is counted and marked as the number of electric changes. The change duration is calculated based on the number of electric changes, the window length d, and the sampling interval.

[0048] Furthermore, the method for calculating the optimal transition curve includes:

[0049] Determine a set of setting parameters, the setting parameter set including a starting current, an ending current, a transition time, a time step, a current change rate limit, and a current range limit;

[0050] Construct a smooth objective function. Based on the set parameter set and the smooth objective function, use the gradient descent method to dynamically adjust the charging current of each current control point to obtain the optimal transition curve.

[0051] In the process of gradually adjusting the charging current in the charging state data based on the optimal transition curve, sliding mode control is used to track and control the charging current.

[0052] The technical effects and advantages of the dual-mode switching control method for a charging adapter of the present invention are as follows:

[0053] By analyzing battery response characteristics using a pulse charging test method, the system can accurately identify the charging requirements of different battery types, automatically match the switching parameter set, and achieve intelligent adaptation to different battery types. During the charging process, it continuously collects multi-dimensional status data such as charging current, charging voltage, and battery surface temperature. Based on dynamic trend analysis and a nonlinear weight fusion model, it accurately assesses the real-time charging status of the battery, providing a reliable basis for subsequent mode switching. Integrating external factors such as ambient temperature, the improved swarm intelligence optimization algorithm dynamically adjusts the switching parameter set to generate a switching threshold set. Compared with the traditional single voltage threshold, this significantly improves the dynamics and adaptability of the switching strategy. A gradient transition strategy is used to calculate the optimal transition curve, and sliding mode control is used to track and adjust the charging current in real time to achieve smooth switching of charging modes, avoid damage to the battery caused by sudden changes in charging current, and improve charging efficiency and safety. This effectively solves the problems of fixed parameters, response lag, and poor safety in the switching control of traditional charging adapters, improving the applicability and performance of charging adapters in battery charging scenarios, ensuring the optimal charging state for different battery types, thereby extending battery life and improving the charging experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a flow chart of a charging adapter dual-mode switching control method according to embodiment 1 of the present invention. DETAILED DESCRIPTION

[0055] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0056] Example 1

[0057] See also Figure 1 As shown, this embodiment provides a dual-mode switching control method for a charging adapter, the method comprising:

[0058] Step S1: A pulse charging test method is used to obtain a battery response characteristic curve, and the battery type is identified according to the battery response characteristic curve, and a switching parameter set is automatically matched based on the battery type.

[0059] The pulse charging test method is a non-continuous charging technology. The specific steps include:

[0060] Step S101: A person skilled in the art sets a pulse sequence based on practical experience; the pulse sequence includes a pulse current, a charging time, and a rest time;

[0061] Step S102: applying a current pulse to the battery based on the pulse sequence, and recording the battery response data within each pulse cycle; the battery response data includes the battery terminal voltage, the battery surface temperature, etc.;

[0062] Step S103: Construct a battery response characteristic curve based on the battery response data within each pulse cycle; the battery response characteristic curve includes a time-voltage curve, a time-battery surface temperature curve, etc.; that is, the battery response characteristic curve is a complete dynamic data set of battery response data such as voltage and battery surface temperature recorded within each pulse cycle, which changes over time and is the basis for identifying battery types and matching switching parameters.

[0063] Methods for identifying battery types based on battery response characteristic curves include:

[0064] Construct a multi-physical quantity coupling model based on the battery response characteristic curve; the multi-physical quantity coupling model includes a sub-models, where a is the number of curves in the battery response characteristic curve, and the sub-models correspond one-to-one to the curves in the battery response characteristic curve; the sub-models include an equivalent circuit model, an equivalent thermal network model, etc.; the battery response characteristic curve is input into the multi-physical quantity coupling model, and a multi-objective optimization method is used (i.e., simultaneously minimizing the fitting error of each curve in the battery response characteristic curve to ensure that the difference between the output curve of the multi-physical quantity coupling model and the battery response characteristic curve obtained by the pulse charging test method is minimized) to fit the multi-physical quantity coupling model and obtain model inverse parameters; the model inverse parameters include ohmic internal resistance, capacitance, heat capacity, thermal resistance, etc.;

[0065] A type mapping matrix is ​​preset, and the type mapping matrix includes parameter ranges corresponding to different battery types. The parameter range includes the numerical range corresponding to each parameter in the model inverse parameter. The type mapping matrix is ​​preset by technical personnel in this field according to actual conditions; battery types include lithium ion, lithium iron phosphate, lithium manganese oxide, etc.; the model inverse parameters are compared with each parameter range in the type mapping matrix respectively, and the parameter range in which each parameter in the model inverse parameter is within the corresponding numerical range is screened out, and marked as a matching range, and the battery type corresponding to the matching range is obtained.

[0066] The method for automatically matching the switching parameter set based on the battery type includes:

[0067] A preset switching parameter set includes b sets of switching parameter sets, where b is the number of battery types. Each switching parameter set corresponds to a battery type one-to-one and is pre-set by a person skilled in the art based on actual conditions. The switching parameter set includes a charge state threshold, a change rate threshold, a duration threshold, a voltage threshold, and a temperature threshold. Based on the identified battery type, the corresponding switching parameter set is automatically matched from the switching parameter set.

[0068] It should be noted that since different types of batteries have significant differences in chemical composition, internal structure and electrical characteristics, the corresponding charging requirements are also different; therefore, it is necessary to identify the battery type and automatically match the corresponding switching parameter set to ensure the safety and efficiency of the charging process, optimize battery life, prevent overcharging and damage, and thus realize the intelligent adaptation of the charging adapter to multiple battery types.

[0069] Step S2: continuously collecting charging status data according to a preset sampling interval to form a status data set.

[0070] Charging status data is data related to the current charging process and status of the battery, and is used to describe the real-time situation of the battery during the charging process; charging status data includes but is not limited to charging voltage, charging current, battery surface temperature and battery internal resistance; among them, the charging voltage and charging current are respectively obtained by a voltage sensor and a current sensor installed on the charging adapter, and the battery surface temperature is obtained by a thermistor fixed on the surface of the battery shell; the method for collecting the battery internal resistance is: the real-time collected charging voltage, charging current and battery surface temperature are input into the equivalent circuit model in sequence, and the equivalent circuit model is fitted using online parameter estimation algorithms such as Kalman filtering and recursive least squares to obtain the battery internal resistance; the sampling interval is pre-set by a technician in this field according to the actual charging situation of the battery.

[0071] Step S3: performing trend analysis calculation on the state data set to obtain a set of change rates, which includes a voltage change rate and a current change rate.

[0072] Methods for obtaining the current change rate include:

[0073] The charging currents in the state data set are sorted in ascending order according to the corresponding acquisition time (i.e., sorted from early to late) to generate a current sequence; the adjacent change rates between each two adjacent charging currents in the current sequence are calculated in sequence to generate a change rate sequence; the adjacent change rates are calculated as follows: for two adjacent currents in the current sequence, the current in the latter position is subtracted from the current in the former position, and the result is divided by the preset sampling interval to obtain the adjacent change rates; the window length is dynamically set, and the change rate sequence is divided into windows based on the window length to obtain c change rate windows, each of which includes d adjacent change rates, where c is an integer greater than 1 and d is the window length; the adjacent change rates in each change rate window are fitted with a linear regression method to obtain a change trend, and the corresponding current characteristic line is obtained, and the slope of the current characteristic line is used as the current change rate; the linear regression method is a prior art, and the specific process will not be described in detail here.

[0074] The method for obtaining the voltage change rate is the same as the method for obtaining the current change rate.

[0075] The steps to dynamically set the window length include:

[0076] Step S301: Preset a type factor mapping table, which includes different battery types and corresponding response speeds. The response speed refers to how quickly the battery reacts to changes in charging current, i.e., how quickly the battery current changes when a charging action is applied externally. The type factor mapping table is pre-set by those skilled in the art based on actual conditions. Based on the identified battery type, the corresponding response speed is obtained from the type factor mapping table.

[0077] Step S302: Calculate the corresponding standard deviation based on all adjacent change rates and mark it as the degree of fluctuation. The calculation method of the standard deviation is based on the existing technology, and the specific calculation process will not be described in detail here.

[0078] Step S303: construct multiple fuzzy sets for response speed and fluctuation degree respectively; for example, the fuzzy sets corresponding to response speed are fast, medium, slow, etc.; the fuzzy sets corresponding to fluctuation degree are large standard deviation, medium standard deviation, low standard deviation, etc.;

[0079] Step S304: The response speed and the degree of fluctuation are converted into the membership of each corresponding fuzzy set through fuzzification technology. Fuzzification technology is the process of converting precise numerical values ​​into the membership of the corresponding fuzzy set. Fuzzification technologies include triangular membership function and trapezoidal membership function. For example, if the value of the response speed is high, the membership of the fast speed is inferred to be 0.9, the membership of the medium speed is 0.3, and the membership of the slow speed is 0.

[0080] Step S305: All memberships are input into the trained membership analysis model to predict the corresponding membership set; wherein the membership analysis model is a deep neural network model, which is a prior art and the specific training process is not described in detail here; the membership set includes the membership of each window level, such as large window, medium window, small window, etc.;

[0081] Step S306: Set a corresponding level interval for each window level and calculate the corresponding interval mean; wherein the interval mean is the mean of the maximum and minimum values ​​of each level interval; based on the membership degree of each window level and the interval mean, the center of gravity method is used to dynamically calculate the window length.

[0082] The expression for window length is:

[0083] Where l is the window length, ω g is the membership degree of the g-th window level, k g is the window mean of the g-th window level, g∈[1,h], h is the number of window level types;

[0084] It should be noted that the reason for dynamically setting the window length based on the response speed and the degree of fluctuation is that the response speed reflects how quickly the battery reacts to changes in the charging current; the faster the response speed, the more suitable it is to use a smaller window to quickly capture changes; the slower the response speed, the larger the window is required to cover its lag effect; and the degree of fluctuation reflects the severity of the change in the charging current; the greater the degree of fluctuation, the shorter the window length needs to be to increase the response sensitivity; the smaller the degree of fluctuation, the larger the window length should be to smooth the noise; by dynamically setting the window length based on these two factors, it is possible to more accurately capture trend changes and improve the intelligence and stability of the charging strategy.

[0085] Step S4: Construct a nonlinear weight fusion model to dynamically evaluate the battery charging state based on the rate of change set and the real-time state data in the state data set.

[0086] The nonlinear weight fusion model is a mathematical model that dynamically assigns weights to different input data and combines them using nonlinear functions. The specific construction process includes determining the input variables, designing the weight distribution mechanism, and selecting the weight fusion function, and is specifically constructed by technical personnel in this field based on actual conditions.

[0087] Methods for dynamically evaluating the battery state of charge include:

[0088] Different digital labels are set for different battery types and marked as type labels; the ambient temperature and charging time are obtained, the ambient temperature is obtained by a temperature sensor, and the charging time is obtained by a timer; the ambient temperature is the external air temperature of the space where the charging adapter is located, which is used to reflect the thermal conditions of the current working environment; the charging time is the total time elapsed from the start of charging the battery to the current moment; the type label, ambient temperature and charging time are used as analysis data, and the analysis data is input into a trained weight distribution model to predict the corresponding weight set; the weight distribution model is a deep neural network model, and the weight set includes the weight coefficient corresponding to each data in the battery status data, and the battery status data includes the voltage change rate, the current change rate and the charging status data;

[0089] Real-time status data is obtained from the status data set, where the real-time status data is the charging status data corresponding to the current moment; the voltage change rate and current change rate corresponding to the real-time status data are obtained from the change rate set, and used together with the real-time status data as real-time data; the corresponding real-time change rate is calculated for each data in the real-time data, and the total value of the change rate is calculated by summing up; based on the real-time change rate and the total value of the change rate, the weight coefficient corresponding to each data in the real-time data is corrected to obtain the corrected weight coefficient; technical personnel in this field preset a weight fusion function based on factors such as battery characteristics and actual operating experience; the real-time data and the corresponding corrected weight coefficient are substituted into the weight fusion function in sequence to calculate the battery charging status.

[0090] Among them, the battery charging state is a comprehensive indicator of the battery's current charging process, reflecting the health and performance of the battery during charging; the real-time change rate is the rate of change of each data in the real-time data compared to the previous moment; the total change rate is the sum of all real-time change rates; the expression for the correction weight coefficient is: Where, δ new is the correction weight coefficient, δ is the weight coefficient, α is the adjustment coefficient, which is used to control the correction amplitude and is set by those skilled in the art according to actual conditions. p is the ratio of the real-time change rate to the total change rate. The average proportion is the average value of the ratio of all real-time change rates to the total change rate.

[0091] Step S5: Acquire the ambient temperature, and dynamically adjust the switching parameter set in combination with the real-time status data to generate a switching threshold set.

[0092] The steps of generating a switching threshold set include:

[0093] Step S501: Preset a threshold range, and calculate a parameter adjustment range based on the threshold range and a handover parameter set; wherein the threshold range is preset by a person skilled in the art based on actual conditions, the threshold range includes a value range corresponding to each handover parameter, and the parameter adjustment range includes an adjustment range corresponding to each handover parameter;

[0094] Step S502: constructing n adjustment sets based on the parameter adjustment range, where n is an integer greater than 1;

[0095] Step S503: randomly selecting m groups of adjustment sets from the n groups of adjustment sets, and using all the m groups of adjustment sets as particles to construct a particle population, and setting the number of iterations to 0;

[0096] Step S504: constructing a potential function and calculating the potential value corresponding to each particle in the particle population;

[0097] Step S505: dividing the particle population into a plurality of particle sub-populations based on the potential value;

[0098] Step S506: setting a corresponding update mechanism for each particle sub-population;

[0099] Step S507: updating the particles in each particle sub-population according to the update mechanism;

[0100] Step S508: Compare the number of iterations with a preset iteration threshold. If the number of iterations is less than the iteration threshold, merge all particles in the particle sub-populations into the particle population, increase the number of iterations by one, and return to step S504. If the number of iterations is greater than or equal to the iteration threshold, proceed to step S509.

[0101] Step S509: obtaining the optimal particle in the particle group, marking the adjustment set corresponding to the optimal particle as the optimal set, dynamically adjusting the switching parameter set based on the optimal set, and generating a switching threshold set.

[0102] In the above step S501, the method for calculating the parameter adjustment range includes:

[0103] Subtract the corresponding switching parameter in the switching parameter set from the maximum and minimum values ​​in the value range corresponding to each switching parameter in the threshold range to obtain the adjustment range corresponding to each switching parameter and form the parameter adjustment range; for example, the threshold range is [5,10], the switching parameter is 6, and therefore the adjustment range is [-1,4].

[0104] In the above step S502, the method of constructing n groups of adjustment sets based on the parameter adjustment range includes:

[0105] A value is randomly selected from each adjustment range and a set of adjustment sets is constructed; and n sets of adjustment sets are constructed in this way, and all n sets of adjustment sets are different.

[0106] In the above step S504, the potential function is expressed as:

[0107]

[0108] Where V i is the potential value of the i-th particle, lb i is the current variation of the i-th particle, yb i is the voltage variation of the ith particle, cd i is the charging efficiency of the i-th particle, i∈[1,m], These are all proportional coefficients preset by those skilled in the art according to actual conditions.

[0109] Methods for obtaining current variation, voltage variation, and charging efficiency include:

[0110] Based on the adjustment set corresponding to the particle, the switching parameter set is dynamically adjusted to generate a candidate threshold set. The ambient temperature, real-time status data, and candidate threshold set are sequentially input into a trained indicator prediction model to predict the corresponding indicator set. The indicator prediction model is a deep neural network model, and the indicator set includes the degree of current variation, the degree of voltage variation, and charging efficiency.

[0111] Among them, the degree of current variation and the degree of voltage variation are the fluctuation degrees of charging current and charging voltage after the switching parameter set is dynamically adjusted based on the adjustment set corresponding to the i-th particle and the mode is switched according to the candidate threshold set; the charging efficiency is the time required to fully charge the battery after the mode is switched according to the candidate threshold set.

[0112] In the above step S505, the method of dividing the particle population into multiple particle sub-populations includes:

[0113] The particles in the particle population are sorted from large to small according to the corresponding electric potential values ​​to generate a particle sequence; a division standard is preset, and the division standard includes two increasing division coefficients, and the value range of the two division coefficients is (0,1). The division standard is pre-set by those skilled in the art according to actual conditions; the product of the two division coefficients and m is respectively used as the first division number and the second division number; that is, the product of the division coefficient with a larger value and m is the first division number, and the product of the division coefficient with a smaller value and m is the second division number; according to the positive sequence of the particle sequence, each particle is assigned a corresponding serial number, and the serial number range is [1,m]; according to the serial number of each particle, the first particle and the second particle are determined; the particle arranged in front of the first particle and the first particle are divided into a high-quality population; the particle arranged in front of the second particle and behind the first particle and the second particle are divided into an ordinary population; the particle arranged behind the second particle is divided into an inferior population;

[0114] The method for determining the first particle and the second particle is as follows: the first and second division numbers are subtracted from the sequence number of each particle, and the absolute values ​​are taken respectively to obtain the first sequence number difference and the second sequence number difference corresponding to each particle; the particle with the smallest first sequence number difference is taken as the first particle, and the particle with the smallest second sequence number difference is taken as the second particle.

[0115] The above-mentioned step S506: the method of setting a corresponding update mechanism for each particle sub-population includes:

[0116] When the particle sub-population is a high-quality population, the corresponding update mechanism is:

[0117]

[0118] Where x i (t+1) is the updated i-th particle, x i (t) is the i-th particle, x i (t-1) is the i-th particle before the last update, is the gradient of the potential function, η is the convergence factor used to control the convergence speed, β is the momentum factor used to avoid oscillation, and the convergence factor and the momentum factor are both pre-set by those skilled in the art according to actual conditions; the gradient of the potential function is obtained by using a numerical difference method on the potential function. The numerical difference method is a prior art, and the specific process will not be described in detail here.

[0119] When the particle sub-population is a normal population, the corresponding update mechanism is:

[0120]

[0121] Where, F i is the resultant electric force of particle i, ‖Fi ‖ is the magnitude of the resultant electric force of the i-th particle, is the normalized direction vector of the i-th particle, which is used to ensure the correct movement direction and the same step size for each update. γ is the step size factor, σ is the perturbation factor, and N(0,1) is a random number in the standard normal distribution. Both the step size factor and the perturbation factor are preset by those skilled in the art according to actual conditions.

[0122] The resultant electric force F i The expression is:

[0123] Where θ ij is the weight factor between the i-th particle and the j-th particle, V j is the potential value of the jth particle, ‖x j -x i ‖ is the Euclidean distance between the i-th particle and the j-th particle, x j (t) is the jth particle;

[0124] Weight factor θ ij The expression is:

[0125] Where e is a natural constant and ε is an attenuation factor, which is used to control the attenuation speed and determines the influence of the Euclidean distance on the weight factor.

[0126] When the particle sub-population is of inferior quality, the corresponding update mechanism is:

[0127] x i (t+1)=x i (t)+λ×Levy(q);

[0128] Where λ is the scaling factor, Levy(q) is the Levy distribution, and q is the stability index used to control the tail thickness of the Levy distribution, q∈(0,2]. The scaling factor is preset by those skilled in the art based on actual conditions.

[0129] In the above step S509, the optimal particle in the particle group is the particle with the largest potential value in the particle group; the method for dynamically adjusting the switching parameter set based on the optimal set is: add each numerical value in the optimal set to the corresponding switching parameter in the switching parameter set, obtain the switching threshold corresponding to each switching parameter, and form a switching threshold set.

[0130] It should be noted that the purpose of using the improved swarm intelligence optimization algorithm to dynamically adjust the switching parameter set is: by constructing a population containing multiple particles, using the potential function to comprehensively evaluate the particles, and dividing the particles into high-quality, ordinary and low-quality sub-populations according to their performance, and using various update mechanisms such as gradient descent, electric field force drive and stable distribution perturbation for different sub-populations, to give full play to the combination of global search and local optimization capabilities; not only can it effectively find the optimal adjustment set and improve the accuracy of the switching threshold, but it can also enhance the robustness and anti-disturbance ability of the swarm intelligence optimization algorithm, adapt to the dynamic changes of ambient temperature and real-time charging status, ensure that the charging mode switching process is smooth and stable, avoid excessive current and voltage fluctuations and reduced charging efficiency, and ultimately achieve safe, efficient and intelligent charging process management.

[0131] Step S6: The current change rate, battery charging status and real-time status data are integrated and compared with the switching threshold set to perform a multi-dimensional analysis to determine whether to perform mode switching.

[0132] Methods for determining whether to switch modes include:

[0133] The current change rate in the real-time data is marked as the real-time electric change rate, and the real-time electric change rate is compared with the change rate threshold in the switching threshold set; if the real-time electric change rate is less than the change rate threshold, the change duration is calculated based on the change rate set; if the real-time electric change rate is greater than or equal to the change rate threshold, the mode switching is not performed; the change duration, the battery charging state, and the charging voltage and battery surface temperature in the real-time state data are used as judgment data, and each data in the judgment data is compared with the corresponding switching threshold in the switching threshold set; if the battery surface temperature is less than the corresponding switching threshold, and each remaining data in the judgment data is greater than or equal to the corresponding switching threshold, the mode switching is performed; if the battery surface temperature is greater than or equal to the corresponding switching threshold, or there is remaining data in the judgment data that is less than the corresponding switching threshold, the mode switching is not performed;

[0134] Methods for statistically analyzing change durations based on change rate collection include:

[0135] According to the positive order of the change rate sequence, the current change rates in the change rate set are sorted to generate an electric change sequence. The real-time electric change rate in the electric change sequence is selected as the starting point and the electric change sequence is traversed in reverse. If the current change rate is less than the change rate threshold, it is added to the pre-built pass set. If the current change rate is greater than or equal to the change rate threshold, the reverse traversal of the electric change sequence is terminated. The number of current change rates in the pass set is counted and marked as the number of electric changes. The number of electric changes, the window length d, and the sampling interval are multiplied in sequence to obtain the change duration.

[0136] Step S7: If mode switching is performed, a gradient transition strategy is used to calculate an optimal transition curve, and the charging current in the charging state data is gradually adjusted based on the optimal transition curve to complete the charging mode switching.

[0137] Methods for calculating the optimal transition curve include:

[0138] Determine a set of setting parameters, including a starting current, an ending current, a transition time, a time step (i.e., the time interval between each two current control points after the continuous time is discretized, indicating how often the current is controlled), a current change rate limit (i.e., the maximum value that the current change rate can reach), and a current range limit (i.e., the maximum and minimum values ​​that the charging current can reach). The starting current is the charging current in the real-time status data, and the ending current, transition time, current change rate limit, and current range limit are all pre-set by those skilled in the art based on actual conditions.

[0139] A smoothing objective function is constructed to optimize the charging current at each current control point while satisfying the set parameter set, so that the transition process is as smooth as possible. The expression of the smoothing objective function is:

[0140]

[0141] Where Y is the smoothing objective function value, which is used to measure the smoothness of the current change curve. The smaller the value, the smoother the current change. N is the number of current control points, that is, how many current control points the entire transition time is divided into according to the time step. For example, if the transition time is 5s and the time step is 1s, there are 6 current control points. K is the charging current at the Kth current control point, Δt is the time step;

[0142] Based on the set parameter set and the smooth objective function, the gradient descent method is used to dynamically adjust the charging current of each current control point to obtain the optimal transition curve that meets the set parameter set and minimizes the smooth objective function; the gradient descent method is an existing technology, and the specific process will not be described in detail here.

[0143] In the process of gradually adjusting the charging current in the charging status data based on the optimal transition curve, sliding mode control is used to accurately track and robustly control the charging current, thereby effectively improving the stability and anti-interference capability of the charging process, preventing current mutations, ensuring battery safety, and extending battery life. At the same time, smooth switching of charging modes is achieved, improving charging efficiency and overall performance. Sliding mode control is an existing technology, and the specific process will not be described in detail here.

[0144] This embodiment analyzes battery response characteristics using a pulse charging test method, accurately identifying the charging requirements of different battery types and automatically matching the switching parameter set to achieve intelligent adaptation for different battery types. During the charging process, it continuously collects multi-dimensional state data, such as charging current, charging voltage, and battery surface temperature. Based on dynamic trend analysis and a nonlinear weight fusion model, it accurately assesses the battery's real-time charging state, providing a reliable basis for subsequent mode switching. By integrating external factors such as ambient temperature, an improved swarm intelligence optimization algorithm dynamically adjusts the switching parameter set to generate a switching threshold set. Compared to traditional single voltage thresholds, this significantly improves the dynamics and adaptability of the switching strategy. A gradient transition strategy is used to calculate the optimal transition curve, and sliding mode control is used to track and adjust the charging current in real time, achieving smooth charging mode switching, avoiding damage to the battery caused by sudden changes in charging current, and improving charging efficiency and safety. This effectively addresses the issues of fixed parameters, delayed response, and poor safety in traditional charging adapter switching control, improving the applicability and performance of charging adapters in battery charging scenarios, ensuring the optimal charging state for different battery types, thereby extending battery life and improving the charging experience.

[0145] Example 2

[0146] The present application also provides an electronic device. The electronic device may include one or more processors and one or more memories. The memories may store computer-readable code that, when executed by the one or more processors, may execute the above-described method for controlling dual-mode switching of a charging adapter.

[0147] The method or system according to the embodiment of the present application can also be implemented with the aid of the architecture of the electronic device shown in this application. The electronic device may include a bus, one or more CPUs, ROM, RAM, a communication port connected to a network, input / output, a hard disk, etc. The storage device in the electronic device, such as a ROM or a hard disk, can store a charging adapter dual-mode switching control method provided in this application. Furthermore, the electronic device may also include a user interface. Of course, the architecture shown in this application is only exemplary. When implementing different devices, one or more components in the electronic device shown in this application may be omitted according to actual needs.

[0148] Example 3

[0149] As shown, one embodiment of the present application discloses a computer-readable storage medium. Computer-readable instructions are stored on the computer-readable storage medium. When the computer-readable instructions are executed by a processor, a charging adapter dual-mode switching control method according to an embodiment of the present application described with reference to the above figures can be executed. The storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory. Non-volatile memory may include, for example, read-only memory (ROM), a hard disk, flash memory, etc.

[0150] In addition, according to embodiments of the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the present application provides a non-transitory machine-readable storage medium storing machine-readable instructions that can be executed by a processor to execute instructions corresponding to the method steps provided in the present application, such as a charging adapter dual-mode switching control method. When the computer program is executed by a central processing unit (CPU), the above-mentioned functions defined in the method of the present application are performed.

[0151] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art will be able to modify the technical solutions described in the foregoing embodiments or to substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

[0152] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0153] In the description of the present invention, it should be understood that the terms "first", "second", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0154] In the description of the present invention, unless otherwise specified, "plurality" means two or more.

[0155] In the description of the present invention, “several” means one or more, and “a large number” means two or more.

[0156] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0157] The formulas in this manual are all dimensionless and calculated using numerical values. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formulas are set by technicians in this field based on actual conditions.

[0158] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

Claims

1. A charging adapter dual-mode switching control method, characterized in that: include: Step S1: obtaining a battery response characteristic curve using a pulse charging test method, identifying the battery type based on the battery response characteristic curve, and automatically matching a switching parameter set based on the battery type; Step S2: continuously collecting charging status data according to a preset sampling interval to form a status data set; Step S3: performing trend analysis and calculation on the state data set to obtain a set of change rates, which includes a voltage change rate and a current change rate; Step S4: constructing a nonlinear weight fusion model to dynamically evaluate the battery charging state based on the rate of change set and the real-time state data in the state data set; Step S5: Acquire the ambient temperature, and dynamically adjust the switching parameter set in combination with the real-time status data to generate a switching threshold set; Step S6: integrating the current change rate, battery charging state and real-time state data, and performing multi-dimensional comparative analysis with the switching threshold set to determine whether to perform mode switching; Step S7: If mode switching is performed, a gradient transition strategy is used to calculate an optimal transition curve, and the charging current in the charging state data is gradually adjusted based on the optimal transition curve to complete the charging mode switching.

2. The dual-mode switching control method for a charging adapter according to claim 1, characterized in that: Methods for identifying battery types based on battery response characteristic curves include: According to the battery response characteristic curve, a multi-physical quantity coupling model is constructed; the multi-physical quantity coupling model includes a sub-models, where a is the number of curves in the battery response characteristic curve; the battery response characteristic curve is input into the multi-physical quantity coupling model, and the multi-objective optimization method is used to fit the multi-physical quantity coupling model to obtain the model inverse parameters; a type mapping matrix is ​​preset, and the type mapping matrix includes parameter ranges corresponding to different battery types, and the parameter range includes the numerical range corresponding to each parameter in the model inverse parameters. The model inverse parameters are compared with each parameter range in the type mapping matrix respectively, and the parameter range in which each parameter in the model inverse parameters is within the corresponding numerical range is screened out and marked as a matching range, and the battery type corresponding to the matching range is obtained.

3. The dual-mode switching control method for a charging adapter according to claim 2, wherein: Methods for obtaining the current change rate include: The charging currents in the state data set are sorted in ascending order according to the corresponding acquisition time to generate a current sequence; the adjacent change rates between each two adjacent charging currents in the current sequence are calculated in sequence to generate a change rate sequence; the window length is dynamically set, and the change rate sequence is divided into windows based on the window length to obtain c change rate windows, each of which includes d adjacent change rates, where d is the window length; the adjacent change rates in each change rate window are fitted with a linear regression method to obtain the corresponding current characteristic line, and the slope of the current characteristic line is used as the current change rate; The method for obtaining the voltage change rate is the same as the method for obtaining the current change rate.

4. The charging adapter dual-mode switching control method according to claim 3, characterized in that: The steps to dynamically set the window length include: Step S301: Preset a type factor mapping table, which includes different battery types and corresponding response speeds; obtain the corresponding response speed from the type factor mapping table according to the identified battery type; Step S302: Calculate the corresponding standard deviation based on all adjacent change rates and mark it as the degree of fluctuation; Step S303: construct multiple fuzzy sets for response speed and fluctuation degree respectively; Step S304: converting the response speed and the fluctuation degree into the membership degree of each corresponding fuzzy set through fuzzification technology; Step S305: Input all the membership degrees into the trained membership analysis model to predict the corresponding membership set; wherein the membership set includes the membership degree of each window level; Step S306: setting a corresponding level interval for each window level and calculating the corresponding interval mean; based on the membership degree and interval mean of each window level, dynamically calculating the window length using the centroid method.

5. The dual-mode switching control method for a charging adapter according to claim 4, characterized in that: Methods for dynamically evaluating the battery state of charge include: Different digital labels are assigned to different battery types and marked as type labels. The ambient temperature and charging time are obtained, and the type label, ambient temperature, and charging time are used as analysis data. The analysis data is input into a trained weight distribution model to predict a corresponding weight set. The weight set includes a weight coefficient corresponding to each data point in the battery status data, which includes the voltage change rate, current change rate, and charging status data. Acquire real-time status data from the status data set, where the real-time status data is the charging status data corresponding to the current moment; acquire the voltage change rate and current change rate corresponding to the real-time status data from the change rate set, and use them together with the real-time status data as real-time data; calculate the corresponding real-time change rate for each data in the real-time data, and calculate the total value of the change rate by summing them up; based on the real-time change rate and the total value of the change rate, correct the weight coefficient corresponding to each data in the real-time data to obtain the corrected weight coefficient; preset a weight fusion function, and substitute the real-time data and the corresponding corrected weight coefficient into the weight fusion function in sequence to calculate the battery charging status.

6. The charging adapter dual-mode switching control method according to claim 5, characterized in that: The steps of generating a switching threshold set include: Step S501: Preset a threshold range, and calculate a parameter adjustment range based on the threshold range and a switching parameter set; Step S502: constructing n adjustment sets based on the parameter adjustment range, where n is an integer greater than 1; Step S503: randomly selecting m groups of adjustment sets from the n groups of adjustment sets, and using all the m groups of adjustment sets as particles to construct a particle population, and setting the number of iterations to 0; Step S504: constructing a potential function and calculating the potential value corresponding to each particle in the particle population; Step S505: dividing the particle population into a plurality of particle sub-populations based on the potential value; Step S506: setting a corresponding update mechanism for each particle sub-population; Step S507: updating the particles in each particle sub-population according to the update mechanism; Step S508: Compare the number of iterations with a preset iteration threshold. If the number of iterations is less than the iteration threshold, merge all particles in the particle sub-populations into the particle population, increase the number of iterations by one, and return to step S504. If the number of iterations is greater than or equal to the iteration threshold, proceed to step S509. Step S509: obtaining the optimal particle in the particle group, marking the adjustment set corresponding to the optimal particle as the optimal set, dynamically adjusting the switching parameter set based on the optimal set, and generating a switching threshold set.

7. The charging adapter dual-mode switching control method according to claim 6, characterized in that: In step S505, the method of dividing the particle population into a plurality of particle sub-populations includes: The particles in the particle population are sorted from large to small according to the corresponding electric potential values ​​to generate a particle sequence; a division standard is preset, and the division standard includes two increasing division coefficients; the product of the two division coefficients and m is respectively used as the first division number and the second division number; according to the positive order of the particle sequence, each particle is assigned a corresponding serial number; according to the serial number of each particle, the first particle and the second particle are determined; the particle that is arranged in front of the first particle and the first particle are divided into a high-quality population; the particle that is arranged in front of the second particle and behind the first particle and the second particle are divided into an ordinary population; and the particle that is arranged behind the second particle is divided into an inferior population.

8. The dual-mode switching control method for a charging adapter according to claim 7, wherein: Methods for determining whether to switch modes include: The current change rate in the real-time data is marked as the real-time electric change rate, and the real-time electric change rate is compared with the change rate threshold in the switching threshold set; if the real-time electric change rate is less than the change rate threshold, the change duration is statistically calculated based on the change rate set; if the real-time electric change rate is greater than or equal to the change rate threshold, the mode switching is not performed; the change duration, the battery charging status, and the charging voltage and battery surface temperature in the real-time status data are used as judgment data, and each data in the judgment data is compared with the corresponding switching threshold in the switching threshold set; if the battery surface temperature is less than the corresponding switching threshold, and each remaining data in the judgment data is greater than or equal to the corresponding switching threshold, the mode switching is performed; if the battery surface temperature is greater than or equal to the corresponding switching threshold, or there is remaining data in the judgment data that is less than the corresponding switching threshold, the mode switching is not performed.

9. The charging adapter dual-mode switching control method according to claim 8, characterized in that: Methods for statistically analyzing change durations based on change rate collection include: According to the positive order of the change rate sequence, the current change rates in the change rate set are sorted to generate an electric change sequence. The real-time electric change rate in the electric change sequence is selected as the starting point and the electric change sequence is traversed in reverse. If the current change rate is less than the change rate threshold, it is added to the pre-built pass set. If the current change rate is greater than or equal to the change rate threshold, the reverse traversal of the electric change sequence is terminated. The number of current change rates in the pass set is counted and marked as the number of electric changes. The change duration is calculated based on the number of electric changes, the window length d, and the sampling interval.

10. A charging adapter dual-mode switching control method according to claim 9, characterized in that: Methods for calculating the optimal transition curve include: Determine a set of setting parameters, the setting parameter set including a starting current, an ending current, a transition time, a time step, a current change rate limit, and a current range limit; Construct a smooth objective function. Based on the set parameter set and the smooth objective function, use the gradient descent method to dynamically adjust the charging current of each current control point to obtain the optimal transition curve. In the process of gradually adjusting the charging current in the charging state data based on the optimal transition curve, sliding mode control is used to track and control the charging current.

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