A method for generating an emergency charging solution for portable motorcycles based on plug-in design

Abstract

The present application provides a method for generating an emergency charging solution for a portable motorcycle based on a plug-in design, comprising: obtaining capacitor voltage parameters and capacitor internal resistance parameters of each capacitor layer, using temperature and current data during the charging and discharging process as input, and using the voltage and internal resistance parameters of each capacitor layer as output, training a nonlinear mapping model, and optimizing the capacitance, temperature, and current parameters of the nonlinear mapping model through cross-validation; during the charging and discharging process of the plug-in charging module, collecting temperature and current data of each capacitor layer in real time, inputting the data into the nonlinear mapping model, and predicting the voltage and internal resistance parameters of each capacitor layer to obtain the dynamic changes of the capacitor layer parameters during the charging and discharging process.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to a method for generating an emergency charging solution for a portable motorcycle based on a plug-in design. Background Art

[0002] When a motorcycle runs low on power during long outdoor trips, emergency charging is necessary. However, if the voltage and internal resistance of each capacitor layer in the motorcycle's charging module are unbalanced, charging efficiency will be too slow, making emergency charging impossible. Therefore, developing an emergency charging solution for plug-in portable motorcycles requires balancing the voltage, internal resistance, and other parameters within the plug-in charging module's layered capacitor structure. This balancing control presents numerous technical challenges. First, the voltage and internal resistance parameters of the different capacitor layers are affected by temperature and current during the charging and discharging process, exhibiting complex nonlinear characteristics. This nonlinearity makes it difficult to precisely control the charge and discharge states of each capacitor layer, thus impacting the performance and reliability of the entire charging module. Second, to ensure consistent discharge depth across each capacitor layer, real-time monitoring and dynamic balancing control of the voltage and internal resistance parameters of each capacitor layer are required. However, in the actual operating environment of the charging module, temperature fluctuations and fluctuations in charge and discharge currents can interfere with the accuracy of capacitor parameter measurements, complicating the design of a balancing control strategy. Furthermore, in a plug-in design, adapting to frequent plug-in and unplugging scenarios places higher demands on the integrated design of the charging module. In summary, how to achieve accurate modeling, parameter identification, and balanced control of layered capacitor structures under complex working conditions is a key issue that needs to be overcome in portable motorcycle emergency charging technology. Summary of the Invention

[0003] The present invention provides a method for generating an emergency charging solution for a portable motorcycle based on a plug-in design, which mainly includes:

[0004] Obtain the capacitance voltage parameters and capacitance internal resistance parameters of each capacitor layer, use the temperature and current data during the charge and discharge process as input, and the voltage and internal resistance parameters of each capacitor layer as output to train a nonlinear mapping model, and optimize the capacitance, temperature, and current parameters of the nonlinear mapping model through cross-validation;

[0005] During the charging and discharging process of the plug-in charging module, the temperature and current data of each capacitor layer are collected in real time and input into the nonlinear mapping model to predict the voltage and internal resistance parameters of each capacitor layer to obtain the dynamic changes of the capacitor layer parameters during the charging and discharging process;

[0006] The predicted voltage of each capacitor layer is compared with the preset voltage balance threshold to determine whether the voltage of each capacitor layer is balanced. If not, the voltage of each layer is adjusted through the energy transfer circuit until a balanced state is reached. The difference between the internal resistance of each capacitor layer and the predicted internal resistance parameter is calculated, and the internal resistance compensation value is determined based on the difference. The capacitor layer whose internal resistance difference is greater than the preset internal resistance difference is compensated;

[0007] The calculated voltage difference and internal resistance difference of each capacitor layer are used as inputs to the preset fuzzy control algorithm, and the charge and discharge current adjustment value of each capacitor layer is used as output. By fuzzifying the input variables, the target charge and discharge current control value of each capacitor layer is output. The target charge and discharge current control value is transmitted to the current regulation circuit of the plug-in charging module to adjust the charge and discharge current of each capacitor layer in real time.

[0008] During the emergency charging process of a plug-in portable motorcycle, the temperature, current, voltage, and internal resistance parameters of each capacitor layer of the plug-in charging module are continuously monitored. Based on the prediction results of the nonlinear mapping model and the target charge and discharge current control quantity, the charge and discharge current of each capacitor layer is dynamically optimized to achieve balanced charging and discharging of the layered capacitor structure.

[0009] During the emergency charging process based on the plug-in portable motorcycle, the motorcycle's power level is also monitored in real time, and an emergency charging plan for the portable motorcycle is generated based on the motorcycle's power level. If the motorcycle's power level is monitored to be in a preset low power range, the balancing control of the discharge of the layered capacitor structure of the plug-in charging module is triggered. If the monitored motorcycle power level is higher than the preset high power threshold, the frequency of the balancing control is gradually reduced until the motorcycle's power reaches full charge, and the balancing control is stopped and charging is terminated.

[0010] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0011] The present invention discloses a method for generating an emergency charging solution for a portable motorcycle based on a plug-in design. The method predicts the voltage and internal resistance parameters of each capacitor layer in real time by establishing a nonlinear mapping model between the capacitor layer parameters and the temperature and current, and dynamically adjusts the charge and discharge current according to the prediction results. During the charging process, the present invention continuously monitors the power level of the motorcycle and adaptively adjusts the balancing control strategy according to the power status. When the power level is in the low power range, the balanced discharge control of the layered capacitor structure is triggered; when the power level exceeds the high power threshold, the balancing control frequency is gradually reduced until it is fully charged and stopped. The present invention realizes precise charge and discharge regulation of each capacitor layer through a fuzzy control algorithm, effectively solving the problems of voltage imbalance and internal resistance difference in the plug-in portable charging process, and improving the charging efficiency and battery life. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1The present invention is a flowchart of a method for generating an emergency charging solution for a portable motorcycle based on a plug-in design.

[0013] Figure 2 This is a schematic diagram of a method for generating an emergency charging solution for a portable motorcycle based on a plug-in design according to the present invention.

[0014] Figure 3 This is another schematic diagram of a method for generating an emergency charging solution for a portable motorcycle based on a plug-in design according to the present invention. DETAILED DESCRIPTION

[0015] To further understand the content of the present invention, the present invention is described in detail with reference to the accompanying drawings and examples. The present application is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are intended only to illustrate the relevant invention and are not intended to limit the invention. It should also be noted that, for ease of description, only portions relevant to the invention are shown in the accompanying drawings.

[0016] like Figure 1-3 In this embodiment, a method for generating an emergency charging solution for a portable motorcycle based on a plug-in design may specifically include:

[0017] Step S101, obtain the capacitance voltage parameters and capacitance internal resistance parameters of each capacitance layer, use the temperature and current data during the charge and discharge process as input, and use the voltage and internal resistance parameters of each capacitance layer as output to train a nonlinear mapping model, and optimize the capacitance, temperature, and current parameters of the nonlinear mapping model through cross-validation.

[0018] The temperature signal and current signal during the charging and discharging process are obtained from a parameter acquisition device, and the parameter acquisition device records the voltage value and internal resistance value at the corresponding moment; based on the temperature signal and the current signal, abnormal fluctuation points are removed by a data cleaning unit to obtain a standardized training data set; for the standardized training data set, a data partitioning unit is used to perform cross-grouping, and a training data subset and a verification data subset are generated according to the proportion of the training set; a nonlinear mapping function from the temperature signal and the current signal to the voltage value and the internal resistance value is established by a neural network mapper, and the parameters of the nonlinear mapping function are trained using the training data subset to obtain a first-round optimization parameter set; if the average error value on the verification data subset is higher than the average error threshold, a gradient descent optimizer is used to iteratively update the first-round optimization parameter set until the average error value is lower than the average error threshold.

[0019] Specifically, a parameter acquisition device acquires temperature and current signals during the charging and discharging process. The same acquisition device is used to record the voltage and internal resistance values ​​at the corresponding moments. A data cleaning unit removes abnormal fluctuations to generate a standardized training dataset. A data partitioning unit performs a 50% cross-grouping of the standardized training dataset. Training and validation data subsets are generated according to a partitioning rule where the training set accounts for 80%. A nonlinear mapping function from temperature and current to voltage and internal resistance is established using a neural network mapper. Parameter training for this nonlinear mapping function is performed using the training data subset. A validation error calculator calculates the mapping error on the validation data subset. A grid search method is used to optimize and adjust the capacitance coefficient, temperature coefficient, and current coefficient in the mapping function to obtain a first-round optimized parameter set. Based on this first-round optimized parameter set, the parameters are iteratively updated using a gradient descent optimizer. Cross-validation is performed using the remaining unused validation data until the average error across all validation data subsets falls below a preset threshold. The final optimized nonlinear mapping parameter set is then output. The parameter acquisition device uses a high-precision sampling circuit to synchronously collect key parameters of the capacitor layer during the charging and discharging process. The temperature signal sampling frequency is set to 10 Hz, with a measurement accuracy of 0.1 degrees Celsius and a measurement range of -40 to 125 degrees Celsius. The current signal sampling frequency is 1 kHz, with a measurement accuracy of 0.01 amperes and a measurement range of 0 to 10 amperes. The voltage signal sampling accuracy is 0.01 volts, and the internal resistance is measured using the AC impedance method at a measurement frequency of 1 kHz. The data cleaning unit processes outliers on the collected raw data. When the temperature change rate exceeds 5 degrees Celsius per second or the current change exceeds 2 amperes per microsecond, the data point is marked as an outlier and removed. A 50% cross-grouping method divides the standardized data set into 5 equal parts, selecting 4 parts as training sets each time and the remaining part as validation sets, achieving 5 rounds of cross-validation. In actual operation, for a total of 10,000 valid data sets, each round contains 8,000 training sets and 2,000 validation sets. The neural network mapper employs a three-layer structure. The input layer contains two nodes, representing temperature and current; the hidden layer has 16 nodes; and the output layer contains two nodes, representing voltage and internal resistance. The activation function is the hyperbolic tangent function. During parameter training of the nonlinear mapping function, the initial values ​​of the capacitance coefficient, temperature coefficient, and current coefficient were set to 0.1, 0.01, and 0.05, respectively. A grid search method was used to search for capacitance within the range of 0.05 to 0.5, with a step size of 0.05; for temperature coefficient within the range of 0.005 to 0.05, with a step size of 0.005; and for current coefficient within the range of 0.02 to 0.2, with a step size of 0.02. The root mean square error (RMS) was calculated on the validation set to identify the parameter combination that minimized the error. Based on the first round of optimization, a gradient descent optimizer employed an adaptive learning rate, initially set to 0.01. If the error did not decrease after three consecutive rounds of validation, the learning rate was reduced by a factor of 0.5.During the validation process, the relative error for voltage prediction was required to be less than 3%, and the relative error for internal resistance prediction was required to be less than 5%. In actual verification, at a temperature of 25 degrees Celsius and a charging current of 1 ampere, the predicted voltage was 2.7 volts, while the measured value was 2.75 volts, with a relative error of 1.8%. The predicted internal resistance was 150 milliohms, while the measured value was 155 milliohms, with a relative error of 3.2%. Under extreme operating conditions, the internal resistance prediction error increased slightly at -20 degrees Celsius, but remained within 5%, meeting engineering application requirements. The nonlinear mapping model, optimized through cross-validation, demonstrated excellent prediction capabilities across various temperature and current conditions, achieving particularly high accuracy within the common operating temperature range of capacitors (0 to 60 degrees Celsius). The nonlinear mapping model accurately reflects the effects of temperature and current on capacitor layer performance. The model's predictions can be used to optimize capacitor operating parameter settings and select appropriate charge and discharge currents at different ambient temperatures, ensuring both charge and discharge efficiency and avoiding overheating due to excessive charge and discharge currents. The model effectively reflects the effects of temperature on internal resistance and the nonlinear characteristics of high current charge and discharge. In practical applications, this model can be used to estimate capacitor parameters online and determine capacitor performance status.

[0020] In step S102, during the charging and discharging process of the plug-in charging module, the temperature and current data of each capacitor layer are collected in real time and input into a nonlinear mapping model to predict the voltage and internal resistance parameters of each capacitor layer to obtain the dynamic changes of the capacitor layer parameters during the charging and discharging process.

[0021] Receive the surface temperature distribution data and current change data of the capacitor layer collected by the plug-in charging module, and generate a real-time monitoring data set with a timestamp based on the temperature distribution data and current change data according to a fixed sampling time interval; use a data preprocessing unit to standardize the real-time monitoring data set, and predict the voltage parameters and internal resistance parameters of the capacitor layer through a nonlinear mapping model based on the standardized data; extract timing features based on the predicted voltage parameters and internal resistance parameters, and a state identifier generates a timing correspondence between the voltage parameters, internal resistance parameters, temperature data, and current data based on the timing features; use a Kalman filter to smooth the voltage parameters and internal resistance parameters in the timing correspondence, and obtain a dynamic change curve of the capacitor layer parameters based on the smoothed parameters combined with the working state of the plug-in charging module.

[0022] Specifically, the plug-in charging module's multi-channel measurement device collects real-time temperature distribution data on the capacitor layer's surface. A high-precision current sensor records current changes during the charging and discharging process, generating a time-stamped real-time monitoring data set based on fixed sampling intervals. A data preprocessing unit then normalizes the temperature distribution and current data for this real-time monitoring data set. This data is then fed into a nonlinear mapping model, which continuously predicts the capacitor layer's voltage and internal resistance parameters based on a preset sampling period. Voltage and internal resistance time series features are extracted from these continuous predictions. A state identifier is used to determine the capacitor layer's charge and discharge state in real time. A dynamic feature extractor generates a time series correspondence between the voltage and internal resistance parameters and the temperature and current data. Based on this time series correspondence, the predicted voltage and internal resistance sequences are smoothed using a Kalman filter. The predicted parameters are then corrected based on the plug-in charging module's operating status to generate a dynamic curve of the capacitor layer's parameters. The plug-in charging module's multi-channel measurement device employs eight temperature sensing points on the capacitor layer's surface, using platinum resistance temperature sensors with a sampling frequency of 10 Hz and a measurement accuracy of 0.1 degrees Celsius. The high-precision current sensor uses a Hall effect current sensor with a sampling frequency of 1 kHz and a measurement accuracy of 0.01 amperes. The timestamp accuracy in the real-time monitoring data set is millisecond-level, recording the specific acquisition time of each sampling point. The data preprocessing unit normalizes the temperature data, mapping the temperature range of -40 to 125 degrees Celsius to the interval 0 to 1. The current data range of -10 to 10 amperes is mapped to the interval -1 to 1. The nonlinear mapping model uses a preset sampling period of 100 milliseconds and completes the prediction calculation of voltage and internal resistance parameters once within each sampling period. When the temperature distribution of the capacitor layer is uneven, with a maximum temperature difference exceeding 20 degrees Celsius, the model assigns different weights to each temperature measurement point. The voltage time series features include three dimensions: absolute voltage value, voltage rate of change, and voltage fluctuation amplitude. The internal resistance time series features include three dimensions: absolute internal resistance value, internal resistance temperature coefficient, and internal resistance current coefficient. The state identifier determines the charge and discharge state by multiplying the voltage and current. A positive value indicates a charging state, and a negative value indicates a discharging state. The dynamic feature extractor uses a 200-millisecond time window and performs a sliding average on each feature. The Kalman filter's state vector contains two variables: voltage and internal resistance. The parameters of the observation noise covariance matrix are set according to sensor accuracy, while the process noise covariance matrix is ​​adaptively adjusted based on the charging and discharging conditions. In practical applications, when the capacitor is charged at a constant current of 1 ampere at a constant temperature of 25 degrees Celsius, the error between the predicted and measured voltage values ​​is within 20 millivolts, and the error between the predicted and measured internal resistance values ​​is within 10 milliohms. The plug-in charging module operates in three modes: standby, charging, and discharging. In the charging state, the input current is positive, and the voltage gradually rises to the rated value of 2.7 volts. In the discharging state, the output current is negative, and the voltage gradually decreases, entering a protection state when it drops to 2.0 volts.The dynamic curve shows that at 25 degrees Celsius, during the 1 ampere constant current charging process, it takes 300 seconds for the capacitor layer voltage to rise from 0 to 2.7 volts, and the internal resistance remains at around 150 milliohms. When the ambient temperature rises to 75 degrees Celsius, charging under the same conditions only takes 250 seconds, and the internal resistance drops to 120 milliohms. The real-time monitoring and prediction of dynamic parameter changes show that there is an obvious nonlinear relationship between the voltage and internal resistance parameters of the capacitor layer and temperature and current. Rising temperature will cause the internal resistance to decrease and the charging and discharging efficiency to improve. However, excessively high temperatures will accelerate the aging of the capacitor. When the temperature exceeds 85 degrees Celsius, the internal resistance value fluctuates abnormally. During large current charging and discharging, due to the influence of Joule heat, the temperature distribution of the capacitor layer is uneven, and the temperature difference between each point can reach 15 degrees Celsius, resulting in significant differences in local internal resistance.

[0023] In step S103, the predicted voltage of each capacitor layer is compared with the preset voltage balance threshold to determine whether the voltage of each capacitor layer is balanced. If not, the voltage of each layer is adjusted through the energy transfer circuit until a balanced state is reached; the difference between the internal resistance of each capacitor layer and the predicted internal resistance parameter is calculated, and the internal resistance compensation value is determined according to the size of the difference, and the capacitor layer whose internal resistance difference is greater than the preset internal resistance difference is compensated.

[0024] A voltage detector is used to compare and calculate the predicted voltage value of the capacitor layer with the preset voltage balance threshold to obtain the voltage deviation and the inter-layer voltage difference value; based on the comparison result of the voltage deviation and the preset threshold range, an energy transfer channel is established between adjacent capacitor layers through an energy transfer controller; for the energy transfer channel, the energy transfer time and the energy transfer current are set according to the inter-layer voltage difference value, and the internal resistance difference value of each capacitor layer is calculated by an internal resistance comparator; for the internal resistance difference value, if it exceeds the preset difference value, the internal resistance compensation parameter is generated by the compensation parameter generator, and the internal resistance compensation process is tracked in real time through the Kalman filter until the internal resistance difference value drops below the preset difference value.

[0025] Specifically, a voltage detector is used to compare and calculate the predicted voltage value of each capacitor layer with a preset voltage balance threshold, and a voltage deviation calculator is used to obtain the voltage deviation of each layer and the voltage difference between layers. If the voltage deviation exceeds the preset threshold range, the capacitor layer is marked as an object to be adjusted. For the object to be adjusted, an energy transfer controller is used to establish an energy transfer channel between adjacent capacitor layers, and the energy transfer time and energy transfer current are set according to the voltage difference between layers until the voltage deviation of each layer drops below the preset threshold. Using the predicted internal resistance parameter, the internal resistance difference value of each capacitor layer is calculated by the internal resistance comparator. If the internal resistance difference value exceeds the preset difference value, the compensation parameter generator generates the internal resistance compensation parameter and the compensation adjustment curve. Based on the compensation adjustment curve, the internal resistance compensation controller applies compensation adjustment to the capacitor layer according to the compensation parameter, and uses the Kalman filter to track the internal resistance change during the compensation process in real time until the internal resistance difference value drops below the preset difference value. The voltage detector measures the voltages of multiple capacitor layers. In a typical three-layer capacitor bank, the voltage balance threshold is set at 2.7 volts. The measured voltages for the top, middle, and bottom layers are 2.9 volts, 2.6 volts, and 2.5 volts, respectively. A voltage deviation calculator calculates the deviations from the balance threshold to be 0.2 volts, -0.1 volts, and -0.2 volts, respectively. The maximum interlayer voltage difference is 0.4 volts, exceeding the preset 0.2 volt threshold. The energy transfer controller uses flying capacitors to transfer energy between adjacent layers. Based on the maximum interlayer voltage difference of 0.4 volts, the controller sets a 500 mA energy transfer current. Energy transfer from the highest-voltage top layer to the middle layer occurs with a 100 microsecond switching cycle. The energy transfer continues until the interlayer voltage difference drops below 0.1 volt, a process that lasts approximately 200 milliseconds. Energy transfer from the middle layer to the bottom layer uses the same current and lasts 150 milliseconds. The internal resistance comparator detected predicted internal resistance parameters for the capacitor layers of 145 milliohms, 150 milliohms, and 165 milliohms, respectively. The measured internal resistance values ​​were 148 milliohms, 152 milliohms, and 180 milliohms, respectively. The internal resistance differences were 3 milliohms, 2 milliohms, and 15 milliohms, respectively. The third layer exceeded the preset 10 milliohm difference. The compensation parameter generator generated a compensation parameter of 20 milliohms for the third layer. The compensation adjustment curve used a piecewise linear approach, completing the compensation process within 5 seconds. After receiving the compensation adjustment curve, the internal resistance compensation controller compensated for the third layer's capacitance by varying the amplitude and duration of the charge and discharge currents. During the compensation process, the Kalman filter's state estimation equation included two variables: the internal resistance value and the rate of change of the internal resistance. The measurement noise standard deviation was set to 5 milliohms. Initially, the internal resistance dropped rapidly, from 180 milliohms to 165 milliohms in 2 seconds. It then slowly adjusted to 158 milliohms over the next 3 seconds. Ultimately, the internal resistance difference dropped to 8 milliohms, meeting the preset difference requirement. The entire process of voltage balance and internal resistance compensation demonstrates the characteristics of a series capacitor group. Voltage balance is achieved through energy transfer between upper and lower adjacent layers, preventing the voltage of a single capacitor from being too high.Internal resistance compensation reflects differences in capacitor performance. Large differences in internal resistance indicate that the performance of the capacitor has deteriorated. Compensation adjustment brings the internal resistance characteristics closer to normal values, improving the consistency of the entire capacitor group. In actual applications, when the ambient temperature rises from 25 degrees Celsius to 75 degrees Celsius, the overall internal resistance of the three-layer capacitor decreases by approximately 20%, but the relative differences remain stable, indicating that the compensation effect is not affected by temperature. During the compensation control process, the two key parameters, voltage and internal resistance, influence each other. The capacitor layer with higher internal resistance changes voltage more rapidly during the charge and discharge process, which can easily cause voltage imbalance between layers. By coordinating the timing of voltage balance control and internal resistance compensation control, voltage balance is achieved first, followed by internal resistance compensation, avoiding interference between the two control processes and ensuring the stability of the control effect. The control process adopts a closed-loop method, tracking parameter changes in real time to ensure control accuracy.

[0026] In step S104, the calculated voltage difference and internal resistance difference of each capacitor layer are used as the input of the preset fuzzy control algorithm, and the charge and discharge current adjustment amount of each capacitor layer is used as the output. By fuzzifying the input variables, the target charge and discharge current control amount of each capacitor layer is output, and the target charge and discharge current control amount is transmitted to the current regulation circuit of the plug-in charging module to adjust the charge and discharge current of each capacitor layer in real time.

[0027] The voltage difference and internal resistance difference between each capacitor layer are obtained through a voltage internal resistance detection device, and a fuzzy variable processing unit is used to generate a fuzzy membership value for the voltage difference and the internal resistance difference; based on the fuzzy membership value, a fuzzy control rule library with temperature and current compensation is used to perform fuzzy reasoning operations to obtain the fuzzy control quantity of the charge and discharge current of each capacitor layer; for the current fuzzy control quantity, a center of gravity method defuzzification unit is used to calculate the real-time charge and discharge current adjustment quantity of each capacitor layer, and a charge and discharge current instruction with amplitude limitation is generated based on the adjustment quantity; the charge and discharge current instruction with amplitude limitation is used to generate a switch control signal in a pulse width modulator, and the charge and discharge current is adjusted according to the current detection signal of the plug-in charging module.

[0028] Specifically, the voltage difference and internal resistance difference between each capacitor layer are obtained through a voltage internal resistance detection device, a fuzzy variable processing unit is used to divide the voltage difference and internal resistance difference into language value intervals, and a membership function calculator is used to generate corresponding fuzzy membership values.

[0029]

[0030] , μV(x) represents the membership function of the voltage difference, x represents the actual value of the voltage difference, a represents the lower threshold of the voltage difference, and b represents the upper threshold of the voltage difference.

[0031]

[0032] , μ R (y) represents the membership function of the internal resistance difference, y represents the actual value of the internal resistance difference, c represents the lower limit threshold of the internal resistance difference, and d represents the upper limit threshold of the internal resistance difference.

[0033]

[0034] , F(z) represents the comprehensive membership value, w i Represents the weight coefficient of the i-th membership function, μ i (z) represents the value of the i-th membership function, n represents the total number of membership functions, and the weight coefficient is preset. According to the fuzzy membership value, a fuzzy control rule base based on temperature and current compensation is used to perform fuzzy reasoning operations, and the fuzzy control quantity of the charge and discharge current of each capacitor layer is generated by a fuzzy relationship mapper to achieve the mapping of the voltage difference and the internal resistance difference to the current control quantity. For the current fuzzy control quantity, a centroid method defuzzification unit is used to calculate the real-time charge and discharge current regulation quantity of each capacitor layer, and the current limit processor is used to constrain the amplitude of the regulation quantity to generate a charge and discharge current instruction with amplitude limitation. Using the charge and discharge current instruction with amplitude limitation, a corresponding switch control signal is generated in the pulse width modulator, and real-time closed-loop regulation is performed according to the current detection signal of the plug-in charging module to achieve dynamic control of the charge and discharge current of each capacitor layer. The voltage and internal resistance detection device recorded voltage values ​​of 2.9 volts, 2.6 volts, and 2.5 volts across the three capacitor layers, respectively. The calculated voltage differences between adjacent layers were 0.3 volts and 0.1 volts, and the internal resistance differences were 5 milliohms and 15 milliohms. The fuzzy variable processing unit divides the voltage difference into seven linguistic value intervals: negative large, negative medium, negative small, zero, positive small, positive medium, and positive large, with a corresponding domain range of -0.5 to 0.5 volts. The internal resistance difference is also divided into five linguistic value intervals: zero, small, medium, large, and maximum, with a domain range of 0 to 20 milliohms. The fuzzy control rules for temperature and current compensation utilize a two-dimensional rule table containing 35 basic rules. At 25 degrees Celsius, when the voltage difference is 0.3 volts and the internal resistance difference is 5 milliohms, the corresponding rule output is a medium charging current. When the temperature rises to 75 degrees Celsius, due to the general decrease in internal resistance, the rule output adjusts to a low charging current under the same conditions. The fuzzy relationship mapping uses the Mamdan i reasoning method to map the membership values ​​of the voltage difference and internal resistance difference to the charge and discharge current regulation value.

[0035]

[0036] , μ out Represents the membership value of the output, μV represents the membership function of the voltage difference, μR represents the membership function of the internal resistance difference, μI represents the membership function of the current regulation amount, and x i 、y i 、zi Represent the corresponding input variable values. During the centroid defuzzification process, the fuzzy control variable is converted into a specific current regulation value. The current limit processor sets the upper limit of the charging current to 2 amps and the upper limit of the discharging current to 1.5 amps. In actual operation, when the voltage difference is 0.3 volts, the calculated charging current regulation is 0.8 amps. When the voltage difference decreases to 0.1 volts, the charging current decreases to 0.3 amps. For an internal resistance difference of 15 milliohms, the compensation current is set to 0.5 amps. The switching frequency of the pulse width modulator is set to 20 kHz, with a dead time of 2 microseconds. Current detection uses a Hall effect sensor with a sampling frequency of 10 kHz and a detection accuracy of 0.01 amps. In closed-loop control, when the actual charging current exceeds the command value by 10%, the controller adjusts the current to within the command range within 100 microseconds. The measured voltage difference gradually decreased from 0.3 volts to 0.05 volts in 5 seconds, and the internal resistance difference decreased from 15 milliohms to 8 milliohms in 10 seconds. The entire fuzzy control process reflects comprehensive consideration of temperature and current. At low temperatures, due to the generally high internal resistance, the charging current should not be set too high, even in the presence of large voltage differences. At high temperatures, however, the reduced internal resistance leads to improved charging efficiency, allowing a smaller charging current to achieve voltage balance. By adjusting the charge and discharge currents in real time, the system ensures voltage balancing speed while avoiding the impact of excessive charge and discharge currents on capacitor life. During the dynamic adjustment process, the two key parameters, voltage and internal resistance, mutually constrain each other. Large internal resistance differences can lead to increased voltage imbalance during the charge and discharge process, which in turn affects the measurement accuracy of internal resistance. Through fuzzy control methods, a nonlinear mapping relationship between voltage difference, internal resistance difference, and charge and discharge current is established, achieving coordinated control of multiple mutually coupled parameters.

[0037] Step S105, during the emergency charging process based on the plug-in portable motorcycle, continuously monitor the temperature, current, voltage and internal resistance parameters of each capacitor layer of the plug-in charging module, and dynamically optimize the charge and discharge current of each capacitor layer based on the prediction results of the nonlinear mapping model and the target charge and discharge current control amount, so that the layered capacitor structure can achieve balanced charging and discharging.

[0038] The temperature, current, voltage, and internal resistance parameters of the motorcycle during charging are acquired through a multi-channel collector of a plug-in charging module. The temperature, current, voltage, and internal resistance parameters are filtered and denoised by a parallel processor to obtain charging load characteristics. Based on the charging load characteristics, a nonlinear mapping model is used to predict the capacitor layer parameters, and deviation data between the predicted values ​​and actual values ​​of the parameters of each capacitor layer are generated. With respect to the deviation data, the target current control amount is compared in a charge and discharge equalizer, and the compensation current value of each capacitor layer is calculated by a dynamic optimizer. Based on the compensation current value, the charge and discharge current of each capacitor layer is closed-loop controlled in a balancing controller, and the controller is adjusted to a balanced charge and discharge state through a feedback regulator.

[0039] Specifically, the plug-in charging module's multi-channel data collector monitors the portable motorcycle's temperature, current, voltage, and internal resistance parameters in real time during charging. A parallel processor filters and de-noises the collected data, generating charging load and motor power characteristics based on the motorcycle's operating condition identifier. Based on these charging load characteristics, a nonlinear mapping predictor predicts the capacitor layer parameters in real time. A deep neural network learns the changing patterns of temperature, current, voltage, and internal resistance, generating deviation data between the predicted and actual values ​​of each capacitor layer's parameters. Based on this deviation data, the charge and discharge equalizer compares the target current control variable, and a dynamic optimizer calculates the required compensation current value for each capacitor layer. The current distributor adjusts the charge and discharge current of each capacitor layer in real time. Based on this compensation current value, the equalization controller dynamically compensates according to the operating status of the plug-in charging module. A feedback regulator maintains closed-loop control of the charge and discharge current of each capacitor layer until a balanced charge and discharge state is achieved. The multi-channel data collector simultaneously collects battery pack parameters during the portable motorcycle's charging process. The temperature sampling frequency is 10 Hz, the sampling accuracy is 0.1 degrees Celsius, and the measurement range covers -20 to 85 degrees Celsius. The current sampling frequency is 1 kHz, with a measurement accuracy of 0.01 amperes and a range of 0 to 10 amperes. The motorcycle operating condition identifier categorizes the motor's operating conditions into four categories: idle, light load, medium load, and heavy load, corresponding to power loads of 100 watts, 300 watts, 500 watts, and 800 watts, respectively. The nonlinear mapping predictor is built based on a deep neural network. The input layer contains two parameters: temperature and current. The hidden layer has a three-layer structure with 16 neurons per layer. The output layer contains the predicted voltage and internal resistance values. In actual application, when the motor is in a heavy load state (800 watts), the capacitor layer temperature increases from 25 degrees Celsius to 45 degrees Celsius, and the predicted internal resistance decreases from 150 milliohms to 130 milliohms, with the deviation from the measured value remaining within 5%. The charge and discharge equalizer calculates the compensation current based on the deviation between the predicted and measured values. When the voltage deviation exceeds 0.1 volt, the compensation current is set to 0.5 amperes. The dynamic optimizer uses an adaptive regulation method, adjusting the compensation current every 50 milliseconds under light load conditions and shortening the adjustment period to 20 milliseconds under heavy load conditions. The current distributor dynamically adjusts the charging current ratio of the three-layer capacitor, maintaining a maximum deviation within 10%. The balancing controller adopts a closed-loop control structure with a sampling period of 100 microseconds and a control period of 1 millisecond. When the plug-in charging module detects a connection to the charging port, the controller completes initialization within 10 milliseconds. When the motor is idling, the controller limits the charging current to less than 1 ampere; when the motor enters a heavy load state, the upper limit of the charging current is increased to 2 amperes. The proportional coefficient and integral time of the feedback regulator automatically adjust according to the operating conditions. Under light load conditions, a larger integral time is used to ensure control stability, while a smaller integral time is used under heavy load conditions to improve response speed. During the emergency charging process of portable motorcycles, changes in motor load directly affect the temperature distribution and internal resistance characteristics of the capacitor layer.When the road is flat and the motor is lightly loaded, the temperature of the capacitor layer is uniform, the internal resistance difference is small, and the charge and discharge control is mainly based on stability. When the motorcycle climbs a slope or accelerates rapidly, the motor enters a heavy-load state, the temperature of the capacitor layer rises rapidly and is unevenly distributed, and the internal resistance shows a large difference. At this time, the controller prioritizes ensuring charge and discharge balance. During the control process, the four key parameters of temperature, current, voltage, and internal resistance are coupled with each other. The increase in temperature causes the internal resistance to decrease, and the charge and discharge efficiency to improve, but at the same time increases the parameter differences between the capacitor layers. High current charging and discharging exacerbates temperature unevenness and affects voltage balance. Through real-time monitoring and prediction, combined with dynamic compensation based on operating conditions, the capacitor layer can maintain a balanced charge and discharge state under various operating conditions. In actual applications, even when the motor power changes frequently, the voltage difference between the capacitor layers is controlled within 0.1 volts, and the internal resistance difference is controlled within 10 milliohms.

[0040] Step S106, during the emergency charging process based on the plug-in portable motorcycle, the motorcycle power is also monitored in real time, and an emergency charging plan for the portable motorcycle is generated based on the motorcycle power. If the motorcycle power is monitored to be in a preset low power range, the balancing control of the discharge of the layered capacitor structure of the plug-in charging module is triggered. If the monitored motorcycle power is higher than the preset high power threshold, the frequency of the balancing control is gradually reduced until the motorcycle power reaches full charge, and the balancing control is stopped and charging is terminated.

[0041] Real-time power data of a motorcycle battery pack is obtained through a power monitor, and a power change curve is recorded based on the real-time power data; a comparison result of a power monitoring value and a preset threshold is generated based on the power change curve, and a power state determination is performed on the comparison result; if the power state determination result is lower than a preset low power threshold, balanced discharge control is performed, and a neural network predictor is used to generate discharge control parameters for a layered capacitor structure; based on the discharge control parameters, a power change trend is tracked in a charging state monitor, and if the power change trend reaches a preset full-charge state, a charging stop signal is generated and the charging circuit is disconnected.

[0042] Specifically, a power monitor collects the real-time power level of the motorcycle battery pack. A data processing unit records the power level curve. An emergency charging strategy is developed based on a charging plan generator, generating a comparison between the monitored power level and a threshold value. Based on this comparison, a threshold determiner determines the power level. If the monitored power level falls below a preset low power threshold, a balanced discharge controller is activated, generating discharge control parameters for the layered capacitor structure using a neural network predictor. Based on these discharge control parameters, the balanced discharge controller sets a discharge balancing frequency, and the layered discharge unit controls the discharge of the capacitor structure. If the monitored power level exceeds a preset high power threshold, the balanced control frequency is reduced according to an adaptive adjustment curve. Based on this adaptive adjustment curve, a charging state monitor continuously tracks power level trends. A full charge determiner determines whether the motorcycle battery pack has reached full charge. A charge terminator generates a charge stop signal and disconnects the charging circuit. The power monitor uses a high-precision fuel gauge chip to monitor the motorcycle battery pack in real time, with a sampling frequency of 10 Hz and a power measurement accuracy of 0.1%. The power level curve recorded by the data processing unit is subjected to a sliding average over a 10-second time window to eliminate the effects of transient fluctuations. The charging plan generator sets three charging strategies based on the state of charge: a fast charge strategy for battery levels below 20%, a standard charge strategy between 20% and 80%, and a trickle charge strategy for battery levels above 80%. The threshold detector sets the low charge threshold at 25% and the high charge threshold at 85%. When the battery level is detected to be below 25%, the neural network predictor generates discharge control parameters based on the motorcycle's driving conditions and ambient temperature. The predictor utilizes a three-layer neural network architecture. The input layer contains three parameters: battery level, temperature, and load power. The hidden layer contains 16 neurons. The output layer generates two control parameters: discharge current and discharge time. The balancing controller dynamically adjusts the discharge balancing frequency based on the battery level. At low battery levels, the balancing control frequency is set to 50 Hz to ensure fast response. The layered discharge unit alternates the discharge of the three-layer capacitor structure, with a discharge time of 20 milliseconds per layer and a switching time of 2 milliseconds between layers. When the battery level exceeds 85%, an adaptive regulation curve exponentially reduces the control frequency, decreasing by 0.7 times for every 5% increase in battery level. The charge status monitor uses a coulomb counter to accumulate charge in real time, with a monitoring accuracy of 1 milliampere-hour. A fully charged state is defined as when the charging current drops to 0.05 times the rated value for more than 10 minutes. In practice, emergency charging is initiated when the motorcycle's charge level reaches 15%, with the layered capacitors discharging at a current of 2 amperes, increasing at a rate of approximately 0.5% per minute. After the charge reaches 30%, the discharge current drops to 1.5 amperes, and the rate of increase slows to 0.3% per minute. The charge level changes reflect the characteristics of different charging stages. During low charge phases, charging speed is prioritized, with the capacitor layers discharged using a high current for balanced discharge.As the battery level increases, the charging speed gradually decreases, prioritizing charging balance. When the battery is near full charge, a low current is used to prevent overcharging. Throughout the entire process, dynamic adjustment of the balancing control frequency ensures a balance between charging efficiency and battery safety. Temperature has a significant impact on the charging process. At 25°C, charging efficiency is highest, with a charge from 20% to 80% in just 40 minutes. When the temperature drops to 0°C, the charging speed slows by 50% due to increased battery internal resistance. When the temperature rises to 45°C, while the charging speed increases, the controller actively reduces the charging current, extending the charging time and protecting the battery. Through real-time monitoring and intelligent control, the system ensures safe and reliable charging under various environmental conditions.

[0043] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but also encompasses other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the concept of this application. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A method for generating an emergency charging solution for a portable motorcycle based on a plug-in design, characterized in that: The method comprises: Obtain the capacitance voltage parameters and capacitance internal resistance parameters of each capacitor layer, use the temperature and current data during the charge and discharge process as input, and the voltage and internal resistance parameters of each capacitor layer as output to train a nonlinear mapping model, and optimize the capacitance, temperature, and current parameters of the nonlinear mapping model through cross-validation; During the charging and discharging process of the plug-in charging module, the temperature and current data of each capacitor layer are collected in real time and input into the nonlinear mapping model to predict the voltage and internal resistance parameters of each capacitor layer to obtain the dynamic changes of the capacitor layer parameters during the charging and discharging process; The predicted voltage of each capacitor layer is compared with the preset voltage balance threshold to determine whether the voltage of each capacitor layer is balanced. If not, the voltage of each layer is adjusted through the energy transfer circuit until a balanced state is reached. The difference between the internal resistance of each capacitor layer and the predicted internal resistance parameter is calculated, and the internal resistance compensation value is determined based on the difference. The capacitor layer whose internal resistance difference is greater than the preset internal resistance difference is compensated; The calculated voltage difference and internal resistance difference of each capacitor layer are used as inputs to the preset fuzzy control algorithm, and the charge and discharge current adjustment value of each capacitor layer is used as output. By fuzzifying the input variables, the target charge and discharge current control value of each capacitor layer is output. The target charge and discharge current control value is transmitted to the current regulation circuit of the plug-in charging module to adjust the charge and discharge current of each capacitor layer in real time. During the emergency charging process of a plug-in portable motorcycle, the temperature, current, voltage, and internal resistance parameters of each capacitor layer of the plug-in charging module are continuously monitored. Based on the prediction results of the nonlinear mapping model and the target charge and discharge current control quantity, the charge and discharge current of each capacitor layer is dynamically optimized to achieve balanced charging and discharging of the layered capacitor structure. During the emergency charging process based on the plug-in portable motorcycle, the motorcycle's power level is also monitored in real time, and an emergency charging plan for the portable motorcycle is generated based on the motorcycle's power level. If the motorcycle's power level is monitored to be in a preset low power range, the balancing control of the discharge of the layered capacitor structure of the plug-in charging module is triggered. If the monitored motorcycle power level is higher than the preset high power threshold, the frequency of the balancing control is gradually reduced until the motorcycle's power reaches full charge, and the balancing control is stopped and charging is terminated.

2. The method according to claim 1, characterized in that The method of obtaining capacitance voltage parameters and capacitance internal resistance parameters of each capacitance layer, taking temperature and current data during the charge and discharge process as input and taking voltage and internal resistance parameters of each capacitance layer as output, training a nonlinear mapping model, and optimizing capacitance, temperature, and current parameters of the nonlinear mapping model through cross-validation includes: Acquire temperature signals and current signals during the charging and discharging process from a parameter acquisition device, wherein the parameter acquisition device records voltage values ​​and internal resistance values ​​at corresponding moments; According to the temperature signal and the current signal, abnormal fluctuation points are removed by a data cleaning unit to obtain a standardized training data set; For the standardized training data set, cross-grouping is performed using data partitioning units, and a training data subset and a validation data subset are generated according to the proportion of the training set; Establishing a nonlinear mapping function from the temperature signal and the current signal to the voltage value and the internal resistance value through a neural network mapper, and training the parameters of the nonlinear mapping function using a training data subset to obtain a first round of optimized parameter set; If the average error value on the validation data subset is higher than the average error threshold, the first round optimization parameter set is iteratively updated using a gradient descent optimizer until the average error value is lower than the average error threshold.

3. The method according to claim 1, characterized in that During the charging and discharging process of the plug-in charging module, the temperature and current data of each capacitor layer are collected in real time, inputted into the nonlinear mapping model, and the voltage and internal resistance parameters of each capacitor layer are predicted to obtain the dynamic changes of the capacitor layer parameters during the charging and discharging process, including: Receive the capacitor layer surface temperature distribution data and current change data collected by the plug-in charging module, and generate a real-time monitoring data set with a time stamp according to a fixed sampling time interval; A data preprocessing unit is used to perform standardization processing on the real-time monitoring data set, and a nonlinear mapping model is used to predict the voltage parameter and internal resistance parameter of the capacitor layer according to the standardized data; Extracting time series features based on the predicted voltage parameters and internal resistance parameters, and generating a time series correspondence between the voltage parameters, internal resistance parameters, temperature data, and current data based on the time series features by the state identifier; A Kalman filter is used to smooth the voltage parameters and internal resistance parameters in the time sequence correspondence, and the smoothed parameters are combined with the working state of the plug-in charging module to obtain a dynamic change curve of the capacitance layer parameters.

4. The method according to claim 1, wherein The method compares the predicted voltage of each capacitor layer with a preset voltage balance threshold to determine whether the voltage of each capacitor layer is balanced. If not, the voltage of each layer is adjusted by an energy transfer circuit until a balanced state is reached. The method calculates the difference between the internal resistance of each capacitor layer and the predicted internal resistance parameter, determines the internal resistance compensation value based on the difference, and compensates the capacitor layer whose internal resistance difference is greater than the preset internal resistance difference. The method includes: A voltage detector is used to compare and calculate the predicted voltage value of the capacitor layer with the preset voltage balance threshold to obtain the voltage deviation and the inter-layer voltage difference value; According to the comparison result of the voltage deviation and the preset threshold range, an energy transfer channel is established between adjacent capacitor layers through an energy transfer controller; For the energy transfer channel, the energy transfer time and the energy transfer current are set according to the inter-layer voltage difference value, and the internal resistance difference value of each capacitor layer is calculated by an internal resistance comparator; For the internal resistance difference value, if it exceeds the preset difference value, the compensation parameter generator generates an internal resistance compensation parameter, and the internal resistance compensation process is tracked in real time through the Kalman filter until the internal resistance difference value drops below the preset difference value.

5. The method according to claim 1, wherein The calculated voltage difference and internal resistance difference of each capacitor layer are used as inputs of a preset fuzzy control algorithm, and the charge and discharge current adjustment amount of each capacitor layer is used as output. By fuzzifying the input variables, a target charge and discharge current control amount of each capacitor layer is output, and the target charge and discharge current control amount is transmitted to the current regulation circuit of the plug-in charging module to adjust the charge and discharge current of each capacitor layer in real time, including: The voltage difference and internal resistance difference between each capacitor layer are obtained by a voltage and internal resistance detection device, and a fuzzy variable processing unit is used to generate a fuzzy membership value for the voltage difference and the internal resistance difference; According to the fuzzy membership value, a fuzzy control rule base of temperature and current compensation is used to perform fuzzy reasoning operation to obtain the fuzzy control quantity of the charge and discharge current of each capacitor layer; For the current fuzzy control quantity, a centroid method defuzzification unit is used to calculate the real-time charge and discharge current adjustment quantity of each capacitor layer, and a charge and discharge current instruction with a limit is generated according to the adjustment quantity; The charge and discharge current instruction with amplitude limitation is adopted to generate a switch control signal in a pulse width modulator, and the charge and discharge current is adjusted according to the current detection signal of the plug-in charging module.

6. The method according to claim 1, characterized in that The method comprises continuously monitoring the temperature, current, voltage, and internal resistance parameters of each capacitor layer of the plug-in charging module during the emergency charging process of the plug-in portable motorcycle, and dynamically optimizing the charge and discharge current of each capacitor layer based on the prediction results of the nonlinear mapping model and the target charge and discharge current control amount, so as to achieve balanced charge and discharge of the layered capacitor structure, including: The temperature, current, voltage and internal resistance parameters of the motorcycle during charging are acquired through a multi-channel collector of the plug-in charging module. The temperature, current, voltage and internal resistance parameters are filtered and de-noised by a parallel processor to obtain charging load characteristics; According to the charging load characteristics, a nonlinear mapping model is used to predict the capacitance layer parameters, and deviation data between the predicted value and the actual value of each capacitance layer parameter is generated; For the deviation data, the target current control amount is compared in the charge and discharge equalizer, and the compensation current value of each capacitor layer is calculated by the dynamic optimizer; According to the compensation current value, closed-loop control is performed on the charge and discharge current of each capacitor layer in the balance controller, and the controller is adjusted to a balanced charge and discharge state through a feedback regulator.

7. The method according to claim 1, characterized in that During the plug-in portable motorcycle emergency charging process, the motorcycle power level is monitored in real time, and a portable motorcycle emergency charging plan is generated based on the motorcycle power level. If the motorcycle power level is detected to be within a preset low power range, a balancing control of the discharge of the layered capacitor structure of the plug-in charging module is triggered. If the monitored motorcycle power level is higher than a preset high power threshold, the frequency of the balancing control is gradually reduced until the motorcycle power level reaches full power, at which point the balancing control is stopped and charging is terminated, including: Acquire real-time power data of the motorcycle battery pack through a power monitor, and record a power change curve based on the real-time power data; Generating a comparison result between a power monitoring value and a preset threshold value according to the power change curve, and determining a power state based on the comparison result; If the state of charge determination result is lower than a preset low-charge threshold, balanced discharge control is performed, and a neural network predictor is used to generate discharge control parameters for the layered capacitor structure; The charge change trend is tracked in the charging state monitor according to the discharge control parameter. If the charge change trend reaches a preset full charge state, a charging stop signal is generated and the charging circuit is disconnected.

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